Study on network analysis of the 7th Framework Programme participation : methodological annex.
Date: 2016-08-23
Institutions: Directorate-General for Research and Innovation
Subjects: EU research policy, Framework Programme for Research and Development, competitiveness, innovation, research policy, research report, scientific cooperation

Study on Network 
Analysis of the 7th 
Framework Programme 
Participation 
Methodological Annex 

EN 

 
 
 
 
 
 
EUROPEAN COMMISSION 

Directorate-General for Research and Innovation 
Directorate A –Policy Development and Coordination 
Unit A5 — Evaluation 

Contact: Rosalinde van der Vlies 

E-mail:  RTD-NA@ec.europa.eu  

RTD-PUBLICATIONS@ec.europa.eu 

European Commission 
B-1049 Brussels 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EUROPEAN COMMISSION 

Study on the Network 
Analysis of the 7th 
Framework Programme 
Participation 

Methodological Annex 

Prepared by 

Directorate-General for Research and Innovation 

2015 – Evaluation 

 
 
 
 
 
 
 
 
 
 
 
 
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CONTENTS 

TABLES ....................................................................................................... IV 

FIGURES .................................................................................................... VII 

1. 

2. 

INTRODUCTION ..................................................................................... 1 

LOGIC OF FP7 INTERVENTION BASED ON RESEARCH NETWORKS ................ 3 

Reviewed information for the logic model of networking in FP7 .................... 4 
Main components of the logic model ......................................................... 6 
Use of the logic model in the present study .............................................. 12 

3. 

SOCIAL NETWORK ANALYSIS (SNA) AND BIBLIOMETRICS ......................... 13 

Data sources ........................................................................................ 13 
Quantitative indicators ........................................................................... 15 
Social network indicators ........................................................................ 15 
Retrieval of FP-related publications .......................................................... 21 
Bibliometric indicators  computed  using  the  publications  retrieved  from 

the WoS ...................................................................................... 28 

Addressing the specific questions using social network and bibliometric 

indicators .................................................................................... 31 

SURVEY OF FP7 PARTICIPANTS ............................................................... 41 

REGRESSION ANALYSES ........................................................................ 69 

CASE STUDIES...................................................................................... 83 

Pre-testing and pilot testing of the interview protocol ................................ 97 
Recruitment .......................................................................................... 98 
Recording and interview transcription ...................................................... 99 
Interview analysis ................................................................................. 99 
Quality control and analysis .................................................................. 100 
Reporting and case study format ........................................................... 100 

4. 

5. 

6. 

7. 

BALANCE IN NETWORK TEAM COMPOSITION (COMPOSITE INDICATOR) .... 102 

Methods ............................................................................................. 102 

8.  WORKSHOP WITH VARIOUS STAKEHOLDERS .......................................... 105 

Content preparation............................................................................. 108 
Logistics ............................................................................................. 109 

9. 

EVIDENCE TABLES .............................................................................. 111 

10.  APPENDICES ....................................................................................... 126 

Appendix I: Literature Review on SNA (Task 1) ....................................... 126 

Introduction—Collaborative  Research  Networks 

(Data  & 

Methods)...................................................................... 126 
Methods and analyses................................................................. 130 
References ................................................................................ 143 

Appendix  II:  Overview  of  networking  activities  under  FP7  and  its 

different specific programmes and thematic priorities (Task 2)......... 151 

 
 
 
R1—Headline  overall  figures  (i.e.  number  of  participants, 
sectors,  countries,  regions  in  total  and  per  project)  by 
programme,  specific  programme  and  broad/thematic 
area ............................................................................ 151 

R2  &  R3—Network  structure  and  its  characteristics  by  level  of 
analysis  (i.e.  organisational,  regional  and  national)  as 
well  as  by  programme,  specific  programme  and 
broad/thematic area ...................................................... 155 

R4 & R6—Key actors (i.e. participants, regions and countries) by 
different positions, their roles and their prestige  in the 
network: central actors/brokers and structural holes by 
programme and specific programme ................................ 160 

R5—Key  groups  (e.g.  clusters,  components  and  k-cores)  and 
their  compositions  (i.e.  participants,  regions  and 
countries)  by  programme,  specific  programme  and 
broad/thematic area ...................................................... 170 

R7—Role  of  industry  (SMEs  in  particular),  universities,  public 
authorities, national and European agencies relevant to 
the  particular  programme,  specific  programme  and 
broad/thematic area ...................................................... 178 

R8—Key  findings  from  the  above  points  pertaining  to  network 
dynamics  over  time  (mainly  through  a  comparison  at 
the programme level; i.e. FP6 vs. FP7) ............................ 186 

R9—Highlight of remarkable specificities per specific programme 

and broad/thematic area ................................................ 187 
Appendix III: Cross-case analysis and synthesis (Task 4) ......................... 190 
Why Case studies? ..................................................................... 190 
Case study approach .................................................................. 190 
Potentials associated with the network approach ............................ 192 
SQ1:  How  do  the  FP  projects link  separate  fields  of  knowledge, 
bridge  different  fields  of  science  and  facilitate  new 
scientific disciplines? ...................................................... 193 

SQ2: How do the FP projects link different types of organisations 
(universities, industry, SMEs, etc.) and bridge the flow 
of innovation ideas between them? .................................. 194 

SQ3:  How  have 

the  FP  projects  contributed 

collaborations 
organisations? .............................................................. 195 

integration 

and 

the 

of 

to  new 
new 

SQ5: How have FP project results been used as an input for new 
FP  projects,  and  how  has  knowledge  generated  in 
previous FPs been transformed into new knowledge? ......... 196 

SQ6 and SQ7: How do the FP projects contribute to international 
research  relations  between  the  EU  and  third  countries 
and  foster  a  wider  participation  of  relevant  actors  and 
stakeholders within the ERA? .......................................... 197 

FQ1:  How  does 

the  network  approach  promoted  and 
implemented  by  FP7  contribute  to  the  achievement  of 
EU Research Policy objectives? (cross-case synthesis) ....... 198 

FQ2:  Potentials  and  limits  of  the  network  approach  promoted 
and 
implemented  by  FP7  approach?  (cross-case 
synthesis) .................................................................... 199 
Appendix IV: Case studies (Task 4) ....................................................... 202 

 
 
 
Case  study  I:  AgriPolicy,  Enlargement  Network  for  AgriPolicy 

Analysis ....................................................................... 202 

Case study II: ATLAS, Development of laser-based technologies 
and prototype instruments  for  genome-wide chromatin 
immunoprecipitation analyses ......................................... 208 

Case  Study  III:  Climate  for  Culture,  Damage  risk  assessment, 
economic 
for 
sustainable  preservation  of  cultural  heritage  in  the 
times of climate change ................................................. 216 

impact  and  mitigation  strategies 

Case Study IV: DELTA-MIN, Mechanisms of Mineral Replacement 

Reactions ..................................................................... 225 

Case  Study  V:  E3ARTHS,  Exoplanets  and  early  earth 

atmospheric research: theories and simulations ................ 232 
Case Study VI: EMIF, European Medical Information Framework ...... 239 
Case  Study  VII:  GP-TCM,  Good  Practice  in  Traditional  Chinese 

Medicine Research in the Post-Genomic Era ...................... 246 

Case Study VIII: LASERLAB EUROPE II, The Integrated Initiative 

of European Laser Research Infrastructures ...................... 255 

Case  Study  IX:  RESPECT,  Relating  Expectations  and  Needs  to 
the  Participation  and  Empowerment  of  Children  in 
Clinical Trials ................................................................ 264 

Case  Study  X:  SOMMACT,  Self  Optimising  Measuring  Machine 

Tools ........................................................................... 273 
Case Study XI: SSRR, Smart Structured Rotating Reactors .............. 280 
Case  Study  XII:  TRAIN  2009,  Training  through  research 

application Italian initiative ............................................. 288 

 
 
 
 
 
 
TABLES 

Table I  

Fundamental and Specific Questions of the Tender Specifications ...... 1 

Table II 

Share of papers with an acknowledgement in the WoS, 2002–2013..... 
 .............................................................................................. 23 

Table III 

Share  of  papers  with  an  acknowledgement  in  the  WoS  by  FP7 
thematic priority, 2010–2013 ..................................................... 24 

Table IV 

Recall  of  WoS  papers 
funding 
acknowledgements based on the whole of WoS and the subset of WoS 
papers matched to FP7 project participants .................................. 25 

tagged  as  FP7-related  using 

Table V 

Proportion  of  projects  in  CORDA  appearing  in  SESAM  by  sub-
programme .............................................................................. 27 

Table VI 

Characterising the match of papers in SESAM with the WoS ........... 28 

Table VII 

Comparison of the centrality of countries, FP6 versus FP7 .............. 34 

Table VIII 

Descriptive statistics and how they will support the Specific Evaluation 
Questions (SQ) and the multivariate regressions ........................... 36 

Table IX 

Survey questions addressing the Fundamental Evaluation Questions ... 
 .............................................................................................. 47 

Table X 

Survey questions addressing the Specific Evaluation Questions ....... 48 

Table XI 

Composite indicators ................................................................. 74 

Table XII 

Spearman correlation matrix of the dependent variables ................ 80 

Table XIII 

Factor  loadings  of  the  dependent  variables  on  the  five  factors 
identified  through  the  exploratory  factor  analysis  using  IPA  factoring
 .............................................................................................. 81 

Table XIV 

Legend  for  the  selected  dependent  variables  to  be  used  in  the 
regression models ..................................................................... 82 

Table XV  

Evidence  Table  SQ1—Integrating  Key  Findings  from  all  Collection 
Methods .................................................................................. 112 

Table XVI  

Evidence  Table  SQ2—Integrating  Key  Findings  from  all  Collection 
Methods .................................................................................. 115 

Table XVII   Evidence  Table  SQ3—Integrating  Key  Findings  from  all  Collection 
Methods .................................................................................. 118 

Table XVIII   Evidence  Table  SQ4—Integrating  Key  Findings  from  all  Collection 
Methods .................................................................................. 119 

 
 
 
Table XIX  

Evidence  Table  SQ5—Integrating  Key  Findings  from  all  Collection 
Methods .................................................................................. 120 

Table XX  

Evidence Table SQ6 (broadened to include integration both within and 
beyond  the  ERA)—Integrating  Key  Findings  from  all  Collection 
Methods .................................................................................. 121 

Table XXI  

Evidence  Table  SQ7  (restrained  to  cross-regional  integration  within 
the ERA)—Integrating Key Findings from all Collection Methods ..... 124 

Table XXII 

Average  number  of  actors  per  project  by  specific  programme  and 
broad/thematic area (FP6 and FP7) ............................................ 153 

Table XXIII  Number  of  projects  in  which  a  country  participated  alongside  the 
number  of  distinct  organisations  that  participated  from  the  given 
country for the 25 most participating countries under FP7 (comparison 
with FP6 is provided) ................................................................ 154 

Table XXIV  Number  of  projects  in  which  a  NUTS  3  region  participated  alongside 
the  number  of  distinct  organisations  that  participated  from  the  given 
region  for  the  25  most  participating  regions  under  FP7  (comparison 
with FP6 is provided) ................................................................ 155 

Table XXV 

Network  statistics  depicting  the  structure  of  the  co-participation 
networks of various programmes at the level of organisations, regions 
(NUTS 3) and countries ............................................................ 159 

Table XXVI  Hubs  at  the  national  and regional  levels in  FP6,  FP7  and  FP7  specific 
programmes ............................................................................ 163 

Table XXVII  Brokers at the national and regional levels in FP6, FP7 and FP7 specific 
programmes ............................................................................ 164 

Table XXVIII  Hubs  at  the  organisational  level  in  FP6,  FP7  and  FP7  specific 
programmes ............................................................................ 168 

Table XXIX  Brokers  at  the  organisational  level  in  FP6,  FP7  and  FP7  specific 
programmes ............................................................................ 169 

Table XXX 

Size  of  the  highest  order  k-core  in  the  co-participation  network  of 
each programme, specific programme, and broad/thematic area ... 173 

Table XXXI  Dispersion  of  actors  in  the  largest  sub-group  of  the  co-participation 
networks  of  the  framework  programmes  as  revealed  through  the 
analysis of clusters ................................................................... 175 

Table XXXII  Share  of  participations  by  sector  for  FP6  and  FP7  as  well  as  for  the 
specific programmes, thematic areas and broad areas of FP7 ........ 182 

Table XXXIII  Expected  to  observed  ratio  of  the  proportion  of  organisations  in  the 
1st decile of the betweenness centrality  distribution  (i.e.,  in the  10% 
most  central  organisation)  by  sector  for  each  programme,  specific 
programmes and broad/thematic area ........................................ 184 

 
 
 
Table XXXIV  The  10  most  central  actors  (based  on  betweenness  centrality)  by 
sector for FP7 overall ................................................................ 185 

 
 
 
FIGURES 

Figure 1 

Logic model of networking in FP7 participation .............................. 11 

Figure 2 

Fictitious  comparison  of  countries’  affinities  in  co-participation  in  FP6 
projects and co-participation in FP7 projects ................................. 35 

Figure 3 

Survey participants by country ................................................... 43 

Figure 4 

Distribution of responses across the field period ............................ 44 

Figure 5 

Final field report: participation and completion rates. .................... 45 

Figure 6 

Drop-out rates .......................................................................... 46 

Figure 7 

Joining of datasets .................................................................... 76 

Figure 8 

Overall FP6 network at the NUTS 3 level ..................................... 157 

Figure 9 

Overall FP7 network at the NUTS 3 level ..................................... 158 

Figure 10 

Relationship  between  the  affinities  of  country  pairs  under  the  co-
participation networks of FP6 and FP7 ........................................ 165 

Figure 11 

Country change in attractiveness between FP7 and FP6 ................ 167 

Figure 12 

Size  of  the  largest  sub-network  in  each  programme,  specific 
programme, and broad/thematic areas using weak components .... 172 

Figure 13 

Size  of  the  largest  sub-network  in  each  programme,  specific 
programme, and broad/thematic area using clusters .................... 174 

Figure 14 

Co-participation network of countries in FP7 projects .................... 177 

Figure 15 

Network graphs showing, to the left, a highly homophilic network and, 
to the right, a somewhat heterophilic network ............................. 178 

Figure 16 

Distribution  of  the  number  of  distinct  organisations  per  sector: 
comparison between FP6 and FP7 .............................................. 180 

Figure 17 

Distribution  of  in  the  number  of  projects  in  which  organisations 
participated per sector: comparison between FP6 and FP7............. 181 

Figure 18 

Heat  map  of  expected  to  observed  ratio  of  the  number  of 
organisations  in  betweenness  centrality  bins  (deciles)  as  an  indicator 
of dominance in co-participation networks ................................... 183 

Figure 19 

Map of the participating partners ............................................... 204 

Figure 20 

Map of the participating partners ............................................... 211 

Figure 21 

Map of the participating partners ............................................... 219 

Figure 22 

Map of the participating partners ............................................... 227 

 
 
 
Figure 23 

Map of the participating partners ............................................... 235 

Figure 24 

Map of the participating partners ............................................... 241 

Figure 25 

Map of the participating partners ............................................... 249 

Figure 26 

Map of the participating partners ............................................... 257 

Figure 27 

Map of the participating partners ............................................... 266 

Figure 28 

Map of the participating partners ............................................... 275 

Figure 29 

Map of the participating partners ............................................... 283 

Figure 30 

Map of the participating partners ............................................... 290 

 
 
 
 
 
1.  INTRODUCTION 

The present Methodological Annex is a companion document to the main evaluation 
of  ‘Network  Analysis  of  the  Seventh  Framework  Programme  Participation’.  This 
evaluation is one of several preparatory studies for the overall ex-post evaluation of 
the  Seventh  Framework  Programme  (FP7),  which  is  to  be  carried  out  two  years 
following  the  completion  of  the  Programme.1  This  annex  refers  to  the  various 
methodological  instruments  that  have  been  implemented  towards  producing  a 
network  analysis  of  FP7  participation  to  address  the  study's  Specific  Evaluation 
Questions  (SQ1  to  SQ8)  as  well  as  its  two  Fundamental  Questions  (FQ1  and  FQ2) 
(see Table I below). 

Table I  

Fundamental  and  Specific  Questions  of 
Specifications 

the  Tender 

Source: 

Science-Metrix 

This annex starts with a description of the FP7 intervention logic depicting its main 
‘inputs’,  ‘activities’,  ‘outputs’,  and  ‘immediate  outcomes’  and  how  they  are 
measured/reported  using  the 
instruments  towards 
addressing  the  study’s  evaluation  questions  (SQ1-SQ8  and  FQ1-FQ2).  The 
methodological instruments are as follows: 

following  methodological 

  Social Network Analyses (SNA) and Bibliometrics (Section 3): SNA of FP7 
participation  is  first  used  to  describe  the  attributes  (in  particular  as  relates  to 

1 Official Journal of the European Union. (2006). Decision No 1982/2006/EC. 

1 

Call for Tenders Question #Survey QuestionFQ1FQ1.  To what extent the network approach, promoted and implemented by FP7, contributed to the achievement of EU Research Policy objectives?            FQ 1.1  What are the effects of networks in terms of strengthening the overall innovation capabilities at local, regional and national level?           FQ 1.2 How the networks contributed to the improvement of the competitiveness of national economies? FQ2FQ2.  What are the potentials and limits of this approach?SQ1Based on the assumption that innovation demands recombination of existing knowledge and new and scientific perspectives: to what extent does the FP link separate fields of knowledge, bridge different fields of science, and facilitate new scientific disciplines? Hypothesis: High diversity of linked knowledge increases the probability of the emergence of innovation.SQ2Based on the assumption that success of innovation depends on the link between basic research, applied research, and business/industry: to what extent does the FP link different types of organisations (universities, industry, SMEs, etc.) and bridge the flow of innovation ideas between them? Hypothesis: Links between different types of organisations foster success of innovation.SQ3Based on the assumption that high density networks are fostering competitiveness of EU research: to what extent has the FP contributed to new collaborations and the integration of new organisations? How many new collaborations emerged due to the FP? Hypothesis: New organisations in the network and new links between existing organisations in the network indicate a way to a common ERA.SQ4Based on the assumption that research collaboration is the core idea of ERA bringing competitive advantage: where are the structural holes (missing links) in the structure of the FP network, and who benefits from them? Hypothesis: By identifying and bridging structural holes ERA will be more integrated and EU research more competitive.SQ5Based on the assumption that scientific innovations are based on the accumulation of knowledge, and looking at network dynamic over time, to what extent are FP project results used as an input for new FP projects, and to what extent has knowledge generated in previous FPs been transformed into new knowledge? Hypothesis: Chaining the scientific outcomes gives good indications that ERA is working in the long-term and is well organised and plannedSQ6Based on the assumption that openness to international knowledge sharing is important for innovations and competitiveness of the EU: to what extent does the FP contribute to international research relations between the EU and third countries (patterns by countries and specific research areas)?SQ7Based on the assumption that FPs should play a proactive role in developing excellence across all countries and regions in the ERA: to what extent do FPs contribute to the integration of countries and regions and foster a wider participation ofrelevant actors and stakeholders?SQ8Where applicable and feasible the contractor will analyse the trends and developments and compare with the situation in the previous Framework Programme (FP6).  
 
 
                                                
the networking dimensions implicitly defined in SQ1 to SQ7) and evolution (from 
FP6 to FP7; i.e. SQ8) of the projects funded under FP7. Bibliometrics is used as 
a  complementary  tool  to  measure  the  scientific  outputs  of  FP7  projects,  which 
are later incorporated, together with SNA data, into the regression analyses (see 
below); 

  Survey  (Section  4)  of  FP7  participants  to  investigate  the  networking,  and  its 
coordination,  that  took  place  under  FP7  as  well  as  the  tangible  outputs  and 
immediate  outcomes  that  resulted  from  the  established  partnerships  towards 
addressing  FQ1.  The  survey  also  investigated  some  of  the  limitations  of  the 
networking approach (FQ2); 

  Regression  analyses  (Section  5)  integrating  SNA,  survey  and  bibliometric 
data to test the underlying hypotheses of the SQs, to identify which networking 
dimensions  are  more  conducive  to  the  achievement  of  EU  research  policy 
objectives  (FQ1),  as  well  as  to  investigate  some  of  the  limitations  of  the 
networking approach (FQ2); 

  A set of case studies (Section 6)—including a semi-structured interview with 
selected project participants—to expand the analysis of the SQs from a different 
perspective than the SNA and survey analyses. This will help nuance/confirm the 
findings  obtained  in  these  early  stages  of  the  project.  The  case  studies  aim  to 
describe and explain how and why the developments related to the foundation, 
formulation,  sustainment,  and  conclusion  stages  of  selected  collaborative 
networks have occurred; 

  Balance in network team composition (composite indicator) (section 7) 
a  composite  indicator  to  measure  the  effect  of  the  number  of  actors  on  the 
outputs/outcomes of the FP projects. 

  A  workshop (Section 8)  with academics, practitioners and representatives of 
the EC Directorate-General for Research and Innovation to validate and discuss 
early findings towards identifying key policy recommendations to be considered 
in implementing Horizon 2020; and 

  A  set  of  evidence  tables  (Section  9)  integrating  the  key  findings  stemming 

from the above methods for each of the SQs. 

2 

 
 
2.  LOGIC OF FP7 INTERVENTION BASED ON RESEARCH NETWORKS 

The  Seventh  Framework  Programme  was  structured  around 
four  specific 
programmes identified in the Decision No 1982/2006/EC of the European Parliament 
and Council of 18 December 2006:2 

  Cooperation:  support  [is]  provided  for  trans-national  cooperation  at  an 
appropriate scale across the  Union and beyond,  in  a number of thematic areas 
corresponding to major fields of the progress of knowledge and technology . . .; 

 

Ideas:  activities  [are]  implemented  by  a  European  Research  Council  (‘ERC’), 
which  [enjoys]  a  high  degree  of  autonomy  to  develop  very  high-level  frontier 
research  at  European  level,  building  on  excellence  in  Europe  and  raising  its 
profile at international level . . .; 

  People:  building  on  the  positive  experiences  with  the  ‘Marie  Curie  Actions’ 
under  previous  Framework  Programmes,  the  ‘People’  programme  [encourages] 
individuals to enter the profession of researcher; structure the research training 
offer  and  options;  encourage  European  researchers  to  stay  in,  or  return  to, 
Europe;  encourage  intersectoral  mobility,  and  attract  researchers  from  all over 
the world to Europe; and 

  Capacities: the use and development of research infrastructures [is] optimised 
along with the strengthening of innovative capacities of SMEs and their ability to 
benefit from research; the development of regional research-driven clusters and 
the coherent development of research policies at national and Community level 
[are]  supported;  the  research  potential  in  the  Union’s  convergence  and 
outermost  regions  [is]  unlocked;  science  and  society  [are]  brought  closer 
together  . . .;  and  horizontal  actions  and  measures  in  support  of  international 
cooperation [are] undertaken. 

The  networking  approach  to  the  execution  of  R&D  was  supported  across  the 
majority  of  these  specific  programmes,  although  it  could  take  different  forms 
depending  on  the  objectives  of  each  specific  programme  and  the  characteristics  of 
funding  schemes.  This  implies  that  research  networks  funded  under  the  Seventh 
Framework  Programme  are  diverse,  ranging  from  ad  hoc  or  established  R&D 
consortia to mobility grants between research institutions. However, there was one 
exception to the application of the networking approach in the Seventh Framework 
Programme. It concerns mainly the support of individual researchers who carry out 
frontier  research  in  the  context  of  the  Ideas  specific  programme,  although  such 
research  can  be  carried  out  by  trans-national  teams.  In  this  case,  the  ‘European 
added  value’  lay  in  bringing  the  competition  between  researchers  at  the  national 
and regional levels to the European level.3 

The  Seventh  Framework  Programme,  particularly  through  the  Cooperation  specific 
programme,  put  strong  emphasis  on  cross-disciplinary  research  between  R&D 
performers  and  research  collaborations  between  R&D  performers  belonging  to 

2  See,  also,  European  Commission.  2005.  Building  the  ERA  of  Knowledge  for  Growth, 

COM(2005) 118 final. 

3  European  Commission.  2007.  FP7  in  Brief  –  How  to  get  involved  in  the  EU  7th  Framework 
Programme  for  Research,  Luxembourg:  Office  for  Official  Publications  of  the  European 
Communities. 

3 

 
 
                                                
different  institutional  sectors—namely,  higher  education,  government,  business 
enterprise (including SMEs), and private non-profit.4 

The following paragraphs present the information used to construct the logic model 
on  the  functioning  of  the  Seventh  Framework  Programme  and  the  promoted 
network approach, its main components (inputs, activities, outputs, and outcomes), 
and its use in the present study. 

Reviewed information for the logic model of networking in FP7 

Often  used  in  evaluation,  logic  models  are  causal  visual  representations  of 
programmes  that  show  how  a  programme  is  intended  to  work—that  is,  how 
resources  that  are  available  to  deliver  to  the  programme  are  converted  into 
programme  activities,  which,  in  turn,  produce  intended  results.  Logic  models  are 
therefore typically constructed by distinguishing between programme inputs (i.e. the 
resources that are required to operate the programme), activities (i.e. the activities 
that  used  resources  to  achieve  the  intended  results),  outputs  (i.e.  the  amount  of 
work that is done as the programme is implemented), and outcomes (i.e. the short-, 
medium-, and long-term intended results that are linked to programme activities). 

Logic  models  do  not  necessarily  intend  to  show  one-to-one  causality  relationships 
between each element of these categories since relationships can be more complex. 
For instance, in a logic model, each activity  has at least one output, but there can 
be  several  outputs  for  particular  activities.  Logic  models  have  several  benefits 
justifying their use in the present study. For instance, they help develop a common 
language  among  stakeholders,  document  and  emphasise  explicit  outcomes,  clarify 
knowledge about what works and why, identify important variables to measure and 
enable more effective use of evaluation resources, and provide a credible reporting 
framework. 

An  in-depth  review  of  relevant  European  documents  was  performed  to  extract  key 
information on the inputs, activities, outputs, and outcomes of the four core specific 
programmes  under  the  Seventh  Framework  Programme.  In  particular,  all  the 
relevant  European  Commission  Communications  and  Decisions  of  the  European 
Parliament  and  of  the  Council  establishing  the  main  components  and  objectives  of 
the programme were considered: 

 

Proposals  related  to  the  Seventh  Framework  Programme,  notably  the  2005 
European Commission’s Proposal for a Decision of the European Parliament and 
of  the  Council  concerning  the  seventh  framework  programme  of  the  European 
for  research,  technological  development  and  demonstration 
Community 
activities (2007 to 2013) (COM(2005) 119 final); Proposal for a Council Decision 
concerning  the  specific  programme:  ‘Cooperation’  implementing  the  7th 
Framework  Programme  (2007-2013)  of  the  European  Community  for  research, 
technological development and demonstration activities  (COM(2005) 440 final); 
Proposal  for  a  Council  Decision  concerning  the  specific  programme:  ‘Ideas’ 
implementing  the  7th  Framework  Programme  (2007-2013)  of  the  European 

4  The  emphasis  of  the  Seventh  Framework  Programme  on  cross-disciplinary  research  is 
explicitly  stated  in  the  Council  decision  of  15  December  2006  (15965/06)  on  the 
Cooperation  programme.  See,  also,  European  Commission.  (2005).  Impact  Assessment 
and  Ex  Ante  Evaluation.  Commission  Staff  Working  Paper.  Annex  to  the  Proposal  for  the 
Council and European Parliament decisions on the 7th Framework Programme, SEC(2005) 
430. 

4 

 
 
                                                
Community 
for  research,  technological  development  and  demonstration 
activities (COM(2005) 441 final); Proposal for a Council Decision concerning the 
specific  programme:  ‘People’  implementing  the  7th  Framework  Programme 
(2007-2013)  of 
technological 
development and demonstration activities (COM(2005) 442 final); and Proposal 
for  a  Council  Decision  concerning  the  specific  programme: 
‘Capacities’ 
implementing  the  7th  Framework  Programme  (2007-2013)  of  the  European 
Community 
for  research,  technological  development  and  demonstration 
activities (COM(2005) 443 final). 

the  European  Community 

research, 

for 

  Official  decisions  adopting  the  Seventh  Framework  Programme,  including 
Decision No 1982/2006/EC of the European Parliament and of the Council of 18 
December 2006 concerning the Seventh Framework Programme of the European 
for  research,  technological  development  and  demonstration 
Community 
activities  (2007-2013);  Regulation  (EC)  No  1906/2006  of  the  European 
Parliament  and  of  the  Council  of  18  December  2006  laying  down  the  rules  for 
the  participation  of  undertakings,  research  centres  and  universities  in  actions 
under the Seventh Framework Programme and for the dissemination of research 
results (2007-2013); the Council Decision of 19 December 2006 concerning the 
the  Seventh  Framework 
Specific  Programme  Cooperation 
Programme  of 
technological 
development  and  demonstration  activities  (2007  to  2013)  (2006/971/EC);  the 
Council  Decision  of  19  December  2006  concerning  the  specific  programme: 
Ideas  implementing  the  Seventh  Framework  Programme  of  the  European 
Community 
for  research,  technological  development  and  demonstration 
activities (2007 to  2013) (2006/972/EC); the  Council Decision  of 19 December 
2006  concerning  the  specific  programme  People  implementing  the  Seventh 
Framework Programme of the European  Community  for research, technological 
development  and  demonstration  activities  (2007  to  2013)  (2006/973/EC);  and 
Council  Decision  of  19  December  2006  on  the  Specific  Programme:  Capacities 
implementing  the  Seventh Framework Programme of  the European  Community 
for  research,  technological  development  and  demonstration  activities  (2007  to 
2013) (2006/974/EC). 

the  European  Community 

implementing 

research, 

for 

  Specialised  European  Commission  documents  on  the  European  Research  Area, 
including  the  2007  Green  Paper  –  The  European  Research  Area:  New 
Perspectives  (COM(2007) 161 final); the 2008 Report  of the ERA  Expert Group 
on  Challenging  Europe’s  Research:  Rationales  for  the  European  Research  Area 
(ERA);  and  the  2012  Report  of  the  High  Level  Panel  on  the  Socio-Economic 
Benefits of the European Research Area. 

 

European  Commission  documents  dedicated 
to  European  citizens  and 
researchers  such  as  the  2007  Pocket  Guide  on  FP7  in  Brief  –  How  to  get 
involved in the EU 7th Framework Programme for Research. 

Additional  emphasis  was  placed  on  documents  related  to  the  preparation  and 
governance  of  the  Seventh  Framework  Programme.  These  include  the  impact 
assessment  and  ex  ante  evaluation  of  the  programme  as  well  as  the  interim 
evaluation. 

All the above information was analysed to identify  important assumptions on FP7’s 
functioning  in  the  four  specific  programmes  of  interest  for  this  study  (namely, 
Cooperation,  Ideas,  People,  and  Capacities)  and  expected  results,  and  to  reflect 
these  in  a  logic  model.  This  implies  that  the  logic  model  does  not  cover  activities 
related  to  the  Joint  Research  Centre  and  European  Atomic  Energy  Community 
(Euratom).  Furthermore,  the  logic  model  does  not  include  activities  on  the 
governance  aspect  of  FP7  as  it  focus  only  on  the  execution  of  R&D.  Only  indirect 

5 

 
 
activities  implemented  on  the  basis  of  calls  for  proposals  are  retained  in  the  logic 
model.  

Main components of the logic model 

Having  described  the  information  that  was  analysed  to  identify  important 
assumptions on FP7’s functioning and the basic concepts surrounding logic models, 
the  following  paragraphs  explain  the  logic  model  that  was  constructed  for  the 
purpose of the present study. 

One  of  the  key  underlying  assumptions  of  the  logic  of  European  intervention  in 
research  is  that  cooperation  and  networking  foster  positive  outcomes  such  as 
increased  European  competitiveness  and  better  integration  of  the  European 
Research Area, which, in turn, are expected to foster socioeconomic betterment. 

The inputs or resources required to develop and maintain the programme activities 
have been grouped in the logic model according to individual resources provided for 
the four main specific programmes at the heart of the present study. 

  Documentary  resources  based  on  legal  framework.  These  inputs  primarily 
include  all  the  decisions  and  regulations  related  to  the  Seventh  Framework 
Programme; that is, the legal framework. The decision for the launching of the 
programme  explains  its  background,  the  strategic  objectives  and  the  rationale 
associated  with  the  specific  programmes  and  activities  as  well  as  the  funding 
schemes that broadly define how indirect actions are supported and which types 
of  activities  are  supported.  The  decision  on  the  rules  of  participation  by  legal 
entities  (such  as  research  organisations,  universities  and  industry)  in  actions 
supported  by  the  programme  includes  the  definition  of  ‘legal  entity’,  the 
minimum  conditions  for  participation  in  the  specific  programmes  and  activities 
such as those related to the creation of pan-EU collaborative research networks, 
the  rules  for  participation  of  entities  from  non-EU  countries,  and  the  rules  for 
the  Community  financial  contribution  (e.g.  co-funding  principle).  The  decisions 
for the launching of the  specific programmes, as well as the  work  programmes 
and  calls  for  proposals,  define  the  type(s)  of  scheme(s)  used  to  fund  different 
actions, the categories of participants who can benefit from it, and the types of 
activities  (e.g.  R&D,  training,  dissemination,  and  other  related  activities)  that 
can be funded through each of them. 

 

 

Financial  resources.  The  inputs  also  include  the  overall  budget  of  the 
programme and the budget allocation among the different specific programmes 
and activities. Given that a large  share of indirect  actions are  supported based 
on a co-funding principle, these financial inputs or resources are complemented 
by  those  originating  from  other  sources,  including  public  sources  at  the 
international,  national  and  regional/local  levels  as  well  as  private  sources  (e.g. 
industry, private non-profit sector). 

IT resources. In addition to legal and financial ones, resources include databases 
and  the  IT  dissemination  tools  to  administer  the  funds  and  projects  supported 
by the programme, to report on the outputs from the latter, and to disseminate 
information on calls for proposals and final results of projects. Examples of these 
databases and dissemination tools are the E-CORDA, RESPIR, and CORDIS. 

  Human  Resources.  Finally,  other  resources  are  human  resources  such  as  the 
researchers and related staff involved in R&D activities and related activities and 
the staff in EU institutions and agencies in charge of the implementation of the 
programme. 

6 

 
 
The above inputs and resources link directly to the activities carried by research and 
non-research  legal  entities  through  the  four  specific  programmes  of  the  Seventh 
Framework  Programme  under  consideration  in  the  present  study:  Cooperation, 
Ideas, People, and Capacities. 

  R&D  activities.  R&D  activities  can  be  directly  linked  to  particular  specific 
programmes,  though  they  can  be  indirectly  covered  under  other  specific 
programmes.  Many  of  these  activities  require  networking  among  different 
entities, with an assumption that networking generates more benefits than costs 
(i.e. coordination costs). Activities undertaken by research entities can consist of 
trans-national  cooperation  across  the  European  Union  and  beyond.  These 
activities  are  those  primarily  implemented  under  the  Cooperation  specific 
programme.  Other  activities  performed  by  research  entities  are  undertaken  in 
the  context  of  research  mobility  between  institutions  and  institutional  sectors, 
often  located  in  different  countries.  These  activities  are  expected  to  have  an 
impact  on  the  career  development  of  researchers.  They  are  chiefly  supported 
under  the  specific  programme  People.  Research  entities  also  carry  out  frontier 
research.  Activities  in  support  of  the  development  of  frontier  research  are 
typically financed under the specific programme Ideas. Other research activities 
undertaken  by  research  entities  participate  in  the  development,  use,  and 
upgrade  of  research  infrastructure.  These  activities  are  supported  through  the 
specific  programme  Capacities.  Research  activities  are  also  performed  in  the 
context  of  collaborative  networks  encompassing  SMEs  and  other  research 
entities  such  as  research  organisations  with  the  aim  of  SMEs  acquiring  R&D 
capacities.  These  activities  are  supported  through  the  specific  programme 
Capacities,  though  the  most  R&D-performing  SMEs  can  be  supported  in  the 
specific programme Cooperation. Finally, some research activities bring together 
regional  actors  involved  in  research.  They  are  supported  primarily  through  the 
specific programme Capacities. 

  Non-R&D  activities.  While  the  above  activities  concern  R&D  activities,  entities, 
whether  research  ones  or  not,  can  participate  in  non-R&D  activities  such  as 
training  and  dissemination.  These  activities  can  be  financed  in  various  specific 
programmes  such  as  Cooperation  and  Capacities.  These  activities  are  typically 
supported  as  coordination  and  support  actions  and  do  not  necessarily  entail 
networking. 

The  different  activities  carried  out  by  legal  entities, primarily  research  ones, in  the 
context  of  the  Seventh  Framework  Programme  are  expected  to  lead  to  outputs. 
These  outputs  can  be  diverse,  reflecting  the  various  types  of  supported  activities 
across  the  four  specific  programmes,  legal  entities  involved  (e.g.  such  as  research 
organisations, universities, industry), types of R&D carried out (e.g. basic research, 
applied  research,  experimental  development),  and  fields of  science  and  technology 
in  which  R&D  is  performed.  It  is  almost  impossible  to  establish  a  one-to-one 
causality  link  between  each  activity  and  a  particular  output,  as  projects  can 
generate several types of outputs. Outputs from the supported activities can consist 
of the following categories:5 

  New  S&T  knowledge.  This  includes  publication  of  academic  articles,  books  and 
proceedings; presentation of results at conferences or workshops; development 

5  This  typology  of  outputs  is  based  on  Jonathon  N.  Cummings,  and  Sara  Kiesler  (2007). 
Coordination  costs  and  project  outcomes  in  multi-university  collaborations.  Research 
Policy, 36(10), pp. 1620-1634. 

7 

 
 
                                                
of  a  new  model  or  approach  in  the  field;  patent  applications;  awards  for 
contributions to the field; etc. 

  New scientific and technological tools. These outputs include the development of 
new  abstract  methodologies;  development  of  a  new  kind  of  tangible 
instruments;  creation  of  new  software;  creation  of  new  hardware;  creation  of 
new datasets; creation of data repositories; creation of surveys; etc. 

  Research  training.  These  outputs  consist  of  career  progress  in  terms  of 

seniority; more stable career positions (e.g. tenure track); etc. 

  Outreach.  These  outputs  translate  into  new  links  established  with  entities  that 
did  not take part in the  supported projects and  that  help disseminate research 
findings  to  a  wider  audience.  These  include  partnerships  with  industry, 
research 
museums,  healthcare 
performing organisations, civil society organisations, etc. 

institutions,  policy-makers,  government 

 

 

Innovation: New products introduced and new processes implemented.6 

Leverage:  Main  research  results  used  as  a  basis  or  key  inspiration  for  other 
projects;  by-products  used  as  a  basis  or  key  inspiration  for  other  projects; 
successful  application  for  new  funding  to  further  develop  research  results 
obtained in the context of the supported projects; successful application for new 
funding  to  further  develop  applications  created  in  the  context  of  the  supported 
projects; etc. 

In  the  context  of  the  promoted  networking  approach  in  the  Seventh  Framework 
Programme,  an  assumption  is  made  that  collaborative  research  is  more  likely  to 
achieve  the  above  outputs  than  research  undertaken  by  individual  entities. 
Furthermore,  while  these  different  outputs  can  be  equally  achieved  by  research 
projects  involving  individual  entities,  collaborative  projects  can  lead  to  distinct 
collaboration outputs,7 including the establishment of lasting partnerships with new 
entities  at  various  geographic  levels  (e.g.  local/regional,  national,  EU,  and 
international);  the  establishment  of  lasting  partnerships  with  new  entities  from 
different institutional sectors (e.g. higher education, government, industry); and the 
establishment of  lasting partnerships with  new entities in different fields of science 
and technology. 

The  above  outputs  are  expected  to  lead  to  different  intended  results  in  the  short, 
medium,  and  long  term.  To  a  large  extent,  these  intended  results  reflect  the 
objectives  of  the  Seventh  Framework  Programme  to  be  achieved  through  the 
promotion of the networking approach. 

Immediate outcomes are short-term results emerging directly from the programme 
outputs. Four types of immediate outcomes can be identified: 

 

Enhanced  integration  of  research.  This  relates  to  the  integration  of  research 
conducted  in  the  European  Research  Area  through  having  enhanced  the 
cooperation  activities.  This  first  outcome  can  be  closely  associated  with  the 
specific  programme  Cooperation.  The  integration  of  research  can  result  from 

6 New products and processes are defined in the context of the present study according to the 
definitions  of  product  and  process  innovations  from  the  OECD  and  Eurostat  Oslo  Manual 
(OECD  and  Eurostat,  (2005),  Oslo  Manual:  Guidelines  for  Collecting  and  Interpreting 
Innovation Data. 3rd Edition. Paris, France: OECD Publishing). 

7 Ibid. 

8 

 
 
                                                
enhanced collaboration between research entities from: the European Research 
Area  and  third  countries  (i.e.  increased  international  collaboration);  different 
fields  of  science  and  technology  (i.e.  increased  multi-  and  inter-disciplinary 
research  and  research  borrowing);  different  EU  regions  and  countries  (i.e. 
increased  EU  collaboration);  and  same  institutional  sectors  and/or  fields  of 
science and technology (increased critical mass). 

Enhanced  integration  of  science,  industry,  and  society.  The  second  type  of 
immediate  outcome  relates  to  the  integration  of  science,  industry  and  society, 
namely  through  stimulating  the  collaborative  efforts  between  higher education, 
the  government  sector,  the  business  enterprise  sector,  and  other  entities  such 
as  civil  organisations.  This  outcome  can  be  associated  with  the  four  specific 
programmes. 

Enhanced integration of careers. The third type of immediate outcome relates to 
the integration of careers in the European Research Area, namely by supporting 
the mobility of researchers throughout Europe and beyond. This outcome can be 
related to the specific programme People. 

Enhanced  integration  of  competencies  and  capacities.  The  fourth  type  of 
immediate outcome refers to the integration of competencies and capacities by 
facilitating  the  creation  of  joint  infrastructure  and  new  research  capabilities  to 
trigger the production  of  new knowledge. This outcome can be associated with 
the specific programmes Capacities and People. 

 

 

 

Intermediate  outcomes  are  medium-term  results  emerging  from  the  immediate 
outcomes. They include the following: 

  Stronger  knowledge  transfer  between  regions  and  countries.  This  first  type  of 
intermediate  outcome  comprises  a  stronger  process  of  knowledge  transfer 
between  countries/regions  within  and  outside  the  European  Research  Area 
through  enhanced  collaboration  between  research  entities  and  facilitated 
researcher mobility. 

  Stronger  knowledge  transfer  between 

institutional  sectors.  The  second 
intermediate outcome includes an enhanced knowledge transfer among research 
entities and researchers from different institutional sectors, notably between the 
public  research  sector  (i.e.  higher  education  and  government)  and  the  private 
one (i.e. industry and private non-profit sector). 

  Greater diversity in research. The third intermediate outcome consists of greater 
diversity in European research through the emergence of new lines of research 
by  the  integration  of  tools,  concepts  and  theories,  information,  and  methods 
from different disciplines. 

  Research  performance  enhanced.  The  fourth  intermediate  outcome  is  the 
enhancement  of  research  performance  through  the  achievement  of  positive 
critical  mass  and  research  efficient  gains  made  possible  by  the  pooling  of 
financial  and  human  resources  in  similar  fields  of  science  and  technology  and 
the reduction of efficiency losses due to the duplication of research undertaken 
by dispersed research entities across the European Research Area. 

  Attraction and retention of world class researchers. A fifth intermediate outcome 
is  the  attraction  and  retention  of  world  class  researchers  in  the  European 
Research Area. 

  Research capacities and competencies strengthened. Finally, a last intermediate 
outcome  is  the  strengthening  of  research  capacities  and  competencies  in  the 
European Research Area. 

9 

 
 
In terms of long-term outcomes, it is expected that if the intermediate outcomes are 
achieved, these will translate into the following: 

  More skilled human resources in research and technology in Europe. New people 
will  be  involved  in  research  fields,  and  established  research  performers  will 
remain  in  or  be  attracted  to  institutions  across  the  EU.  Thus,  the  human 
potential  in  research  and  technology  in  Europe  will  be  strengthened  both 
quantitatively and qualitatively. 

 

 

 

The  enhancement  of  the  dynamism,  creativity  and  excellence  of  European 
research at the frontier of knowledge. Investigator-driven basic research based 
on excellence is conducted. 

The  establishment/consolidation  of  instruments  to  better  respond  to  emerging 
scientific, industrial, policy and societal needs. Use of research infrastructures is 
optimised and new facilities and instruments for research are developed. 

Trans-national  cooperation  at  every  scale  across  the  EU  will  be  strengthened. 
Increased  collaboration  and  networking  involving  people  from  various  sectors 
(industry,  society,  research)  and  levels  (EU,  national,  regional)  throughout  the 
EU. 

10 

 
 
 
Figure 1 

Logic model of networking in FP7 participation 

11 

InputsParticipants/stakeholders Member States representatives Research workers from universities and research centres Research performers Industrial/business partners SMEs Industrial/business associations Non-profit organisations Regional authorities European organisations Civil society International organisations Organisation and researchers from third countries Students (postgraduate)Immediate outcomesOutputsActivitiesIntermediate outcomesLong-term outcomesStrategic outcomesInstruments to better respond to emerging scientific, industrial, policy and societal needs enabledStrengthen, qualitatively and quantitatively, human potential in research and technology in EuropeDynamism, creativity and excellence of European research at the frontier of knowledge enhancedTrans-national cooperation at every scale across EU strengthenedResearch performance enhanced (critical mass)Greater diversity in researchKnowledge transfer between countries and regions (EU and outside) strengthenedResearch capacities/competences strengthenedKnowledge transfer between institutional sectors strengthenedAttraction/retention world class researchers Research integrationMultidisciplinary/interdisciplinary/borrowing enhancedInternational and intra-EU collaboration enhancedCollaboration between research entities in same institutional sectors and/or disciplines enhancedScience, industry and society integrationCollaboration between research entities in the public and private sectors (including civil society organisations) enhancedCareers in European research integrationMobility of researchers reinforced (between countries and within/across institutional sectors)Competences and capacities integrationJoint research infrastructures developedCollaborations (e.g. new networks involving new actors and other disciplines)New S&T knowledge (e.g. publications and patents)Research training (e.g. progress in seniority, more stable careers)Leveraging (e.g. use of research findings for other projects, successful application for further funding)New S&T tools (e.g. methods, software, instruments)Outreach (e.g. new links with industry, museums, civil society organisations, and policymakers)Innovations (e.g. new products, new processes)Provision of support for transnational cooperation (across the Union and beyond)Provision of support for research projects involving SMEs/SMEs associations Provision of support for researcher mobility and career development (e.g. training)Provision of support for development/upgrade/use of research infrastructuresProvision of support for high-level frontier researchProvision of support for coordination of regional actors involved in research (e.g. division of responsibilities, meetings)Financial resources (FP7 budget, budget allocation to programmes and activities, and co-funding from other public and private sources)FP7 legal framework (background, objectives, specific programmes, funding schemes, work programmes, rules for participation [EU/ non-EU legal entities], incl. minimum networking conditions)Human resources (e.g. researchers, EU staff)IT resources for FP7 administration and dissemination (e.g. E-CORDA, RESPIR, and CORDIS)Overall innovation capabilities at local, regional and national level and  improvement of the competitiveness of national economies strengthenedAchievement of EU Research Policy objectives: - Sustainable economic growth;- Creation of jobs- Social and environmental well-beingFeedback loopProvision of support for non-R&D activities (e.g. dissemination, studies)New research capacities acquiredProduction of new high-end knowledge facilitated 
 
 
Use of the logic model in the present study 

The logic model and its components are essential tools to  address all the questions 
raised in the terms of reference, and especially the first fundamental question of this 
study:  ‘How  far  did  the  network  approach,  promoted  and  implemented  by  FP7, 
contribute  to  the  achievement  of  EU  Research  Policy  objectives?’  The  multiple 
research  methods  and  data  collection  tools  (documentary  review,  network  analysis 
using E-CORDA, SESAM and the Web of Science bibliometric database, survey, case 
studies  with  semi-structured  interviews)  have  been  used  to  answer  the  evaluation 
questions  given  the  specific  strengths  associated  with  each  method.  The 
CORDA/network  and  the  survey  analyses  have  mainly  measure  ‘what’,  ‘to  which 
extent’, ‘where’ and ‘who’; the interviews used in the case studies were mostly focus 
on ‘how’ and ‘why’. 

The  CORDA/network  analysis  are  first  concentrated  on  the  ‘inputs’  and  ‘activities’ 
components  of  the  logic  model  to  identify  the  attributes  of  FP7  projects  (e.g. 
collaborative  projects  involving  several  disciplines,  public  and  private  entities, 
entities in the same institutional sector, institutions working in the same discipline, 
entities  from  non-EU  countries,  and  entities  across  and  within  EU  countries  and 
regions) implicitly defined by the specific questions SQ1, SQ2, SQ3, SQ4, SQ6, and 
SQ7,  as  well  as  the  properties  of  larger  networks  supported  by  FP7  in  different 
activities.  These  components  can  also  be  used  to  explain  the  evolution  of  the 
attributes of projects funded under the Sixth and Seventh Framework Programmes 
and of the properties of larger networks supported by these successive programmes 
(SQ8). 

The  logic  model  was  used  as  a  basis  for  structuring  the  survey  questions.  The 
survey  analysis  has  make  use  of  the  ‘inputs’,  ‘activities’,  ‘outputs’,  and  ‘immediate 
outcomes’  components  to  examine  the  extent  to  which  the  network  approach 
supported  by  the  Seventh  Framework  Programme  has  led  to  the  tangible  outputs 
and immediate outcomes depicted in the logic model, using descriptive statistics and 
multivariate  statistics  (regression  analysis).  In  the  regression  analysis,  the  various 
assumptions  associated  in  particular  with  the  specific  evaluation  questions  SQ1, 
SQ2,  SQ3,  SQ4,  SQ6,  and  SQ7  were  tested.  The  dependent  variables  are  the 
various  outputs  and  immediate  outcomes  measured  through  the  survey  and 
bibliometric  indicators,  and  the  independent  variables  are  the  indicators  created 
from  the  survey  and  network  analysis  based  on  the  ‘inputs’  and  ‘activities’ 
components  of  the  logic  model.  It  is  important  to  note  that  the  limitations  of  the 
networking  approach  have  also  been  investigated  in  the  regression  analysis  by 
integrating  other  independent/control  variables,  such  as  project  budget  amounts 
(and the EC contribution), the country and region of participants, the start and end 
year  of  projects,  the  type  of  research  undertaken,  the  type  of  participants  (e.g. 
firm), and the coordination activities undertaken during the project. 

The logic model was also used as a basis for structuring and formulating the semi-
structured  interview  questions  used  as  part  of  the  case  studies.  The  case  studies 
deepen  the  analysis  of  the  specific  Task  3  questions  (from  SQ1  to  SQ8)  from  a 
different  perspective  than  the  CORDA/network  and  survey  analyses.  The  case 
studies also address specific questions listed in the terms of reference  for the case 
studies.  In  doing  so,  the  interviews  and  case  studies  make  use  of  the  different 
components  of  the  logic  model,  as  they  describe  and  explain  how  and  why  the 
developments  related  to  the  foundation,  formulation,  sustainment,  and  conclusion 
stages of selected collaborative networks have occurred. 

12 

 
 
3.  SOCIAL NETWORK ANALYSIS (SNA) AND BIBLIOMETRICS 

This  section  describes  the  network  analysis  approach  that  was  used  for  this 
evaluation.  Network  statistics  were  first  produced  to  satisfy  the  requirements  of 
Task  2  in  the  Terms  of  Reference.  Several  descriptive  statistics  and  visual 
representations of networks that are presented in the final report of this evaluation 
were prepared to fulfil each of the requirements. The descriptive statistics were then 
used  in  combination  with  bibliometric  statistics  to  explicitly  address  each  of  the 
Specific  Questions  of  this  evaluation,  in  part  through  regression  analyses  (see 
Section 5).  

A  description  of  the  data  sources  (CORDA,  WoS  and  SESAM)  is  presented  first, 
followed  by  the  presentation  of  a  list  of  quantitative  indicators  providing  a  broad 
overview  of  some  of  the  general  characteristics  of  FP7  projects.  Social  network 
indicators, which represent the core of the network analysis, are presented next at 
various  levels  and  the  section  ends  with  the  description  of  the  probability  affinity 
index. Subsequently, the methodological approach developed to gather the required 
data  to  compute  the  bibliometric  indicators  measuring  FP7-related  research 
publications  using  the  Web  of  Science  (WoS)  database  is  presented  alongside  the 
definition  of  the  computed  bibliometric  indicators.  Finally,  this  section  ends  with  a 
description of the analyses (aside from the regression analyses, which are described 
in Section 5) performed using SNA and bibliometric indicators to address each of the 
Specific  Questions  (SQ1  to  SQ8).  It  is  accompanied  by  a  table  mapping  the 
computed network statistics with the relevant Specific Questions (Table VIII). 

Data sources 

The  network  indicators  and  analyses  were  essentially  computed  using  the 
information on all participants in each FP7 project contained in CORDA. In addition, 
some  bibliometric  data  was  used  to  complement  the  information  from  CORDA. 
Indeed,  although  CORDA  was  suitable  for  performing  the  majority  of  the  network 
analyses  for  this  project,  some  analyses  could  not  be  performed  solely  based  on 
information  contained  in  CORDA.  Bibliometric  data,  based  on  WoS,  were  used  for 
two specific analyses: 

1.  Determining  the  interdisciplinarity  of  projects  using  the  scientific  publications 
that  were  linked  to  each  participant  (SQ1)  in  FP7  since  the  analysis  of 
disciplinarity/interdisciplinarity proved impossible using CORDA alone. This could 
only  be  performed  for  FP7  since  no  contact  information  was  provided  for  FP6 
projects.  

2.  Providing  data  on  scientific  collaboration  between  countries  to  describe  the 
structure of the network of collaboration between countries before FP7 support. 
Through  a  comparative  analysis  with  the  networks  based  on  CORDA  (FP6  and 
FP7), this helped identify changes in collaboration affinities. 

At  the  onset  of  this  evaluation,  the  use  of  SESAM  data  was  also  planned  to 
complement  the  information  available  in  the  WoS.  However,  after  the  match  of 
SESAM  on  the  WoS,  it  was  discovered  that  this  would  not  lead  to  major 
improvements  in  the  coverage  of  FP7-supported  publications  and  thus  SESAM  was 
not used in the end. 

13 

 
 
 
 
CORDA 

The Commission has provided  data on  individual FP7  projects and all organisations 
participating in each project. The list contains 22,228 projects that started between 
2007 and 2014. The number of participants in each project ranges from 1 to 98. The 
following  sections  present  the  different  fields  that  were  used  for  the  network 
analysis. 

Standardisation of organisations’ names 

The names of organisations listed in CORDA are standardised, and each organisation 
usually appears under a single name. However, this was not the case in the data for 
FP6.  Each  organisation  was  listed  under  different  names  (e.g.  LINKOEPING 
UNIVERSITY,  LINKOEPINGS  UNIVERSITET,  LINKOPING  UNIVERSITET,  LINKOPING 
UNIVERSITY,  LINKOPINGS  UNIVERSITEIT,  LINKOPINGS  UNIVERSITET).  In  order  to 
conduct  the  analysis  on  network  dynamics  over  time,  the  name  of  organisations 
were standardised under a single name for both FP6 and FP7. 

Coding of organisations by type 

In  order  to  analyse  the  role  of  the  different  types  of  organisation  (e.g.  industry, 
public  authorities,  higher  education)  in  the  networks,  each  organisation  in  CORDA 
had to be coded to a specific type. Although this information was available for both 
FP6  and  FP7,  the  quality  was  variable  and  certain  records  were  relatively  sparse, 
meaning that the sector type had to be validated manually for each organisation. In 
the end, organisations were coded under five sectors for FP7 organisations based on 
the sectors provided in CORDA: 

  Higher Education 

  Research Organisation 

 

 

Public Body 

Private 

  Others 

However, the coding provided for FP6 organisations was slightly different from that 
in  FP7  and  in  order  to  be  able  to  compare  results  between  both  FPs,  aggregates 
similar across the FPs had to be created. Indeed, the ‘Public Body’ category was not 
present in FP6, most of the organisations falling under this category being included 
in the ‘Others’ category. For FP7, more refinement was provided in CORDA with the 
category ‘Public Body’. Thus, when data by sectors are compared from FP6 to FP7, 
the following categories are used: 

  Higher Education 

  Research Organisation 

 

 

Public Body & Others 

Private 

In this case, ‘Higher Education’, ‘Research Organisation’ and ‘Private’ are extensively 
based on the coding provided in  CORDA, while  the  ‘Public Body &  Others’  category 
encompasses ‘Public Body’ and ‘Others’ for FP7, and ‘Others’ only for FP6 since this 
category already encompassed most public authority organisations. 

14 

 
 
Quantitative indicators 

Throughout this report, a large number of indicators based on information available 
in CORDA were produced to provide a general overview of the FPs. These indicators 
were  computed  when  needed  for  specific  analyses,  and  could  be  computed  at 
different levels, ranging from statistics at the project level, for different geographical 
entities  (e.g.  per  country,  NUTS3  regions)  or  for  FP  subsets  (e.g.  per  programme, 
thematic). Trends over time were also produced when appropriate, again based on 
information available in CORDA. Next are short descriptions of these indicators. 

Number  of  participants:  The  count  of  distinct  participants  based  on  contact 
information. 

Number of organisations: The count of distinct organisations. 

Number of countries: The count of distinct countries. 

Number of ERA countries: The count of distinct ERA countries. 

Number of non-ERA countries: The count of distinct non-ERA countries. 

Number of NUTS3 regions: The count of distinct NUTS3 regions. 

Number  of  sectors:  The  count  of  distinct  sectors  of  activity  involved  on  each 
project based on sector coding of organisations. 

Number of projects: The count of distinct projects related to the entity. 

For many of the above indicators, averages by project were also produced. In these 
cases,  it  is  important  to  note  that  duplicates  are  incorporated  in  the  computation 
since  entities  at  stake  are  often  involved  on  more  than  one  project.  Furthermore, 
these  averages  are  based  on  the  total  number  of  projects overall,  which  results  in 
scores indicating the average presence of the entity across all projects. For instance, 
685  distinct  British  organisations  were  involved  on  1,262  FP7  projects  in  2008. 
However,  these  685  organisations  were  in  fact  present  2,212  times  on  these 
projects since some were active on more than one project. In the end, FP7 projects 
from  2008  involved  on  average  0.81  British  organisations  based  on  the  fact  that 
2,731  FP7  projects  started  in  2008  (i.e.  2,212  British  organisations/2,731  FP7 
projects= 0.81 British organisation/FP7 project). 

Social network indicators 

Social network analysis (SNA) includes a set of methods to map and measure (using 
various  indicators)  the  ties  between  individuals  and  groups  (e.g.  organisations, 
regions, countries). In science policy and research evaluation, network analysis can 
be used to examine scientific collaboration (e.g. through analysis of co-authorships) 
and knowledge diffusion (e.g. through citation analysis). A network can be pictured 
as  nodes  representing  the  actors  or  performing  entities  (e.g.  researchers, 
organisations,  countries)  and  ties  representing  scientific  collaboration  (e.g. 
participation in FP projects, co-authorship of scientific publications) and by extension 
knowledge flows (e.g. citation ties). 

The  network  structure  is  determined  by  the  ties  (or  links)  between  the  actors.  In 
turn, the structure determines the network’s efficiency at transmitting knowledge to 
all actors and the position of each actor in the network, from which one can assess 

15 

 
 
the ‘role’ played by each actor in the network. Thus, role is not an intrinsic property 
of  the  actor  but  an  emergent  property  of  the  network.  Similarly,  the  network 
emerges  from  non-directed  collaborative  activities  of  each  actor.  As  such,  the 
network  is  mostly  self-organised,  and  might  only  optimally  transmit  knowledge  by 
serendipity.  Policy  intervention  can  influence  individual  decisions  on  collaboration 
and can therefore shape the structure of the network by obtaining desired outcomes 
(e.g. enhanced integration of ERA countries, knowledge transfer from universities to 
SMEs, smart specialisation). 

In this study, various networks were produced to map the networking effect of FP7 
in  Europe  and  internationally.  To  further  characterise  the  impact  of  FP7  on 
networking,  these  networks  were,  for  example,  compared  with  one  another  (e.g. 
across  programmes  to  monitor  their  evolution/dynamics)  as  well  as  with  idealised 
networks  (e.g.  for  assessing  the  density  of  a  network  compared  with  a  theoretical 
maximum).  Such  characterisation  implied  measuring  many  aspects  of  networks; 
below is a detailed list of network indicators that were used to provide evidence to 
address  the  Specific  Questions  of  this  evaluation  and  act  as  input  for  the 
multivariate regressions.  

Most  of  the  network  analyses  performed  in  this  evaluation  was  based  on  the  co-
participation  of  researchers  and  organisations  (and,  by  extension,  regions  and 
countries)  in  projects  supported  by  the  FP.  However,  while  the  number  of 
participants in the specific programmes of Cooperation (SP1) and Capacities (SP4) is 
generally  above  one  and  thus  makes  it  possible  to  perform  network  analyses,  the 
mode  for  participating  in  the  Ideas  (SP2)  and  People  (SP3)  programmes  is  clearly 
one,  with  more  than  85%  of  projects  having  only  one  participating  organisation. 
This  means  that  the  indicators  for  these  specific  programmes  had  to  be  calculated 
on a small number of relatively small projects. It is be important to keep this clear 
distinction  between  the  two  groups  of  programmes  in  mind  when  computing  the 
indicators  and,  moreover,  when  using  the  results  to  address  the  evaluation 
questions. 

In  order  to  perform  the  network  analysis  and  compute  the  network  indicators,  the 
networks were constructed at four levels: 

Country:  Each  country is an  actor tied to other countries based  on the number  of 
co-participations in FP projects 

NUTS: Each region (NUTS) is an actor tied to other regions based on the number of 
co-participations in FP projects 

Organisation:  Each  organisation  is  an  actor  tied  to  other  organisations  based  on 
the number of co-participations in FP projects 

2-mode network participant-project: Each project is an actor tied to participants 
(i.e. organisations) that are themselves linked to other projects. 

Gephi,  a  network  graphing  software,  was  used  to  process  data  and  produce  a 
representation  of  the  strength  of  the  relationships  between  the  entities  at  each  of 
these four levels. A ‘spring–embedding’ algorithm was used to establish the relative 
locations of the institutions in the graphic representation. Each node consists of an 
entity  represented  by  a  circle.  Links  between  nodes  represent  collaborations 
between institutions. 

16 

 
 
The network indicators that were used in this evaluation can be broadly categorised 
into three large groups: actor-level, dyads and network-level. The following sections 
present brief definitions for each of the network indicators in this analysis. 

Social network indicators: Actor-level 

Number of nodes: The number of actors in a given network. 

Unweighted degree: The degree of an actor is the number of ties adjacent to the 
actor.  For  example,  this  represents  the  number  of  institutions  with  whom  an 
institution  collaborates.  The  normalised  version  of  this  indicator  is  obtained  by 
dividing the degree by the number of possible neighbours for each of the N actors in 
the  network,  i.e.  N-1  possible  neighbours.  The  average  unweighted  degree  of  all 
actors in the network was also  computed to obtain the network level equivalent of 
this indicator. 

Weighted  degree:  The  weighted  degree  takes  into  account  the  size  of  the  tie 
between actors. The size of the actors is the number of occurrences of collaboration 
(e.g.  number  of  co-participation  in  FP  projects,  number  of  co-authorships  in 
publications).  The  normalised  version  of  this  indicator  is  obtained  by  dividing  the 
weighted degree  by  the  number  of  possible  neighbours  for  each  of  the  N  actors  in 
the network, i.e. N-1 possible neighbours. The average weighted degree of all actors 
in  the  network  was  also  computed  to  obtain  the  network  level  equivalent  of  this 
indicator. 

Closeness centrality: The average distance from a given actor to all other actors 
in  the  network.  An  actor  directly  connected  to  every  other  actor  in  the  network 
would  score  1,  the  highest  possible  closeness  centrality  score.  Closeness  centrality 
for  actors  disconnected  from  the  core  of  the  network  is  defined  as  ‘infinite’.  The 
weighted  version  of  the  indicator,  which  takes  into  account  the  extent  of  the  tie 
between actors, was used throughout the analysis for this report. No normalisation 
was necessary for this indicator as it is already normalised. The average normalised 
closeness  centrality  of  all  actors  in  the  network  was  also  computed  to  obtain  the 
network level equivalent of this indicator. 

Betweenness  centrality:  Betweenness  centrality  measures  how  often  an  actor 
appears on the shortest paths between actors in the network. The weighted version 
of the indicator takes into account the extent of the tie between actors. In the end, 
the  normalised  weighted  version  of  this  indicator  was  computed  by  dividing  the 
betweenness  centrality  score  by  the  total  number  of  shortest  paths  between  all 
actors in the network (i.e. for N actors, ((N-1)(N-2)/2) shortest paths). The average 
normalised betweenness centrality of all actors in the network was also computed to 
obtain the network level equivalent of this indicator. 

Eigenvector  centrality:  A  measure  of  the  level  of  integration  of  an  actor  in  a 
collaboration  network.  The  level  of  integration  of  actors  within  a  collaboration 
network is reflected by the number of actors to which they are connected, and the 
quality of their collaborations (i.e. the strength of the ties measured by the number 
of  co-authored  publications  and  the  importance  of  the  actors  to  which  they  are 
connected  in  the  network).  The  mathematical  definition  of  eigenvector  centrality  is 
such that the centrality score of an actor in a network is proportional to the sum of 
the centrality scores of all actors connected to it. Thus, this indicator offers a good 
appreciation  of  both  the  number  and  quality  of  an  entity’s  collaborations  because 
connections  to  high-scoring  actors  (which  represent  the  entities  in  the  network) 
contribute  more  to  the  score  of  that  entity  than  equal  connections  to  low-scoring 
actors.  An  actor  scoring  high  for  this  indicator  operates  closer  to  the  core  of  the 

17 

 
 
network  (i.e.  it  is  central  and  highly  important  to  the  network’s  structure)  than  a 
low-scoring institution, such that eigenvector centrality provides a good appreciation 
of  the  integration  of  individual  countries  within  a  network;  that  is,  the  higher  the 
score the more integrated the country. The weighted version of the indicator takes 
into  account  the  size  of  the  tie  between  actors.  The  average  weighted  eigenvector 
centrality and average unweighted eigenvector centrality of all actors in the network 
were also computed to obtain the network level equivalents of these indicators. 

Clustering coefficient: The clustering coefficient, when applied to a single actor, is 
a measure of how complete the neighbourhood of an actor is. The neighbourhood of 
an actor, u, is the set of actors connected to u. If every actor in the neighbourhood 
of  u  is  connected  to  every  other  actor  in  the  neighbourhood  of  u,  then  the 
neighbourhood  of  u  is  complete  and  will  have  a  clustering  coefficient  of  1.  If  no 
actors in  the  neighbourhood  of u are  connected,  then  the clustering coefficient  will 
be 0. 

Broker:  A  broker  is  an  actor  that  creates  the  only  connection  between  two 
components of a network. In a large, dense network with only one component, this 
strict definition of broker is not useful. Alternatively, the brokers can be identified as 
the  actors  with  the  highest  betweenness  centrality  in  the  network.  It  is  this  latter 
definition that is used when brokers are addressed in this report. 

Bridge: A bridge is a tie that connects two components in a network. 

Social network indicators: Dyads 

Number  of  dyads:  This  indicator  counts  the  number  of  researcher/organisation 
pairs in a network. By measuring dyads over time, it is possible to assess continuity 
and network dynamics.  

Social network indicators: Network-level 

Average degree: This is the average of degrees of each actor in the network. The 
higher the average degree, the more  connected is the network.  When the average 
degree  is  equal  to  the  number  of  actors  minus  one  (N-1),  then  the  density  of  the 
network is maximal as each actor is connected to all other actors. 

Average weighted degree: This is the average of weighted degrees of all actors in 
the network. 

Network  density:  This  is  the  ratio  of  the  number  of  ties  in  the  network  over  the 
number  of  possible  ties  in  the  network.  This  indicator  measures  how  close  the 
network is to being  complete.  A complete graph has all possible ties and a density 
equal  to  1.  For  instance,  in  a  three-node  graph  in  which  there  are  two  edges,  the 
graph  density  would  be  0.67  as  three  ties  are  possible  as  a  maximum  for  this 
configuration. 

Average  clustering  coefficient:  When  applied  to  an  entire  network,  it  is  the 
average clustering coefficient of all the actors in the network (see the description of 
the clustering coefficient presented earlier in the method). 

Components:  A  component  is  a  subgroup  of  actors,  whereby  all  actors  are linked 
by  at  least  one  path.  Actors  in  the  component  are  also  disconnected  from  other 
actors in the complete network. The concept of component is closely related to the 
notion of cohesion, another measure of the degree of connectedness in networks. 

18 

 
 
Homophily:  Homophily  is  the  tendency  of  individuals  to  associate  and  collaborate 
with  similar  others.  For  example,  if  researchers  collaborate  more  with  other 
researchers from their country than with researchers from other countries then the 
network  is  homophilic  in  regard  to  the  country.  Homophily  is  thus  the  opposite  of 
integration.  Traditionally,  homophily  is  calculated  by  comparing  the  average  path 
length of actors that share a characteristic to the average path length of all actors in 
the  network.  In  the  following  example,  the  average  path  length  between  any  two 
actors is 1.8, but the average  path length between white actors (or between black 
actors)  is  1,  meaning  that  the  network  is  homophilic  in  regard  to  the  colour  of 
actors. 

However,  in  the  case  of  networks  with  disconnected  components,  this  definition  of 
homophily is mostly useless because it becomes impossible to compute the average 
path length of disconnected actors. This is a problem for this project since many of 
the networks at stake involve high numbers of disconnected actors. Consequently, a 
modified  version  of  this  indicator  had  to  be  developed  to  provide  insights  on 
homophily in the networks. For a given network, the frequency of collaborations for 
each  type  of  pairs  (e.g.  pairs  of  thematics,  sectors,  countries,  etc.)  is  computed 
based  on  the  total  number  of  collaborations  in  the  network.  Then,  the  number  of 
collaborations of each pair in a random network of the same size (e.g. same number 
of actors and collaborations) is calculated to obtain a reference level. For homophilic 
pairs, the expected frequency is:  

𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦𝐻𝑜𝑚 = (

𝑁1
𝑁

) (

𝑁1 − 1
𝑁 − 1

) 

where N is the total number of actors in the network and N1 is the number of actors 
for this specific network level (e.g. sector, thematic, country). For heterophilic pairs, 
the expected frequency is: 

𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦𝐻𝑒𝑡 = (

𝑁1
𝑁

) (

𝑁2
𝑁 − 1

) + (

𝑁2
𝑁

) (

𝑁1
𝑁 − 1

) 

where N is again the total number of actors in the network, and N1 and N2 are the 
numbers  of  actors  for  the  two  specific  network  levels  involved  in  the  pairing  (e.g. 
sectors,  thematics,  countries).  For  both  homophilic  and  heterophilic  pairs,  the 
expected  occurrences  are  then  obtained  by  multiplying  the  frequency  by  the  total 
number of collaborations in the network, which represents the weighted sum across 
all the dyads in the network. 

Next,  the  sum  of  observed  collaborations  for  all  homophilic  pairs  is  divided  by  the 
sum of expected collaborations of all homophilic pairs to provide a ratio of observed 
over expected frequencies. The same process is repeated for heterophilic pairs, then 
the ratio of homophilic pairs is divided by the ratio of heterophilic pairs, with a score 
above  1.00  indicating  that  homophilic  pairs  are  more  present  than  expected 
compared  to  heterophilic  pairs  and  thus  the  network  is  homophilic,  while  a  score 
below  1.00  indicates  that  heterophilic  collaborations  are  more  frequent  than 
expected and thus the  network  is heterophilic. The  logarithm  value of this score  is 
presented  in  this  report  in  order  to  bound  the  indicator  between  -1.00  and  1.00, 
with  0  acting  as  the  neutral  value  at  which  the  network  is  neither  homophilic  nor 

19 

 
 
 
heterophilic,  while  scores  above  0  indicate  homophily  and  scores  below  0  indicate 
heterophily in the network. 

While  the  above  indicator  presents  a  homophily  score  for  the  network  as  a  whole, 
ratios  of  observed  over  expected  frequencies  for  each  type  of  pairs  (e.g.  pairs 
between sectors, thematics, countries, etc.) can be computed in the same manner, 
providing  information  on  homophily  at  these  levels.  For  instance,  while  a  network 
can  be  homophilic,  some  pairings  might  not  be  and  thus  scores  at  the  pair  level 
helps highlight different patterns in the network. Ratios over 1.00 indicate pairs that 
are  more  frequent  than  expected  in  the  network  while  scores  below  1.00  indicate 
the reverse case. 

Cliques and n-cliques: Another concept for studying subgroups in a network is the 
concept of clique. The latter can be defined as the maximal complete sub-network of 
three  or  more  actors.  In  such  a  maximal  complete  sub-network,  all  actors  are 
adjacent to the others. It is important to note that an actor can belong to different 
cliques  in  a  network.  Because  a  clique  is  a  restrictive  definition  of  a  cohesive 
subgroup,  other  related  concepts  such  as  n-cliques  have  been  suggested  in  SNA 
literature. An n-clique is a sub-graph in which each pair of nodes is connected by a 
path of length n or less. 

K-cores:  A  k-core  is  a  sub-network  in  which  each  actor  is  adjacent  to  at  least  a 
minimum  number,  k,  of  other  actors  in  the  sub-network.  This  concept  is  closely 
related to that of degree centrality since an actor is a member of a k-core if it has at 
least a degree centrality of k in the group. The concept of  k-core can be useful for 
identifying  the  more  important  actors  in  ensuring  cohesion  in  a  network,  namely 
those actors that belong in higher-level k-cores. 

Modularity: Actors in a complete network can be partitioned into a varying number 
of  subgroups  called  clusters  (or  communities)  based  on  the  degree  to  which  ties 
between actors are homogeneously distributed among all actors; the more ties are 
heterogeneously distributed, the more clusters there will be. The optimal partition is 
considered to be the one which maximises ‘modularity’, which measures the density 
of ties within clusters as compared to ties between clusters. The higher the maximal 
modularity  for  a  given  network,  the  more  clustered  the  network  with  a  lesser 
density of ties between clusters than within them. 

Clusters: See the description of modularity above. 

Gini  coefficient:  As  was  already  discussed,  averages  were  computed  for  some  of 
the  most  important  indicators  above  to  summarise  the  different  distributions  of 
scores  and  provide  grounds  for  comparison.  However,  given  that  averages  can  be 
highly  affected  by  outliers,  especially  with  low  numbers  of  data  points,  Gini 
coefficients were also produced to assess whether extreme values are prominent or 
not  in  the  different  distributions.  The  Gini  coefficient  is  a  measure  of  statistical 
dispersion  that  is  usually  used  to  highlight  inequalities  in  income  distribution  of 
nations’ residents. For this project, the indicator is used to identify areas where the 
average  scores  might  be  strongly  affected  by  only  a  few  extreme  values,  in  which 
cases the Gini coefficients will be close to 1.00, the maximum case of inequality. On 
the  contrary,  if  all  the  values  are  equal  in  the  distribution,  the  distribution  is 
perfectly equal and the value of the Gini coefficient is 0. Note that the median was 
also used to complement the Gini coefficient for some analyses. 

Probabilistic affinity index: In order to predict, given certain simple entries,  the 
expected  collaboration  potential  of  countries,  probability  affinity  indexes  were 
computed  for  pairs  of  countries  for  different  periods  (e.g.  FP6,  FP7,  prior  to  FP7 
using  scientific  literature)  to  detect  affinities  and  evolution  in  these  affinities.  The 

20 

 
 
probability  affinity  index  takes  into  account  the  total  amount  of  collaborations  and 
redistributes  it  among  the  different  countries,  according  to  their  size.  The 
probabilistic  affinity  index  compares  the  observed  occurrence  of  a  dyad  to  its 
expected occurrence  based  on  the  random  assortment  of  actors  in  the  network.  It 
ranges from -1 (maximum departure below expectation) to +1 (maximum departure 
above  expectation).  Only  dyads  with  an  expected  occurrence  of  at  least  30  are 
presented  in  this  report  as  including  all  dyads  would  bring  a  lot  of  noise  into  the 
analysis. 

Retrieval of FP-related publications 

Retrieval of FP7-related publications in WoS using contact emails in CORDA 

The  production  of  bibliometric  indicators  on  the  scientific  outputs  resulting  from 
specific  FP7  projects  requires  the  identification  of  the  peer-reviewed  scientific 
publications  produced  by  each  individual  FP7-supported  projects,  as  well  as  the 
subsequent extraction of such papers from bibliographic databases amenable to, for 
example,  citation  analysis  (e.g.  the  Web  of  Science  [WoS]  produced  by  Thomson 
Reuters  and  Scopus  produced  by  Elsevier).  A  multi-step  approach  was  used  to 
achieve this work: 

1.  Extraction  of  FP7-supported  publications 

from  the  WoS  using 

funding 

acknowledgements; and 

2.  Extraction  of  additional  FP7-supported  publications  from  the  WoS  using  the 
European Commission’s internal database for monitoring the research outputs of 
FP7 projects (i.e. SESAM). 

Retrieval of FP7-related publications in the WoS using funding acknowledgements 

The  simple  extraction  of  all  publications  acknowledging  FP7  support  in  the  WoS  is 
not in itself sufficient for the task since the publications must then be attributed to a 
specific  project  in  order  to  be  used  in  the  regression  analyses.  Thus,  the  scientific 
publications  authored  by  scientific  contacts  on  FP7-projects  were  first  identified  in 
the  WoS  assuming  that  most  papers  resulting  from  a  given  FP7  project  should 
include the name of the principal investigator (PI)—i.e. usually the scientific contact 
on  a  project.  This  procedure  involved  matching  the  scientific  contacts’  emails  as 
indexed in CORDA with the emails of authors in the WoS. All publications linked to 
these  emails  were  retrieved,  even  those  from  projects  funded  through  other 
sources;  in  other  words,  the  retrieved  publications  include  more  than  just  FP7-
supported  publications.  Unfortunately,  this  process  could  not  be  achieved  for  the 
previous  FP  since  only  the  administrative  contacts  were  available  for  FP6  projects, 
not  the  scientific  contacts,  which  are  essential  to  the  extraction  of  information  on 
scientific publications. 

These publications constituted the initial seeds from which the publication portfolios 
of  scientists  participating  in  FP7  were  built.  These  seeds  were  then  used  in 
combination  with  advanced  disambiguation  algorithms  to  expand  the  pool  of 
publications associated  with  FP7 researchers. However,  it is important  to note that 
not  all  publication  portfolios  built  in  this  manner  were  kept  for  the  final  analyses. 
Indeed,  a  list  of  filters  were  tested  and  applied  in  the  end  to  only  keep  data  that 
would  provide  robust  results  by  removing  false  positives  (i.e.  portfolios  in  which 
publications were erroneously assigned to a given researchers). Below is the list of 
applied filters: 

21 

 
 
Subfield  diversity:  For  each  email  (i.e.  FP7  researcher),  an  index  characterising 
the diversity of subfields in which the attributed publications fall was computed. This 
index takes account of both the number of subfields found within the publication set 
of a researcher as well as of the existing distances between these subfields (e.g. the 
presence  of  both  economics  and  high-energy  physics  in  a  given  portfolio  is  much 
less  likely  than  the  presence  of  applied  physics  and  high-energy  physics  since  the 
subfields  in  the  latter  pair  are  much  closer  to  one  another  than  they  are  in  the 
former  pair).  The  publication  portfolios  for  which  diversity  exceeded  a  specific 
threshold  were  removed  after  manual  validation  demonstrated  that  false  positives 
were too frequent beyond this threshold.  

Number  of  countries/emails:  The  publication  portfolios  that  were  linked  to 
addresses from more than 10 countries or 15 different emails were filtered out, with 
the exception of publications in a set of subfields that frequently have large sets of 
authors  per  publications  (e.g.  Nuclear  &  Particle  Physics,  Fluids  &  Plasmas, 
Astronomy & Astrophysics, Applied Physics and Chemical Physics).  

Surname  proximity:  The  syntactical  proximity  between  surnames  found  in  the 
portfolios  of  a  given  researcher  was  computed  and  the  portfolios  for  which  the 
proximity was too weak were removed. 

Given  name  proximity:  The  syntactical  proximity  between  given  names  found  in 
the portfolios of a given researcher  was computed and the portfolios for which the 
proximity was too weak were removed. 

At  this  stage,  the  selected  portfolios  included  all  publications  produced  by  the 
corresponding researchers, not just those related to FP7 funding. 

Identification of FP-supported publications using funding acknowledgements in WoS 

FP7-supported  publications 
funding 
acknowledgements. At first, the search was not restricted to the publications found 
in the portfolios built in the previous step to cover the entire database. This allowed 
us  to  attribute,  at  a  later  stage,  FP7-supported  publications  to  specific  projects 
through: 

identified  based  on 

in  the  WoS  were 

 

 

The intersection between the pool of papers acknowledging FP7 support and the 
publication portfolios built in the previous step; and 

The  search  for  specific  FP7  project  acronyms  and/or  unique  identifiers  in  the 
funding acknowledgment section of papers acknowledging FP7 support.  

Firstly,  note  that  funding  acknowledgements  in  the  WoS  became  more  available 
starting in  2009  with  nearly  50%  of  all publications indexed  in  the  WoS  having  an 
acknowledgement (Table II). The core period in which papers related to a given FP 
is  expected  to  be  observed  starts  two  years  after  the  start  of  the  first  batch  of 
projects  related  to  it  and  extends  to  two  years  after  the  end  of  the  last  batch  of 
projects.  For  FP6,  the  first  sizeable  batch  of  projects  started  in  2003  and  the  last 
sizeable batch of projects started in 2007 (core period = 2005−2012). For FP7, the 
first sizeable batch of projects started in 2008 and the last sizeable batch of projects 
started in 2013 such that supported papers should continue to get published in the 
years  to  come  (core  period  =  2010−).  Consequently,  data  on 
funding 
acknowledgements  in  WoS  do  not  allow  a  good  identification  of  FP6-supported 
papers  in  the  early  years  of  the  core  period  during  which  one  would  expect  to 
observe  them  (i.e.  2005–2012)  compared  to  FP7-supported  papers  (i.e.  2010–). 
Indeed,  the  share  of  papers  with  funding  acknowledgement  information  stands  at 
32%  for  the  core  period  for  FP6-supported  papers  compared  to  58%  for  FP7-

22 

 
 
supported papers (Table II). For some analyses performed on the aggregated set of 
FP6- and FP7-supported papers (i.e. not by project), this problem is not expected to 
cause  important  bias  because  the  total  samples of  FP6-  and  FP7-supported  papers 
are  quite  large  overall  (i.e.  about  50,000  papers  for  FP6  and  80,000  for  FP7).  For 
analyses  performed  at  the  project  level  (e.g.  in  the  regression  analysis),  FP6  data 
was not considered. 

Table II 

Share of papers with an acknowledgement in the WoS, 2002–
2013 

Note: 

Source: 

FA  =  Acknowledgement.  Based  on  a  random  sampling  of  100  papers  with  an 
acknowledgement  in  the  WoS,  it  appears  that  nearly  all  such  papers  (99%) 
provide  information  on  the  funding  sources  of  the  authors.  We  therefore 
assume  that  the  authors  of  all  such  papers  would  have  acknowledged  FP7  if 
they had been supported by the programme. The core period where supported 
papers  are  expected  starts  two  years  after  the  start  of  the  first  batch  of 
projects related to a programme and extends to two years after the end of the 
last  batch  of  projects.  For  FP6,  the  first  sizeable  batch  of  projects  started  in 
2003  and  the  last  sizeable  batch  of  projects  started  in  2007  (core  period  = 
2005−2012). For FP7, the first sizeable batch of projects started in 2008 and 
the last sizeable batch of projects started in 2013 such that supported papers 
should continue to get published in the years to come (core period = 2010−). 
Computed by Science-Metrix using WoS data (Thomson Reuters) 

for  the  European  Commission—developed  a  match  between 

Science-Metrix—within the context of another project entitled ‘Analysis and Regular 
Update  of  Bibliometric  Indicators’  (Contract  number:  2009_S_158-229751)  and 
its 
performed 
classification of journals by scientific subfield and the  17 thematic priorities funded 
under  FP7.  Using  this  match,  a  comparative  analysis  of  the  availability  of  funding 
acknowledgements  by  thematic  in  the  WoS  was  also  performed  (Table  III).  This 
analysis shows that the availability of funding acknowledgements in the core period 
for  FP7-supported  papers  (i.e.  2010−)  is  highly  variable  across  thematics  with  the 
Socio-Economic Sciences (12%) and Humanities (28%) having the smallest share of 
their  publications  for  which  an  acknowledgement  is  available,  whereas  other  areas 
present  much  higher  shares  (e.g.  Nanosciences  and  Nanotechnologies  with  86% 
availability).  As  a  result,  mixing  data  for  FP7  projects  in  the  Socio-Economic 
Sciences  and  other  thematics  with  a  better  coverage  could  introduce  biases  in  the 
regression analyses to be performed (see Section 5).  

To circumvent this limitation, a dummy variable (C13; see Section 5) was added to 
the  regression  analysis  to  control  for  differences  attributable  to  the  main  scientific 
discipline  of  a  given  project. 
funding 
acknowledgements for papers falling within the 17 FP7 thematic priorities (grouped) 
in  this  period  is  generally  high  within  the  publication  portfolios  of  FP7  participants 
(72%)  compared  to  the  WoS  (53%)  indicating  that  the  population  of  researchers 
participating  in  FP7  likely  acknowledge  more  frequently  their  funding  sources  than 

the  availability  of 

Interestingly, 

23 

YearPapers with FAAll papersShare with FAPapers with FAAll papersShare with FA2002960814,5180%6863,5670%20031,225853,6390%11968,7780%20042,224898,1790%18376,8610%20052,945944,5600%22784,0740%20062,711998,3910%23489,7810%20075,7691,049,3961%57794,3101%2008189,7211,130,85517%21,974100,73122%2009548,1791,185,50646%62,294103,93560%2010662,9091,226,13054%77,401108,76271%2011745,5571,301,23757%86,446115,68775%2012773,8051,300,26960%85,637111,60377%2013845,9031,365,18562%80,504102,76878%Core period for FP6-supported papers (2005–2012)2,931,5969,136,34432%334,790808,88341%Core period for FP7-supported papers (2010–)3,028,1745,192,82158%329,988438,82075%World levelPapers matched to FP7 project participants 
 
 
other  researchers,  or  perhaps  they  simply  publish  more  frequently  in  journals  for 
which such information is available. As  a result, the recall of FP7-supported papers 
is good as shown below. 

Table III 

Share of papers with an acknowledgement in the WoS by FP7 
thematic priority, 2010–2013 

Source: 

Computed by Science-Metrix using WoS data (Thomson Reuters) 

FP7-supported publications  (as  well  as  FP6-supported  papers)  were  extracted  from 
the WoS using a keyword-based approach searching for terms specifically related to, 
for  example,  the  European  Commission,  the  Framework  Programme,  specific  sub-
programmes,  and  specific  call  identifiers.  Search  for  terms  not  specific  to  FP7  only 
(e.g. ‘European Union’) were combined with terms related to FP7 project acronyms 
and/or unique identifiers. 

Using  the  above  data  combined  with  the  results  of  the  retrieval  of  FP7-supported 
papers,  some  inferences  were  made  to  assess  the  overall  recall  of  FP7-supported 
papers.  The  results  of  those  inferences  are  presented  in  Table  IV.  These  results 
show  that  72%  of  the  papers  with  a  funding  acknowledgement  in  WoS  (i.e.  those 
papers that can be identified as FP7-related using the above approach) that should 
be tagged as FP7-supported were so. If we expand the estimation of this recall to all 
papers in WoS (i.e. including those without a funding acknowledgement that cannot 
be identified with the above approach), the percentage drops to 54%, which is still a 
sizeable sample. Indeed, it tells us that 54% of all FP7-supported publications found 
in the WoS were successfully identified.  

For analyses performed in aggregate (i.e. using all FP7-supported papers grouped), 
this sample provides reliable measures. Indeed, statistical analysis usually use only 
a  small  fraction  of  the  total  population,  which  highlights  the  robustness  of  the 
results  obtained  with  FP7-supported  publications  when  analysed  in  aggregates.  On 
the  other  hand,  when  the  results  are  used  at  the  project  level  (as  in  the 
regressions),  a  54%  recall  could  lead  to  important  biases.  Interestingly  though, 
67% of SESAM  items with a funding acknowledgement to FP7 in WoS (i.e.  77% of 
SESAM  items  present  in  the  WoS)  were  labelled  as  FP7-supported  based  on  the 
papers  matched  to  FP7  project  participants.  Assuming  that  FP7  outputs  indexed  in 
SESAM from the final reports of FP7 projects are comprehensive, these data suggest 
that  the  recall  obtained  by  FP7  project  in  WoS  is  higher  than  the  estimated  54% 
approaching  70%.  In  other  words,  when  a  researcher  acknowledges  funding  from 
FP7, he or she likely does so for most of the papers resulting from the given funding 
source.  The  lower  recall  of  54%  would  be  due  to  projects  in  which  none  or  nearly 
none of the related papers acknowledged funding sources. 

24 

FP7 thematic priorityPapers with FAAll papersShare with FAPapers with FAAll papersShare with FA1. Health1,059,4331,939,42855%125,261166,87075%2a. Food, Agriculture and Fisheries113,683203,11056%10,75014,80273%2b. Biotechnology61,39681,51475%7,1958,63483%3. Information and Communication Technologies93,676160,16958%7,12110,74366%4a. Nanosciences and Nanotechnologies56,32468,38682%6,8237,96786%4b. Materials (exluding Nanotechnologies)123,627185,33067%9,66113,18173%4c. New Production Technologies31,41156,86555%1,8083,15457%4d. Construction and Construction Technologies11,03030,95536%9422,22042%5. Energy69,381120,71557%6,78010,28466%6. Environment (including Climate Change & Earth Sciences)192,651260,98674%25,42630,79383%7a. Aeronautics / 9. Space5,01610,89746%5971,15752%7b. Automobiles1,8312,97562%19426573%7c. Other Transport Technologies43,82885,35451%2,7795,26553%8a. Socio-Economic Sciences12,339248,4655%1,0589,19712%8b. Humanities4,787107,9664%5441,92828%10. Security12,76922,90556%1,3011,92168%Total FP7 Thematic Priorities1,893,1823,586,02053%208,240288,38172%Unclassified1,199,7731,707,65770%124,035153,34981%World levelPapers matched to FP7 project participants 
 
 
Table IV 

Recall  of  WoS  papers  tagged  as  FP7-related  using  funding  acknowledgements  based  on  the  whole  of 
WoS and the subset of WoS papers matched to FP7 project participants 

Note: 

Source: 

FA = funding acknolwedgment. ‡ 40,308 FP7 project participants contributed to the production of the 740,706 papers mostly from 
2007 to 2013 (2014 is highly incomplete at the time these data are produced) for a yearly average of 2.6 papers per researcher. Ⱡ 
Note  that  publication  portfolios  for  the  scientific  contacts  of  FP7  projects  could  only  be  constructed  for  roughly  80%  of  all  FP7 
projects  in  CORDA.  *  The  number  of  false  negatives  (i.e.  matched  papers  truly  acknowledging  FP7  support  that  were  not 
successfully tagged as FP7-supported by our automated query searching for references to FP7 acknowledgements) was obtained 
by  multiplying  the  number  of  matched  papers  with  a  funding  acknowledgement  not  tagged  as  FP7-supported  by  5%  (i.e.  the 
estimated  proportion  of false negatives  among  these  papers,  which  is  based on  a  manual  validation  of  a random  sample  of 200 
such papers). † The estimated number of FP7-supported papers (including those without a funding acknowledgement) is based on 
the  shares  of  papers  matched  to  FP7  project  participants  during  the  core  period  (i.e.  2010–2013)  where  supported  papers  are 
expected  (See  Table  III).  The  estimation  assumes  that  when  authors  acknowledge  funding,  they  do  so  for  all  relevant  sources. 
Note that the precision reached in tagging papers as being FP6- or FP7-supported is very high approaching 100% (i.e. negligible 
shares  of false positives;  based on  a random sample  of 100  papers  tagged  as  FP6-supported  and of  100  papers  tagged  as FP7-
supported). 
Computed by Science-Metrix using WoS data (Thomson Reuters)

25 

FP7 ThemeTotalwith FAwith FA & tagged as FP7-supportedwith FA not tagged as FP7-supportedNo. of false negatives*with FA that should be tagged as FP7-supportedEstimated no. of FP7-supported papers (including those without FA)†Recall of those with FA that should be tagged as FP7-supportedRecall of those that should be tagged as FP7-supported (including those without FA)1. Health288,001159,85917,120142,7398,56425,68434,21667%50%2a. Food, Agriculture and Fisheries25,41613,42999312,4367461,7392,39557%41%2b. Biotechnology14,3659,1561,3507,8064681,8182,18274%62%3. Information and Communication Technologies17,6738,6051,9916,6143972,3883,60283%55%4a. Nanosciences and Nanotechnologies12,0778,3481,5716,7774071,9782,30979%68%4b. Materials (exluding Nanotechnologies)22,37212,1171,23310,8846531,8862,57365%48%4c. New Production Technologies5,2162,2224351,78710754294680%46%4d. Construction and Construction Technologies3,4191,131881,0436315135558%25%5. Energy15,5928,0318307,2014321,2621,91466%43%6. Environment (including Climate Change & Earth Sciences)51,21932,2163,75928,4571,7075,4666,62069%57%7a. Aeronautics / 9. Space2,101771876844112824868%35%7b. Automobiles4532455219312648782%60%7c. Other Transport Technologies8,4843,3233302,99318051096565%34%8a. Socio-Economic Sciences14,8391,2821771,105662432,11573%8%8b. Humanities3,118667755923611139268%19%10. Security3,0811,6282071,4218529243271%48%Unclassified253,280154,09024,605129,4857,76932,37440,02576%61%Total740,706‡417,12054,903362,21721,73376,636101,37772%54%Recall based on matched papers relative to the whole of WoSⱠ-------47%36%1. Health--23,182--34,77946,33267%50%2a. Food, Agriculture and Fisheries--1,431--2,5063,45157%41%2b. Biotechnology--1,893--2,5503,06074%62%3. Information and Communication Technologies--4,523--5,4258,18483%55%4a. Nanosciences and Nanotechnologies--2,259--2,8443,32079%68%4b. Materials (exluding Nanotechnologies)--1,854--2,8363,86965%48%4c. New Production Technologies--928--1,1572,01880%46%4d. Construction and Construction Technologies--137--23455258%25%5. Energy--1,351--2,0543,11666%43%6. Environment (including Climate Change & Earth Sciences)--5,559--8,0849,79069%57%7a. Aeronautics / 9. Space--127--18736268%35%7b. Automobiles--78--9513082%60%7c. Other Transport Technologies--531--8201,55365%34%8a. Socio-Economic Sciences--313--4303,74073%8%8b. Humanities--115--16960168%19%10. Security--336--47470071%48%Unclassified--38,806--51,05963,12676%61%Total--83,423--115,704153,90672%54%Papers matched to FP7 project participants (publication year  2007)WoS papers (no limitation based on the portfolios constructed for FP7 participants; publication year  2007) 
 
 
Furthermore, to further reinforce assurance that statistics presented from the above 
set  of  papers  are  robust,  manual  validations  were  performed.  First,  a  random 
sample of 100 publications published during the core FP7 period (2010–2013) in the 
WoS  and  presenting  funding  acknowledgements  were  sampled,  and  99%  of  these 
publications  had  valid  funding  information.  Afterwards,  another  random  sample  of 
100  publications  that  were  tagged  as  FP7-supported  with  the  keyword  approach 
were  sampled  and  99%  of  these  were  valid  FP7-supported  publications.  The  same 
analysis  was  performed  for  publications  tagged  FP6-supported,  with  a  precision 
score  of  97%.  Following  this  slightly  lower  precision,  work  was  carried  out  to  try 
improving the result, but it was not possible to make important gains in this regard.  

Finally,  a  random  sample  of  100  papers  matched  to  the  scientific  contacts  of  FP7 
projects that have a funding acknowledgement in WoS but that  were not tagged as 
FP6-  or  FP7-supported,  was  analysed.  This  test  indicates  that  a  precision  of  about 
97% was reached, indicating  that 97% of these  publications were correctly  tagged 
as  not  being  FP6-  or  FP7-supported.  Again,  work  was  carried  out  to  try  improving 
the results, but it was not possible to make major gains in this regard. 

In  the  end,  the  final  selection  of  FP7-supported  publications  by  scientific  contact 
allowed  the  production  of  bibliometric  statistics  per  scientific  contact,  participating 
organisation  and  project.  Note  that  the  statistics  are  more  reliable  for  the  higher 
aggregation level  (i.e.  project level).  Therefore,  in the  regression  analyses,  project 
level statistics were used. 

Retrieval of FP7-related publications in the WoS using SESAM 

The European Commission’s internal system for monitoring FP7 saw the integration 
of a new tool in late 2012 with the release of the SESAM Research Performance and 
Impact Reporting tool (RESPIR). 

RESPIR  is  a  user  friendly  online  reporting  tool  capable  of  reading  and  processing 
data  on  approved  FP7  projects’  final  reports  and  presenting  a  comprehensive 
summary of statistics and indicators at various levels of aggregation—from FP7 as a 
whole  to  Specific  Programmes,  Theme,  Priority  Areas,  and  even  sub-themes  and 
sub-areas. 

This  application  provides,  for  the  first  time  in  the  history  of  the  Framework 
Programmes  implementation,  a  solid  quantitative  and  some  qualitative  basis  for 
properly and objectively assessing FP7 outputs and results. 

More  specifically,  the  tool  presents  statistical  data  on  research  outputs  (peer-
reviewed  applications,  applications  for  patents,  gender  and  ethical  issues,  etc.) 
based  on  FP7  project  Final  Reports  that  are  submitted  and  registered  in  the 
SESAM  application.  RESPIR  reports  on  data  derived  from  projects  administered  by 
DG RTD and the Research Executive Agency (REA).8 

However, based on informal information provided to Science-Metrix by Commission 
officials,  it  appears  that  data  integrated  into  the  SESAM  database  could  include 
material  from  both  progress  and  final  reports.  This  could  have  proved  to  be 
problematic  for  measuring  research  outputs  as  data  on  ongoing  projects  would 
certainly  be  incomplete,  whereas  those  on  completed  projects  could  contain  some 
biases (e.g. when based on progress reports rather than on final reports). To partly 
circumvent this limitation, Science-Metrix limited the analysis to projects completed 

8  European  Commission.  (2013).  Sixth  FP7  Monitoring  Report:  Monitoring  Report  2012. 

http://ec.europa.eu/research/evaluations/pdf/archive/fp7_monitoring_reports/6th_fp7_m
onitoring_report.pdf. 

26 

 
 
                                                
in  2013  at  the  latest.  Currently,  most  projects  in  SESAM  (99.6%)  were  completed 
before 2014.  

Another  limitation  of  the  SESAM  database  is  that  it  only  covers  projects 
administered  by  DG  RTD  and  the  Research  Executive  Agency  (REA).  It  therefore 
lacks information on projects administered, for example, by the European Research 
Council,  DG  CONNECT,  DG  ENER  and  DG  MOVE.  This  inevitably  led  to  an  unequal 
representation of projects by FP7 sub-programme. For instance,  Table V shows the 
share  of  FP7  projects  in  CORDA  that  appears  in  SESAM  for  each  of  the  four  sub-
programmes  covered  in  this  study.  One  can  see  that  projects  from  the  Idea  sub-
programme  are  not  at  all  represented  in  SESAM.  As  previously  stated,  the 
limitations  in  SESAM/WoS  were  to  be  compensated  for  by  using  both  information 
sources,  meaning  that  the  findings  on  research  outputs  based  on  both  SESAM  and 
the  WoS  would  be  much  more  reliable  than  those  based  on  only  one  of  these  two 
approaches. 

Table V 

Proportion of projects in CORDA appearing in SESAM by sub-
program 

Source: 

Computed by Science-Metrix using SESAM data 

However, if the analysis is limited to projects for which an output can be measured 
in both SESAM and the WoS, no data can be obtained for the Ideas sub-programme. 
Furthermore,  because  only  a  small  share  of  projects  in  CORDA  actually  appears  in 
SESAM  for  the  other  three  sub-programmes,  limiting  the  analysis  to  projects  for 
which  an  output  can  be  measured  with  both  databases  might  result in  a  restricted 
pool  of  projects  for  the  regression  analyses.  Thus,  using  the  previous  approach 
based  on  the  publication  portfolios  (i.e.  those  that  were  built  for  the  scientific 
contacts of FP7 projects in WoS) alone appears more promising. Indeed, as will be 
seen below, this approach provides a more comprehensive coverage of FP7 projects. 
Finally, adding information from SESAM for a subset only of the projects covered in 
the  WoS  would  create  a  bias  in  the  regression  analyses.  Indeed,  the  projects  for 
which information would be available from  both  sources  would be at an advantage 
relative to those for which information is only available from the WoS. 

Nevertheless, the match of SESAM with the WoS was performed and characterised. 
The  outcomes  of  this  analysis  are  presented  in  Table  VI.  Overall,  of  the  close  to 
32,000 items present in SESAM, a little more than 20,000 could be matched to WoS 
(63%). Of the 20,000 items, 15,200 were retrieved in WoS within papers matched 
to  FP7  project  participants,  which  represent  76%  of  all  SESAM  items  matched  to 
WoS. Furthermore about 16,900 SESAM items present a funding acknowledgement 
in WoS (84% of SESAM items in  WoS). Of  these, 77% acknowledged FP7 funding, 
but 10% acknowledged FP6 funding. In addition, 4% acknowledged EC funding, but 
lacked  information  to  establish  the  specific  source  of  funding.  Finally,  15%  did  not 
acknowledge EC funding at all. Thus, it is safe to assume that about a quarter (10% 
relate to  FP6  and  15%  relate  to  funding  sources  other  than  those  from  the  EC)  of 
SESAM items might not actually relate (at least directly) to FP7 funding. 

27 

SP1CooperationSP2IdeaSP3PeopleSP4CapacitiesNumber of projects in CORDA7,1453,8039,2931,853Number of projects in SESAM1,41103,17268220%0%34%37%Number of projects with > 1 publication89502,04924213%0%22%13% 
 
 
In the end, the SESAM items acknowledging FP7 support in the WoS only represent 
a proportion of 15% compared to all WoS papers tagged as FP7-supported, which is 
quite  low.  This  indicates  that  the  first  approach  based  on  publication  portfolios  is 
much  more  comprehensive  in  terms  of  retrieving  FP7-supported  publications  than 
that  based  on  SESAM.  In  fact,  using  the  publications  obtained  using  the  match  of 
SESAM to the WoS  would only add about 8%  of papers to those obtained with the 
approach based on publication portfolios; indeed, of the 13,107 papers from SESAM 
that acknowledge FP7 funding, only 33% (about 4,300) are not already covered by 
those  found  in  the  publication  portfolios  (nearly  55,000).  Based  on  the  above 
findings—and the fact that incorporating two sources of data for a subset only of the 
measured projects would create a bias in their favour in the regression analyses—it 
was  decided  that  SESAM  did  not  provide  enough  enhancement  to  the  coverage  of 
FP7-supported publications in the WoS and was thus discarded from the analysis. 

Table VI 

Characterising the match of papers in SESAM with the WoS 

Note: 

Source: 

† The sum across the four groups exceeds 100% as some papers acknowledge 
support from both FP6 and FP7. 
Computed by Science-Metrix using SESAM data 

Bibliometric indicators  computed using  the  publications  retrieved from the 

WoS 

Number  of  publications:  The  traditional  widespread  publication  count  is  one 
means  of  measuring  and  comparing  the  production  of  various  aggregates  (e.g. 
individuals,  organisations,  regions  and  countries).  It  can  also  be  used  to  evaluate 
output  in  individual  disciplines,  such  as  philosophy  and  economics,  and  to  track 
trends in research fields, collaborative research and many other aspects of research 
output. A number of other indicators can also be derived from these simple counts. 
Full and fractional counting are the two ways of counting the number of papers. In 
this  project,  only  the  full  counting  method  has  been  used.  In  the  full  counting 
method, each paper is counted once  for  each entity listed in the address field. For 
example, if a paper is authored by two researchers from the University of Oslo, one 

28 

GroupNumberNo. of FP7 projects in CORDA22,228No. of FP7 projects in SESAM5,233FP7 projects in SESAM as a share of FP7 projects in CORDA24%No. of FP7 projects in SESAM with papers matched in WoS2,735FP7 projects in SESAM with papers matched in WoS as a share of FP7 projects in CORDA12%Items in SESAM31,730Items in SESAM matched to WoS20,044Share of items matched to WoS63%SESAM items (in WoS) within papers matched to FP7 project participants15,219Recall of SESAM items (in WoS) based on papers matched to FP7 project participants 76%SESAM items with a funding acknowledgement in WoS 16,924Share of SESAM items with a funding acknowledgement in WoS84%Proportion acknowledging FP7 funding†77%Proportion acknowledging FP6 funding†10%Proportion acknowledging EC funding (not enough information to establish the specific source of funding)†4%Proportion without an acknowledgement to EC funding†15%Recall of SESAM items with a funding acknowledgement to FP7 in WoS  based on WoS papers tagged as FP7-supported using the automated query searching for reference to FP7 within the acknowledgement section of papers84%Recall of SESAM items with a funding acknowledgement to FP7 in WoS  based on papers matched to FP7 project participants and tagged as FP7-supported using the automated query searching for reference to FP7 within the acknowledgement section of papers67%No. of SESAM items acknowledging FP7 support in WoS (13,107) as a proportion of WoS papers tagged as FP7-supported (88,054)15% 
 
 
from  the  University  College  London  (UCL)  and  one  from  the  University  of 
Washington, the paper will be counted once for the University of Oslo, once for the 
University College London and once for the University of Washington. It will also be 
counted  once  for  Norway,  once  for  the  UK  and  once  for  the  US.  When  it  comes  to 
groups  of  institutions  (e.g.  research  consortia)  or  countries  (e.g.  EU-28),  double 
counting is avoided. This means that if authors from Norway and France co-publish 
a paper, when counting papers for the EU-28, this paper will be credited only once, 
even though each country will have been credited with one publication count. 

Average  of  relative  citations  (ARC):  The  ARC  is  an  indicator  of  the  scientific 
impact  of  papers  produced  by  a  given  entity  (e.g.  the  world,  a  country,  an 
institution)  relative  to  the  world  average  (i.e.  the  expected  number  of  citations). 
Because it is based on the citations received by the actual publications of an entity, 
it  is  said  to  be  a  direct  measure  of  scientific  impact.  The  number  of  citations 
received by  each  publication  is  counted  for  the  year  in  which  it  was  published  and 
for all the subsequent years. 

To account for different citation patterns across fields and subfields of science (e.g. 
there  are  more  citations  in  biomedical  research  than  in  mathematics),  each 
publication’s citation count is divided by the average citation count of all publications 
that were published the same year in the same subfield to obtain a Relative Citation 
count  (RC).  The  ARC  of  a  given  entity  is  the  average  of  the  RCs  of  the  papers 
belonging  to  it.  An  ARC  value  above  1  means  that  a  given  entity  is  cited  more 
frequently  than  the  world  average  while  a  value  below  1  means  the  opposite.  The 
ARC  was  not  computed  for  the  publications  in  2012  since  Science-Metrix  does  not 
compute  this  indicator  without  a  citation  window  of  at  least  three  years  (i.e. 
publication  year  +  two  years).  Note  that  the  ARC  can  be  computed  for  all 
publications as well as for co-publications. 

Average  of  relative  impact  factors  (ARIF):  The  ARIF  is  a  measure  of  the 
scientific  impact  of  papers  produced  by  a  given  entity  (e.g.  the  world,  a  country) 
based on the impact factors of the journals in which they were published. As such, 
the  ARIF  is  an  indirect  impact  metric  reflecting  the  scientific  ‘quality’  measured  by 
the average citation rate of the publication venue instead of the actual publications. 

Thomson Reuters calculates an annual impact factor (IF) for each journal based on 
the number of citations it received in the previous two years relative to the number 
of  papers  it  published  in  the  previous  two  years.  Thus,  each  journal’s  IF  will  vary 
from year to year. The IF of a journal in 2007 is equal to the number of citations to 
articles  published  in  2006  (8)  and  2005  (15)  divided  by  the  number  of  articles 
published in 2006 (15) and 2005 (23) (i.e. IF = numerator [23]/denominator [38] = 
0.605).  However,  as  Archambault  pointed  out  (2009),  this  indicator  carries  the 
weight of history and of many choices that were made a long time ago, when their 
effect had not yet been thoroughly studied. For example, Moed and colleagues have 
described  the  effect  of  the  observed  asymmetry  between  the  numerator  and 
denominator of the Thomson Reuters’ IF:  

ISI classifies documents in types. In calculating the nominator of the IF, ISI counts 
citations  to  all  types  of documents,  while  as  citable  documents  in  the  denominator 
ISI  includes  as  a  standard  only  normal  articles,  notes  and  reviews.  However, 
editorials, letters and several other types are cited rather frequently in a number of 
journals. When they are cited, these types do contribute to the citation counts in the 

29 

 
 
IF’s numerator, but are not included in the denominator. In a sense, the citations to 
these documents are ‘for free’.9 

In this study, Science-Metrix therefore computes and uses a symmetric IF based on 
the  document  types  that  are  used  throughout  this  entire  project  for  producing 
bibliometric data.  

The  IF  of  publications  is  calculated  by  ascribing  to  them  the  IF  of  the  journal  in 
which they are published for the year in which they are published using a  five-year 
citation  window  (publication  year  and  four  previous  years).  Subsequently,  to 
account  for  different  citation  patterns  across  fields  and  subfields  of  science  (e.g. 
there are more citations in biomedical research than mathematics), each paper’s IF 
is divided by the average IF of all papers that were published the same year in the 
same subfield to obtain a Relative Impact Factor (RIF). The ARIF of a given entity is 
the average of its RIFs (i.e. if an institution has 20 papers, the ARIF is the average 
of  20  RIFs:  one  per  paper).  When  the  ARIF  is  above  1,  it  means  that  the  entity 
scores  higher  than  the  world  average;  when  it  is  below  1,  it  means  that,  on 
average,  the  entity  publishes  in  journals  that  are  not  cited  as  often  as  the  world 
level. Although this indicator was not presented in this report, it is available in the 
report’s  companion  database.  Note  that  the  ARIF  can  be  computed  for  all 
publications and for co-publications. 

Multidisciplinarity  of  FP  projects:  In  order  to  measure  the  multidisciplinarity  of 
projects, the diversity of disciplines of participants was measured. The discipline of 
each scientific contact was determined by retrieving its scientific publications  in the 
WoS  (see  above).  The  discipline  was  identified  using  the  Science-Metrix  journal 
classification.  The  major  advantage  of  this  approach  is  that  a  baseline  similarity 
matrix between disciplines  can  be built  using citation flows among specialities. The 
mix and proximity of specialities within FP projects can then be compared with this 
baseline to compute a measure of multidisciplinarity that takes account of both the 
diversity of specialties among  a given  project’s researchers as well as the inherent 
proximity between them. For example, a co-participation between a biochemist and 
an organic chemist contributes less to multidisciplinarity than collaboration between 
an  astrophysicist  and  a  parasitologist.  A  score  of  multidisciplinarity  was  computed 
for  each  project  and  averaged  for  the  whole  network  by  programme,  by  thematic 
area, and for FP7 for the global period (i.e. 2007–2014) as well as for two separate 
periods  (i.e.  2007–2010  and  2011–2014) to  look  at  trends  over  time.  Trends  over 
time  had  to  be  limited  to  FP7  since  it  was  not  possible  to  build  portfolios  of 
publications for FP6 given that scientific contacts were not provided for FP6. 

Interdisciplinarity of various sets of publications: The approach used consists 
of measuring the diversity of disciplines (i.e. interdisciplinarity) involved in a single 
paper relative to a reference  set  of papers,  while  taking into account the distances 
(or  similarities)  between  scientific  disciplines  (e.g.  two  disciplines  that  are  tightly 
connected  will  contribute  less  to  a  paper’s  diversity  than  two  disciplines  that  are 
somewhat  disconnected).  The  rationale  behind  this  approach  is  based  on  the 
assumption  that  highly  interdisciplinary  research  is  more  likely  to  generate 
innovative results. 

9  Moed,  H.F.,  Van  Leeuwen,  T.N.,  and  Reedijk,  J.  (1999).  Towards  appropriate  indicators  of 
journal  impact.  In  Universidad  de  Colima.  Proceedings  of  the  Seventh  Conference  of  the 
International Society for Scientometrics and Informetrics (pp. 341–354). Colima, Mexico, 
UCOL. 

30 

 
 
                                                
This  indicator  actually  measures  the  variety  of  disciplines  (i.e.  scientific  subfields 
based  on  Science-Metrix’s  ontology)  cited  in  a  given  paper.  Subsequently,  the 
interdisciplinarity of an entity (i.e. a researcher, a research group, an institution) is 
obtained by averaging the scores of its papers. This indicator is based on academic 
work  carried  out  by  a  number  of  scholars  (including  Rafols,  Meyer  and  Stirling). 
Taking  this  work  as  a  base,  Science-Metrix  measured  the  entropy  of  each  paper 
within  the  database  by  comparing  the  frequency  distribution  of  subfields  within  its 
references  to  a  proximity  matrix  between  scientific  subfields.  This  matrix  provides 
the pairwise similarity of subfields based on their co-occurrence patterns within the 
references of individual scientific papers in the database as a whole, which is to say, 
the  statistical likelihood of  two papers  from  different  subfields  being  cited  together 
by  a  third  paper.  Using  this  proximity  matrix  in  computing  the  interdisciplinarity 
indicator gives more weight to unusual co-citation patterns relative to those that are 
very common (e.g. a co-citation between clinical medicine and biomedical research 
contributes less to interdisciplinarity than a co-citation between clinical medicine and 
visual and performing arts). 

Addressing  the  specific  questions  using  social  network  and  bibliometric 

indicators  

Below is a brief description of the rationale behind the proposed analysis of SNA and 
bibliometric indicators to address each of the specific questions. Note that to provide 
satisfactory  answers  to  the  specific  questions,  evidence  was  drawn  from  SNA  and 
bibliometric indicators, surveys, regression analyses and case studies; however, this 
section  only  presents  the  social  network  and  bibliometric  analysis  performed  on 
each SQ. 

SQ1:  To  assess  to  what  extent  the  FP  links  separate  fields  of  knowledge,  bridges 
various  fields  of  science,  and  facilitates  new  scientific  disciplines,  three  analyses 
were performed using SNA and bibliometric data. 

The first analysis consisted in comparing the multidisciplinarity (see above definition 
of this indicator) across specific programmes as well as across thematic/broad areas 
to determine the extent to which FP7 linked researchers from different disciplines. 

The  second  analysis  studied  the  extent  to  which  projects  from  different 
thematic/broad  areas  were  interconnected  through  researchers  participating  in 
multiple  projects.  In  networks,  individuals  and  organisations  select  partners  based 
on  not  only  scientific  or  technological  merit  but  also  other  characteristics  such  as 
geographical  proximity,  common  language,  shared  disciplines,  and  friendship.  This 
tendency of individuals and organisations to associate and bond with similar peers is 
called  homophily.  A  simple  method  to  measure  the  cross-fertilisation  of  various 
fields of science in networks supported by the FP would be to use the department of 
the contact scientist provided in CORDA (after minimal cleaning) and to measure the 
distribution  of  the  variety  of  departments  in  the  network.  A  homogeneous 
distribution  of  similar  departments  in  the  network  would  be  indicative  of  an 
interdisciplinary network, whereas a heterogeneous distribution characterised by the 
amalgamation  of  similar  departments  into  clusters  would  be  indicative  of  a 
homophilic  network.  In  the  former  case,  the  evidence  would  suggest  that  the  FP 
contributed  to  bridging  scientific  disciplines,  which  is  thought  to  promote  the 
emergence of innovation through knowledge recombination. 

However,  it  was  not  possible  to  obtain  reliable  information  on  the  departmental 
affiliation  (e.g.  mathematics,  biology,  mechanical  engineering)  of  researchers  in 
CORDA.  Indeed,  this  information  is  very  sparse.  Furthermore,  even  if  the 

31 

 
 
information  was  fully  available,  it  would  remain  an  imperfect  source  of  data  for 
measuring  interdisciplinarity.  Indeed,  a  partnership  between  the  biochemistry 
department  and  the  molecular  biology  department  would  be  considered  as 
interdisciplinary  as  a  partnership  between  the  department  of  astrophysics  and  the 
department  of  parasitology.  Yet,  the  two  departments  in  the  latter  case  are  much 
more dissimilar than in the former case. 

Consequently, an alternate source of information was required to perform a test for 
the  presence  of  homophily  in  the  networks  (by  programme,  FP7  thematic  areas, 
etc.;  see  Section  3)  of  FP7  participants.  Science-Metrix  used  the  peer-reviewed 
papers—as  indexed  in  the  Web  of  Science  (WoS)—of  FP-supported  researchers  to 
assign them a specialty (see Section 3). The specialty in which a researcher is most 
active is identified based on the journal category in which most of the researcher’s 
scientific  papers  are  published.  The  journal  categories  are  those  found  in  Science-
the  WoS.10  A  score  of 
Metrix’s 
multidisciplinarity  was  computed  for  each  project  and  averaged  for  the  whole  for 
each network analysed  under  FP7 (see  above  definition of the multidisciplinarity of 
FP7  projects).  A  comparison  with  FP6  could  have  been  interesting.  However,  since 
no  information  has  been  provided  to  Science-Metrix  on  the  scientific  contacts  for 
each FP6 project, it was not possible to identify the peer-reviewed papers of specific 
FP6 projects in the WoS. 

journal-based  classification  applied 

to 

Next,  to  assess  whether  or  not  researchers  participating  in  more  than  one  project 
could have contributed to the flow of information between different thematic/broad 
areas,  projects  were  linked  together  when  a  researcher  participated  in  both  (or 
more)  projects.  Then  the  homophily  (see  section  above  for  a  definition  of  the 
homophily)  of  the  linked  projects  was  evaluated  based  on  the  similarity  between 
thematic/broad area. 

Finally,  a  regression  analysis  was  performed  to  see  if  the  multidisciplinarity  of  the 
project  team  translated  into  interdisciplinary  work  as  measured  from  their  FP7-
supported publications (see Section 5). 

SQ2:  Innovation  has  widely  been  described  as  a  triple-helix  model  within  which 
interactions  between  various  actors  (e.g.  universities  and  research  institutes, 
industry [such as SMEs], and government) are its key driver. To assess how far the 
FP  ties  different  types  of  organisations  and  bridges  the  flow  of  innovation  ideas 
between them the following approach will be used. Using CORDA data, this question 
was  addressed  by  measuring  inter-sectorial  co-applications  as  well  as  the  diverse 
types  of  organisations  involved  in  FP6  and  FP7.  Subsequently,  a  test  of  sectorial 
homophily was also conducted to detect clustering of organisations by sector in the 
network.  To  assess  the  influence  FP7  had  on  the  clustering  of  sectors,  homophily 
scores  were  compared  between  FP6  and  FP7  networks.  Moreover,  since  business 
enterprises  (including  SMEs)  play  an  important  role  in  fostering  innovation  within 
collaborative projects, their integration in FP7 compared to FP6 was evaluated. This 
was  done  by  comparing  the  sectorial  shares  of  project  collaboration  for  specific 
programmes, thematic/broad areas and for all projects in FP6 and FP7. 

10  Archambault  É.,  Caruso  J.,  and  Beauchesne  O.  (2011).  Towards  a  Multilingual, 
Comprehensive and Open Scientific Journal Ontology, in Noyons, B., Ngulube, P. and Leta, 
J.  Proceedings  of  the  13th  International  Conference  of  the  International  Society  for 
Scientometrics and Informetrics (ISSI) (pp. 66–77). Durban, South Africa.  

32 

 
 
                                                
focusing  on  an  analysis  of 

SQ3:  To  assess  the  effect  of  the  FP  on  the  creation  of  new  collaboration  and 
integration  of  new  organisations,  various  measures  were  computed  using  CORDA 
data 
to  FP7.  The  main 
measures/indicators  used  are  number  of  actors,  new  actors,  vanishing  actors, 
number  of  dyads,  new  dyads,  vanishing  dyads,  net  flow  of  dyads,  and  network 
density. The analysis was carried on the overall networks of FP6 and FP7 but also on 
the specific programmes and thematic/broad areas (for FP7). 

from  FP6 

trends 

SQ4: In order to detect and characterise the presence of structural holes in an FP-
supported network, the structure of the network was evaluated. This was first done 
through  visual  inspection and  then  the  structure  was  confirmed  by  SNA  indicators. 
Given the core-periphery structure observed, the betweenness centrality was highly 
informative as it reflects the number of times a given actor is found on the shortest 
path between two other actors. The betweenness centrality was computed at every 
level  (i.e.  organisations,  NUTS  3  regions  or  countries)  and  its  distribution  was 
evaluated  with  the  Gini  coefficient.  Moreover  the  density  of  the  networks  was  also 
assessed. 

Also, in a network without structural holes, each actor tends to be equally connected 
to all other actors in the network. When one or a few actors dominate the network 
with  a  high  concentration  of  the  ties  incident  to  them,  they  are  knowledge 
information  hubs  in  the  network  and  the  information  flow  must  go  through  these 
actors  to  diffuse  to  all  regions  in  the  network.  Since  these  large  information  hubs 
often  fall  on  the  shortest  path  between  other  actors  in  the  network,  they  are 
characterised  by  strong  betweenness  centrality.  Thus,  the  distribution  of 
betweenness centrality scores across actors can be used to analyse structural holes. 
In  a  network  with  no  structural  holes,  the  distribution  of  betweenness  scores  will 
tend to be uniform across actors. A strong concentration of high betweenness scores 
for a few actors with most other actors having relatively small scores is indicative of 
the  presence  of  structural  holes,  especially  in  highly  clustered  networks  (i.e.  those 
with high modularity). 

Sectorial betweenness centrality was evaluated with a heat map (which is based on 
the  presence  of  sectors  into  the  decile  distribution  of  the  betweenness  centrality 
versus  their  expected  distribution).  It  was  hence  possible to  see  which  sectors  are 
more distributed at periphery and which ones are more central.  

SQ5: To assess how far FP project results are used as an input for new FP projects, 
and to what extent knowledge generated in previous FPs has been transformed into 
new knowledge, the approach, based on WoS data, consisted of examining the FP7 
supported  scientific  papers  to  determine  the  extent  to  which  they  refer  to 
publications  produced  through  support  from  FP6.  By  referring  to  FP6-supported 
scientific  articles,  the  authors  acknowledge  the  research  done  previously  and  they 
can  align  their  research  based  on  the  knowledge  generated  through  FP6.  FP-
supported  papers  were  identified  in  the  Web  of  Science  database  using  various 
methods described in the sub-section ‘Retrieval of FP-related publications’. 

SQ6:  To  determine  to  which  extent  the  FP  contributed  to  international  research 
relations, quantitative indicators on countries involved in FP6 and FP7 projects were 
compared. These include the number of ERA, non-ERA, EU-15, and EU-28 countries 
participating in  FP7,  as well as the  average number  of countries per project  at  the 
specific programme and thematic/broad area level. To assess the level of integration 
of  entire  networks,  the  mean  (or  median)  of  centrality  scores  was  analysed;  in 
addition,  various  measures  of  the  dispersion  of  these  scores  (e.g.  variance  in 
centrality scores, skewness of centrality scores) were also performed. 

33 

 
 
Table VII 

Comparison of the centrality of countries, FP6 versus FP7 

Note: 

Source: 

The  Wilcoxon  signed-rank  test  was  used  to  compare  the  distribution  of 
centrality  scores  of  countries  within  groups  of  the  ERA  network  from  FP6  to 
FP7. When the Z value (i.e. [sum of signed ranks]/square root [sum of squared 
ranks])  is  positive,  differences  tend  to  be  greater  than  zero  (i.e.  there  is 
generally  an  increase  in  the  centrality  of  countries),  whereas  when  it  is 
negative,  differences  tend  to  be  less  than  zero  (i.e.  there  is  generally  a 
decrease in the centrality of countries). The increase in centrality is statistically 
significant  when  the  p-value  is  smaller  than  0.05  (indicated  by  an  asterisk; 
one-tailed test). 
Computed by Science-Metrix using FP data 

The dispersion of centrality scores in a network is important in assessing the level of 
centralisation  in  this  network  (i.e.  how  much  centrality  is  concentrated  on  a  few 
actors).  Commonly,  centrality  is  highly  concentrated  in  a  network  of  scientific 
collaboration,  with  a  few  hubs  (highly  important  actors  to  the  network  structure) 
having high centrality scores and a majority of relatively unimportant actors to the 
network structure having low centrality scores. When the centrality of a majority of 
countries  is  increased  (as  revealed  by  the  mean/median  centrality  score)  and  the 
dispersion  or  concentration  of  centrality  scores  is  reduced,  countries  are,  on  the 
whole, more integrated and the network is more balanced, with smaller inequalities 
between  actors.  Using  CORDA  data,  this  analysis  can  easily  be  conducted  at  both 
the  international  and  ERA  levels  to  assess  the  integration  of  countries.  Table  VII 
shows a comparison of the centrality measures for some groups of countries under 
FP6 and FP7. A non-parametric test was used to determine whether the differences 
were statistically significant. 

However  centrality  measures  are  scale  sensitive  (the  more  projects  a  country 
collaborates  on,  the  higher  its  centrality  score  will  be),  so  to  assess  if  FP7  had  an 
impact  on  the  collaboration  preferences  of  countries  an  additional  analysis  was 
developed.  A  matrix  of  probabilistic  affinity  index  (PAI)  of  every  collaborating 
country  pair  was  computed.  The  PAI  compares  the  observed  occurrence  of  a  dyad 
(country pair) to its expected occurrence based on the random assortment of actors 
in  the  network  with  a  view  to  compensate  for  scale  effects.  The  indicator  scores 
range  from  -1  (extremely  low  affinity  between  countries)  to  +1  (extremely  high 
affinity  between  countries).  To  reduce  noise,  the  PAI  scores  were  computed  for 
dyads  with  an  expected  occurrence  of  at  least  30  collaborations.  The  PAI  was 
calculated for the co-participation of countries in FP6 projects and in FP7 projects as 
well  as  for  the  co-authorship  of  scientific  articles  supported  by  the  FPs  (for  the 
retrieval  of  supported  papers,  see  above).  To  evaluate  the  shift  (or  absence  of)  in 
the  collaboration  pattern  from  FP6  to  FP7,  the  two  matrices  were  compared, 
correlated  and  the  correlation  was  depicted  on  a  graph.  This  was  done  for  the 
matrices  of  FP  projects’  co-participation  as  well  as  for  the  matrices  of  scientific 
articles’ co-authorship. 

34 

Titre de l'étude     Wilcoxon signed-rank test (FP6 versus FP7) Group   % of countries with  increased ranks   Z score   p-value EU27   74%   1.970   0.025* EU12 (new)   92%   2.746   0.003* EU15 (old)   60%   0.398   0.346 ERA   79%   2.707   0.004* Non-ERA   71%   2.397   0.009*   
 
 
Figure 2 

Fictitious  comparison  of  countries’  affinities  in  co-participation 
in FP6 projects and co-participation in FP7 projects 

For  example,  in  Figure  2,  countries  A  and  C  exhibit  a  negative  affinity  in  co-
participation  of  FP6  projects  (left)  but  have  a  higher  than  expected  affinity  in  co-
participation  of  FP7  projects  (right).  Thus,  in  this  case,  the  two  countries  have 
increased their collaboration from the FP6 to FP7 period. If the values in the matrix 
of co-participation are all close to zero (meaning no preference); this means that FP 
support  has  a  nil  effect.  If  the  two  matrices  are  highly  correlated,  then  one  can 
conclude  that  the  ‘natural  affinity’  in  research  has  been  reproduced  in  co-
participations  in  the  FP  and  that  the  latter  does  not  entail  positive  (or  negative) 
discrimination.  

Also, to explore the attractiveness of countries, the affinity was averaged for every 
pair a country is found in. This was done for every country for both FP6 and FP7 and 
the  difference  between  the  two  averages  gave  the  change  in  attractiveness  of  a 
country. 

SQ7:  The  integration  and  participation  of  regions  (NUTS  3)  was  evaluated  by 
computing indicators at the NUTS 3 level with data from the CORDA database. Such 
indicators  include  the  number  of  NUTS  3  regions  participating  in  FP7,  the  average 
number of NUTS 3 regions per project, the average number of projects per NUTS 3 
region, the density of the NUTS 3 network, the average betweenness centrality and 
the  average  degree  of  the  NUTS  3  networks.  The  density  gives  information  about 
the  completeness  of  the  network  (a  higher  density  means  a  more  connected 
network),  the  average  betweenness  centrality  measures  how  well  regions  are 
connected to important regions, and the average degree provides information about 
the  average  number  of  partners  each  region  has.  Lastly,  to  assess  integration,  an 
affinity  index  measuring  the  affinity  between  each  region’s  pairs  was  computed  in 
the same manner as in SQ6 (with regions instead of countries). Affinities were then 
compared  between  FP6  and  FP7  and  the  highest  differences  in  attractiveness  were 
highlighted. 

35 

Matrix of affinity in co-authorshipMatrix of affinity in co-participationCountry ACountry BCountry CCountry DCountry ECountry ACountry BCountry CCountry DCountry ECountry A0.85-0.76-0.010.23Country A0.810.580.020.08Country B0.850.36-0.10.68Country B0.81-0.81-0.20.01Country C-0.760.360.710.07Country C0.58-0.810.120.11Country D-0.01-0.10.71-0.46Country D0.02-0.20.12-0.61Country E0.230.680.07-0.46Country E0.080.010.11-0.61Matrix of affinity in co-authorshipMatrix of affinity in co-participationCountry ACountry BCountry CCountry DCountry ECountry ACountry BCountry CCountry DCountry ECountry A0.85-0.76-0.010.23Country A0.810.580.020.08Country B0.850.36-0.10.68Country B0.81-0.81-0.20.01Country C-0.760.360.710.07Country C0.58-0.810.120.11Country D-0.01-0.10.71-0.46Country D0.02-0.20.12-0.61Country E0.230.680.07-0.46Country E0.080.010.11-0.61 
 
 
 
Table VIII 

Descriptive statistics and how they will support the Specific Evaluation Questions (SQ) and the multivariate 
regressions 

Requirement  Variable 

Network-
level 
(actors) 

Other 
dimensions 

Details 

Evaluation 
questions 
directly 
addressed with 
this 
information1  

Variables 
for  multi-
variate 
regression 
at  project 
level2 

1- Provide the 
headline overall 
figures – number 
of participants, 
number of 
networks created 
in total and per 
specific 
programme; 
participating 
countries, 
participating 
organisations 
(type) 

2- Network 
structure and its 
characteristics 
(core vs. 
periphery): 

Number of 
participants 

NA 

 Program 
 Thematic 

area 

 FP6 vs. FP7 

 Number of participants (organisations, NUTS, 

countries) in each project 

 Number of organisations in each programme, 

thematic area, country and overall 

 Average number of participants/project for 
programme, thematic area, organisation, 
country and overall. 

 Data per start year and overall 

SQ6 - to what 
extent does the FP 
contribute to 
international 
research relations 
between the EU 
and third countries 
SQ8 - Comparison 
with FP6 

Number of 
participants 
(organisation
s, NUTS, 
countries: 
ERA, non-
ERA and 
overall) 
(most SQ, 
more 
specifically 
SQ6 and 
SQ7) 

Number of networks 

NA 

 Program 
 Thematic 

area 

Number of networks (projects) in each 
programme, thematic area, organisation, NUTS, 
country and overall 

Actor-level 
 Degree centrality 
 Closeness centrality 
 Betweenness 

centrality 
 Eigenvector 
centrality 

Network-level 
 Centralisation 
 Modularity 
 Graph density 

 Country 

(ERA, non-
ERA) 
 NUTS 
 Organisatio

n 

 Program 
 Thematic 

area 

 Centrality indicators will be computed for each 
actor (country, NUTS, organisation), for each 
programme, thematic area and overall 
 Graph density and centralisation will be 

computed for each programme, thematic area 
and overall 

 Additionally, all network-level indicators will be 

useful to assess the overall structure of the 
network (e.g. small world, distributed clusters 
or networked communities) 

SQ6 - to what 
extent does the FP 
contribute to 
international 
research relations 
between the EU 
and third countries 
SQ7 - to what 
extent FPs 
contribute to 
integrate countries 
and regions 
SQ8 - Comparison 
with FP6 

Using 2-mode 
network 
(project-
contact 
scientist) 
measure the 
centrality of 
projects (e.g. 
eigenvector 
as a proxy for 
density for 
SQ3, 
betweenness 
as a proxy for 
brokers for 
SQ4) 

36 

 
 
  
  
Requirement  Variable 

Network-
level 
(actors) 

Other 
dimensions 

Details 

Evaluation 
questions 
directly 
addressed with 
this 
information1  

Variables 
for  multi-
variate 
regression 
at  project 
level2 

3- Networks 
numbers and 
structure per 
specific 
programme; per 
specific thematic 
area 

4- Key actors by 
different 
positions, their 
roles, and their 
prestige in the 
network: central 
actors, brokers, 
bridges, 
amplifiers, filters, 
multiple and 
simple relays 
(key 
organisations in 
terms of 
rankings) 

5- Identify core 
clusters, 
components, 
cores, and 
cliques (key 
networks) 

See requirement 1 
and 2 above 

See 
requirement 
1 and 2 
above 

See 
requirement 
1 and 2 
above 

See requirement 1 and 2 above 

 Degree centrality 
 Closeness centrality 
 Betweenness 

centrality 
 Eigenvector 
centrality 

Organisation 

 Program 
 Thematic 

area 

Brokers, bridges 

Organisation 

 Program 
 Thematic 

area 

Amplifiers, filters, 
multiple and simple 
relay 

Organisation 

  Program 
 Thematic 

area 

 Components 
 Clusters 
 Cores 
 Cliques 

 Country 
 NUTS 
 Organisatio

n 

  Program 
 Thematic 

area 

No definition of these concepts in the context of 
social network analysis was found despite a 
thorough search of available literature. It would 
be useful if the Commission could provide an 
operational definition of these concepts. 

We suggest computing the strict versions of 
these indicators first and then relaxing the 
conditions to obtain more useful results, if 
needed. For example, we foresee that all 
networks may have only one component or one 
large component with some small isolated 
components. It could then be useful to filter out 
some of the weaker edges to identify more sub-
networks. Similarly, the strict definition of 
cliques could be relaxed to use an alternative 
such as n-cliques or n-clans 

Number of 
brokers/bridg
es associated 
to each 
project (SQ4) 

Potentially: 
number of 
Amplifiers, 
filters and 
relays 
associated 
with each 
project (SQ4) 

37 

 
 
  
  
  
  
  
  
  
  
  
  
Requirement  Variable 

Network-
level 
(actors) 

Other 
dimensions 

Details 

6- Identify 
structural holes 
(missing ties 
preventing a 
more balanced 
network); How 
FP contribute to 
bridge over 
structural holes 

 Distribution of 
betweenness 
(weighted/unweight
ed) among actors 

 Matrix of co-

authorship of 
countries in science 
generally 

& 
 Matrix of co-

participation in FP6 

Versus 
 Matrix of co-

participation in FP7 

 Country 
 NUTS 
 Organisatio

n 

 Program 
 Thematic 

area 

 FP6 vs. FP7 

 In a network with no structural holes, the 
betweenness of each actor will tend to be 
equal. A large diversity of betweenness scores 
indicates the presence of structural holes. 
Actors with high betweenness scores 
potentially ‘benefit’ from these structural holes 
By color-coding (gradient) the actors based on 
their betweenness scores, we can determine 
the position of structural holes in each 
network (missing ties between low 
betweenness actors) 

 Also, to assess if FP7 had an impact on 

bridging structural holes, we will compare the 
matrix of co-participation of countries in FP7 
with a similar matrix for FP6 and also with the 
matrix of co-authorship between countries 
before FP7 

Evaluation 
questions 
directly 
addressed with 
this 
information1  

Variables 
for  multi-
variate 
regression 
at  project 
level2 

SQ4 - where are 
the structural holes 
(missing ties) in 
the structure of the 
FP network, and 
who benefits from 
them 
SQ8 - Comparison 
with FP6 

Average 
and/or max 
betweenness 
of 
participants 
in each 
project (SQ4) 

7- Assess the 
role of industry, 
SMEs, public 
authorities, 
national and 
European 
agencies, etc., 
relevant to the 
particular specific 
programme/the
matic area 

Actor-level 
 Degree centrality 
 Betweenness 

centrality 
 Eigenvector 
centrality 

 Brokers 

Network-level 
 Homophily 

Organisation 
(analysis 
based on the 
classification 
of 
organisation 
by sector) 

 Program 
 Thematic 

area 

 FP6 vs. FP7 

 First, different indicators (degree centrality, 

betweenness centrality, eigenvector centrality, 
brokers) will be produced for each sector in 
each network to determine if the organisations 
in the sector are core or peripheral in the 
network and also to assess the propensity of 
organisations in different sectors to be in a 
position to act as broker in the network 

 Then, the homophily of networks will be used 
to determine if organisations from each sector 
are distributed randomly in the network or are 
clustered in some areas and absent in others 

SQ2 - to what 
extent does the FP 
tie different types 
of organisations 
(universities and 
research institutes, 
industry, SME, 
etc.) and bridge 
the flow of 
innovation ideas 
between them 
SQ8 - Comparison 
with FP6 

Presence/abs
ence of 
organisations 
for different 
sectors 
Number of 
sectors 
involved 
(SQ2) 

38 

 
 
Requirement  Variable 

Network-
level 
(actors) 

Other 
dimensions 

Details 

8- Describe 
network 
dynamics over 
time (duration, 
stability or 
variation) 

Actor-level 
 No. of organisations 
 No. of new 

organisations 

 Vanishing 

organisations 

 Net flow of 

organisations 

Dyad-level 
 No. of dyads 
 New dyads 
 Vanishing dyads 
 Net flow of dyads 

Network-level 
 Network density 

Organisation 

 Program 
 Thematic 

area 

 The network of organisations in FP7 will be 
compared to the network of organisation in 
FP6 in order to determine if new organisations 
are involved and if organisations are 
collaborating with new partners 

 The density of networks will be compared 

 FP6 vs. FP7 

between FP6 and FP7 

 In contrast, organisations and pairs of 

organisations that extend from FP6 and FP7 
contribute to the chaining of knowledge. 

9- Identify trends 
and specificities 
per specific 
programme and 
thematic area 

Trends and 
specificities will be 
identified within other 
requirements 

Trends and 
specificities 
will be 
identified 
within other 
requirements 

Trends and 
specificities 
will be 
identified 
within other 
requirements 

Trends and specificities will be identified within 
other requirements 

Variables 
for  multi-
variate 
regression 
at  project 
level2 

Number of 
lasting dyads 
(SQ3 and 
SQ5) 

Evaluation 
questions 
directly 
addressed with 
this 
information1  
SQ3 - to what 
extent has the FP 
contributed to new 
collaboration and 
integration of new 
organisations? How 
many new 
collaborations 
emerged due to 
the FP 
SQ5 - to what 
extent are FP 
project results 
used as an input 
for new FP 
projects, and to 
what extent 
knowledge 
generated in 
previous FPs has 
been transformed 
into new 
knowledge? 
SQ8 - Comparison 
with FP6 

39 

 
 
  
  
Requirement  Variable 

Network-
level 
(actors) 

Other 
dimensions 

Details 

10- 
Interdisciplinarity 
(not  required  per 
se  but  essential 
to  answer 
the 
specific 
questions) 

 Interdisciplinarity 

Index 

 Homophily 

2-mode 
project-
contact 
scientist 

 Program 
 Thematic 

area 

 The  interdisciplinarity  index  will  be  computed 

for each project 

 Homophily  of  disciplines  will  be  computed  on 
2-mode networks to determine if projects from 
different disciplines are closely connected 

1Regressions will be used to supplement these findings. 
2Most of these regressions will feed evaluation questions. Please see Section 5 for more details.

Evaluation 
questions 
directly 
addressed with 
this 
information1  

Variables 
for  multi-
variate 
regression 
at  project 
level2 

SQ1  -  To  what 
extent  does  the  FP 
tie  separate  fields 
knowledge, 
of 
different 
bridge 
field  of 
science, 
and  facilitate  new 
scientific disciplines 
SQ8  -  Comparison 
with FP6 

Level 
of 
interdisciplina
rity (SQ1) 

40 

 
 
4.  SURVEY OF FP7 PARTICIPANTS 

Sampling Procedure 

The  overall  survey  sample  of  around  8,000  potential  respondents  was  compiled 
based on the full set of raw CORDA provided by the European Commission’s services. 
As outlined in the  inception report, this selective  approach was adopted in  line  with 
the tender specifications to prevent an undue burden on FP7 participants, who were 
also approached by other evaluation projects conducted in parallel at the time of this 
study.  In  a  very  first  step,  Science-Metrix  centrally  removed  all  information  on 
projects  conducted  under  EURATOM,  JRC  (direct  actions)  and  all  other  activities 
outside  the  perimeters  of  the  four  specific  programmes,  which,  according  to  the 
tender  specifications,  should  not  be  part  of  the  study  and  thus  had  to  be  removed 
from  all  analyses.  Furthermore,  all  the  information  on  the  Networks  of  Excellence 
(NoE)  and  ERANETs  was  removed  from  the  raw  CORDA  dataset.  This  left  data  on 
21,969 projects in the dataset. 

The  remaining  CORDA  used  for  the  sampling  process  thus  contained  116,032  of 
originally more than  120,200 participant datasets. For the purposes of this study, a 
dataset was defined as one distinct FP7 participant in the context of one specific FP7 
project,  taking  into  account  that  some  FP7  participants  have  taken  part  in  different 
projects  with  different  characteristics  leading  to  varying  experiences.  Analytically, 
focusing each request for participation on a particular project allowed us to ascertain 
that  an  equally  focused  perspective  was  taken  by  all  respondents  and  that  one 
person’s varying experiences do not blur his or her assessment in the questionnaire. 
To address those  respondents with  appropriate in-depth knowledge on the projects’ 
character  and  results,  participants  were  addressed  through  scientific  contacts.  In 
cases  where  a  participant  had  listed  more  than  one  scientific  contact  (there  were 
19,603 more scientific than administrative contacts), one was selected on a random 
basis. 

Before  the  actual  sampling  process  started,  the  CORDA  database  was  cleaned  of 
corrupted  datasets  (of  which  there  were  relatively  few)  and  datasets  that  lacked 
information on crucial attributes, most importantly the scientific co-ordinator’s email 
address  (of  which  there  were  comparatively  many,  around  5%).  Every  dataset  that 
included information like ‘   ’, ‘NULL’, ‘-’, or other characters that did not conform to 
an email address format were excluded from the sampling process. Moreover, all FP7 
participants from  outside the  EU’s Member  States or  FP7  Associated  Countries were 
excluded from the population, firstly, since the analytical focus of the study is on the 
latter  two groups,  and  secondly,  since  experience  from  previous  studies  shows  that 
few responses can be expected from this sub-group. 

To  ascertain  a  representative  yet  fully  random  sample,  a  stratified  approach  was 
chosen that takes into account the three core dimensions  ‘start date of the project’, 
‘country  affiliation  of  the  participant’  and  ‘FP7  sub-programme’.  As  the  former  two 
can  be  manifested  in  too  high  a  number  to  stratify  even  a  large  population  like 
CORDA, these were assigned to a more limited number of groups, such as  ‘projects 
started in the first half of the support period’, ‘projects started in the second half of 
the  support  period’,  and  nine  groups  of  Member  States  and  FP7  Associated 
Countries.11  

11 G1: DE, AT; G2: FR; G3: IT; G4: DK, SE, FI; G5: UK, IE; G6: BE, NL, LU; G7: ES, PT; G8: 
HU, CZ, PL, CY, RO, BG, SI, SK, HR, EE, LT, LV, EL, MT; G9: RS, IS, MK, MD, ME, BA, AL, 
FO, LI, CH, NO, IL, TR 

41 

 
 
                                                
Finally,  the  remaining  CORDA  population  of  FP7  participants  was  stratified  along 
these  groups  into  72  sub-classes  (such  as  ‘first  half  of  period-projects  from  France 
under the Cooperation  programme’) from which, in turn, individual random samples 
were drawn and re-aggregated into the final sample. In technical terms, the process 
was  implemented  through  complex,  tailor-made  SQL-scripting  in  an  ORACLE 
environment. 

The overall sample of participants obtained was made up of slightly more than 8,000 
potential  respondents  (8,076)  with  formally  valid  emails.  A  number  slightly  above 
8,000 was chosen as spot-checks revealed that some email addresses were factually 
very unlikely to be accurate, even though they met all the formal requirements for an 
email  (‘a@b.com’,  ‘na@na.na’,  etc.).  This  issue  will  be  discussed  further  in  the 
description of the survey execution below. 

Pre-testing 

Once a first draft was made, the questionnaire was discussed extensively among the 
project partners so that an adapted version was included in the revised version of the 
inception  report.  This  version  was  approved  as  a  basis  for  further  work  by  the 
European  Commission  in  April  2014.  At  this  point,  an  online  version  of  the  draft 
questionnaire  enabled  a  more  broad-based  approach  to  pre-testing.  Further 
modifications  and  improvements  to  wording  and  presentation  were  made  on  the 
basis  of  internal,  senior  level  reviews  and  staff  discussions  at  Fraunhofer  ISI. 
Following  a  final  review  by  the  lead  partner,  the  draft  version  of  the  online 
questionnaire was sent to the European Commission for approval on 06/05/2014.  

Once  it  had  been  officially  approved,  the  Fraunhofer  ISI  started  pre-testing  the 
questionnaires  with  external  experts  in  six  detailed  phone  interviews  that  lasted 
between  30  minutes  and  one  hour.  Following  this,  the  resulting  draft  questionnaire 
was sent to a stratified random sample of more than 80 FP7 participants, who were 
invited  to  submit  comments  via  the  EFS  survey  tool’s  comment  functionality.  The 
sample  required  was  established  based  on  exactly  the  same  sampling  methodology 
used for the full sample. The process used ensured that only those participants were 
addressed who  would not be  part of the actual  sample  later—based on the premise 
that participants who have already committed 30-45 minutes to a pre-test should not 
be  approached  for  a  second  time  by  the  same  evaluation  project.  Due  to  the 
shortage of time, pre-testers were given a week to respond and around 10 complied. 
With  this  last  step  completed,  the  questionnaire  was  finalised  and  prepared 
technically for the launch of the full-scale survey. 

42 

 
 
Figure 3 

Survey participants by country 

Source: 

Matching of Survey and CORDA, Fraunhofer ISI 

Survey Implementation and Field Phase 

On  19/05/2014,  the  survey’s  potential  participants  received  an  invitation  by  email, 
which  was  automatically  sent  as  either  .html  or  pure  text  format  according  to  the 
configuration  of  the  recipient  server.  To  ensure  a  professional  impression,  all  the 
emails were personalised and included a reference to the addressee’s academic title, 
if  present.  Furthermore,  the  invitation  included  an  official  letter  from  the  European 
Commission  as  an  attachment.  With  a  view  to  potential  concerns  with  regard  to 

43 

050100150200250300350400FOBAISLUMKLVCYHRMTLTEERSSKBGTRROSICZIEHUNOILPLFIDKPTATELCHSEBENLFRESUKITDE 
 
 
privacy, another attachment included a detailed project-specific privacy statement as 
well as later options to insist on anonymisation and to opt out of being contacted for 
interviews.  To  ensure  that  answers  were  given  for  the  intended  project,  the 
respective project name was included in the email heading.  

The survey was open for participation from  19/05/2014 to 16/06/2014. As Figure  4 
documents,  the  distribution  of  responses  across  the  field  period  did  not  reveal  any 
irregularities.  As  to  be  expected,  the  largest  number  of  responses  occurred  during 
the  days  following  the  first  invitation  (16/06/2014)  as  well  as  following  the  first 
(28/05/2014) and second (06/06/2014) reminders with a generally decreasing trend 
and  a  constant  baseline  of  around  20  daily  responses  on  weekdays.  The  average 
number  of  responses  per day  amounted  to  89.5,  the  average  number  of  completed 
responses to 66.5. 

Throughout  the  period,  invited  participants  were  consulted  by  email  and,  in  specific 
cases,  by  phone.  Responsibilities  for  the  survey  were  made  transparent  so  that 
participants  with  grievances  or  questions  could  personally  address  the  project 
manager. 

Figure 4 

Distribution of responses across the field period 

Source: 

EFS Survey, Fraunhofer ISI 

44 

 
 
 
The  final  total  sample  size  amounted  to  8,448,  of  which  7,591  respondents  could 
actually be reached (i.e. their emails did not return error messages) (see Figure 5). 
2,595 of the addressees started to answer the questionnaire (34.19% response rate) 
and  1,929  completed  it  in  full  (25.41%).  Of  those,  1,508  could  be  identified  as 
funded  under  the  Co-operation  programme,  154  were  funded  under  the  Ideas 
programme,  503  were  funded  under  the  People  programme,  and  426  were  funded 
under the Capacities programme. 

Figure 5 

Final field report: participation and completion rates. 

Source: 

EFS Survey, Fraunhofer ISI 

As can be seen, the overall sample documented by the survey software exceeds the 
original sample of 8,076. This difference is due to the fact that, as documented in the 
original proposal and the inception report, factually invalid email addresses were not 
simply  accepted  as  such  but  inspected  manually  and  attempts  made  to  reach  the 
respective persons at, for example, their new workplaces or via a more current email 
address.  In  detail,  we  obtained  all  the  emails  that  had  technically  bounced  through 
antispameurope.com  and  then  disregarded  all  the  addresses  that  allowed  the 
conclusion that our survey server was being denied delivery as a potential source of 
spam.  In  other  cases  where  we  found  indications  that,  for  example,  ‘an  email  no 
longer exists at this server’, we tried to identify an alternative email address through 
web  searches and targeted emails inquiring  about  the respective co-ordinator’s new 
organisational association. Moreover, targeted replies to the project’s dedicated email 
inbox were taken into account. 

In  general  terms,  the  25.41%  completion  rate  obtained  constitutes  a  substantially 
above-average  achievement  when  compared  to  similar  surveys,  even  taking  into 
account  that  this  one  was  run  under  the  official  mandate  of  the  European 
Commission.  The  originally  promised  overall  sample  size  of  600  was  able  to  be 
exceeded by far.  Moreover, the fact  that 74.34% of those  respondents  who  opened 
the survey’s first page went on to answer all its questions indicates that it was well 
designed and favourably received  by its target group.  As illustrated in  Figure 6, the 
drop-out rate is more or less constant across the different questions, indicating that 
none  of  them  deterred  an  above-average  share  of  respondents.  Against  this 

45 

 
 
 
background,  there  is  no  reason  to  assume  a  selection  bias  in  drop-outs.  Instead,  it 
seems  likely  that  the  reasons  for  not  fully  completing  the  survey  are  in  most  cases 
unrelated  to  the  survey  as  such  and  have  more  to  do  with  a  general  lack  of  time, 
being interrupted, and so forth. These conclusions are backed up by the fact that the 
number  of  complaints  in  the  form  of  phone  calls,  emails,  and  negative  text  field 
entries  was  notably  low  in  light  of  the  comparatively  large  sample  population  and 
below average when compared to other surveys. 

Figure 6 

Drop-out rates 

Source: 

EFS Survey, Fraunhofer ISI 

Nonetheless,  the  fact  that  some  respondents  did  drop  out  during  completion  of  the 
survey  will  result  in  slightly  differing  overall  samples  for  the  individual  survey 
questions  (n).  Against  the  above  background  and  as  the  differences  are 
comparatively  small,  however,  no  specific  efforts  had  to  be  undertaken  to  further 
reduce the sample to those that answered all the questions. 

Addressing the specific questions using survey data 

This section states more explicitly which survey questions have been used to address 
each of the specific evaluation questions in the Tender Specifications. As can be seen 
in Table IX and Table X, most specific questions are reflected directly in one or more 

46 

 
 
 
survey  questions  as  are  the  key  underlying  aspects  of  the  study’s  two  fundamental 
questions. 

First,  preliminary  descriptive  analyses  have  been  conducted  to  examine  the  basic 
distributions  of  the  variables  as  well  as  their  differing  patterns  across,  for  example, 
different  FP7  sub-programmes,  thematic  areas,  and  participating  Member  States, 
regions  and  organisations.  Section  4  shows  how  the  survey  information  is 
incorporated  into  the  regression  models  that  were  tested,  the  main  goal  being  to 
shed further light on the impact of networking on the creation of beneficial outcomes. 

Table IX 

Survey  questions  addressing  the  Fundamental  Evaluation 
Questions 

Note: 

FQ = Fundamental Question 

47 

Call for Tenders Question #Survey QuestionFQ1 & FQ2Success in networking2.1 Was a project manager assigned to the FP7 project?2.2 Was a steering committee convened to define the main scientific orientations of the FP7 project and to monitor its scientific quality’?2.3 Which types of resources were shared by the FP7 project partners?2.4 How often and in which manner was the work of the FP7 project partners coordinated through personal meetings?2.5 How often and in which manner was the work of the FP7 project partners coordinated by electronic means of communication (phone, video conference etc.)?2.7 Which internal channels knowledge exchange between the project partners were used in the FP7 project?Research outputs3.13 Has the FP7 project resulted in the production of any of the following types of scientific & technological knowledge by your organization?3.14 Has the FP7 project resulted in the development of any of the following types of scientific & technological tools by your organization?3.15 How many additional researchers joined your team within your organization for the period of the FP7 project?3.16 How many researchers have remained / will likely remain with your current organization after the for-mal end of the FP7 project?3.17 Did the FP7 project, during its life-cycle, establish links between your organisation and other organisations that are not part of the project? 3.18 Please indicate if the FP7 project has prompted your organisation to introduce product innovations?3.19 Please indicate if the FP7 project has prompted your organization to introduce process innovations?Research outcomes4.9To what extent has the FP7 project contributed to resolving or addressing the following Grand Challenges identified by the European Union?4.10 Has the FP7 project been successful in training junior researchers from your organisation in the sense that, as a result of the project-related activities: 4.11 Has your FP7 project been successful at developing senior researchers’ careers in the sense that, as a result of the project-related activities:4.12 Has your FP7 project been successful in attracting internationally renowned researchers from outside the European Union and FP7 associated countries?4.13 Will your organisation use main results of this FP7 project as the basis or key inspiration for another fu-ture project?4.14 Will your organisation use by-products of this FP7 project as the basis or key inspiration for another future project?4.15 To what extent has your FP7 project contributed to the further development of existing joint research infrastructures?4.16 To what extent has your FP7 project contributed to the creation / establishment of new joint research infrastructures?4.17 To what extent has your FP7 project initiated a new line of research? 
 
 
Table X 

Survey questions addressing the Specific Evaluation Questions 

Note: 

SQ = Specific Question 

48 

Call for Tenders Question #Survey QuestionSQ11.1 Which fields of science and technology did your FP7 project cover?1.2 Which fields of science and technology are typically covered by your organisation?3.4 Among these partners [collaborated with before], how many covered fields of science and technology not typically covered by your organisation?3.8 Among these [outside FP6&7] partners, how many covered fields of science and technology not typically covered by your organisation? 3.12 Among these new partners, how many covered fields of science and technology not typically covered by your organisation? 4.2 To what extent has the FP7 project helped your organisation to integrate separate contributions from researchers from disciplines that are not at the core of its usual research activities? 4.3 To what extent has the FP7 project helped your organisation to integrate into its main research effort techniques, concepts, and/or theories from disciplines that are not at the core of its usual research activities? 4.4 To what extent has the FP7 project helped your organisation to borrow methods, skills, or theories from disciplines that are not at the core of its usual research activities?4.19 To what degree do you consider the collaboration with a heterogeneous group in terms of fields of science and technology to hinder the progress of your FP7 project? SQ2On the linking of the various research performers:3.3 Among these partners [collaborated with before], how many belong to the following types of organisations?3.7 Among these [new] partners, how many belong to the following types of organisation?3.11 Among these new partners, how many belong to the following types of organisation?On the quality of the linking and networking: 2.4 How often and in which manner was the work of the FP7 project partners coordinated through personal meetings?2.5 How often and in which manner was the work of the FP7 project partners coordinated by electronic means of communication (phone, video conference etc.)?2.6 Did/Does your organisation use any specific IT tech-nologies to share FP7 project related information?2.7 Which internal channels knowledge exchange be-tween the project partners were used in the FP7 pro-ject?4.8 To what extent has the FP7 project allowed you to enhance knowledge transfer... 4.18 To what degree do you consider the collaboration with a heterogeneous group in terms of types of research performing organisation (e.g. industry, higher education, government) to hinder the progress of your FP7 project? SQ33.9 With how many new project partners have you established co-operations in the context of the Seventh Framework Programme?3.11 Among these new partners, how many were from the following geographic areas?3.12 Among these new partners, how many belong to the following types of organisation?4.5 To what extent has the FP7 project allowed you to strengthen linkages with research performers within the same institutional sector (e.g. industry, government, higher education, private non-profit)?4.6 To what extent has the FP7 allowed you to strengthen linkages with research performers working in similar fields of science and technology? SQ45.3 Please give an assessment with regard to the representation of certain groups of partners in the project consortium5.4 If you could restart the project, would you include more or less of the following types of partners to achieve your specific projects’ stated objectives?SQ5On findings and collaborators from previous FPs: 3.1 With how many of your FP7 project partners did you collaborate in the context of the Sixth Framework Programme?3.20 Did your project draw on findings from the Sixth Framework Programme?On subsequent collaborations:3.21 Has your organisation used the main results of this FP7 project as the basis or key inspiration for another, subsequent project?3.22 Has your organisation used by-products of this FP7 project as the basis or key inspiration for another subsequent project?SQ63.2 Among these partners [collaborated with before], how many were from the following geographic areas?3.6 Among these [outside FP6&7] partners, how many were from the following geographic areas?3.10 Among these new partners, how many were from the following geographic areas?4.1 To what extent has the FP7 project allowed you to strengthen collaborations with international part-ners outside the European Union and the FP7 asso-ciated countries?SQ73.2 Among these partners [collaborated with before], how many were from the following geographic areas?3.6 Among these [outside FP6&7] partners, how many were from the following geographic areas?3.10 Among these new partners, how many were from the following geographic areas?3.17 Did the FP7 project, during its life-cycle, establish links between your organisation and other organisations that are not part of the project?4.7 To what extent has the FP7 project allowed you to strengthen linkages with research performers at regional/local levels within your country? SQ83.1 With how many of your FP7 project partners did you collaborate in the context of the Sixth Framework Programme?3.5 With how many of your FP7 project partners did you collaborate outside the Sixth and Seventh Framework Programmes before your current FP7 project started?3.9 With how many new project partners have you established co-operations in the context of the Seventh Framework Programme?  
 
 
Survey Questionnaire 

Study on Network Analysis 
of the FP7 Participation 

- FINAL SURVEY QUESTIONNAIRE -  

May 19, 2014 
prepared by  
Science-Metrix Inc. 
and their subcontractor 
Fraunhofer ISI  
for the European Commission 
Directorate-General for Research and Innovation 

Survey Purpose: 

In preparation of the final evaluation of the 7th Framework Programme for Research 
and  Technical  Development  (FP7),  the  Directorate-General  for  Research  and 
Innovation  of  the  European  Commission  has  commissioned  a  Study  on the  Network 
Analysis of the FP7 Participation. Within the context of this study Science-Metrix Inc. 
and  their  subcontractor  Fraunhofer  ISI  carry  out  an  online  survey  on  the  nature, 
challenges, and opportunities of networking in projects supported under FP7.  

As a beneficiary of FP7 funding, you are kindly asked to participate. Please note that 
this invitation is extended to beneficiaries of all four FP7 sub-programmes, explicitly 
including  people,  ideas,  and  capacities.  The  survey  will  seek  to  study  the 
phenomenon of networks from a broader perspective, including formal co-operation, 
mobility, as well as co-operation in the creation of capacities. 

Who should complete this questionnaire?  

The FP7 scientific coordinator within the contacted institute or another person with a 
similar  degree  of  in-depth  knowledge  and  expertise  concerning  the  project.  The 
respondent  should  be  of  sufficient  seniority  to  provide  well-founded  assessment  of 
the project’s origin, nature, outcomes, and impact. 

Changes in Responsibility:  

This online survey is addressed to institutions’ main contact persons as listed  in the 
European Commission FP7 database. Should responsibility have been re-assigned or 
you  be  reading  this  email  in  deputy  of  a  person  that  has  in  the  meantime  left  your 
institution  –  please forward  the link to the  survey  to  the  appropriate person. Thank 
you very much for your support! 

Please 
Irrelevant or not applicable questions will be filtered automatically. 

questions, 

complete 

unless 

otherwise 

all 

instructed. 

Confidentiality and Data Protection:  

All personal information collected through the questionnaire or to be connected to it 
(see below) will be treated as strictly confidential  under the provisions of Regulation 

49 

 
 
 
 
 
(EC) No 45/2001. It will neither to be divulged to the European Commission nor any 
other third parties or entities.  

Planned Record Linkage:  

To  increase  the  analytical  potential  of  the  study,  Science-Metrix  and  Fraunhofer  ISI 
will  combine  the  data  obtained  from  this  survey  with  data  from  the  European 
Commission database on the 7th Framework Programme. The two sets of data will be 
matched  for  the  sole  purpose  of  conducting  the  planned  evaluation  and  can  be 
anonymised on request. 

Furthermore,  Science-Metrix  and  their  subcontractor  Oxford  Research  will  at a  later 
stage  of  the  study  seek  to  contact  selected  project  coordinators  and  participants  to 
conduct in-depth, qualitative telephone interviews. Should this not appear acceptable 
to them, respondents will be given the option to opt out of being contacted. 

Assistance  

If  you  have  any  questions  concerning  this  online  survey,  of  need  assistance 
completing this questionnaire, please contact Fraunhofer ISI at  
Phone:   
Fax: 
E-mail:  

>>>> >>>>>>>>> 
>>>> >>>>>>>>> 
fp7networks@isi.fraunhofer.de 

Person in charge of the implementation of the survey:  

>>>> >>>>>>>>> 

Fraunhofer ISI 
Breslauer Strasse 48 
76139 Karlsruhe 
GERMANY 

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Cooperation and Capacities only 

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Cooperation and Capacities only 

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Cooperation and Capacities only 

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Cooperation and Capacities only 

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Cooperation and Capacities only 

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Cooperation and Capacities only 

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Cooperation and Capacities only 

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5.  REGRESSION ANALYSES  

The networking characteristics used as explanatory (independent) variables were 
collected through Social Network Analysis (SNA) of FP7 participation data (performed 
by Science-Metrix using CORDA [European Commission] and the Web of Science 
[WoS, Thomson Reuters]; see Section 3 for details on the methods used in 
computing these variables), as well as collected through a survey of FP7 participants 
(performed by Fraunhofer ISI; see Section 4). They cover each of the specific 
evaluation questions from SQ1 through SQ7.12  

Not specific to a given SQ 

  General_SQ: Number of partners on FP7 projects (Source: SNA) 

Specific to a given SQ 

  SQ1: Interdisciplinarity of FP7 projects (Source: SNA and Bibliometrics) 

  SQ2_a:  To what extent has the  FP7 project allowed you  to strengthen linkages 

with researchers from different institutional sectors? (Source: Survey) 

  SQ2_b:  Number  of  institutional  sectors  among  the  participants  of  a  given  FP7 

project (Source: SNA) 

  SQ3_a:  Share  of  new  partners  for  a  given  participant  on  a  project  (Source: 

Survey) 

  SQ3_b:  Number  of  new  dyads  on  a  project  (i.e.  new  organisational  pair  not 
present  across  FP6  projects;  Source:  SNA)  showing  the  appearance  of  new 
partnerships from FP6 to FP7 

  SQ3_c: Number of lasting dyads on a project (normalised against the maximum 
possible number of lasting dyads based on the number of persistent actors on a 
project)  showing  continuity  in  organisational  partnerships  from  FP6  to  FP7 
(Source: SNA) 

  SQ4_a:  If  you  could  restart  the  project,  would  you  include  more  or  less  of  the 

following types of partners... (organisational type)? (Source: Survey) 

  SQ4_b:  If  you  could  restart  the  project,  would  you  include  more  or  less  of  the 

following types of partners... (geographical)? (Source: Survey) 

  SQ4_c:  If  you  could  restart  the  project,  would  you  include  more  or  less  of  the 

following types of partners... (field)? (Source: Survey) 

  SQ4_d:  Betweenness  centrality  of  projects  in  a  two-mode  network  linking 

projects through their scientific/administrative contact (Source: SNA) 

  SQ4_e:  Number  of  bridges  in  a  project  (i.e.  sum  of  the  degrees  of  a  project's 
their 

projects 

through 

linking 

in 

brokers) 
scientific/administrative contact (Source: SNA) 

two-mode  network 

a 

12  Some  survey  variables  were  omitted  if  their  relevant  SQ  was  adequately  addressed  using 
CORDA and WoS data alone (i.e. SQ1: Extent to which the FP7 project helped organisation 
strengthen linkages with researchers working in dissimilar fields of science and technology 
and  SQ6:  Extent  to  which  the  FP7  project  allowed  for  strengthened  collaborations  with 
researchers outside the European Union and the FP7-associated countries). 

69 

 
 
                                                
  SQ5_a:  To  what  degree  did  your  project  draw  on  findings  from  the  Sixth 

Framework Programme? (Source: Survey) 

  SQ5_b: Average number of citations to FP6-supported papers per FP7-supported 

papers, per project (Source: Bibliometrics) 

  SQ5_c: Share of FP7-supported papers acknowledging both FP6 and FP7 funding 

(Source: Bibliometrics) 

  SQ6_a:  Number  of  non-ERA  countries  per  project  showing  the  level  of 

international partnerships beyond the ERA (Source: SNA) 

  SQ6_b:  Number  of  ERA  countries  per  project  showing  the  level  of  international 

partnerships within the ERA (Source: SNA) 

  SQ7_a: Did the FP7 project establish links between your organisation and other 

organisations that are not part of the project? (Source: Survey) 

The  positive  research  and  innovation  outputs/outcomes  to  be  used  as  response 
(dependent)  variables  (see  listing  in  Table  XIV)  were  measured  through  the  survey 
and  through  a  bibliometric  analysis  using  the  SESAM  and  WoS  databases  (see 
Section 3). 

The  initial  analytical  approach  proposed  in  the  inception  report  was  to  rely  on 
mediation analysis, based on the premise that to be successful, networking requires 
active  coordination  within  each  FP7  project.  In  other  words,  it  is  assumed  that  it  is 
not  sufficient  to  link  researchers  from  various  geographical,  organisational  or 
disciplinary  settings  to  achieve  desirable  outputs/outcomes,  but  that  the  individuals 
involved  must  actively  exchange/coordinate  the  joint  undertaking.  Under  such  a 
model,  the  variables  measuring  the  success  in  network  coordination  would  mediate 
part,  or  the  entirety,  of  the  effects  of  the  explanatory  variables  (i.e.  networking 
characteristics)  on  the  response  variables  (i.e.  positive  outputs/outcomes).  This 
analysis was performed in an exploratory manner using variables for both: 

  M1: Extent of coordination through personal meetings (Source: Survey); and 

  M2:  Extent  of  coordination  through  electronic  means  of  communications 

(Source: Survey). 

The results suggested, however, that the assumed interaction effect was in fact not 
as  present  as  expected,  and  it  is therefore  not  logical  to perform  a  full-fledged  test 
for  mediation  using  diverse  regression  analysis.  The  relevant  variables  did  not,  in 
most  cases,  display  a  statistically  significant  effect.  Hence,  the  mediators  were 
included,  alongside  various  controls  and  networking  characteristics,  as  independent 
variables  in  conventional  multiple  regression  models.  In  other  words,  they  were 
treated as regular controls in the models. Where relevant, interaction effects could be 
identified in the exploratory analysis; this will be highlighted in the description. 

To  improve the  regressions,  all  aspects identified  in  the literature  as  well  any  other 
reasonable suggestions have been included as control variables. This has been done 
with due diligence, consultation with various senior researchers and after a sufficient 
amount of practical testing in the process of developing the final regression models. 
Note  that  some  controls  specific  to  given  dependent  variables  were  not 
systematically  included  across  all  models  (see  below).  The  controls  included  in  the 
developed models are: 

  C1:  Start  year  of  FP7  project  (Source:  CORDA;  Rationale:  FP7  projects  which 
started near the end of FP7 [e.g. in 2013] have not had as much time as earlier 
projects  [e.g.  in  2007]  to  produce  positive  outputs/outcomes;  Usage:  All 
regression models). 

70 

 
 
  C2:  Project  duration  (Source:  CORDA;  Rationale:  Longer  FP7  projects  might 
take  more  time  to  generate  positive  outputs/outcomes  and  may  produce  more 
outputs/outcomes; Usage: All regression models). 

  C3:  Project  budget  (Source:  CORDA;  Rationale:  FP7  projects  with  greater 
financial resources might produce more outputs/outcomes; Usage: All regression 
models). 

  C4:  Yearly  scientific  production  per  researcher  associated  with  a  given  FP7 
project  prior  to  FP7  (in  the  five  years  prior  to  the  project  start  year;  Source: 
Bibliometrics;  Rationale:  Many  network  statistics  (e.g.  SQ4_d  and  SQ4_e,  see 
above) are known to correlate strongly with scientific output. As such, a project 
in which participants previously had large scientific outputs could be expected to 
show  higher  centrality  in  the  network  based  on  CORDA  as  well  as  a  larger 
production  of  FP7-supported  papers  than  a  project  in  which  participants 
previously  had  a  rather  small  scientific  production.  Usage:  The  inclusion  of  this 
control in all models to be tested will  enable  the incorporation of a lag  effect to 
better capture the role of networking on FP7-related outputs/outcomes). 

  C5:  Did  your  organisation  play  a  role  in  defining  the  objectives  of  the  FP7 
project?  (Source:  Survey;  Rationale:  Participants  with  different  roles  in  a 
project  might  have  different  perceptions  of  the  success  of  the  project  both  in 
terms  of  networking  and  production  of  positive  outputs/outcomes;  Usage:  All 
regression models). 

  C6: Did your organisation play a role in defining the content and the scope of its 
work programme? (Source: Survey; Rationale: Participants with different roles 
in a project might have different perceptions of the success of the project both in 
terms  of  networking  and  production  of  positive  outputs/outcomes;  Usage:  All 
regression models). 

  C7: Did your organisation play a role in defining the size and membership of the 
consortium?  (Source:  Survey;  Rationale:  Participants  with  different  roles  in  a 
project  might  have  different  perceptions  of  the  success  of  the  project  both  in 
terms  of  networking  and  production  of  positive  outputs/outcomes;  Usage:  All 
regression models). 

  C8:  Was  a  project  manager  assigned  to  the  FP7  project?  (Source:  Survey; 
Rationale: Projects with an assigned project manager might be more successful 
in terms of networking; Usage: All regression models). 

  C9:  Was  your  organisation  mainly  involved  in  the  FP7  project  as  a  research 
performer?  (Source:  Survey;  Rationale:  Participants  with  different  roles  in  a 
project  might  have  different  perceptions  of  the  success  of  the  project  both  in 
terms  of  networking  and  production  of  positive  outputs/outcomes;  Usage:  All 
regression models). 

  C10:  Did  your  organisation  receive  any  external  financial  support  for  your  FP7 
project? (Source: Survey; Rationale: Projects in which participants managed to 
secure  external  funds  might  be  more  attractive/innovative  for  their  respective 
budget size. As such, this variable controls for an important success factor that is 
not necessarily related to networking. Usage: All regression models). 

  C11:  Set  of  dummies  to  control  for  the  sub-programme  to  which  a 
participant/project belongs (C11_a = Cooperation, C11_b = Capacities, C11_c = 
Ideas,  C11_d  =  People;  Source:  CORDA;  Rationale:  The  sub-programmes 
under FP7 are diverse in actions and objectives; Usage: All regression models). 

  C12: Set of dummies to control for the main objective of a given FP7 project in 
terms  of  its  relevance  to  advance  fundamental  research  (high  vs.  low)  and 
applied research (high vs. low) (C12_a = HH, C12_b = LL, C12_c = LH, C12_d = 

71 

 
 
HL;  Source:  Survey;  Rationale:  Projects  contributing  to  fundamental  research 
are  not  expected  to  produce  the  same  outputs/outcomes  as  are  those 
contributing to applied research; Usage: All regression models). 

  C13:  Set  of  dummies  to  control  for  the  main  scientific  discipline  of  a  given 
project  (C13_a  =  Applied  Sciences;  C13_b  =  Arts  &  Humanities,  C13_c  = 
Economics  &  Social  Sciences,  C13_d  =  General  Sciences,  C13_e  =  Health 
Sciences, C13_f = Natural Sciences; Source: Survey; Rationale: Researchers in 
diverse  disciplines  often  exhibit  different  production  patterns  (e.g.  scientists  in 
the health sciences often publish more as well as more often cite their peers than 
researchers in the health sciences; also, as is explained in Section 3, the recall of 
FP7-supported  papers  varies  a  lot  across  FP7  themes);  Usage:  All  regression 
models.) 

  C14: Average number of ERA countries per paper among those produced by FP7 
participants  associated  with  a  given  FP7  project  prior  to  FP7  (in  the  five  years 
prior to the project start year;  Source:  Bibliometrics;  Rationale:  The extent of 
scientific co-authorship at time t is highly  dependent  upon  the  same  variable at 
time  t-1.  Thus,  FP7  teams  who  collaborated  a  lot  prior  to  FP7  are  expected  to 
score  higher  on  this  variable  for  their  FP7-supported  papers  compared  to  those 
who  did  not;  Usage:  Only  in  the  regression  model  looking  at  the  effect  of 
networking (at the level of the ERA) within the context of a given FP7 project on 
the  actual  networking  (at  the  level  of  the  ERA)  taking  place  through  co-
authorship among the resulting FP7-supported papers. 

  C15: Average number of non-ERA countries per paper among those produced by 
FP7 participants associated to a given FP7 project prior to FP7 (in the  five years 
prior to the project start year;  Source:  Bibliometrics;  Rationale:  The extent of 
scientific co-authorship at time t is highly  dependent  upon  the  same  variable at 
time  t-1.  Thus,  FP7  teams  who  collaborated  a  lot  prior  to  FP7  are  expected  to 
score  higher  on  this  variable  for  their  FP7-supported  papers  compared  to  those 
who  did  not;  Usage:  Only  in  the  regression  model  looking  at  the  effect  of 
networking  (outside  the  ERA)  within  the  context  of  a  given  FP7  project  on  the 
actual  networking  (outside  the  ERA)  taking  place  through  co-authorship  among 
the resulting FP7-supported papers. 

  C16:  Average  of  the  Relative  Impact  Factors  (ARIF)  of  papers produced  by  FP7 
participants  associated  with  a  given  FP7  project  prior  to  FP7  (in  the  five  years 
prior  to  the  project  start  year;  Source:  Bibliometrics;  Rationale:  The  level  of 
scientific impact at time t is highly dependent upon the same variable at time t-1. 
Thus, FP7 teams with a high impact prior to FP7 are expected to score higher on 
this  variable  for  their  FP7-supported  papers  compared  to  those  with  a  smaller 
impact; Usage: Only in the regression model looking at the effect of networking 
on the scientific impact (i.e. ARIF) of the resulting FP7-supported papers. 

  C17: Transdisciplinarity of papers produced by FP7 participants associated with a 
given  FP7  project  prior  to  FP7  (in  the  five  years  prior  to  the  project  start  year; 
Source:  Bibliometrics;  Rationale:  The  level  of  transdisciplinarity  at  time  t  is 
highly  dependent  upon  the  same  variable  at  time  t-1.  Thus,  FP7  teams  with  a 
high transdisciplinarity prior to FP7 are expected to score higher on this variable 
for 
those  with  a  smaller 
transdisciplinarity;  Usage:  Only in the regression model looking at  the  effect of 
networking (especially the interdisciplinarity of projects) on the transdisciplinarity 
of the resulting FP7-supported papers. 

their  FP7-supported  papers  compared 

to 

Conditioning of survey data for the regression analyses 

Once  the  survey  was  completed,  the  raw  data  were  processed  and  cleaned. Due  to 
the  unusually  high  rate  of  participation  and  willingness  of  the  respondents  to 

72 

 
 
complete the individual questions, there were few problems with regard to corrupted 
or obviously inconsistent data. 

For  use  in  the  descriptive  chapter  (Section  4),  most  questions’  results  were 
transferred  into  overview  tables  and  figures  as  directly  as  possible  and  included  in 
the  comprehensive  Excel  data  book  submitted  together  with  this  report.  For  all 
questions, cross-tabulations were performed  for the four  different  sub-programmes. 
Beyond  this,  the  Excel  data  book  intentionally  follows  the  structure  of  the 
questionnaire page by page and question by question. 

Furthermore, some prominent tables and exhibits were included in the main section 
of  the  report  and  adapted  to  their  use  therein.  To  a  limited  extent,  data  had  to  be 
recoded  and  new  variables  had  to  be  calculated.  Where  this  is  the  case,  respective 
notes are made below the tables and figures concerned. 

For the subsequent multivariate regression analysis, however, the often complex and 
multidimensional information collected via the survey questions had to be transferred 
into  a  format  compatible  with  the  need  for  one-dimensional,  metric,  ordinal  or 
dichotomous variables that could be introduced into analytical models. In detail, the 
composite indicators that were constructed are presented in Table XI. 

73 

 
 
 
Table XI 

Composite indicators 

Name 

Basis  

Calculated as 

(Survey question) 

Dependent variables 

Breadth 

of 

knowledge 

Outputs.1 

Sum of all types 

creation 

Breadth 

of 

S&T 

tools 

Outputs.2 

Sum of all types 

creation 

Triggering of innovations 

Outputs IV.1&2 

OR connection 

Transfer of results 

Outcomes III.2 

Mean of both scales 

Facilitation of careers 

Outcomes IV 

Sum of all ‘Yes’ categories 

Independent variables 

SQ1: Interdisciplinarity 

Outcomes 

Relation:  Mean  of  scales  Q1  to 

scale Q2 

SQ2:  Linking  organisation 

Outcomes II.1 

Relation:  Scale  Q1.1  to  scale 

types 

Q1.2 

SQ3: Involving new partners  Output VII 

Share  of  new  partners  in  total 

partners 

SQ4: Structural holes 

Satisfaction II.2 

Sum  over  all  variables  (coded 

SQ5: Building on FP6 

Output VIII 

Mean of all scales 

SQ6: 

Extra-European 

Outcomes II.2.4 

(clean for n/a) 

linkages 

1-3) 

SQ7: Wider participation 

Outputs III 

Sum  of  all  categories,  except 

‘no’ 

Interaction terms 

Personal coordination 

Coord. 

activities 

Mean of all scales 

III 

Electronic coordination 

Coord. 

activities 

Mean of all scales 

Control variables 

IV 

Defining the project 

Introduction II.1 

high/central = 1, else = 0; sum 

of three 

Main objectives 

Introduction II.2 

by  4  categories:  high-high, 

high-low, ... 

Research performer vs. user 

Introduction II.3 

performer or both = 1, else = 0 

External funding 

Introduction III.2 

variable:  ‘yes’  categories  =  1, 

no = 0 

74 

 
 
 
 
 
 
 
 
 
 
Project manager 

Coord. activities.1  variable:  ‘yes’  categories  =  1, 

no = 0 

Source: 

Fraunhofer ISI. 

Integration of all data sources 

The final dataset used for the multivariate analysis was generated as follows based 
on three different sources of information (Figure 7): 
 

a  condensed  excerpt  of  the  CORDA  database,  which  combines  relevant 
project and participant level data (generated from  project and participant level 
data in an earlier step), 

 

 

the dataset resulting from the network analysis (which was in turn established 
based on the analysis of CORDA project level data), 

the dataset resulting  from  the  survey-based analysis  based on the collection 
of data on the participant-in-a-specific-project level (as elaborated in the section 
on the survey above). 

To combine all the information necessary to generate the variables for and perform 
the  synthetic  multivariate analysis,  data  from  the  different  sections  of  the  analysis 
were  merged  through  relational  database  queries  and  re-exported  into  a  final 
common dataset.  

As  Figure  7  outlines,  information  from  the  network  analysis  had  to  be  matched  at 
project level, whereas the single observation in the final dataset was the participant-
in-a-specific-project  level  as  developed  for  the  survey  and  already  mirrored  in  the 
condensed CORDA dataset. This was achieved without any problem using a standard 
1:n matching. 

75 

 
 
Figure 7 

Joining of datasets 

Source: 

Fraunhofer ISI 

Overall,  information  was  matched  irrespective  of  whether  entries  on  some  of  the 
survey questions were incomplete. In contrast, cases from the network analysis and 
the  condensed  CORDA  dataset  were  dropped  if  there  were  no  corresponding 
respondents in the survey. Hence, the size  of  the  final dataset is equivalent to the 
final number of survey responses at n=2,591. 

Correlation and exploratory factor analyses 

Note that most of the concepts captured by  the  SNA and survey are  complex, and 
as such, they are often characterised using multiple indicators/survey questions. As 
a result, it is highly likely that some redundancy exists in this multivariate dataset. 
The various dependent variables (i.e. explanatory, mediators and controls) collected 
using  SNA  (as  well  as  bibliometric  methods)  and  the  survey  were  therefore 
investigated through a correlation analysis as well as an exploratory factor analysis 
(EFA)  to  highlight  any  redundancy  in  the  dataset  that  should  be  removed  prior  to 
performing  the  multiple  regression  analyses.  This  is  important  as  multicollinearity 
can  lead  to  spurious  conclusions  regarding  the  significance  of  the  regression 
coefficients and to coefficients of unexpected sign.13 Note that some of the concepts 

13 Zar, J.H. (1999). Biostatistical Analysis. Fourth Edition, Prentice Hall, New Jersey, p. 425. 

76 

 
 
 
                                                
measuring  the  positive  outputs/outcomes  through  the  survey  are  perhaps  even 
more  complex.  Some  of  the  survey  questions  pertaining  to  these  aspects  were 
therefore  condensed  into  composites  to  be  included  as  response  variables  in  the 
regression analysis. 

The  results  of  the  correlation  and  factor  analyses  are  presented  in  Table  XII  and 
Table XIII; note that pairwise deletion was used such that the full sample is included 
(N  =  2,587).  Prior  to  performing  these  analyses,  the  frequency  distributions  of  all 
variables  were  examined  to  see  whether  or  not  the  indicators  needed  to  be 
transformed.  Indeed,  the  normality  of  individual  variables  is  an  assumption 
underlying  many  correlation  coefficients  and  factoring  methods  used  in  performing 
EFA.  As  expected,  many  indicators  had  a  high  positive  skew,  with  most  of  the 
observations  having  low  scores  and  a  few  having  high  scores  (i.e.  having  heavy 
tails).  As  such,  some  of  them  were  log  transformed  (specifically  General_SQ, 
SQ3_a,  SQ3_b,  SQ3_c,  SQ4_d,  SQ4_e,  SQ6_a  and  SQ6_b)  while  others  (SQ5_b, 
SQ5_c) were transformed using  the  fifth  root of their  value. These transformations 
also  improved  the  linearity  of  the  relationships  between  all  observed  variables. 
Nevertheless, the resulting distributions did not always fully satisfy the condition of 
normality.  Consequently,  the  correlation  matrix  presented  in  Table  XII  presents 
Spearman’s Rank-Order Correlation Coefficients, which are adequate for non-normal 
distributions.  Similarly,  the  factor  analysis  presented  in  Table  XIII  was  performed 
using  Iterated  Principal  Axis  (IPA)  factoring,  which  does  not  rely  on  any 
distributional assumption.14 

The  main  finding  from  these  analyses  is  that  there  is  not  a  large  amount  of 
redundancy (multicollinearity) in this dataset. Firstly, only a handful of the pairwise 
correlation  coefficients  in  Table  XII  (0.6%  of  all  pairs)  have  a  score  above  0.8  or 
below  -0.8.  Secondly,  the  five  factors  identified  using  EFA  (Table  XIII)  altogether 
explain only 43% of the total variance in the dataset. Nevertheless, some variables 
related  to  the  same  or  similar  concepts  and  that  appear  redundant  could  be 
identified. 

The  highest  degree  of  collinearity  occurs  between  the  variables  loading  the  most 
heavily on the first factor, which explains 20% of the total variance in the dataset. 
The  variable  showing  the  strongest  correlation  with  this  factor  is  the  number  of 
participants  (i.e.  general_SQ;  R  =  0.99,  Table  XIII).  Since  this  is  a  key  variable 
characterising the extent of networking within each FP7 project, it was decided that 
it should be kept. The other variables loading strongly on this factor include: 

  Number of institutional sectors (SQ2_b, R = 0.82); 

  Number of new dyads (SQ3_b, R = 0.94); 

  Betweenness centrality of projects (SQ4_d, R = 0.91); 

  Number of bridges in a project (SQ4_e, R = 0.82); and 

  Number of ERA countries per project (SQ6_b, R = 0.92). 

These variables also correlate strongly with the number of participants, as shown in 
Table  XII.  Since  there  is  at  least  one  alternative  variable  for  each  of  these  SQs, 

14 Fabrigar, L.R., Wegener D.T., MacCallum R.C.,  and Strahan E.J. (1999). Evaluating the use 
of Exploratory Factor Analysis in psychological research. Psychological Methods, 4(3), 272-
299. 

77 

 
 
                                                
these dependent variables were dropped for the upcoming regression analyses with 
the exception of SQ4_d. 

Both SQ4_d and SQ4_e relate to a core concept of the assumption underlying SQ4 
(i.e. by identifying and bridging structural holes, ERA will be more integrated and EU 
research more competitive) and SQ4_d is better aligned with this concept; indeed, it 
takes account of both the diversity of brokers and bridges associated with a project, 
not just the number of bridges. 

The  second  factor,  which  explains  7%  of  the  total  variance  in  the  dataset,  also 
highlights  some  amount  of  redundancy  among  variables  related  to  the  same 
concept.  Two  distinct  sets  of  variables  are  loading  on  this  factor;  they  can  be 
distinguished  by  looking  at  their  correlation  patterns  in  Table  XII.  The  first  set 
includes  three  variables  related  to  similar  survey  questions—namely,  ‘If  you  could 
restart  the  project,  would  you  include  more  or  less  of  the  following  types  of 
partners...  (organisational  type  =  SQ4_a;  geographical  type  =  SQ4_b  and  field  = 
SQ4_c)’. To reduce the number of variables and the redundancy among them, these 
three variables were combined into a composite variable in the upcoming regression 
analyses (name of new variable = SQ4_a-c). 

The  second  set  of  variables  loading  on  this  factor  also  relates  to  similar  survey 
questions—namely,  ‘Did  your  organisation  play  a  role  in  defining  the  objectives  of 
the FP7 project?’ (C5), ‘Did your organisation play a role in defining the content and 
the  scope  of  its  work  programme?’  (C6),  and  ‘Did  your  organisation  play  a  role  in 
defining the size  and membership of the consortium?’  (C7). To reduce  the number 
of variables and the redundancy among them, these three variables were combined 
into  a  composite  variable  in  the  upcoming  regression  analyses  (name  of  new 
variable  =  C5-7).  Table  XIV  provides  the  legend  for  the  selected  variables  used  in 
the regression analyses. 

To  investigate  the  influence  of  the  entire  range  of  networking  characteristics  that 
correspond  to  the  specific  evaluation  questions  in  relation  to  the  positive 
outputs/outcomes  of  FP7  projects,  the  role  of  the  selected  explanatory  variables 
(either  raw  or  composite  variables)  was  explored  using  regression  analysis  where 
(dependents =  scientific research  outputs measured through bibliometric methods) 
and (dependents = various outcomes measured through the survey)  

To  investigate  the  influence  of  the  entire  range  of  networking  characteristics  that 
correspond  to  the  specific  evaluation  questions  in  relation  to  the  positive 
outputs/outcomes  of  FP7  projects,  the  role  of  the  selected  explanatory  variables 
(either  raw  or  composite  variables)  was  explored  using  regression  analysis  in 
relation  to  two  sets  of  dependent  variables.  The  first  set  consisting  of  scientific 
research  outputs  measured  through  bibliometric  methods  and  the  second  set 
corresponding to various outcomes measured through the survey. 

Modelling Approach 

As  the  data  did  not  give  any  indication  to  the  contrary,  standard  modelling 
approaches  such  as  ordinary  least  squares  or  binominal  logit  regression  models 
were used. 

A note on limitations 

Note  that  despite  the  broad  coverage  of  potential  variables  that  could  foster  the 
creation  of  positive  outputs/outcomes  under  FP7,  there  are  undoubtedly  a  number 
of  further  factors  as  well  as  idiosyncrasies  at  play  in  a  programme  as  diverse  in 

78 

 
 
actions  and  objectives  as  FP7.  Consequently,  impressive  model  fits  cannot  be 
expected, nor were they the objective. The main purpose of the modelling exercise 
remains to identify whether the SQ-related independent variables exert a significant 
influence  on  a  number  of  outputs  or  outcomes.  That  further  factors  beyond  the 
control  variables  are  at  play  is  neither  disputed  nor  confirmed  by  the  analytical 
approach.  As  such,  the  key  findings  highlighted  through  the  regression  analysis 
should be viewed as preliminary evidence  of which factors have  a significant effect 
on  producing  positive  outputs/outcomes,  and  are  presented  in  this  report  when 
confirmed by other lines of evidence. 

79 

 
 
Table XII 

Spearman correlation matrix of the dependent variables 

Note: 

Source: 

The full definitions of these dependent variables (i.e. explanatory variables, mediators and controls) are presented above. In the 
regression analysis, the mediators are treated as regular controls (see explanation above). 
Computed by Science-Metrix and Fraunhofer ISI using CORDA, WoS and Survey data

80 

General_SQSQ1SQ2_aSQ2_bSQ3_aSQ3_bSQ3_cSQ4_aSQ4_bSQ4_cSQ4_dSQ4_eSQ5_aSQ5_bSQ5_cSQ6_aSQ6_bSQ7_aM1M2C1C2C3C4C5C6C7C8C9C10C11_aC11_bC11_cC11_dC12_aC12_bC12_cC12_dC13_aC13_bC13_cC13_dC13_eC13_fC14C15C16Explanatory VariablesSQ10.57SQ2_a-0.05-0.08SQ2_b0.760.53-0.05SQ3_a-0.31-0.08-0.10-0.17SQ3_b0.970.58-0.050.78-0.28SQ3_c0.430.25-0.020.36-0.060.29SQ4_a0.110.08-0.080.110.090.10-0.02SQ4_b0.020.030.010.040.170.010.050.50SQ4_c-0.020.070.010.010.14-0.020.010.330.62SQ4_d0.890.57-0.050.70-0.230.860.410.060.00-0.02SQ4_e0.760.53-0.050.62-0.200.730.400.080.040.000.86SQ5_a0.210.15-0.010.200.010.170.260.070.150.090.200.24SQ5_b0.160.08-0.030.10-0.030.100.170.000.020.030.210.220.09SQ5_c0.200.11-0.020.14-0.020.140.23-0.030.020.010.260.290.140.52SQ6_a0.330.23-0.030.29-0.110.330.150.040.010.000.290.240.160.100.09SQ6_b0.890.51-0.030.70-0.200.850.450.090.06-0.010.810.710.240.180.230.27SQ7_a0.000.03-0.230.050.21-0.010.040.140.110.03-0.010.010.18-0.010.020.000.04MediatorsM10.07-0.030.110.010.010.040.070.080.080.050.050.040.040.040.020.060.08-0.05M20.080.010.120.03-0.040.060.060.040.060.050.070.050.020.020.030.030.03-0.080.44ControlsC1-0.07-0.03-0.01-0.11-0.03-0.05-0.170.020.000.01-0.08-0.07-0.150.06-0.03-0.13-0.13-0.01-0.01-0.04C20.300.15-0.080.16-0.140.260.180.030.020.020.270.200.010.240.190.080.26-0.070.080.080.14C30.790.50-0.050.57-0.200.750.430.050.020.010.730.630.170.170.230.200.69-0.060.040.08-0.010.53C4-0.02-0.04-0.04-0.08-0.02-0.060.04-0.050.000.010.040.03-0.080.410.34-0.05-0.02-0.080.00-0.010.160.230.11C50.000.02-0.070.020.140.000.090.03-0.010.020.010.030.17-0.030.010.010.030.15-0.03-0.03-0.03-0.060.03-0.01C60.030.06-0.080.060.120.030.100.020.010.020.040.050.16-0.020.020.000.050.12-0.04-0.04-0.04-0.050.060.000.77C7-0.040.01-0.060.000.12-0.040.040.02-0.020.02-0.02-0.010.16-0.03-0.02-0.01-0.030.16-0.10-0.08-0.02-0.08-0.03-0.030.610.60C80.190.16-0.020.17-0.070.190.13-0.02-0.010.020.200.160.070.110.100.070.170.05-0.05-0.02-0.010.080.200.080.050.050.06C90.050.04-0.030.030.060.030.080.040.080.070.040.020.140.030.080.010.060.060.030.04-0.050.050.120.040.110.130.120.01C10-0.02-0.01-0.04-0.030.01-0.050.02-0.030.000.02-0.04-0.020.080.070.050.04-0.020.11-0.06-0.06-0.06-0.01-0.010.020.020.020.030.030.09C11_a0.550.44-0.020.500.010.550.350.04-0.040.010.550.450.150.040.100.260.52-0.010.070.05-0.160.190.61-0.060.060.080.010.120.13-0.06C11_b0.090.010.030.10-0.010.080.06-0.040.04-0.010.060.100.080.030.04-0.020.110.04-0.07-0.05-0.14-0.21-0.07-0.060.020.040.020.03-0.110.02-0.52C11_c-0.34-0.280.01-0.35-0.34-0.20-0.34-0.31-0.11-0.05-0.08-0.13-0.34-0.010.130.34-0.160.05-0.13-0.15-0.11-0.14-0.07-0.04-0.30-0.11C11_d-0.56-0.410.00-0.51-0.56-0.37-0.54-0.47-0.18-0.05-0.11-0.24-0.56-0.020.26-0.25-0.600.10-0.02-0.040.03-0.09-0.010.08-0.58-0.22-0.12C12_a0.040.03-0.120.010.010.030.04-0.020.030.010.050.030.040.040.030.000.030.08-0.04-0.030.020.080.100.080.130.140.120.080.160.040.08-0.06-0.04-0.02C12_b0.070.050.080.09-0.080.080.00-0.02-0.04-0.070.070.06-0.04-0.03-0.020.030.07-0.030.040.030.02-0.05-0.02-0.04-0.06-0.07-0.04-0.01-0.10-0.050.010.07-0.05-0.04-0.14C12_c-0.29-0.270.08-0.330.01-0.32-0.13-0.030.000.04-0.29-0.28-0.090.060.05-0.11-0.26-0.080.010.000.080.09-0.190.15-0.07-0.11-0.06-0.10-0.030.05-0.28-0.060.250.25-0.34-0.12C12_d0.160.160.020.190.050.190.050.110.070.040.160.140.06-0.14-0.120.050.140.000.040.05-0.06-0.090.09-0.19-0.040.00-0.040.02-0.04-0.070.150.06-0.13-0.17-0.34-0.12-0.29C13_a0.200.14-0.010.180.030.210.110.030.020.020.180.170.14-0.23-0.170.030.13-0.010.030.04-0.08-0.100.15-0.310.030.060.04-0.020.05-0.070.170.06-0.13-0.180.06-0.01-0.320.34C13_b-0.08-0.04-0.03-0.070.04-0.07-0.08-0.01-0.020.02-0.10-0.10-0.03-0.10-0.02-0.01-0.070.090.02-0.03-0.02-0.06-0.12-0.11-0.03-0.03-0.01-0.03-0.02-0.01-0.110.040.080.05-0.070.050.03-0.03-0.09C13_c-0.06-0.070.03-0.020.04-0.05-0.030.05-0.020.00-0.07-0.100.05-0.12-0.07-0.030.030.140.01-0.06-0.03-0.12-0.17-0.17-0.01-0.020.000.010.04-0.050.000.020.01-0.03-0.030.050.020.02-0.120.08C13_d0.030.06-0.010.040.000.020.040.010.090.070.030.040.08-0.04-0.020.040.020.08-0.03-0.01-0.05-0.040.01-0.090.030.030.030.05-0.010.01-0.030.09-0.04-0.030.020.07-0.060.030.030.17-0.01C13_e0.050.08-0.080.050.000.050.020.000.010.060.080.02-0.120.280.21-0.010.05-0.030.00-0.020.110.220.150.35-0.01-0.03-0.040.090.060.030.14-0.15-0.03-0.020.10-0.030.09-0.13-0.34-0.07-0.13-0.13C13_f-0.15-0.140.02-0.200.01-0.19-0.04-0.030.050.09-0.13-0.15-0.040.160.100.01-0.15-0.070.040.060.000.07-0.060.170.01-0.01-0.01-0.050.020.08-0.17-0.030.120.170.07-0.100.23-0.17-0.17-0.10-0.20-0.06-0.03C140.090.02-0.020.07-0.010.040.130.030.040.010.080.080.070.350.280.000.130.03-0.03-0.040.110.080.090.320.040.020.020.090.060.06-0.050.09-0.080.030.010.000.07-0.10-0.20-0.08-0.01-0.010.130.12C150.01-0.07-0.06-0.07-0.06-0.040.040.040.020.050.020.020.020.330.250.200.00-0.030.01-0.030.010.130.020.33-0.03-0.04-0.020.040.000.06-0.110.050.050.07-0.01-0.030.17-0.20-0.23-0.12-0.14-0.090.100.210.25C16-0.05-0.040.01-0.10-0.01-0.100.130.01-0.010.02-0.010.03-0.020.280.11-0.14-0.10-0.05-0.03-0.020.180.200.120.35-0.04-0.05-0.020.07-0.050.01-0.03-0.070.160.030.02-0.030.18-0.18-0.280.01-0.09-0.040.220.130.210.29C17-0.010.30-0.070.030.090.02-0.03-0.01-0.030.020.080.09-0.050.120.04-0.04-0.02-0.010.010.020.180.100.000.11-0.03-0.03-0.040.030.01-0.040.12-0.160.00-0.010.040.05-0.04-0.07-0.260.070.07-0.030.36-0.08-0.02-0.120.20 
 
 
Table XIII 

Factor loadings of the dependent variables on the five factors 
identified  through  the  exploratory  factor  analysis  using  IPA 
factoring 

Note: 

Source: 

The  full  definitions  of  these  dependent  variables  (i.e.  explanatory  variables, 
mediators  and  controls)  are  presented  above.  In  the  regression  analysis,  the 
mediators  are  treated  as  regular  controls  (see  explanation  above).  The 
dummies (i.e. variables C11a-d, C12a-d and C13a-f) were not included in the 
factor analysis. 
Computed  by  Science-Metrix  and  Fraunhofer  ISI  using  CORDA,  WoS  and 
Survey data 

81 

Factors12345Explanatory VariablesGeneral_SQ0.99-0.04-0.04-0.06-0.10SQ10.700.01-0.09-0.110.17SQ2_a-0.07-0.080.02-0.10-0.06SQ2_b0.82-0.02-0.08-0.08-0.03SQ3_a-0.230.26-0.08-0.030.12SQ3_b0.94-0.05-0.08-0.11-0.05SQ3_c0.550.050.080.10-0.05SQ4_a0.080.530.32-0.290.00SQ4_b0.020.740.51-0.410.02SQ4_c-0.010.620.39-0.340.06SQ4_d0.910.00-0.08-0.02-0.04SQ4_e0.82-0.040.030.020.04SQ5_a0.270.240.010.09-0.10SQ5_b0.18-0.100.450.340.21SQ5_c0.25-0.060.300.270.14SQ6_a0.290.020.030.02-0.17SQ6_b0.92-0.02-0.03-0.03-0.11SQ7_a0.060.27-0.060.06-0.02MediatorsM10.050.110.10-0.130.02M20.050.040.07-0.140.00ControlsC1-0.04-0.090.180.080.30C20.27-0.130.280.080.24C30.81-0.070.140.030.12C4-0.03-0.140.520.410.30C50.090.55-0.380.520.11C60.120.54-0.370.500.12C70.030.47-0.320.430.07C80.230.010.050.090.05C90.080.180.030.070.03C10-0.020.050.050.10-0.08C140.090.010.390.38-0.27C15-0.05-0.010.470.39-0.36C16-0.12-0.120.340.200.26C170.07-0.08-0.19-0.240.79Percent of Total Variance Explained20.17.06.45.84.0 
 
 
Table XIV 

Legend for the selected dependent variables to be used in the 
regression models 

Note: 
Source: 

*Only used in specific regression models not shown here. 
Produced by Science-Metrix and Fraunhofer ISI 

82 

LabelNameTypeTransformationSourceExplanatory VariablesGeneral_SQNo. of partnersSingleNatural LogSNASQ1InterdisciplinaritySingleRawSNA & BibliometricsSQ2_aExtent of intersectorial linkagesSingleRawSurveySQ3_aShare of new partnersSingleNatural LogSurveySQ3_cNo. of lasting dyads (normalised)SingleNatural LogSNASQ4_a-cIf you could restart the project, would you include more or less of the following types of partners...(Overall)CompositeRawSurveySQ4_dBetwenness centrality of projectsSingleNatural LogSNASQ5_aTo what degree did your project draw on findings from the Sixth Framework Programme?SingleRawSurveySQ5_bAvg. no. of citations to FP6-supported papers per FP7-supported papers per projectSingleFifth rootBibliometricsSQ5_cShare of FP7-supported papers acknowledging both FP6 and FP7 FundingSingleFifth rootBibliometricsSQ6_aNo. of non-ERA countries per project SingleNatural LogSNASQ7_aDid the FP7 project establish links between your organisation and other organisations that are not part of the project?SingleRawSurveyControlsM1Coordination through personal meetings SingleRawSurveyM2Coordination through electronic means of communications SingleRawSurveyC1Start year of FP7 project SingleRawCORDAC2Project duration SingleRawCORDAC3Project budget SingleNatural LogCORDAC4Yearly sci. production prior to FP7SingleNatural LogBibliometricsC5-7Did your organisation play a role in defining …(Overall)CompositeRawSurveyC8Was a project manager assigned to the FP7 project? SingleRawSurveyC9Was your organisation mainly involved in the FP7 project as a research performer?SingleRawSurveyC10Did your organisation receive any external financial support for your FP7 project?SingleRawSurveyC11_a-dDummies for sub-programsSingleRawSurveyC12_a-dDummies for fundamental vs. applied res.SingleRawSurveyC13_a-fDummies for disciplinesSingleRawSurveyC14*Avg. no. of ERA countries per paper prior to FP7SingleNatural LogBibliometricsC15*Avg. no. of non-ERA countries per paper prior to FP7SingleNatural LogBibliometricsC16*Sci. impact of papers prior to FP7SingleRawBibliometricsC17*Transdisciplinarity of papers prior to FP7SingleRawBibliometrics 
 
 
 
6.  CASE STUDIES 

This  study  consisted  of  a  total  of  twelve  case  studies  in  addition  to  one  pilot  case 
study. The approach used was the generalised success case method. The main goal 
of  this  methodological  instrument  was  to  explain  the  factors  associated  with 
successful  outputs  and  outcomes,  making  it  extremely  appropriate  considering  the 
fundamental  questions  raised  by  the  Commission  for  this  study.  The  case  studies 
were performed by Oxford Research with support from Science-Metrix, especially for 
the use of the network analysis data, and from the Fraunhofer ISI, for the use of the 
survey data.  

The  case  studies  developed  a  narrative  of  the  interactions  between  project 
participants  at  the  first  stages  of  collaboration  (i.e.  foundation  and  formulation 
stages, sustainment stage) and the outputs and outcomes of the collaboration (i.e. 
conclusion stage) based on the scientific and, potentially, the commercial application 
and/or social outcomes of the research.  

Each of the case studies involved:  

1.  carrying out desk  research—and in particular reading  official  progress and final 

reports  from  13  projects  submitted  to  the  European  Commission  and/or  its 

agencies;  

2.  examining the network analysis data available on these 13 cases;  

3.  examining the survey data for the 13 cases;  

4.  performing  a  minimum  of  4  interviews  per  case  for  the  12  case  studies  (3 

interviews  were  conducted  for  the  pilot  and  55  interviews  were  conducted  for 

the 12 case studies); and  

5.  triangulating and meta-analysing these four sources of data.  

A further, final, stage of meta-analysis consisted of performing a cross-case analysis 
to  address  three types  of questions:  (1)  study  the questions  listed  in  the  terms  of 
reference  specifically  for  the  case  studies,  (2)  provide  a  rich  qualitative  view  for 
specific questions raised in Task 3 of the terms of reference to provide an in-depth 
analysis  of  these  key  questions  in  the  draft  and  final  reports;  and  (3)  study  the 
fundamental questions.  

The  case  studies  were  written  in  a  narrative  style  that  can  be  read  as  standalone 
documents.  In  other  words,  the  case  studies  are  sufficiently  detailed  so  that  a 
reader  can  gain  an  understanding  of  the  purpose,  role,  context,  and  impact of  the 
funding  instruments  but  also  of  the  project  or  network  being  described.  The  key 
findings and lessons learned  from  the  case  studies and the cross-case analysis are 
integrated  in  the  final  study  report  and  are  particularly  useful  in  drawing  policy 
recommendations.  

The generalised success case method approach 

The  advantage  of  case  study  research  is  that  it  generates  insight  into  specific 
contextual mechanisms (George & Bennett, 2005). A previous study into the impact 
of the framework programmes found that case studies provide  ‘a rich way to get a 
qualitative understanding of FP impacts, [as they allow] the researcher to move with 
the twists and turns of emerging evidence’ (Arnold et al, 2011). For this evaluation, 
the  focus  was  placed  on  performing  an  in-depth  examination  of  the  potentials  and 

83 

 
 
limitations  of  networking  in  achieving  EU  Research  Policy  objectives  rather  than 
simply describing processes. 

An  important  aspect  of  the  study  was  to  understand  not  only  how  knowledge  is 
created through research collaboration and networking, but also how the transition 
from  invention  to  innovation  occurs  within  and  outside  networks.  The  analysis 
therefore focused on the factors that contributed to successful process, such as the 
impact on networking and collaboration, as well as on outcome at the project level 
and  across  networks.  This  meant  that  the  case  studies  could  not  have  hard 
boundaries,  as  the  intention  was  to  follow  the  varying  pathways  through  which 
knowledge is transmitted to the broader economy and society. 

The  success  case  method  is  based  largely  on  the  work  of  Robert  Brinkerhoff.15  It 
differs  from  traditional  case  study  approaches  in  that  it  explicitly  embraces  the 
general  concept  of  learning.  Traditionally,  the  success  case  method  was  used  to 
study training and education programmes but it can also be used as a more general 
method. The organising principles of the generalised case study method include: 

 

 

 

 

Purposive versus random sampling 

Focus on learning 

Inclusion of successful cases and less successful cases 

Emphasis on conditions and variables that are critical to success rather than on 
description of the case itself 

This  approach  is  focused  on  producing  concrete  evidence  of  the  effect  of  an 
intervention  that  supports  learning  by  individuals  and  organisations  (or  the  lack  of 
it) in ways that stakeholders and decision-makers find compelling, relating verifiable 
incidents in specific behaviours that can be convincingly shown to lead to worthwhile 
results. The advantage of the method is that it captures holistic information on the 
impact of the intervention with the changes it generated. The goals of programmes, 
projects,  and  initiatives  that  support  R&D  as  research  are  knowledge  creation, 
knowledge  diffusion,  and  learning  at  the  individual,  organisational,  and  system 
levels. The benefits of the support are meant to move beyond the recipients of the 
support. 

The generalised success case method approach recognises the inherent ‘messiness’ 
of  research  outputs  by  seeking  and  identifying  the  actors  and  factors  leading  to 
success. Given the unique pathways to success based on the potentially unexpected 
directions  in  which  research  can  move,  this  approach  is  therefore  as  it  does  not 
attempt to find a panacea. It stresses the importance of environmental, cultural and 
system  factors  that  interplay  with  learning  to  make  it  a  success.  This  non-
reductionist approach yields more realistic policy recommendations as it will eschew 
suggestions of interventions that would likely fail as other necessary factors for their 
success  may  not  be  present  in  a  given  setting.  It  is  for  this  reason  that  case 
selection is important, in order to balance successful cases with less successful ones 
or  cases  where  an  important  variable  is  absent,  and  ensure  that  the  study 
represents  the  various  types  of  project  and  includes  the  perspective  of  more  than 
one institutional type. 

15  The  present  text  borrows  widely  from  Brinkerhoff  (2005).  Used  with  permission  from  the 

author. 

84 

 
 
                                                
Case study selection 

The  method  for  choosing  cases  and  analysing  the  selected  cases  can  scarcely  be 
separated  when  the  focus  of  a  work  is  on  one  or  a  few  instances  of  some  broader 
phenomenon. Yet choosing good cases for small samples is a challenging endeavour 
(Seawright  &  Gerring,  2008).  Case  selection  has  the  same  twin  objectives  as  any 
type  of  sampling; that  is, to  obtain  a  sample  while  generating  useful  variations  on 
the dimensions of theoretical interest. 

To  obtain  a  broadly  representative  sample  while  focusing  on  identifying  successful 
and less successful cases, 13 FP7 projects were chosen, one as a pilot case and 12 
for specific case studies, with the emphasis being primarily placed on examining the 
importance  of  various  success  factors.  Several  types  of  cases  were  selected  to 
provide  a  wealth  of  qualitative  evidence.  More  specifically,  six  highly  successful 
cases  were  selected  based  on  a  visibly  large  aspect  examined  in  the  following 
specific questions of Task 3: 

  SQ1—A  project  visibly  linking  separate  fields  of  knowledge,  bridging  different 

fields of science. 

  SQ2—A  project  visibly  linking  different  types  of  organisations  (universities  and 
research  institutes,  industry,  SME,  etc.)  and  clearly  bridging  the  flow  of 
innovation ideas between them. 

  SQ3/SQ7—A  project  that  clearly  contributed  to  new  collaboration  and 
integration  of  new  organisations/countries.  For  instance,  the  studied  project 
could  comprise  several  organisations  from  several  of  the  latest  Member  States 
to join the Union. 

  SQ4—A  project  with  a  capacity  to  solidly  fill  structural  holes  (missing  links)  in 

the previous structure of the network. 

  SQ5—A  project  strongly  building  on  knowledge  generated  in  previous  FPs  and 

which has been transformed into new knowledge. 

  SQ6—A  project  having  strongly  contributed  to  international  research  relations 
between the EU and third countries (patterns by countries and specific research 
areas). 

Some four case studies examined projects where networking was not such a central 
part  of  FP7  support,  as  is  the  case  in  the  Ideas  and  People  specific  programmes. 
Hence,  these  were  not  necessarily  unsuccessful  projects  in  terms  of  networking; 
rather  they  presented  cases  where  networking  may  happen  spontaneously,  or  not. 
The goal here was to examine how projects can have highly positive and somewhat 
less positive outputs and outcomes when networking is not a central aspect of FP7 
support.  One  case  study  with  very  positive  output/outcome  may  have  few 
networking  activities  while  another  may  reveal  intense  networking.  Similarly,  the 
projects  with  fewer  outputs/successful  outcomes  may  include  one  with  few 
networking  activities,  while  the  other  one  may  reveal  intense  networking.  Here 
again,  evidence  on  networking  and  output/outcomes  was  obtained  from  the 
networking  analysis  and  the  survey,  and  the  survey  questions  were  used  to 
corroborate  network  analysis  evidence  suggesting  little  networking  (according  to 
evidence gathered for SQ1 to SQ7). 

The 12 case studies were selected based on the results of the network analysis and 
the  survey.  Different  operationalisation  factors  selected  from  the  network  analysis 
and  the  survey  were  used  to  identify  the  selected  cases.  Under  each  specific 
question  or  case,  two  projects  were  identified.  The  approach  also  recognised  the 
need  for  pragmatism.  If  Oxford  Research  did  not  receive  a  response  from  the 

85 

 
 
project  coordinator  of  one  of  the  cases,  they  moved  on  to  contacting  the  second 
project that was selected. If both of the originally selected cases did not respond or 
did not wish to participate in the case study, a third case was chosen based on the 
same  operationalisation  factors.  The  willingness  of  the  project  coordinator  to 
participate  in  the  case  study  also  affected  whether  Oxford  Research  could  proceed 
with the selected cases.  

The following graph illustrates the operationalisation factors used when selecting the 
12 cases for the case studies. 

Case selection 

SQ1  A  project  visibly  linking  separate  fields  of  knowledge,  bridging  different 

fields of science. 

Operationalisation: 

 

 

 

 

 

v_453 (high score)  

v_454 (high score) 

v_455 (high score) 

v_469 (low score) 

To  what  degree  has  the  FP7  allowed  you  to 
strengthen  linkages  with  researchers  working  in 
fields of science and technology that are not at the 
core of your organisation's usual research activities 

To  what  degree  has  the  FP7  project  allowed  you  to 
strengthen  collaborations  with  researchers  from 
different institutional sectors? 

Interdis  

Interdisciplinarity of FP7 projects 

Selected case(s):  

214539 (Cooperation), 221952 (Cooperation) 

SQ2  A  project  visibly 

types  of  organisations 
(universities  and  research  institutes,  industry,  SME,  etc.)  and 
clearly bridging the flow of innovation ideas between them. 

linking  different 

Operationalisation: 

  No_Sect  

Number  of 
participants of a given FP7 project 

institutional  sectors  among  the 

 

 

 

 

v_227 (low score) 

v_228 (low score) 

v_120 (low score) 

v_121 (low score) 

Selected case(s):  

To  what  degree  do  you  consider  the  collaboration 
with  a  heterogeneous  group  in  terms  of  types  of 
research performing organisations/fields of science 
and technology an obstacle to the progress of your 
FP7 project? 

Please  indicate  if  the  FP7  project  has  prompted 
your  organisation  to  introduce  product/process 
innovations 

115013 (Cooperation), 212239 (Cooperation) 

86 

 
 
 
SQ3/
SQ7 

A  project  that  clearly  contributed  to  new  collaboration  and  integration  of 
new  organisations/countries.  For  instance,  the  studied  project  could 
comprise several organisations from several of the latest MSTs to join the 
Union. 

Operationalisation: 

  CD_ORG_COUNTRY  

Country code 

 

 

 

 

 

 

 

 

 

v_154 (high number)  

v_78 (low score) 

v_79 (low score) 

v_80 (low score) 

v_81 (low score) 

v_83 (low score) 

v_84 (low score) 

v_85 (low score) 

v_86 (low score)  

  No_newD 

  No_ldN 

Selected case(s):  

With  how  many  NEW  project  partners  have 
you  established  co-operations  in  the  context 
of the Seventh Framework Programme? (Only 
projects with at least 20 partners.) 

How often and in which manner was the work 
of  the  FP7  project  partners  coordinated 
through personal meetings? 

How often and in which manner was the work 
of  the  FP7  project  partners  coordinated  by 
electronic  means  of  communication  (phone, 
video conference, etc.)? 

Number of new dyads on a project 

Number  of  lasting  dyads  on  a  project 
(normalised against the maximum possible 
number  of  lasting  dyads  based  on  the 
number of persistent actors on a project) 

211760 (Cooperation)1, 211382 (Capacities)2, 211971 (Cooperation)2 

1Selected because the project contains the latest MSTs 

2 Selected because the project has many new project members and a high level of 
exchange 

SQ4  A project with a capacity to solidly fill structural holes (missing links) in the 

previous structure of the network. 

Operationalisation: 

  BetwA (high number) 

Betweenness  centrality  of  projects  in  a  two-
mode  network  linking  projects  through  their 
scientific/administrative contact 

87 

 
 
 
 
 
 
  No_BridA (high number)  

Number of bridges in a project (i.e. sum of the 
degrees  of  a project's  brokers)  in  a  two-mode 
their 
network 
scientific/administrative contact 

linking  projects 

through 

Selected case(s): 

218802 (Cooperation), 115372 (Cooperation)  

SQ5  A project strongly building on knowledge generated in previous FPs 

and which has been transformed into new knowledge. 

Operationalisation: 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

v_190 (high score)  

v_191 (high score) 

To  what  degree  did  your  project  draw  on  findings 
from  the  Sixth  Framework  Programme  regarding 
own findings under FP6/other findings under FP6? 

Has  the  FP7  project  resulted  in  the  production  of  any 
of  the  following  types  of  scientific  &  technological 
knowledge by your organisation? 

Has  the  FP7  project  resulted  in  the  development  of 
any of the following types of scientific & technological 
tools by your organisation? 

v_426 

v_293 

v_294 

v_295 

v_296 

v_297 

v_298 

v_299 

v_283 

v_284 

v_285 

v_286 

v_287 

v_288 

v_289 

v_290 

v_291 

v_292 

  Avg_Cit 

  Ackn_6a7 

Average number of citations to FP6-supported papers per 
FP7-supported papers, per project 

Share of FP7-supported papers acknowledging both 
FP6 and FP7 funding 

Selected case(s): 

218695 (Cooperation), 226973 (Cooperation) 

88 

 
 
 
 
 
SQ6  A  project  having  strongly  contributed  to  international  research 

relations between the EU and third countries. 

Operationalisation: 

 

 

 

 

v_138 

v_126 

v_158 

v_474 (low score)  

  No_non_ERA 

  No_ERA_C 

Selected case(s):  

All other countries 

To what degree has the FP7 project allowed you 
to  strengthen  collaborations  with  researchers 
from other countries? 

Number  of  non-ERA  countries  per  project 
showing  the  level  of  international  partnerships 
beyond the ERA 

Number of  ERA countries per  project  showing  the 
level of international partnerships within the ERA 

223154 (Cooperation), 212545 (Cooperation) 

Case 
7 

A  project  with  mitigated  success  in  networking  but  encountered 
positive outputs and/or outcomes (Cooperation or Capacities). 

Operationalisation: 

 

 

v_227 (high score) 

v_228 (high score) 

To  what  degree  do  you 
the 
collaboration  with  a  heterogeneous  group  in 
terms  of 
research  performing 
organisations/fields  of  science  and  technology 
an obstacle to the progress of your FP7 project? 

types  of 

consider 

  No_newD 

Number of new dyads on a project 

  No_ldN 

  No_FP7P 

  Avg_imp 

  Avg_Tra 

  Avg_ERA 

  Avg_non 

Number  of 
lasting  dyads  on  a  project 
(normalised  against  the  maximum  possible 
number of lasting dyads based on the number of 
persistent actors on a project) 

Number of papers 

Avg. of Rel. Impact Factors 

Avg. Transdisciplinarity 

Avg. No. of ERA countries per paper 

Avg. No. of non-ERA countries per paper 

89 

 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

v_426 

v_293 

v_294 

v_295 

v_296 

v_297 

v_298 

v_299 

v_283 

v_284 

v_285 

v_286 

v_287 

v_288 

v_289 

v_290 

v_291 

v_292 

Has the FP7 project resulted in the production of any of the 
following  types  of  scientific  &  technological  knowledge  by 
your organisation? 

Has the FP7 project resulted in the development of any of the 
following  types  of  scientific  &  technological  tools  by  your 
organisation? 

Has  the  FP7  project  resulted  in  the  development  of  any  of 
the following types of scientific & technological tools by your 
organisation? 

v_120 (low score) 

v_121 (low score)  

Please  indicate  if  the  FP7  project  has  prompted 
your  organisation  to  introduce  product/process 
innovations. 

Selected case(s): 

232156 (Capacities), 228999 (Cooperation) 

Case 
8 

A  project  visibly  linking  different  types  of  organisations  with 
mitigated success in outputs and/or outcomes. 

Operationalisation: 

  No_Sect  

Number  of 
participants of a given FP7 project 

institutional  sectors  among  the 

 

 

v_227 (low score) 

v_228 (low score) 

To what degree do you consider the collaboration 
with a heterogeneous group in terms of types of 
research  performing  organisations/fields  of 
science  and  technology  an  obstacle  to  the 
progress of your FP7 project? 

  No_FP7P 

  Avg_imp 

  Avg_Tra 

  Avg_ERA 

Avg. of Rel. Impact Factors 

Avg. Transdisciplinarity 

Avg. No. of ERA countries per paper 

90 

 
 
 
 
  Avg_non 

Avg. No. of non-ERA countries per paper 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

v_426 

v_293 

v_294 

v_295 

v_296 

v_297 

v_298 

v_299 

v_283 

v_284 

v_285 

v_286 

v_287 

v_288 

v_289 

v_290 

v_291 

v_292 

Has  the  FP7  project  resulted  in  the  production  of  any  of 
the following types of scientific & technological knowledge 
by your organisation? 

Has the FP7 project resulted in the development of any of the 
following  types  of  scientific  &  technological  tools  by  your 
organisation? 

v_120 (high score)  

v_121 (high score)  

Please  indicate  if  the  FP7  project  has  prompted 
your  organisation  to  introduce  product/process 
innovations. 

Selected case(s): 

213734 (Cooperation), 201938 (Cooperation) 

Case 
9 

A  project  with  mitigated  success  in  networking  but  encountered 
positive outputs and/or outcomes (Ideas or People). 

Operationalisation:  

  No_Sect 

  No_newD 

  No_ldN 

Number of institutional sectors among the participants of 
a given FP7 project 

Number of new dyads on a project 
Number  of  lasting  dyads  on  a  project  (normalised 
against  the  maximum  possible  number  of  lasting  dyads 
based on the number of persistent actors on a project) 

  No_FP7P 

  Avg_imp 

Number of papers 

Avg. of Rel. Impact Factors 

  Avg_Tra 

Avg. Transdisciplinarity 

  Avg_ERA 

Avg. No. of ERA countries per paper 

91 

 
 
 
  Avg_non 

Avg. No. of non-ERA countries per paper 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

v_426 

v_293 

v_294 

v_295 

v_296 

v_297 

v_298 

v_299 

v_283 

v_284 

v_285 

v_286 

v_287 

v_288 

v_289 

v_290 

v_291 

v_292 

Has  the  FP7  project  resulted  in  the  production  of  any  of 
the following types of scientific & technological knowledge 
by your organisation? 

Has the FP7 project resulted in the development of any of 
the  following  types  of  scientific  &  technological  tools  by 
your organisation? 

v_120 (low score) 

v_121 (low score)  

Please  indicate  if  the  FP7  project  has 
prompted  your  organisation  to  introduce 
product/process innovations. 

Selected case(s): 

227577 (Ideas), 209622 (Ideas) 

Case 
10 

A  project  with  intense  networking  and  a  positive  outputs  and/or 
outcomes (Ideas or People). 

Operationalisation:  

  No_Sect 

  No_newD 

  No_ldN 

Number of institutional sectors among the participants of a 
given FP7 project 

Number of new dyads on a project 

Number of lasting dyads on a project (normalised against 
the maximum possible number of lasting dyads based on 
the number of persistent actors on a project) 

  No_FP7P 

  Avg_imp 

Number of papers 

Avg. of Rel. Impact Factors 

  Avg_Tra 

Avg. Transdisciplinarity 

  Avg_ERA 

Avg. No. of ERA countries per paper 

92 

 
 
 
  Avg_non 

Avg. No. of non-ERA countries per paper 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

v_426 

v_293 

v_294 

v_295 

v_296 

v_297 

v_298 

v_299 

v_283 

v_284 

v_285 

v_286 

v_287 

v_288 

v_289 

v_290 

v_291 

v_292 

Has the FP7 project resulted in the production of any 
of  the  following  types  of  scientific  &  technological 
knowledge by your organisation? 

v_120 (low score) 

v_121 (low score)  

Please  indicate  if  the  FP7  project  has  prompted 
your  organisation  to  introduce  product/process 
innovations. 

Selected case(s): 

215360 (People), 246549 (People) 

Case 
11 

A  project  with  mitigated  success  in  networking  and  with  mitigated 
success in outputs and/or outcomes (Ideas or People). 

Operationalisation:  

  No_Sect 

Number  of 
participants of a given FP7 project 

institutional  sectors  among 

the 

  No_newD 

Number of new dyads on a project 

  No_ldN 

  No_FP7P 

  Avg_imp 

Number  of  lasting  dyads  on  a  project  (normalised 
against the maximum possible number of lasting dyads 
based on the number of persistent actors on a project) 
Number of papers 

Avg. of Rel. Impact Factors 

  Avg_Tra 

Avg. Transdisciplinarity 

  Avg_ERA 

  Avg_non 

Avg. No. of ERA countries per paper 

Avg. No. of non-ERA countries per paper 

93 

 
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Has the FP7 project resulted in the production of any of 
the 
technological 
types  of  scientific  & 
knowledge by your organisation? 

following 

Has  the  FP7  project  resulted in  the  development 
of  any  of  the  following  types  of  scientific  & 
technological tools by your organisation? 

v_426 

v_293 

v_294 

v_295 

v_296 

v_297 

v_298 

v_299 

v_283 

v_284 

v_285 

v_286 

v_287 

v_288 

v_289 

v_290 

v_291 

v_292 

v_120 (high score) 

v_121 (high score)  

Please  indicate  if  the  FP7  project  has  prompted 
your  organisation  to  introduce  product/process 
innovations. 

Selected case(s): 

227010 (Ideas), 248997 (People) 

Case 
12 

A  project  with  intense  networking  and  with  mitigated  success  in 
outputs and/or outcomes (Ideas or People). 

Operationalisation:  

  No_Sect 

Number  of  institutional  sectors  among  the  participants  of  a 
given FP7 project 

  No_newD 

Number of new dyads on a project 

  No_ldN 

Number of lasting dyads on a project (normalised against 
the maximum possible number of lasting dyads based on 
the number of persistent actors on a project) 

  No_FP7P 

Number of papers 

  Avg_imp 

Avg. of Rel. Impact Factors 

  Avg_Tra 

Avg. Transdisciplinarity 

  Avg_ERA 

Avg. No. of ERA countries per paper 

94 

 
 
 
 
 
  Avg_non 

Avg. No. of non-ERA countries per paper 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

v_426 

v_293 

v_294 

v_295 

v_296 

v_297 

v_298 

v_299 

v_283 

v_284 

v_285 

v_286 

v_287 

v_288 

v_289 

v_290 

v_291 

v_292 

Has  the  FP7  project  resulted  in  the  production  of  any 
of  the  following  types  of  scientific  &  technological 
knowledge by your organisation? 

Has the FP7 project resulted in the development  of any of 
the  following  types  of  scientific  &  technological  tools  by 
your organisation? 

v_120 (high score) 

v_121 (high score)  

Please  indicate  if  the  FP7  project  has  prompted 
your  organisation  to  introduce  product/process 
innovations. 

Selected case(s): 

215360 (People), 246549 (People) 

In  the  above,  the  level  of  success  of  networking  activities  were  derived  from  the 
network analysis and corroborated by survey responses, while success in outcomes 
were obtained from answers to the survey purposefully designed to identify outputs 
and outcomes.  

Similarly, one project was selected on the basis that it experienced somewhat more 
mitigated  success  in  networking  (based  on  both  network  and  survey  data)  but 
encountered  positive  outputs  and/or  outcomes.  This  counterfactual  case  examined 
the limitations and potentials of networking. The analysis was also to be supported 
by a case where neither networking nor outputs or outcomes materialised to a great 
extent,  and  this  case  was  expected  to  provide  information  on  the  conditions 
required for successful networking and innovation/competitiveness outcomes. 

In  the  table  below,  the  12  case  studies  selected  are  identified  and  the  number  of 
interviews conducted for each case is illustrated.  

95 

 
 
 
 
 
Case 

SQ1 

SQ2 

SQ3
/7 
SQ4 

SQ5 

SQ6 

Case 
7 
Case 
8 
Case 
9 
Case 
10 
Case 
11 
Case 
12 

Refer
ence 
22195
2 
21223
9 
21176
0 
11537
2 
22697
3 
22316
4 
22833
4 
20193
8 
20962
2 
21536
0 
22701
0 
24654
9 

Name 

ATLAS 

Coordi
nator 
x 

Particip
ant 1 
x 

Particip
ant 2 
x 

Particip
ant 3 
x 

Particip
ant 4 

(Particip
ant 5) 

 Interviews 
Completed 
4 

SOMMACT 

AgriPolicy 

EMIF 

Climate for 
Culture 
GP-TCM 

LASERLAB-
EUROPE 
RESPECT 

E3ARTHS 

DELTA-MIN 

SSRR 

TRAIN 2009 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

4 

6 

4 

5 

5 

5 

5 

5 

4 

4 

4 

Total : 

55 

Semi-structured interviews 

While  quantification  in  the  previous  methodological  instruments  is  useful  in 
generating  overall  patterns,  interviews  are  a  powerful  tool,  not  only  for 
contextualising specific cases but also for understanding detailed qualitative aspects. 
Detailed  interviews  were  carried  out  by  Oxford  Research  using  a  semi-structured 
approach  to  provide  qualitative,  in-depth  evidence  on  the  questions  raised  in  this 
study.  More  specifically,  qualitative  information  was  sought  through  interviews  on 
issues where data from the network analysis and survey were either insufficient or 
did  not  fully  capture  the  qualitative  aspects  of  causalities,  motivations,  and 
processes.  A  key  advantage  of  the  interview  method  is  that  it  is  suitable  for 
answering  questions  of  an  in-depth  qualitative  nature  and  for  explorative 
approaches aimed at deepening the qualitative understanding of causalities (why?), 
motivations (to what end?) and processes (in what way?). 

Semi-structured  interviews  were  used  to  obtain  new  information  in  a  pre-defined 
research  area  while  allowing  the  interviewer  to  ask  follow-up  questions  to  pursue 
additional  lines  of  inquiry.  Based  on  the  potential  for  follow-up  questions,  it  was  a 
requirement that interviewers had  deep  and  comprehensive knowledge of both the 
objectives  of  the  study  as  well  as  the  functioning  of  the  network  and  the  overall 
framework programme. Because the semi-structured approach required interviewers 
to have knowledge of the study objectives and the specific dynamics of the network 
to  structure  the  appropriate  questions,  the  team  of  interviewers  was  deliberately 
kept small to ensure that cumulative learning was possible throughout the interview 
programme.  

In  this  type  of  interview  there  is  room  for  deviation  from  the  interview  guide, 
meaning  that  the  researcher  could  follow-up  with  additional  questions  during  the 

96 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
interview. This opened up the possibility for new and unexpected information to turn 
up and was especially useful for understanding the motivations behind actions. The 
above  requirements  were  met  through  several  elements—namely,  well-designed 
interview  questions  and  protocols,  and  a  clear  plan  for  transcription  and  analysis. 
Each of these aspects is detailed in the following sections.  

Interview guide 

The  interview  guide  aimed  to  provide  specific  evidence  that  would  not  have  been 
answered  to  a  sufficient  level  of  depth  through  the  network  analysis  and  the 
surveys.  It  aimed  to  complete  these  tools  where  more  qualitative,  in-depth, 
narrative  examples  were  required  to  provide  a  comprehensive  answer  to  the  eight 
specific questions of Task 3 and to the fundamental questions, the latter sometimes 
requiring  that  evidence  be  mapped  to  the  logic  model  (Section  2).  Importantly, 
however, the core factor driving the interview guide was the need to address the 13 
case  study  questions  raised  in  the  terms  of  reference.  The  questions  were  as 
follows: 

  CSQ1—Design, structure and composition of the network. 

  CSQ2—Mission and interests of various actors. 

  CSQ3—Relevance and appropriateness. 

  CSQ4—Is there an optimal size for the network in terms of number of partners? 

  CSQ5—Which methods could be used to assess the quality of interactions inside 

a network? 

  CSQ6—Did  the  design  of  the  network  encourage  broader  international 

cooperation (widening participation where applicable)? 

  CSQ7—Initiative, management of network, other implementation issues. 

  CSQ8—Duration  of  the  network.  Continuations/stability  of  the  network  over 

several FPs, the role of EC funding in the ‘life’ of the networks. 

  CSQ9—Links with other established networks—Networks of Excellence, ERANets. 

  CSQ10—EC funding: performance and project size. 

  CSQ11—Short-term/long-term objectives. 

  CSQ12—Achievements and lessons: What are the outcomes (joint publications) 
and impacts (structuring ERA, chaining of scientific knowledge) of the FP? What 
are other possible results of the FP that can only be revealed through studying 
relations, by means of network analysis? Which is the impact of these networks 
in  the  territories  of  their  members  (participants)  in  relation  with  the  EU 
Research Policy objectives? 

  CSQ13—Achievements  and  lessons:  On  the  basis  of  network  analysis  results: 
what  are  policy  recommendations  for  fostering  research  and  innovation  in  EU, 
such as bridging identified missing structural links, etc.? 

Pre-testing and pilot testing of the interview protocol 

The questions underwent a testing phase as part of the pilot case study before being 
applied  on  a  wider  scale.  The  pilot  testing  involved  conducting  three  interviews  as 
part  of  the  pilot  case  study.  To  refine  and  validate  the  case  study  interview 
guidelines  that  were  used,  four  preparatory  interviews  were  conducted  with 

97 

 
 
scientific  contacts  such  as  PIs  and  fellows  and  other  relevant  actors  for  which  the 
Commission had contact information. 

The interviews had an exploratory character and were broadly oriented along the 13 
questions  listed  earlier.  Following  the  four  interviews  conducted  for  the  pilot  case 
studies, an internal discussion was held within the project team to discuss potential 
improvements  to  the  interview  protocols.  The  following  aspects  were  reviewed  in-
depth: 

  Structure of data collection format: the data collection format was developed to 

align with the questions raised in the terms of reference.  

 

Timing  and  efficiency:  based  on  the  pilot  interviews,  an  estimate  of  the  time 
commitment  for  each  interview  was  noted.  Avoiding  undue  burden  on  the 
interview  participants  was  prioritised.  Interview  questions  were  not  redundant 
nor duplicated the questions that were already examined in the survey. 

  Quality check and feedback: interviews were be led by different members of the 
Oxford  Research  team  (but  with  all  of  interview  team  present)  to  ensure  a 
common  understanding  of  the  issues  and  nature  of  the  interviews.  Mutual 
exchange of experience with the interviews ensured that quality standards could 
be  applied  across  all  interviews  and  that  inter-interviewer  reliability  was 
assured.  

 

Planning for outcomes: the questions and the responses were reviewed to better 
understand the value-added aspects of  each  question.  Moreover, if  it  appeared 
that  a  more  nuanced  division  of  questions  was  required,  then  the  team 
considered adding follow-up questions.  

Recruitment 

Once  the cases had  been identified, contact  with  the project coordinator or  project 
initiator had been made, and the coordinator agreed to proceed with the case study, 
the  individual  constituent  interviewees  were  identified  and  recruited  for  the  study. 
The interview participants included key actors with varying roles within the network, 
including  project  coordinators  (who  were  also  often  the  initiator),  managers, 
scientific  contacts  such  as  PIs  and  fellows,  and  other  relevant  actors  for  which  the 
Commission may have contact information. A snowballing method for  the selection 
of informants was used; that is, suggestions for key interview contacts were asked 
of  project  coordinators  to  gain  access  to  key  actors  in  the  projects  to  be  studied. 
Furthermore,  Oxford  Research  prioritised  contacting  project  participants  from 
different institutional types and sectors. 

Based on experience with multinational programme recipients, dedicated effort was 
needed to increase the likelihood of participation in the study and to obtain consent 
to  participate  in  the  evaluation.  Indeed,  a  key  consideration  in  the  case  selection 
related  to  the  feasibility  of  obtaining  consent  from  appropriate  members  of  the 
projects. 

The interviewees were contacted on the recommendation of the project coordinator 
with whom  a first  initial conversation or  short interview was held  before interviews 
began  with  other  project  participants.  Interviewees  were  contacted  through  the 
project  coordinators  who  either  provided  the  contact  information  for  potential 
interview  participants,  or  the  project  coordinator  contacted  their  colleagues  for 
Oxford  Research  directly.  Analysts  from  Oxford  Research  then  followed  up  with 
these participants through email or by phone to confirm interviews.  

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Oxford  Research  collaborated  with  a  representative  from  DG  Research  and 
Innovation  to  draft  an  invitation  letter  requesting  the  participation  of  project 
members. This letter was signed  by  a  senior-level  representative. When  contacting 
the potential interview participants, this letter was attached in the initial  email as a 
PDF  file.  The  letter  explained  the  purpose  of  the  study  and  the  use  of  the 
information.  

Recording and interview transcription 

Oxford  Research  recorded  the  interviews  when  the  interview  participant  gave  their 
consent  to  it  and  prepared  detailed  interview  transcripts.  Notes  were  also  taken 
after the interview on the major themes to emerge from the interview, based on the 
impression  of  the  interviewer.  It  is  important  to  keep  in  mind  that  transcriptions 
from  recordings  were  subject  to  interpretation,  thus  it  is  necessary  to  consider 
transcriber  reliability.  A  Category  IV  researcher  was  assigned  to  all  transcription, 
ensuring  consistency.  All  notes  were  reviewed  by  the  interviewer  prior  to  being 
considered complete. 

In  a  situation  where  the  interview  participant  preferred  not  to  be  recorded,  the 
interviewer  and  an  accompanying  researcher 
took  shorthand  notes.  The 
consequence is that the level of detail may have been lower. However, if recording 
the  interview  could  have  impeded  satisfactory  answers,  then  the  emphasis  was 
placed on obtaining the best answers possible and the interview was not recorded. 

Interview analysis 

Up  to  five interviews  were  used  for  each  case.  After  reviewing  all  available  project 
documentation, online sources, and general information on the fields relevant to the 
projects,  information  was  taken  from  the  interview  transcripts  and  structured 
according  to  the  evaluation  question  addressed  and  grouped  by  topics.  Given  the 
open  responses  generated  from  the  semi-structured  approach  taken,  information 
required for particular topics were distributed across several responses, which were 
re-grouped to address the questions specified  in the  terms of reference  specifically 
for  the  case  studies,  in  addition  to  providing  additional  qualifications  to  the  other 
lines of evidence to address the specific questions specified in Task 3 in addition to 
the  fundamental  questions  of  the  study.  Major  themes  were  identified  and 
highlighted,  with  any  discrepancies  between  responses  leading  to  further  analysis 
and follow-up questioning.  

The analysis of interview data drew on the approach of George and Bennett (2005), 
using  process  tracing  as  a  key  instrument  linking  factors  to  outcome.  Specifically, 
the  cases  outlined  the  links  between  types  of  collaboration/networking  and  the 
factors that support them, such as language, trust, working cultures, organisational 
practices, mutual interest, existing partnerships, and other facilitators of knowledge 
transmission.  It  was  through  these  interactive  elements  that  counter-intuitive  or 
unknown  variables  could  be  identified  and  explored,  especially  relevant  given  the 
range of disciplines that could potentially interact and the various pathways through 
which  knowledge  can  be  recombined.  Factors  leading  to  collaboration  or  less 
collaboration  were  highlighted  through  descriptive accounts  of  the  decision-making 
process, which depended heavily on the opportunities generated by the structures of 
networks.  

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Case studies reporting 

It was important to maintain a common structure for the cases without constraining 
the  narrative  element.  Therefore,  while  the  cases  followed  a  common  structure, 
there  was  also  significant  scope  for  highlighting  particular  aspects  of  the  cases 
within each case study.  

The general approach for the analysis of the case study evidence drew on the work 
of  Pawson  and  Tilley  (1997).  The  data  collection  format  was  relatively  stable  and 
enabled  comparisons  between  cases  on  the  one  hand,  but  was  flexible  enough  to 
allow  diversity  in  situations  to  be  included  in  the  case  descriptions,  and  to  allow  a 
rich and illustrative case description, on the other hand.  

Quality control and analysis 

Ensuring  the  reliability  and  validity  of  qualitative  research  was  fundamental  to 
ensuring  confidence  in  the  findings,  especially  given  the  significant  role  that 
individual  researchers  play  in  both  aspects.  Consequently,  safeguards  had  been 
included in the project design to promote reliability. Case studies were developed by 
a  small  team  and  generally  written  by  the  analysts  who  carried  out  the  semi-
structured  interviews  with  the  project  participants.  The  templates  for  the  final 
reporting of the case studies were prepared by the core team from Oxford Research 
in  close  collaboration  with  Science-Metrix  and  Fraunhofer  ISI.  For  the  pilot  case 
study,  results  were  reviewed  collectively  for  interviews  and  that  information 
juxtaposed with  other lines of  evidence, with  Science-Metrix  providing  guidance  on 
how to interpret network analysis findings and practitioners from the Fraunhofer ISI 
providing guidance on survey data.  

Reporting and case study format 

The outcome of the analysis consisted of a short report for each case study (approx. 
6–12 pages). The case studies contained project data, drew on information from the 
desk  research,  network  analysis,  survey  data,  and  the  interviews  to  assess  the 
evaluation questions.  

The  12  individual  case  studies  formed  the  basis  for  the  cross-case  analysis  to 
answer  the  questions  related  to  the  relative  impact  of  cooperation  at  the  network 
level  and  with  project  outcomes.  The  breakdown  of  the  cases  is  provided  in  the 
following points. 

1.  Description of the case (0.5–1 page) 

  Overall context: past collaborations 

  Size of funding 

2.  Formation of partnerships/network (1–2 pages) 

  Design, structure and composition of the network 

  Mission and interests of various actors 

  Considerations on the size of the network (too big, too small?) 

  International cooperation 

3.  Process and implementation (1–1.5 pages) 

  Perceived quality of interactions 

100 

 
 
  Management of network and implementation issues 

4.  Contextual factors (0.5–1 page) 

  Field of science 

  Policy considerations 

5.  Impact at network level (0.5–1 page) 

  Role of EC funding in the life of the network 

  Description of links to other networks 

6.  Outputs, outcomes and impacts—impact at project level (1–3 pages) 

  Publications 

  Chaining of knowledge 

  Evidence of success/non-success from network analysis 

7.  Reflection on success factors (0.5–1 page) 

  Impact on policy 

  Potential lessons and recommendations 

8.  Conclusions (0.5–1 page) 

  Summary of success factors 

  Possible results of the FP that can only be revealed through network analysis 

Cross-case analysis 

The  purpose  of  cross-case  analysis  is  to  delineate  the  combination  of  factors  that 
may  have  contributed  to  the  outcomes  of  the  case,  to  seek  or  construct  an 
explanation  as  to  why  one  case  is  different  from  or  the  same  as  others,  to  make 
sense  of  puzzling  or  unique  findings,  or  to  further  articulate  the  concepts, 
hypotheses, or theories discovered or constructed from the original case. 

Cross-case  analysis  enhances  the  capacity  to  understand  how  relationships  may 
exist  among  discrete  cases,  to  accumulate  knowledge  from  the  original  case,  and 
refine  and  develop  concepts.  Furthermore,  cross-case  analysis  allowed  for  the 
comparison  of  cases  from  one  or  more  contexts,  which  provided  opportunities  to 
learn from different cases and gather critical evidence to modify policy.  

Whereas  the  individual  case  studies  addressed  the  terms  of  reference,  case  study 
sections  as  well  could  shed  light  on  Task  3  specific  questions  and  the  cross-case 
report  focused  on  addressing  the  study’s  fundamental  questions.  While  the  case 
studies on  their own  addressed  questions  relatively  directly,  patterns  of  potentially 
more  complex  factors  affecting  the  potentials  and  limitations  of  networking  were 
identified  through  cross-case  studies.  To  facilitate  the  cross-case  comparison,  the 
cases followed a broadly similar structure overall but, given the narrative approach 
to be taken in each case, flexibility was necessary in terms of the relative emphasis 
of each aspect of the cases.  

The  cross-case  analysis  highlighted  factors  identified  in  individual  cases  but  also 
from higher-level organisational factors such as the role of institutions and national 
factors.  The  cross-case  analysis  serves  as  a  key  input  to  the  study  report  and 
provides  evidence  contributing  to  the  elaboration  of  policy  recommendations,  and 
was also used to highlight the underlying programme assumptions at the workshop. 

101 

 
 
7. 

BALANCE 

IN  NETWORK 

TEAM  COMPOSITION 

(COMPOSITE 

INDICATOR) 

It is not clear from the evidence collected that more collaboration is always better. 
The  study  provided  some  evidence  that  something  more  is  not  always  better 
(especially  in  terms  of  bridging  sectors;  too  many  sectors  appear  to  have  a  slight 
negative effect on a variety of positive outcomes based on regression analyses), or 
at least that more is not perceived as better (survey). 

This  raises  key  questions  on  how  to  achieve  the  proper  balance  in  network  team 
composition.  For  example,  what  would  be  the  optimal  number  of  (1)  participants, 
(2) sectors, (3) ERA countries, (4) non-ERA countries, (5) regions, and (6) fields in 
a project network? 

Methods 

To  address  these  questions,  the  performance  of  FP7  projects  towards  achieving  a 
specific set of positive outcomes was compared across groups of projects of varying 
team size as measured with the: 

1.  Number of participants; 

2.  Number of sectors; 
3.  Presence/absence of private firms (or small and medium enterprises [SMEs]);16 

4.  Number of ERA countries; 

5.  Number of non-ERA countries; 

6.  Number of NUTS3 regions; and 

7.  Multidisciplinarity score of the team composition. 

The  performance  of  FP7  projects  was  measured  through  the  construction  of  a 
composite indicator synthesising the following positive outcomes: 

Bibliometric  statistics  produced  using  CORDA  and  WoS  data  (Science-
Metrix) 

 

Logarithm (to normalise the data in as much as possible) of the number of peer-
reviewed  publications  resulting  from  a  project  (Science-Metrix:  Data  produced 
using CORDA and WoS data); 

  Natural logarithm (to normalise the data in as much as possible) of the average 
number of ERA countries involved on the peer-reviewed publications of a project 

16 Although this does not relate to the size of the team, the integration of private firms, and in 
particular SMEs, is a key component of FP7 towards improving the transfer of knowledge 
to European industry with  the ultimate goal of boosting its international competitiveness. 
For  instance,  the  ‘Research  for  the  Benefit  of  SMEs’  thematic  under  the  Capacities  sub-
programme  aims  to  ‘strengthen  the  “innovation  capacity”  of  small  and  medium-sized 
enterprises (SMEs) in Europe and their contribution to the development of new technology 
based products and markets. The programme will help them outsource research, increase 
their  research  efforts,  extend  their  networks,  better  exploit  research  results  and  acquire 
technological  know-how,  bridging  the  gap  between  research  and  innovation’.  (See 
http://cordis.europa.eu/fp7/capacities/research-sme_en.html) 

102 

 
 
                                                
to  assess  the  extent  to  which  project  partnerships  really  translate  in  co-
publications (Science-Metrix: Data produced using CORDA and WoS data); 

  Natural logarithm (to normalise the data in as much as possible) of the average 
number  of  non-ERA  countries  involved  on  the  peer-reviewed  publications  of  a 
project to assess the extent to which project partnerships really translate in co-
publications (Science-Metrix: Data produced using CORDA and WoS data); 

  Scientific  ‘quality’  (i.e.  Average of Relative Impact Factors) of  a  project’s peer-
reviewed  publications  (Science-Metrix:  Data  produced  using  CORDA  and  WoS 
data); and 

  Average  interdisciplinarity  of  a  project’s  peer-reviewed  publications  to  assess 
the  extent  to  which  the  multidisciplinarity  of  a  project  team  translates  in 
transdisciplinary  work  (Science-Metrix:  Data  produced  using  CORDA  and  WoS 
data). 

Statistics produced using survey data (Fraunhofer ISI) 

  Number of types of S&T knowledge produced as part of a project; 

 

 

The number of researchers that have moved on to more senior positions; 

The  extent  to  which  an  FP7  project  has  helped  the  participants  to  enhance 
knowledge transfer from science to society and to the market; 

  Number of types of S&T tools created as part of a project; and 

  Has  an  FP7  project  prompted  the  participants  to  introduce  any  innovation  (i.e. 

yes/no)? 

Prior  to  computing  the  composite  measure,  a  descriptive  analysis  of  this  set  of 
positive  outcomes  was  performed  through  an  Exploratory  Factor  Analysis  (EFA)  as 
well as a correlation analysis. In both cases, a robust approach suitable to deal with 
variables not following a Gaussian distribution was used (i.e. using iterated principal 
axis  [IPA]  factoring  and  Spearman  correlation  coefficients).  The  main  conclusion 
drawn from both analyses is that there are not strong redundancies in the dataset of 
positive outcomes, with the EFA’s two significant factors only explaining 30% of the 
variance  in  the  dataset  and  the  strongest  correlation  coefficient  between  any  two 
variables in the dataset being equal to 0.42 (R2 = 0.18). It therefore appeared that 
a simple unweighted average of the re-scaled scores across variables would provide 
a  decent  composite  measure  reflecting  equally  well  each  of  the  positive  outcomes 
measured.  However,  missing  data  were  very  common  for  two  variables:  the 
scientific  ‘quality’  and  average  transdisciplinarity  of  a  project’s  peer-reviewed 
publications. Consequently, these two variables were discarded to only retain cases 
without  missing  data  across  all  selected  outcomes  not  requiring  data  imputation. 
This provided a sample of 900 projects. 

The score of a project for a given variable  was then re-scaled between 0 and  1  by 
dividing it by the maximum score across projects. This procedure was performed for 
all  outcomes.  Subsequently,  an  unweighted  average  of  a  project’s  re-scaled  score 
multiplied by 100 was computed  to obtain a composite measure ranging from  0 to 
100. To validate the composite measure,  a  correlation matrix (based on Spearman 
coefficients)  was  then  built  to  examine  the  correlation  pattern  of  the  composite 
indicator with each of its source variables. This led to some manual adjustments in 
the  weighting  of  individual  variables  to  distribute  more  evenly,  in  as  much  as 
possible,  the  percentage  of  the  variance  in  each  of  the  source  variables  which  is 
explained by the composite measure. 

103 

 
 
On  average,  the  final  composite  measure  explains  21%  of  the  variance  in  each  of 
the  positive  outcomes  considered,  the  percentage  of  explained  variance  ranging 
from  a  low  of  17%  (i.e.  the  extent  to  which  an  FP7  project  has  helped  the 
participants  to  enhance  knowledge  transfer  from  science  to  society  and  to  the 
market) to a high of 31% (i.e. the number of types of S&T tools created as part of a 
project).  Consequently,  a  project  performing  well  across  all  outcomes  captured  by 
the composite indicator will likely outperform a project performing really well on one 
or  a  few  dimensions  only;  in  other  words,  the  composite  gives,  to  some  degree, 
more  weight  to  projects  with  a  well-balanced  set  of  outcomes.  It  results  that  in 
cases  of  an  unexpected  finding,  one  should  not  conclude  too  rapidly  that  the  FP7 
projects  bearing  a  specific  characteristic  are  detrimental  to  the  production  of 
positive outcomes. Indeed, the goals targeted across FP7 sub-programmes—as well 
as within a given sub-programme at the thematic/call level—can vary considerably, 
such  that  this  approach  might  not  offer  sufficient  granularity  to  determine  the 
optimal size (for various dimensions) of teams under specific circumstances. In such 
cases,  further  research  would  be  required  to  specifically  measure  the  expected 
outcomes  of  the  thematic/call  of  interest  against  the  specific  networking 
characteristics  that  were  promoted  under  the  corresponding  thematic/call.  This 
would be difficult to achieve here given that the sample sizes available would rapidly 
get very small as one move towards more disaggregated groups of projects. 

104 

 
 
8.  WORKSHOP WITH VARIOUS STAKEHOLDERS 

Oxford  Research  designed,  organised,  and  facilitated  a  workshop  with  Commission 
services  and  practitioners.  Research  staff  from  Science-Metrix  and  Fraunhofer  ISI 
participated  in  presenting  the  quantitative  data  based  on  respectively  the  network 
analysis  and  the  survey  results  as  well  as  contributing  to  the  discussions  and 
answering  questions  from  Commission  staff  and  project  practitioners.  In  line  with 
the objectives set out in the terms of reference, the workshop was held to:  

 

Present findings of the evaluation 

  Confirm the accuracy of the data and analyses presented 

  Deepen the analysis of the report and to discuss potential recommendations 

Each of these objectives required specific approaches, with a blend of presentations, 
question  and  answer  sessions,  and  interactive  dialogue.  Although  the  workshop 
combined a range of approaches, the underlying principle for the overall workshop 
was to facilitate learning. 

The  outcomes  of  the  empirical  research,  the  focus  of  Work  Packages  3,  4,  and  6, 
built a strong base of evidence to be used in the case studies. Drawing lessons from 
the integrated set of evidence required a strategic focus that integrated perceptions, 
strategies,  and  personal  vision.  The  movement  from  evidence  to  action  was  an 
important  purpose  of  the  workshop.  Although  the  workshop  focused  on  verifying 
evidence,  the  highest  value-added  aspect  was  the  opportunity  for  input  from  the 
representatives.  

There were 22 participants at  the workshop,  which  took place at the Commission’s 
premises  in  Brussels.  There  were 11  participants  from  the  Commission,  six  project 
practitioners  had  confirmed  their  attendance  but  only  five  actually  attended  the 
workshop,  and  there  were  seven  representatives  from  the  consortium.  The  project 
budget  covered  the  costs  of  participation  for  the  six  practitioners  identified  during 
the  case  studies.  A  rapporteur  from  Oxford  Research  was  also  assigned  to  the 
workshop.  After  the  workshop,  Oxford  Research  compiled  workshop  minutes  and 
circulated  them  among  the  participants.  These  notes  have  also  contributed  to 
writing the final report.  

Design 

The  design  of  the  workshop  was  based  on  interactive  approaches  that  maximised 
dialogue  while  balancing  the  need  for  the  presentation  of  data.  Given  the  three 
objectives  of  the  workshop  and  the  structure  of  the  project,  the  workshop  was 
broken  into  three  sessions.  Each  session  was  composed  of  two  distinct  types  of 
workshop:  a  formal  presentation  by  the  research  team  followed  by  a  group 
discussion  in  which  the  invited  participants  were  expected  to  contribute.  The three 
sessions  were  grouped  according  to  research  questions.  Results  and  conclusions 
from  the  specific  questions  were  presented,  and  to  conclude  the  presentations, 
discussion questions were posed for all workshop participants to discuss. Workshop 
participants were divided into three groups and  with  each session the groups were 
changed.  Each  group  consisted  of  representatives  from  the  Commission,  project 
practitioners  and  members  of  the  research  team.  The  research  team  sat  in  the 
groups  as  observers  and  did  not  contribute  or  lead  the  direction  of  the  group 
discussion. Kim Møller of Oxford Research, an experienced facilitator, facilitated the 
group  discussions  and  brought  together  the  different  group  inputs  to  form  overall 
conclusions.  

105 

 
 
  Session  1:  Linking  fields  of  knowledge,  types  of  organisations  and 
contributing to new collaboration within FP7: The first session focused on 
three specific evaluation questions.  

  To what extent does the FP link separate fields of knowledge, bridge 
different fields of science and facilitate new scientific disciplines? 

  To what extent does the FP link separate fields of organisations and bridge 

the flow of innovation between them? 

  To what extent has the FP contributed to new collaborations and the 

integration of new organisations?  

The  presentation  focused  on  the  key  findings  from  each  of  the  SQs  with  each 
research  team  presenting  their  data  where  applicable  to  support  the  findings 
based on their own methodology. Throughout the presentation, participants had 
the  opportunity  to  ask  questions  associated  with  the  accuracy  of  the  data  and 
the  validity  of  the  findings.  The  two  following  discussion  questions  were  posed 
for workshop participants to discuss in the first session. 

  Collaboration within fields is still more common than across fields despite 
the fact that FP7 was able to link projects and researchers from different 
fields of S&T who may not normally collaborate. What are the implications of 
this? 

  What are the implications of increased multidisciplinarity and collaborations 
between different types of organisations via FP7—which on the one hand 
may help integrate otherwise disconnected knowledge and promote 
innovation, but on the other hand, also comes with challenges such as 
different work cultures, different expertise and different end goals? 

  Session  2:  Linking  to  new  projects  and  wider  cooperation  beyond  FP7: 
In  the  same  way,  the  second  session  centred  on  three  specific  evaluation 
questions.  

  Where are the structural holes in the structure of the FP network and who 

benefits from them? 

  To what extent are FP project results used as an input for new FP projects, 

and to what extent has knowledge generated in previous FPs been 
transformed into new knowledge? 

  To what extent does the FP contribute to international research relations 
between the EU and third countries and foster a wider participation of 
relevant actors and stakeholders within the ERA? 

All three research teams presented their data to support the key findings under 
each of the SQs. A discussion was then held focusing on these three questions: 

  What are the implications of the finding that all other sectors, aside from 

academia, generally occupy peripheral positions in a network and depend on 
universities or research organisations to bridge knowledge in the network? 

  What are the implications of the finding that grounding FP7 projects in 

earlier work leads to well-organised and planned research, producing more 
beneficial outputs/outcomes in the long run? 

  What are the implications of the finding that FP7-sponsored research 

promotes international research relations between the EU-28 and other non-
associated countries, but, the core of research collaboration networks 
created under FP7 remains focused on Europe? 

106 

 
 
  Session  3:  Achieving  EU  Research  Policy  objectives:  The  final  session 
focused  on  a  broader  specific  evaluation  question  and  concluded  with  a 
discussion  on  the  potentials  and  limits  of  the  network  analysis  approach.  The 
two topics presented on in the last session included: 

  To what extent does the network approach prompted and implemented by 

FP7 contribute to the achievement of EU Research Policy objectives? 

  Potentials and limits of this approach. 

The last session concluded with two questions for discussion: 

  What are the implications of the finding that even though the network 
approach has huge potential for generating direct outputs (e.g. peer-
reviewed articles and spin-off projects), and—in the longer term—that 
networks can potentially generate outcomes such as stronger knowledge 
transfer from research to industry, there are a number of 
limitations/challenges such as: lack of a mutual understanding of the 
project’s objective in multi- and interdisciplinarity projects; differences in 
academic vs. industrial motivation; legal issues such as IPR; geographical 
distances; and large number of project partners. 

  What are your thoughts on the point of view that, potentially, a certain level 

of inequality and structural holes is natural and even beneficial for a 
scientific system as large as the ERA? 

The workshop followed the programme below. 

FP7 Network Analysis Workshop 
November 5, 2014 
Directorate-General for Research & Innovation 

8:30 
9:00 
9:00 
9:30 

– 

Registration and coffee 

– 

Welcome 
  Opening remarks from the Commission 

9:30 
10:50 

– 

Introduction by Kim Møller, Oxford Research 
 

The aims and goals of the workshop 

 

Presentation of the agenda for the workshop 

Session 1: Linking fields of knowledge, types of 
organisations and contributing to new collaboration 
within FP7 
Context: Research findings presented by Science-Metrix, 
Fraunhofer and Oxford Research 
Discussion facilitated by Kim Møller 
 

To  what  extent  does  the  FP  link  separate  fields  of  knowledge, 
bridge  different  fields  of  science  and  facilitate  new  scientific 
disciplines? 

 

 

To  what  extent  does  the  FP  link  separate  fields  of  organisations 
and bridge the flow of innovation between them? 

To  what  extent  has  the  FP  contributed  to  new  collaborations  and 
the integration of new organisations? 

10:50 
11:20 

– 

Coffee Break 

107 

 
 
11:20 
12:45 

– 

Session 2: Linking to new projects and wider 
cooperation beyond FP7 
Context: Research findings presented by Science-Metrix, 
Fraunhofer and Oxford Research 
Discussion facilitated by Kim Møller 
  Where are  the  structural holes in the structure of  the FP network 

and who benefits from them? 

 

 

To what extent are FP project results used as an input for new FP 
projects, and to what extent has knowledge generated in previous 
FPs been transformed into new knowledge? 

To  what  extent  does  the  FP  contribute  to  international  research 
relations  between  the  EU  and  third  countries  and  foster  a  wider 
participation of relevant actors and stakeholders within the ERA? 

12:45 
13:45 
13:45 
15:05 

15:05 
15:30 
15:30 
16:30 

– 

Lunch 

– 

Session 3: Achieving EU Research Policy objectives 
Context: Research findings presented by Science-Metrix, 
Fraunhofer and Oxford Research 
Discussion facilitated by Kim Møller 
 

To  what  extent  does  the  network  approach  prompted  and 
implemented  by  FP7  contribute  to  the  achievement  of  EU 
Research Policy objectives? 

 

Potentials and limits of this approach 

– 

Coffee Break 

– 

Closing 
Led by Kim Møller, Oxford Research 
  Summary 

  Next Steps 

Organisation of the workshop 

Besides  the  design  of  the  workshop,  several  steps  were  taken  to  organise  the 
workshop.  Essentially,  these  steps  fell  under  two  streams,  one  focused  on  the 
preparation of content to support the workshop and the other related to the logistics 
associated with holding a workshop. 

Content preparation 

The  preparatory  stage  had  three  components:  discussion  with  the  Commission 
representatives to set the agenda, confirmation of the participants, and the creation 
of background material. 

  Step 1: Discussion with Commission representatives 

  Step 2: Confirming list  of participants.  Project practitioners were invited to 
the  workshop  based  on  different  criteria,  including  gender,  country,  industry, 
and  role  in  the  project.  A  list  of  potential  participants  was  also  sent  to  the 
Commission to review. Due to the schedules and availability of different project 
practitioners,  however,  not  all  criteria  could  be  met  and  more  flexibility  was 
needed.  

108 

 
 
  Step  3:  Background  materials.  An  introductory  email,  along  with  a  draft 
programme  agenda,  was  sent  to  the  project  practitioners  with  their  invitation. 
The  email  started  with  a  brief  introduction  about  the  purpose  of  the  workshop 
and  the  approach.  Because  of  the  data-intensive  aspects  of  the  presentations, 
and  the  need  to  cover  a  significant  amount  of  material  in  a  short  period,  the 
draft  final  report  including  tables  of  the  primary  data  as  well  as  brief 
explanations  and  analysis  was  also  sent  to  the  project  practitioners  and  the 
Commission  three  days  ahead  of  the  workshop.  The  PowerPoint  presentation 
used in the workshop was also sent to project practitioners and the Commission 
prior to the workshop.  

Logistics 

Preparation for the workshop included the following elements: 

 

 

 

 

 

Element 1: Information for participants 

Element 2: Registration 

Element 3: Workshop materials 

Element 4: Travel and accommodation management 

Element 5: Venue 

Every  step  was  verified  by  a  progressive  checklist  that  was  used  to  ensure  the 
efficiency  of 
the  project 
the  organisation.  The  checklist  evolved  during 
implementation to comply with the organisational needs. 

  Element  1:  Information  to  participants.  An  initial  invitation  email  was  be 
sent  to  all  participants,  which  included  practical  details  such  as  location, 
directions, and instructions for registration. The email included information such 
as  updated  programmes  and  the  structures  of  the  workshop,  and  background 
documents were transmitted to the participants.  

  Element  2:  Registration  management.  The  registration  form  included  the 
following information: Name; First name; Function; Institution; Contact details; 
Language  barriers.  This  information  was  used  in  the  production  of  workshop 
materials: 

  Registration monitoring table 

  Reminder 

  Registration confirmation 

  Information to suppliers and Commission Services (catering, etc.). 

When  necessary,  reminders  were  sent  and  telephone  calls  were  made  to  the 
targeted  participants  who  did  not  reply  to  the  invitation  before  the  deadline. 
After response, confirmation was provided and a reminder was issued one week 
before the workshop. Another element that was required was a language field. 
As the workshop was conducted in English,  any translation services  needed to 
be  identified  in  advance.  Translation  services  at  the  workshop  were  not 
necessary on this occasion.  

  Element  3:  Workshop  material:  Documents  necessary  for  the  smooth 
running  of  the  workshop—badges,  name  tags,  copies  of  presentations, 
background  and  discussion  papers—and  the  list  of  participants  were  prepared 
and provided at the workshop. The following material were produced for running 
the workshop smoothly: 

109 

 
 
  Badges: Participants (including speaker) received at their arrival a badge 
with the following information: first name, last name, and institution.  

  List of participants and attendance lists: Through to the registration system, 
the Commission was informed on demand of the number of participants 
registered for the workshop with details. Commission representatives were 
asked to remark or make suggestions about the participation, such as 
additional participants or scheduling conflicts. Oxford Research was tasked 
with implementing corrective measures if necessary.  

  Copies of the workshop material: For the content of the specific workshop 
and the background and discussion material to be prepared (see above). 

  Workshop programme: For the convenience of the participants and to take 

into account final amendments to the programme, the workshop programme 
was printed and distributed to the participants upon arrival. 

  Element  4:  Travel  arrangement  and  accommodation:  The  number  of 
participants not including Commission representatives was 12. According to the 
Tender Specifications, attendance at the workshop was supported by the project 
budget. To ensure a smooth system, full documentation of expenses, and early 
reservations  to  reduce  prices,  Oxford  Research  arranged  the  travel  and 
accommodation. This included:  

  Booking the rooms for the participants—a group rate was sought to reduce 

the price and to facilitate transport. 

  Travel arrangements for the participants—flights and transport would be 
arranged in groups to minimise duplication of effort. As all of the project 
practitioners came from different cities in Europe, arranging flights in groups 
was not possible. 

  Confirmation of travel and accommodation for participants—reminders were 

issued, and all documentation was managed by Oxford Research. 

  Element  5:  Venue:  The  workshop  was  organised  at  a  Commission  venue. 
Oxford  Research  worked  with  the  Commission’s  support  services  to  arrange 
several practical aspects to facilitate the workshop.  

  Setup of the workshop room: The meeting room was to be prepared. 

  Equipment was reserved by the Commission and their Audio-visual services. 

  Names of workshop participants were given to the Commission ahead of the 

workshop so that project practitioners could access the workshop on 
Commission premises.  

Drafting of workshop proceedings 

Oxford  Research  supplied  a  rapporteur  for  the  meeting  who  was  responsible  for 
drafting the workshop proceedings, including a summary of the analysis of the data, 
conclusions,  and  policy  recommendations.  The  working  language  during  the 
workshop  was  English,  and  all  materials  were  written  in  English.  The  proceedings 
were circulated to all participants within  nine  working days of the workshop, which 
provided  for  additional  dialogue  with  the  research  team  and  allowed  any 
misunderstandings  or  omissions  to  be  added  to  the  official  transcript.  Workshop 
participants then had 10 working days to approve the proceedings. 

110 

 
 
9.  EVIDENCE TABLES  

The key findings stemming from each of the above methods have been synthesised 
for  the  provision  of  informed  policy  recommendations  as  relates  to  the  study’s 
Specific  Evaluation  Questions  (i.e.  SQ1  to  SQ8)  as  well  as  the  two  Fundamental 
Questions (FQ1 and FQ2). This consists of the integration of those key findings that 
appear robust as evidenced through the triangulation of information gathered using 
the  various  methodological  instruments  presented  in  this  methodological  annex. 
These synthetic findings, arranged under each evidence table, are  then used in the 
final  evaluation  report  as the  cornerstone  around  which  the  team  anchors  its  main 
conclusions/policy  recommendations  pertaining  to  the  study’s  evaluation  questions 
(i.e. SQ1-SQ8 and FQ1-FQ2). These tables were mainly used as tools and inspiration 
to  write  the  report.  As  such,  there  might  be  discrepancies  between  the  exact 
wording  of  the  findings  and  even  new  findings  not  found  within  the  tables.  These 
new findings originate from a deeper and more thorough analysis of the data. 

111 

 
 
 
Table XV  

Evidence Table SQ1—Integrating Key Findings from all Collection Methods 

Finding  mapped  to  data 

Social 

Network 

Survey 

Regressions 

Case studies 

Cross-case synthesis 

sources 

Analysis (SNA) 

(Integrating 

SNA 

and survey data) 

Firstly, FP7 
successfully supported 
the creation of multi-
disciplinary research 
teams. 
Secondly, the share of 
project pairs from 
different thematic 
priorities that are 
linked to one another 
by at least one 
common participant 
rose during FP7, 
almost doubling 
between 2007–2010 
and 2011–2014. 

SQ1 Finding 1 
FP7 linked researchers and 
projects from fields of 
science that do not 
otherwise frequently 
exchange knowledge 

SQ1 Finding 2 
FP7 fosters interdisciplinary 
research even in specific 
programmes where 
cooperation and networking 
is not sought 

Many research 
activities funded 
under FP7 have 
succeeded in 
linking different 
fields of science 
and technology. 

Survey 
respondents 
revealed that in 
many cases their 
FP7 project led to 
the emergence of 
a new line of 
research. 
However, the 
causal link 
between this and 
the 
interdisciplinarity 
of projects 
remains to be 
clearly 
established. 

A multi-disciplinary 
approach brings people 
together from different 
disciplines who would 
otherwise not work 
together. 

The multidisciplinarity of 
project teams has a 
slight but significant 
effect on the integration 
of otherwise a priori 
disconnected knowledge 
beyond what can be 
explained solely by the 
average 
interdisciplinarity of the 
papers produced by 
project teams prior to 
their participation in FP7. 

As exemplified by the 
case study selected to 
document the 
assumption that 
increased 
interdisciplinarity 
increases the probability 
of the emergence of 
innovation, the 
interdisciplinary nature 
of projects was 
successful. 

Networks inspire 
researchers to broaden 
their perspective. 

112 

 
 
 
 
 
SQ1 Finding 3 

The majority of the research 
projects in FP7 involved new 
lines of research 

SQ1 Finding 4 

Multidisciplinary research 
formally supported by FP7 
does not appear to be a 
prerequisite to start new 
lines of research 

The following 
question was 
asked to FP7 
participants in the 
survey: ‘To what 
degree has your 
FP7 project 
initiated a new line 
of research?’… 
more than half of 
the survey 
respondents 
(63%) reported 
that their FP7 
projects opened 
up a new field of 
research. 
The very high 
share observed for 
Ideas is not 
surprising since it 
focuses on 
supporting 
excellence to 
achieve frontier 
research. 
However, this, at 
first sight, does 
not support the 
underlying 
hypothesis that 
multidisciplinary 
research 
supported under 
FP7 is a 
prerequisite to 
foster the 
emergence of new 
lines of research. 
Indeed, Section 
7.1.1 has shown 
that both Ideas 
and People were 
not 
multidisciplinary 
compared to 
Capacities and 
Cooperation. 

113 

 
 
 
 
 
 
 
 
 
 
The explanatory 
variables included in the 
regression model was 
ran with innovation as 
the dependent variable 
that was measured 
through the survey, 
where researchers were 
asked to indicate 
whether the FP7 project 
has prompted their 
organisation to introduce 
product or process 
innovations. Based on 
this analysis, 
multidisciplinary research 
has not been shown to 
exert a significant effect 
on triggering innovation 
in the form of new 
products or processes. 

The inter-disciplinary 
nature of projects was 
also the biggest 
challenge (team was 
forced to engage 
participants across 
scientific fields so that 
the entire group would 
understand the inputs 
that each partner was 
contributing). 

SQ1 Finding 5 

No statistically significant 
link was found between 
multidisciplinarity and 
innovative capacity 

SQ1 Finding 6 
Despite increasing linkages, 
project management 
difficulties may be incurred 
by increased 
interdisciplinarity. 

SQ1 Finding 7 
Generally speaking, there 
does not seem to be an ideal 
level of multidisciplinarity to 
obtain the outcomes sought 
in FP7 

The multidisciplinarity 
of project teams does 
not appear to exert a 
positive or negative 
effect on positive 
outcomes measured 
through the composite 
indicator except under 
the People specific 
programme. 

114 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table XVI  

Evidence Table SQ2—Integrating Key Findings from all Collection Methods 

Finding mapped to data 

Social Network 

Survey 

Regressions 

Case studies 

sources 

Analysis (SNA) 

(Integrating SNA 

and survey data) 

Cross-case 

synthesis 

SQ2 Finding 1 
FP6 and FP7 have 
successfully promoted 
intersectoral collaborations 
and the level of intersectoral 
collaboration is largely 
unchanged in FP7 

The results show that 
70% of the core 
collaborative projects 
under FP7 involve three 
or more sectors, thereby 
indicating that it has 
successfully promoted 
intersectoral 
collaborations. This is 
very similar to what was 
achieved under FP6. 

The integration of sectors 
promoted under FP7 
facilitates the flow of 
innovation ideas between 
sectors to a greater 
extent than under FP6. 

SQ2 Finding 2 
Though FP7 strengthened 
links with researchers in 
other sectors, it 
strengthened links even 
more between researchers 
within the same sector 

Inter-
organisational 
collaboration 
plays a 
constitutive role. 

The survey 
results 
corroborate the 
above finding 
obtained from 
the analysis of 
CORDA data, 
wherein at least 
half of the 
respondents 
indicated that 
their FP7 
projects enabled 
them to 
strengthen their 
linkages with 
researchers 
from other 
sectors. 
Importantly 
though, an even 
larger 

115 

 
 
 
 
 
 
 
 
 
 
proportion of 
researchers 
mentioned that 
FP7 
strengthened 
linkages with 
researchers 
from their own 
sector. 

The regression, as seen 
in Table XXII, showed 
that crossing too many 
sectoral boundaries 
between cooperation 
partners decreases to a 
slight degree: 
- The number of types of 
S&T knowledge produced 
as part of a project (or 
the breadth of knowledge 
output); 
- The number of types of 
S&T tools created as part 
of a project (or the 
breadth of S&T tools 
created);  
- The transfer of research 
results to the business 
sector and society;  
- The triggering of 
innovations. 

SQ2 Findings 3 
There is no clear evidence 
suggesting that there is an 
ideal number of sectors in a 
project to achieve the 
outcomes sought in FP7 

Composite indicator, 
There does not appear to 
be a turning point related 
to the number of sectors 
involved in a project 
where the benefits of 
adding more sectors 
decrease. 

SQ2 Findings 4 
Greatly extending 
intersectoral linkages may 
have an adverse effect on 
innovation 

SQ2 Finding 5 
The integration of business 
enterprises, including SMEs, 
in intersectoral partnerships 
fosters innovation 

From the composite 
indicator, looking at 
individual set of 
outputs/outcomes related 
to innovation in the 
business sector, 
Knowledge transfer from 
research to market 

Similar to SQ1, inter-
organisational work can 
present limitations in 
projects, where the 
partners have not 
managed to develop a 
mutual understanding 
of the project’s 
objective and thus 
cannot value the 
diversity in the project. 

116 

 
 
 
 
 
 
 
 
 
 
 
 
 
(Table XXV) and the 
propensity of projects to 
introduce innovation in 
the form of new products 
or processes (Table 
XXVI) are both 
significantly increased in 
the presence of SMEs in 
all specific programmes. 

117 

 
 
 
Table XVII   Evidence Table SQ3—Integrating Key Findings from all Collection Methods 

Finding  mapped  to  data 

Social 

Network 

Survey 

Regressions 

Case studies 

Cross-case 

sources 

Analysis (SNA) 

(Integrating 

SNA 

and 

survey data) 

synthesis 

SQ3 Finding 1 

The FP has greatly facilitated 
new collaborations and 72% 
of the participating 
organisations are new 

71.7% of the 
organisations and 85.9% 
of the inter–
organisational 
collaboration pairs were 
found to be new in FP7. 

There is a high level of 
new organisations from 
across the EU-28, so that 
a strengthening of the 
ERA can be expected. 

FP7 has succeeded in 
bringing together partners 
not previously involved in 
collaborations, thus 
integrating new 
organisations into the 
consortiums. 

SQ3 Finding 2 

New partners can contribute 
to strengthening the 
network and promoting 
innovation 

Involving new 
partners is 
beneficial to the 
breadth of S&T 
tools created. 

Furthermore, new 
partners (in large 
consortia) and 
regular exchange 
between the 
partners is 
conducive to 
prompt innovation. 

As innovation often 
requires a 
multidisciplinary and 
cross-sector approach, 
more networks are 
being developed and 
further strengthened 
by the inclusion and 
addition of new 
organisations and 
partners. 

Networks stimulate 
innovation by 
transferring research-
related knowledge from 
the universities to the 
industry and vice versa. 
The networks cover the 
range from ‘pure basic 
research’, ‘use-inspired 
basic research’ to 
‘applied research’. Two 
thirds of the cases 
studies are examples of 
research projects were 
private sector businesses 
are part of the project 
network. 

118 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table XVIII   Evidence Table SQ4—Integrating Key Findings from all Collection Methods 

Finding  mapped  to 

Social Network Analysis (SNA)  Survey 

Regressions 

Case 

Cross-case 

The collaboration affinity between 
countries has stayed roughly the same 
from FP6 to FP7, apart from a couple of 
outliers. 
In addition, no major change was 
detected in the project co-participation 
patterns and the co-authorship for 
scientific articles from pre-FP7 to FP7 
period. Thus, FP7 does not appear to 
have filled past structural holes in the 
network of FP participation or in the 
scientific literature generally. 
In the deciles corresponding to the 
smallest betweenness centrality scores 
(deciles 4-10), organisations from the 
SME, Private and Public body and 
Others sectors are observed more 
frequently than expected, whereas the 
opposite is true for the Higher 
education and Research organisations 
sectors. The opposite pattern is 
observed for the deciles corresponding 
to the highest betweenness scores. 

Respondents 
indicated that 
more SMEs should 
be involved and 
that the 
interdisciplinarity 
of consortiums 
should be 
strengthened. 

data sources 

SQ4 Finding 1  
At the country level, 
little change was seen 
from FP6 in terms of 
the collaboration 
affinity of countries 
based on co-
participation and co-
publication patterns  

SQ4 Finding 2  

SMEs, the private 
sector, and public 
bodies represent 
missing links in the 
network structure 

SQ4 Finding 3 

As structural holes 
appear to benefit those 
who are centrally 
located, bridging them 
may help distribute 
benefits more equally 

(Integrating  SNA  and 

studies 

synthesis 

survey data) 

Structural holes appear 
beneficial to the output 
(papers) of those who are 
centrally located in the FP7 
network for the People and 
Ideas sub-programmes 
(which are not network-
focused). It is possible that 
by bridging structural holes, 
these benefits may be more 
equally distributed. As 
shown above using SNA, 
academic institutions and 
research organisations are 
usually those which are the 
most centrally located with 
companies at the periphery. 

Limit - Where there is a 
core of partners and a 
periphery of partners, 
the benefits gained vary 
according to the 
partners’ role in the 
network.  
(Based on the SNA, the 
FP7 network is 
characterised by a core-
periphery structure, 
whereby actors are 
closely knit at the core, 
with multiple actors 
distributed at the 
periphery). 

119 

 
 
 
 
 
 
 
 
 
 
 
 
Table XIX  

Evidence Table SQ5—Integrating Key Findings from all Collection Methods 

Finding mapped to data 

Social  Network 

Survey 

Regressions 

Case studies 

Cross-

sources 

Analysis (SNA) 

(Integrating SNA and survey data) 

case 

synthesis 

SQ5 Finding 1  
It appears that knowledge 
transfer did occur to some 
extent from FP6 to FP7 

SQ5 Finding 2 
Findings related to the 
leveraging of knowledge 
produced under FP6 
support the hypothesis of 
SQ5 that grounding FP7 
projects in earlier work 
leads to well-organised 
and planned research, 
producing more beneficial 
outputs/outcomes in the 
long run. 

SQ5 Finding 3  
Evidence suggests that 
knowledge produced 
under the FP will continue 
to be used in subsequent 
projects 

The presence of 
results/knowledge 
transfer is clearly 
observed from FP6 to 
FP7. On average, each 
FP7–supported paper 
cited 0.48 FP6–
supported papers. And 
overall about 50% of 
FP7 papers cited FP6 
papers. 

There is a notable, 
although not 
necessarily substantial 
connection between 
research performed 
under FP6 and 
subsequent projects 
under FP7. 

One or more 
experts from one 
project carried on 
tacit knowledge to 
use in another 
project. 

The use of knowledge produced under FP6 is 
beneficial to the size of FP7-supported 
scientific output, to the scientific impact of 
FP7-supported publications, to the breadth 
of knowledge and S&T tools created, to the 
transfer of research results to the business 
sector and society as well as to career 
effects. Note, however, that the size effects 
are generally small. 

Although it is based solely on the regression 
analysis, this is a strong/reliable finding as a 
significant effect was observed for nearly all 
outputs/outcomes measured both with 
bibliometric indicators and survey indicators. 
In a sense, it is based on two data sources 
since the regression analysis already 
incorporates the SNA and Survey data. 

A substantial share of 
the participants 
currently funded under 
FP7 plan to use the 
results for other 
projects and have 
already applied for 
concrete funding. 

There are examples 
of project partners 
who expressed a 
wish to continue to 
work together on 
new FP supported 
projects and have 
therefore chosen to 
apply for new 
funding or are 
planning to apply. 

120 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table XX  
Key Findings from all Collection Methods 

Evidence  Table  SQ6  (broadened  to  include  integration  both  within  and  beyond  the  ERA)—Integrating 

Finding  mapped 

Social Network Analysis (SNA) 

Survey 

Regressions 

Case 

Cross-

to data sources 

SQ6 Finding 1 

The FP clearly 
contributes to 
international 
research relations, 
and therefore to 
the integration of 
European nations, 
within and beyond 
the ERA. FP7 
actually fostered 
participants to 
increase their 
international 
relations in the 
form of 
international co-
authorships of 
scientific papers. 

International research relations, within and beyond the ERA, are clearly 
apparent based on co-participation data to FP7 projects (e.g. avg. no. 
of countries [from various groups of countries] per project). These 
relations do not appear to be stronger in FP7 relative to FP6 based on 
averages of the number of countries per project as well as on the 
centralities of countries in co-participation networks. However, this is 
not to say that no changes occurred as a result of FP7. Indeed, many 
of the participants in FP7 are new and therefore did not directly benefit 
from the integration fostered under FP6. As such, their tendency to 
collaborate internationally might have been increased as a result of 
their participation to FP7. This is later shown through a comparative 
analysis of their international co-authorship patterns in their papers 
published prior to FP7 and while supported by FP7. 

Additionally, using a measure of affinity between country pairs which is 
less sensitive to scale, it appears that a number of countries (e.g. some 
of the former Yugoslav countries as well as some EU members states 
located on the eastern side of Europe) are more integrated in the co-
participation network under FP7 than under FP6, their average pairwise 
affinities within the network having shown the greatest increase among 
all countries involved.  

Based on the co-publication data of FP6 and FP7, similar findings are 
obtained. For instance, when comparing the international co-authorship 
rates of FP-supported publications to those of papers involving at least 
one ERA country but who were not supported by an FP, it is clear that 
the FPs promoted stronger international research relations to a similar 
extent under FP6 and FP7. This is, as was the case for data based on 
co-participation, especially true within the ERA. In terms of co-
authorship beyond the ERA, only FP7 appears to promote co-authorship 
between ERA and non-ERA countries to a larger extent than is 
observed for papers involving at least one ERA country but who were 
not supported by an FP. As was the case for data based on co-
participation, the magnitude of effect beyond the ERA remains small. 

For FP7 participants, the international co-publishing rate clearly 
increased both within and outside the ERA when comparing their 

FP7-sponsored 
research clearly 
promotes 
international 
research 
relations within 
the ERA (i.e. 
EU-28 and FP7 
associated 
countries) as 
well as between 
the ERA and 
other, non-
associated, 
countries, but 
to a much 
larger extent 
for the former 
than the latter. 

(Integrating 

studies 

case 

SNA and survey 

synthesis 

data) 

Networks 
lead to 
increased 
mobility of 
researchers. 
All the case 
studies are 
examples of 
projects 
where 
partners 
from 
different 
countries 
work 
together and 
travel across 
borders to 
meet. 

121 

 
 
 
 
 
 
 
Finding  mapped 

Social Network Analysis (SNA) 

Survey 

Regressions 

Case 

Cross-

to data sources 

(Integrating 

studies 

case 

SNA and survey 

synthesis 

data) 

papers published before FP7 to their FP7-supported papers. These rates 
and their increase are also notably stronger than for the ERA as a 
whole (excluding papers by FP7 participants) over the same period, 
although to a lesser extent for international partnerships beyond the 
ERA compared to within the ERA. This indicates that the cross-country 
participations promoted by FP7 contributed to increasing the propensity 
of participating researchers to produce their research through 
international partnerships, potentially contributing to furthering the 
integration of European research both within and beyond the ERA to a 
greater extent than has generally been observed for the ERA as a 
whole (i.e. for ERA researchers who did not participate in FP7). 

The stronger integration of ERA and non-ERA countries resulting from 
FP7 is evident when comparing their centralities within the co-
authorship network of publications produced by participants prior to 
FP7 to those within the co-authorship network of FP7-supported 
publications. For ERA countries, this is mostly attributable to the 
increased integration of the latest EU Member States. 

122 

 
 
 
Finding  mapped 

Social Network Analysis (SNA) 

Survey 

Regressions 

Case 

Cross-

to data sources 

SQ6 Finding 2 

Through the 
attraction of 
internationally 
renowned 
scientists as well 
as increased 
outputs/outcomes, 
international 
cooperation under 
FP7 contributes to 
raising the 
international 
competitiveness of 
European science 
& technology likely 
resulting in 
improved 
innovation 
capabilities at the 
national level. 

Projects linking a greater number of ERA countries appear to produce 
more of the desirable outputs/outcomes expected at the outset of FP7 
participation (based on a composite measure of outputs/outcomes). 
This indicates that international research relations within the ERA 
contribute to raising the international competitiveness of European 
science and technology. 

(Integrating 

studies 

case 

SNA and survey 

synthesis 

A substantial 
number of 
respondents 
state that FP7 
funded projects 
successfully 
attracted 
internationally 
renowned 
researchers 
(mainly from 
technologically 
leading nations) 
to the EU, 
fostering wider 
international 
research 
relations 
between EU 
countries and 
beyond. 

Additionally, 
the attraction 
of 
internationally 
renowned 
scientists from 
outside the ERA 
as the potential 
to raise 
research 
excellence, 
thereby 
increasing the 
international 
competitiveness 
of European 
science. 

data) 
It was also found 
that by increasing 
the number of 
participants on FP7 
projects, one might 
achieve larger 
scientific 
production, but 
likely with 
diminishing returns 
as the size of 
projects increases. 

Since the number of 
ERA countries is 
strongly correlated 
with the number of 
participants on a 
project (Corr. Coeff. 
= 0.91), one could 
extrapolate this 
finding, with some 
reserve, to conclude 
that the higher the 
number of 
participants on a 
project, the more 
likely it is that the 
project will produce 
new knowledge 
disseminated in the 
form of scientific 
publications. At 
least, this provides 
additional evidence 
in line with the SNA 
finding that more 
ERA countries on a 
project lead to more 
outputs/outcomes. 

123 

 
 
 
 
 
 
Table XXI  
from all Collection Methods 

Evidence Table SQ7 (restrained to cross-regional integration within the ERA)—Integrating Key Findings 

Finding 

Social Network Analysis (SNA) 

Survey 

Regressions 

Case studies 

Cross-case 

mapped 

to 

data sources 

SQ7- Finding 1  
The FPs 
contributed to 
various 
degree to the 
integration of 
regions and 
fosters a 
wider 
participation 
of relevant 
actors and 
stakeholders 

The collaboration affinity pattern between 
NUTS 3 regions has not fundamentally 
changed when looking at it from a global 
standpoint. 

The average project number per NUTS has 
increase by 40% (compared to FP6). This is 
also supported by an increase of 10% of 
average weighted degree. Hence the 
average number of new partner (unweighted 
degree) might not have increase by much 
but the intensity of collaboration, i.e. the 
number of different projects 2 same partner 
are working on has significantly increased. 

When using the collaboration affinity index 
of NUTS regions to assess their 
attractiveness, we notice that a lot of 
regions which do not have a large focus on 
research are now much more integrated into 
the network. 

Regions not participating in any project has 
drop by almost 60% (62 in FP7 compared to 
147 in FP6). 

When asked to what 
degree has the FP7 
project allowed you to 
strengthen 
collaborations with 
researchers 
from...(various 
geographical areas) 
Respondents indicated 
that collaboration was 
strengthen to a high or 
very high degree more 
than 50% of the time 
with researchers from 
other regions of the 
country. 

SQ7- Finding 2 

NUTS 3 
collaboration 

Composite indicator result: There does not 
appear to be a turning point related to the 
number of NUTS 3 regions involved in a 
project at which point the benefits of adding 
more NUTS 3 regions decrease or level off. 

(Integrating 

SNA 

and survey data) 

The extent of intra-EU co-
authorship on FP7-
supported papers relates 
positively to the number of 
participants, and by 
extension to the number 
of NUTS 3 regions (both 
variables correlate 
strongly, R = 0.98), on a 
project. 

The extent of intra-EU co-
authorship on FP7-
supported papers, and by 
extension to the number 
of NUTS 3 regions (both 
variables correlate 
strongly. R = 0.98), on a 
project relates positively 
with the perception (from 
the survey) that FP7 
projects allowed the 
respondents’ organisations 
to link with other 
organisations that are not 
part of the project. 
This supports the idea that 
by fostering a wider 
participation of relevant 
actors, FP7 leads to a 
better integration among 
NUTS 3 regions. 

The extent of intra-EU co-
authorship on FP7-
supported papers relates 
positively to the number of 
participants, and by 

synthesis 

124 

 
 
 
 
 
 
 
 
 
 
 
 
Finding 

Social Network Analysis (SNA) 

Survey 

Regressions 

Case studies 

Cross-case 

mapped 

to 

data sources 

foster the 
economic 
competiveness 
of Europe 

Indeed, there are significant differences 
across groups for all sub-programmes and 
the mean rank sum increases as the size of 
group increases in all sub-programmes. 
Hence increasing the number of NUTs3 
region will always results in positive 
outcomes and thus better competiveness 
and integration. 

(Integrating 

SNA 

and survey data) 

extension to the number 
of NUTS 3 regions (both 
variables correlate 
strongly, R = 0.98), on a 
project. 

The extent of intra-EU co-
authorship on FP7-
supported papers, and by 
extension to the number 
of NUTS 3 regions (both 
variables correlate 
strongly. R = 0.98), on a 
project relates positively 
with the perception (from 
the survey) that FP7 
projects allowed the 
respondents’ organisations 
to link with other 
organisations that are not 
part of the project. 
This supports the idea that 
by fostering a wider 
participation of relevant 
actors, FP7 leads to a 
better integration among 
NUTS 3 regions. 

synthesis 

125 

 
 
 
10.  APPENDICES 

Appendix I: Literature Review on SNA (Task 1) 

Introduction—Collaborative Research Networks (Data & Methods) 

This  report  reviews  the  main  data  sources  and  methods  used  in  studying 
collaborative research  through  Social  Network Analysis (SNA). It therefore lays the 
ground  for  the  review  of  the  team’s  approach  to  SNA  in  the  context  of  the  FP7 
participation network analysis to be performed for this study; see Updated Methods: 
Network Analysis (WP2) of the Inception Report. 

In  particular,  the  following  review  presents  the  most  frequently  used  concepts, 
associated  terminology  and  the  strengths  and  weaknesses  of  these  concepts  in 
investigating the role of networking in the generation of positive outcomes such as 
the increased research and innovation capacity of FP7 participants. For instance, the 
most appropriate terminologies to be used in the context of this study are identified 
based  on  this  review.  Only  this  terminology  will  be  used  in  the  remaining 
documentation  to  be  delivered  as  part  of  this  study.  For  example,  networks  are 
referred  to  graphs  in  graph  theory,  which  is  the  mathematical  foundation  of  SNA. 
Many  other  terms  used  in  graph  theory  have  their  equivalents  in  the  context  of 
social networks, such as actors for vertices and ties for edges. In the context of this 
study, it makes more sense to speak of networks, actors and their ties. 

Main data sources 

Given the complexity of the concepts at the root of SNA—which covers a variety of 
interactions  among  researchers,  institutions,  institutional  sectors,  and  countries—
collaborative  research  networks  cannot  be  measured  without  ambiguity.  The 
difficulty  in  measuring  collaborative  research  networks  has  been  acknowledged  by 
numerous  scholars  (Subramanyam,  1983;  Melin  and  Persson,  1996;  Katz  and 
Martin, 1997; Laudel, 2002; Clark, 2009; Bozeman, Fay et al,. 2013). For instance, 
Subramanyam  (1983),  cited  by  Clark  (2009),  declares  that  research  collaboration 
‘cannot  be  easily  determined  by  the  usual  methods  of  observation,  interviews  or 
questionnaire because of the complex nature of human interaction  that takes place 
between  or  among  collaborators  over  a  period  of  time.  Both  the  nature  and 
magnitude of contribution of each collaborator are likely to change during the course 
of a research project’. 

Notwithstanding  the  difficulties  in  measuring  collaborative  research  networks, 
scholars  have  used  several  types  of  data—including,  notably,  bibliometric  data, 
administrative  data  on  publicly  funded  R&D  projects,  and  self-reported  and  survey 
data—to measure research collaboration and map networks. 

Data on scientific output 

Among  the  data  used  to  measure  collaborative  research  networks,  bibliometric 
data—namely,  data  on  scientific  publications  and  patents—have  been  the  most 
widely used in the peer-reviewed and grey literature (Katz and Martin, 1997; Clark, 
2009; Bozeman, Fay et al., 2013). 

126 

 
 
 
 
Data on scientific publications 

Scientific publications have traditionally been used as a proxy for scientific activity. 
Measures  of  collaborative  research  networks  using  data  on  scientific  publications 
have for decades mainly relied on co-authorship (De Solla Price and Beaver, 1966; 
Merton,  1973;  Katz  and  Martin,  1997;  Laudel,  2002;  AVEDAS  AG,  CWTS  et  al., 
2009;  Clark,  2009;  Bozeman,  Fay  et  al.,  2013),  and  to  a  lesser  extent,  citation 
networks (Noyons, 1999). 

Among these measures, co-authorship is considered to imply a much stronger bond 
than  citation  networks  (which  have  nodes  indicating  publications  and  the  links 
between  the  nodes,  citations).  Because  authors  who  co-write  publications  are 
generally acquainted,  and publications published  by  multiple authors are  seen  as a 
measurable  output  of  research  collaboration,  the  use  of  co-authorship  data  is 
considered  to  be  a  powerful  source  of  quantitative  data  for  measuring  social 
networks at different levels of analysis: individuals, institutions, institutional sectors, 
regions  and  countries.  Although  co-authorship  has  traditionally  been  used  to  map 
the  scientific  side  of  collaborative  research  networks  among  public  research  actors 
(Katz and Martin, 1997), it has also increasingly been used to depict the structure of 
scientific collaborative networks involving public and private research actors (Calvert 
and  Patel,  2003;  Tijssen,  2012).  Depending  on  the  purpose  of  the  network  study 
and  bibliometric  database  used,  the  node  in  the  collaborative  research  network 
based  on  co-authorship  can  be  the  individual  author,  the  institution,  the  country 
where the institution is located, or its institutional sector. 

The  use  of  data  on  scientific  co-authorship  has  many  advantages  over  other  data 
sources  to  measure  research  collaboration  and  to  examine  collaborative  research 
networks. For instance, results obtained with this type of data are reproducible over 
time,  traceable  year-on-year,  invariant,  verifiable,  and  easily  obtained  through 
different  counting  methods  (fractional  and  full  counts)  (Katz  and  Martin,  1997; 
Bozeman, Fay et al,. 2013; Leydesdorff, Wagner et al., 2013). Furthermore, sample 
sizes  can  be  very  large,  allowing  for  more  statistically  significant  results  than 
qualitative  methods.  In  sum,  co-authorship  data  are  the  largest  source  of  social 
network  data  available  among  researchers  and  readily  accessible  through 
bibliographic databases. 

Despite  these  advantages,  the  measure  of  research  collaboration  has  some 
limitations, which led Katz and Martin to note that ‘co-authorship can never be more 
than  a  rather  imperfect  or  partial  indicator  of  research  collaboration  between 
individuals’  (1997)  and  Bozeman  et  al.  to  affirm  that  ‘co-authorship  is  neither 
necessary nor sufficient to collaboration’ (2013).  

Researchers having worked on a project together may decide to publish findings in 
scientific publications separately (Katz and Martin, 1997) because they disagree on 
them  or  have  different  reward  systems  for  their  careers.  In  contrast,  researchers 
who  did  not  work  jointly  on  a  project  may  nevertheless  co-author  publications  in 
order  to  pool  findings  (Katz  and  Martin,  1997).  Moreover,  co-authorship  can 
overestimate  research  collaboration  when  researchers  co-author  publications  for 
reasons such as material contribution, honorary purposes, unethical behaviour, and 
research  in  big  science  involving  a  large  number  of  researchers  (Hagstrom,  1965; 
Stokes  and  Hartley,  1989;  Clark,  2009).  As  underlined  by  Clark  (2009),  this 
phenomenon of ‘hyper-authorship’ has been  examined  by  several scholars (Cronin, 
2001;  Bozeman  and  Corley,  2004;  Glänzel  and  Schubert,  2005;  Persson  and 
Glänzel, 2014). In his survey of the peer-reviewed literature on academic scientific 
collaboration,  Clark  (2009)  also  underlines  the  phenomenon  of  ‘sub-authorship’, 
whereby individuals and institutions having contributed to the research project have 

127 

 
 
not been included as co-authors in the published work for various reasons, including 
their status (e.g. funding agency, junior researcher) (Heffner,  1981;  Laudel,  2002; 
Cronin, Shaw et al., 2003; Glänzel and Schubert, 2005; Bozeman, Fay et al., 2013). 
Besides,  in  order  to  use  data  on  publications  for  network  analysis  purposes,  these 
data must be cleaned and the names of authors and their affiliations standardised—
a demanding job. 

Data on patents 

Patents  are  often  used  as  a  measure  of  technological  activity  (OECD,  2009). 
Measures of collaborative research networks based on patent data are less common 
than  those  relying  on  scientific  publications.  Patent  data  on  co-inventors  and  co-
applicants  have  been  used  by  scholars  to  map  the  technological  aspect  of 
collaborative  networks,  especially  among  and  within  firms  (Tijssen  and  Korevaar, 
1997;  Guellec  and  van  Pottelsberghe  de  la  Potterie,  2001;  Ter  Wal  and  Boschma, 
2009;  Goetze,  2010;  Inoue,  Souma  et  al.,  2010;  Adams,  Fontana  et  al.,  2013) 
between  the  public  and  the  private  sectors  (Tijssen  and  Korevaar,  1997;  Balconi, 
Breschi  et  al.,  2004;  Baba,  Shichijo  et  al.,  2009;  Lissoni,  2010),  and  between 
countries (Guellec and van Pottelsberghe de la Potterie, 2001, Fraunhofer ISI, Idea 
Consult et al., 2009; Kroll and Stahlecker, 2012). Beyond data on co-inventors and 
co-applicants,  some  studies  have  used  patent  citations  to  map  collaborative 
research  networks,  particularly  knowledge  spillovers 
from  R&D  (Jaffe  and 
Trajtenberg,  2002;  Ruegg  and  Feller,  2003;  Sternitzke,  Bartkowski  et  al.,  2008). 
These  studies  are  nevertheless  rarer,  as  their  main  purpose  is  not  the  measure  of 
effective  collaboration  ties  among  actors  as  is  the  case  for  studies  using  co-
authorship data or patent data on co-inventors and co-applicants. 

Patent applicants or patent holders are the legal persons that legally apply for and 
own  the  patent.  These  legal  persons  can  be  either  institutions  (e.g.  firms  or 
universities)  or  individuals.  Inventors  are  people  who  have  participated  in  the 
development of patented inventions. Patents can be applied for or owned by one or 
more legal persons. Likewise, one or more inventors can be mentioned in a patent. 
By analogy to measures based on co-authorship, patents with several applicants and 
inventors  have  been  the  main  measure  of  collaborative  research  networks  in  the 
peer-reviewed  literature,  although  the  use  of  this  measure  is  more  recent. 
Depending  on  the  purpose  of  the  study,  in  a  collaborative  research  network  based 
on patent data, nodes represent either inventors, applicants, applicants’ institutional 
sectors, inventors’ countries of residence, or applicants’ countries. 

Using patent data to proxy technological collaborative research networks has several 
advantages, including data availability, unique coverage in terms of space and time, 
and the close link to inventions (OECD, 2008; OECD, 2009). 

However,  patent  data  suffer  from  weaknesses  (Archibugi  and  Planta,  1996;  OECD, 
2008; OECD, 2009). Not all inventions are patentable. Changes in patent laws over 
time  also  call  for  caution  when  examining  trends.  Patent  data  are  complex  to 
analyse  as  they  are  the  results  of  complex  administrative  and legal  processes  that 
differ  across  intellectual  patent  regimes.  Not  all  firms  apply  for  patents  since  they 
can  use  alternative  appropriation  mechanisms  such  as  trade  secrecy  and 
trademarks; in contrast, publishing is the norm in the scientific community as it is at 
the  heart  of  the  reward  system.  The  patenting  propensity  of  small  and  medium-
sized  enterprises  is  much  lower  than  that  of  larger  firms.  It  also  differs  across 
industries.  Moreover,  universities  and  government  research  institutions  are 
underrepresented in patenting activities, partly for legal reasons that apply in some 
countries  and  because  of  the  social  norms  prevailing  in  the  scientific  community 
(OECD, 2003). 

128 

 
 
is,  patents  owned  by  at 

These  weaknesses  have  implications  for  the  measure  of  collaborative  research 
networks  based  on  patents  (Ter  Wal  and  Boschma,  2009),  though  these  are  less 
discussed  in  the  peer-reviewed  literature  than  the  implications  for  scientific 
publications. Measures based on patents are only appropriate in technological areas 
were  patents  are  allowed  and  in  industries  that  rely  on  patents  as  their  main 
appropriation mechanism. They are also better at mapping networks involving large 
firms rather than smaller ones. In contrast, they only imperfectly and incompletely 
depict  the  structures  of  public/private  research  networks.  Moreover,  co-patents—
institutions—often  underestimate 
least  two 
that 
collaborative  research,  as  institutions  often  choose  to  distribute  the  patents 
resulting  from  collective  research  among  themselves  and  sign  cross-licensing 
agreements  rather  than  applying  for  joint  patents  (Hagedoorn,  2003).  This 
underestimation  tends  to  be  corroborated  by  the  small  share  of  patents  that  are 
applied for by several institutions (Giuri and Mariani, 2005). Studies based on patent 
citation  maps  imperfectly  identify  real  collaborative  research  networks,  in  contrast 
to  knowledge  flows.  In  addition,  patent  citations  are  not  necessarily  made  by 
inventors (Criscuolo and Verspagen, 2008; Cotropia, Lemley et al., 2013), making it 
difficult  therefore  to  identify  plausible  networks.  Finally,  in  order  to  be  able  to  use 
patent  data  for  network  analysis  purposes,  the  names  of  inventors  and  applicants 
must be fastidiously cleaned and standardised. 

Administrative data on publicly funded R&D projects 

Given  the  limitations  of  data  based  on  scientific  publications  and  patents  for  the 
purpose  of  depicting  collaborative  research  networks,  a  number  of  studies  have 
relied  on  administrative  data  on  publicly  funded  collaborative  R&D  projects  in  the 
United States, Europe, and elsewhere. 

funded  by 

the  European  Commission’s  European 

At  the  EU  level,  the  main  data  sources  have  been  the  CORDIS  projects  and  E-
CORDA  databases,  which  cover  administrative  information  on  the  projects  and 
participants 
framework 
programmes.  The  data  contained  in  these  sources  have  been  used  to  analyse  the 
research  networks  established  by  the  European  Framework  Programmes  between 
institutions  from  various  institutional  sectors  and  countries  (Caloghirou  and 
Vonortas,  2000;  Gusmão,  2000;  Gusmão,  2001;  Breschi  and  Cusmano,  2004; 
Barber,  Krueger  et  al.,  2006;  Roediger-Schluga  and  Barber,  2007;  Tekes,  2008; 
AVEDAS AG, CWTS et al., 2009; Ortega and Aguillo, 2010; Heller-Schuh, Barber et 
al., 2011). Depending on the purpose of the study, nodes in collaborative research 
networks based on CORDIS/CORDA data represent institutions, institutional sectors, 
or institutions’ countries. 

Both  CORDIS  projects  and  E-CORDA  databases  include  valuable  information  for 
examining  research  networks  supported  by  the  European  framework  programmes. 
This  information  includes,  but  is  not  limited  to,  project  objectives,  total  project 
budget  and  EC  control,  project  start  and  end  dates,  administrative  and  scientific 
contact  persons,  the  specific  programmes  and  funding  schemes  supporting  the 
project,  the  names  of  participants,  participants’  institutional  sectors,  participants’ 
roles (whether they are a coordinator or not), and their addresses. 

Despite  this,  these  databases  nonetheless  have  limitations  (Roediger-Schluga  and 
Barber,  2007).  The  records  for  the  different  European  framework  programmes  are 
rarely  complete,  although  there  have  been  improvements  over  time.  For  instance, 
the scientific contact persons are not available for the Sixth Framework Programme 
in E-CORDA, and the email addresses of administrative or scientific contact persons 
are  not  systematically  reported.  Furthermore,  the  classification  of  institutions  by 
institutional  sector  is  not  always  consistent  because  participants  self-select  their 
sector.  In  addition,  the  classification  is  not  based  on  the  recommendations  of  the 

129 

 
 
OECD Frascati Manual (OECD, 2002), and the data on participants, particularly their 
names,  need  to  be  cleaned  and  standardised  to  undertake  any  proper  network 
analysis. Finally, the existence of collaborative projects in these databases does not 
necessarily  mean  that  participants  have  effectively  cooperated  by  pooling  their 
resources. 

Self-reported and survey data 

As  noted  by  Bozeman  et  al.  (2013),  another  main  type  of  data  has  been  used  to 
examine  collaborative  research  networks—namely,  self-reporting  and  survey  data. 
Some  studies  explicitly  ask  individuals  to  provide  information  on  their  partners  or 
different  forms  of  collaboration  (from  informal  to  formal  collaborations)  (Melin, 
2000;  Bozeman  and  Corley,  2004;  Cummings  and  Kiesler,  2007;  Bozeman  and 
Gaughan,  2011;  Jeong,  Choi  et  al.,  2011;  Van  Rijnsoever  and  Hessels,  2011)  in 
order  to  provide  alternative  measures  of  collaboration  and  to  overcome  the 
weaknesses of bibliometric data, in particular hyper-authorship or sub-authorship. 

The  use  of  self-reported  and  survey  data  to  identify  a  variety  of  ties  between 
individuals and institutions nevertheless raised methodological issues. For instance, 
the  concept  of  self-reported  collaboration  may  not  be  specific  enough  from  an 
operational  point  of  view  (Bozeman  and  Corley,  2004).  Moreover,  studies  based 
exclusively on self-reported and survey data to measure research collaboration rely 
only on the perceptions of individuals (Van Rijnsoever and Hessels, 2011). They are 
not  objective  measurements  and  are  prone  to  incorrectness  because  of  biased 
responses. In addition, self-reported and survey data on research collaboration may 
be of low quality because of poor individual or organisational memory (Vasileiadou, 
2009).  Lastly,  these  data  do  not  allow  for  a  dynamic  analysis  of  collaborative 
research  networks,  in  contrast  to  bibliometric  data  and  administrative  data  on 
publicly funded R&D projects in the context of European framework programmes. 

Note that under the design of the current study’s methods (see Updated Methods of 
the  Inception  Report),  data  from  all  three  data  sources  described  above  (i.e. 
bibliometric  data,  administrative  data  on  FP6  and  FP7  projects,  and  self-reported 
and  survey  data)  will  be  collected  to  gather  information  on  collaborative  research 
networks. 

Methods and analyses 

When analysing collaborative research networks based on the above data, the peer-
reviewed and grey literature has relied on several research methods to identify the 
determinants,  processes,  outputs  and  outcomes  of  these  networks.  The  methods 
include  social  network  analysis  (SNA),  multivariate  statistical  analysis,  and  more 
qualitative research methods such as interviews and case studies. 

The use  of these  research methods to  characterise collaborative research networks 
and to examine their determinants, processes, outputs and outcomes are discussed 
below.  Given  the  prevalence  of  network  analysis  in  the  present  study,  the 
paragraphs  below  extensively  present  concepts  associated  with  SNA  and  illustrate 
the  extent  to  which  these  concepts  have  been  used  in  the  empirical  literature  on 
collaborative  research  networks.  Following  is  a  discussion  of  the  advantages  and 
limitations  of  SNA  as  a  method  for  investigating  collaborative  research  networks 
compared  to  other  methods  relying  on  multivariate  statistics,  interviews  and  case 
studies. 

Applied social network analyses (Wasserman and Faust, 1994; Prell, 2012; Borgatti, 
Everett  et  al.,  2013;  Scott,  2013)  consist  of  calculating  ‘a  number  of  metrics  to 

130 

 
 
describe the structure of a network or capture aspects of individuals’ positions in the 
network’  (Borgatti,  Everett  et  al.,  2013).  These  analyses  have  their  foundations  in 
formal,  mathematical,  and  conceptual  models  and  are  characterised  by  the  use  of 
systematic  empirical  data  and  graphical  imagery.  The  basic  hypothesis  underlying 
social  network  analysis  is  that  the  nature  of  the  network  structures  and  structural 
properties  can  impact  the  behaviour  and  performance  of  the  individual  nodes  and 
the entire network (Besussi, 2006). 

In  order  to  review  studies  on  collaborative  research  networks  based  on  SNA,  it  is 
useful to classify these  studies into categories corresponding to the different  levels 
of analysis in SNA (Prell, 2012)—e.g. actor level in complete networks, actor level in 
ego  networks,  dyad  and  triad  levels,  subgroup  level,  and  network  level—and  to 
define the most frequently used concepts.17 The bulk of social network analyses on 
collaborative  research  networks  have  been  based  on  bibliometric  data  (co-
authorship  data  for  scientific  papers  and  co-inventors/assignees  data  for  patents 
analysed  at  several  aggregation  levels)  and  administrative  data  on  publicly  funded 
R&D  projects  such  as  those  extracted  from  the  CORDIS  projects  and  E-CORDA 
databases. Given the nature of the data, these studies have mainly been based on 
undirected  graphs  (i.e.  links  between  nodes  are  reciprocated).  Therefore,  only  the 
most frequently used concepts applicable to undirected graphs are discussed. 

Box 1 

Definitions of basic terms in graph theory and social network 
analysis 

Social network analysis relies strongly on mathematics, in particular graph theory, to analyse 
the  attributes  of  actors  and  networks.  It  is  therefore  necessary  to  define  the  basic  terms  in 
graph  theory  and  social  network  analysis  before  discussing  how  these  are  translated  into 
concepts  in  the  SNA  literature.  Most  of  the  information  presented  below  is  extracted  directly 
from Borgatti et al. (2013) and Wasserman and Faust (1994) and applied mostly to undirected 
graphs. 

Graphs: A graph is a model for a social network that consists of a set of vertices (or nodes, 
points  or  actors)  and  a  set  of  edges  (or  links,  lines  or  ties).  The  edges  connect  pairs  of 
vertices. Graphs may be directed or undirected. In a directed graph, the edges are like arrows 
because they have direction. Undirected graphs are used for relations where direction does not 
make  sense,  but  is  reciprocated.  A  graph  may  be  valued  in  the  sense  that  it  is  possible  to 
assign a value to edges or unvalued in the sense that each edge can only take a dichotomous 
value  (i.e.  0,  1).  In  SNA,  graphs,  vertices,  and  edges  are  also  referred  to  as  networks, 
actors, and ties. These latter terms will be used throughout the study. 

Subgraphs, dyads, and triads: A subgraph (or subgroup/sub-network) of a given graph (or 
network) is a subset of the set of vertices (or actors) of the given graph and the subset of the 
set of edges (or ties) in the given graph. A dyad consists of a pair of vertices (or actors) and 
the  possible  edges  (or  ties)  between  them.  A  triad  is  a  subgraph  consisting  of  three vertices 
(or actors) and the possible edges (or ties) among them. 

Degree: The degree of a vertex (or actor) is the number of edges (or ties) that are incident 
with it. 

Density: The density of a graph (or network) is the number of edges (or ties) in it divided by 
the  maximum  possible  number  of  edges  (or  ties)  determined  by  the  number  of  vertices  (or 
actors). It can also apply to subgraphs (or subgroups). 

Walks,  trails,  and  paths:  A  walk  is  a  sequence  of  vertices  (or  actors)  and  edges  (or  ties), 
starting  and  ending  with  vertices  (or  actors),  in  which  each  vertex  (or  actor)  is  incident  with 

17 Note that although some concepts are used more frequently at specific levels, they can also 
be used at other levels. For instance, some statistics at the actor level can be computed in 
both complete and ego networks although they are presented in either one or the other in 
the  following  sections.  When  relevant,  these  multiple  applications  are  mentioned  in  the 
context of this study. 

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the  edges  (or  ties)  following  and  preceding  it  in  the  sequence.  The  length  of  a  walk  is  the 
number of occurrences of edges (or ties) in it. If a line is included more than once in a walk, it 
is  counted  each  time  it  occurs.  A  trail  is  a  walk  in  which  all  of  the  lines  are  distinct,  though 
some vertices (or actors; one or many) may be included more than once. A path is a walk in 
which all vertices (or actors) and all edges (or ties) are distinct. 

Connected  graphs  and  components:  A  graph  (or  network)  is  connected  if  there  is  a  path 
between every pair of vertices (or actors) in the graph. If a graph is not connected, then it is 
disconnected.  The  vertices  in  a  disconnected  graph  may  be  partitioned  into  two  or  more 
subsets in which there are no paths between the vertices in different subsets. A component is 
a subgraph in which all pairs of vertices are connected through paths and in which vertices are 
not connected to any other vertices in other parts of the overall graph. 

Geodesics,  distance,  and  diameter:  The  shortest path  between  two vertices  (or  actors)  is 
referred  to  as  a  geodesic.  The  distance  between  two  vertices  is  defined  as  the  length  of  a 
geodesic  between  them.  The  diameter  of  a  connected  graph  is  the  length  of  the  largest 
geodesic between any pair of vertices. 

Cutpoints and bridges: A vertex (or actor) is a cutpoint if the number of components in the 
graph that contains  the given node is fewer than the number of components in the subgraph 
that results from deleting that node from the graph. A bridge is an edge (or tie) such that the 
graph containing the edge has fewer components than the subgraph that is obtained after the 
edge is removed. 

Source: Borgatti, Everett et al. (2013), Wesserman and Faust (1994)  

Actor level in complete networks 

One of the most important themes in SNA relates to the analysis of individual actors 
in complete networks and their importance. This notion of importance is approached 
in the SNA literature through the concept of centrality. Various centrality measures 
have  been  proposed.  Some  of  the  main  centrality  measures  are  described  below. 
They are applicable to undirected networks and can be computed taking account or 
not  of  the  value  (or  weight)  of  ties  between  actors  (weighted  or  unweighted 
centrality  measures).18  These  measures  provide  valuable  information  as  to  the 
position of actors in the network. For instance, actors with a low centrality often lie 
at the periphery of a network, whereas actors with a high centrality often lie at the 
core  of  the  network.  This  can  in  turn  inform  the  potential  role  of  actors  in  the 
network (see description of betweenness centrality below). 

  Degree  centrality:  The  simplest  measure  of  actor  centrality  relies  on  the 
perception  that  actors  are  more  important,  and  thus  central,  when  they  have 
the  most  ties  to  other  actors  in  the  network.  It  is  therefore  a  measure  of  the 
level  of  involvement  of  an  actor  in  the  network.  This  measure  of  centrality  is 
referred as degree centrality. It is measured  by  counting the number of actors 
(alters) adjacent to the actor under examination (ego) and can be normalised by 
taking  into  account  the  number  of  nodes  in  the  network  in  order  to  allow  for 
comparisons  between  different  networks.  This  notion  of  centrality  has  the 
advantage  of  simplicity  as  it  underlines  the  activity  of  an  actor.  However,  it  is 
only  a  rough  measure  of  the  importance  of  a  focal  actor  (ego),  it  does  not 
consider  the  centrality  of  the  actors  (alters),  while  the  latter  could  be  either 
central or not in the network (Borgatti, Everett et al., 2013). 

  Eigenvector  centrality:  In  order  to  overcome  this  limitation,  the  concept  of 
eigenvector  centrality  has  been  suggested  in  the  SNA  literature.  Eigenvector 
centrality  is  defined  by  the  sum  of  a  focal  actor’s  ties  to  other  actors  in  the 

18 Note that in collaboration networks based on co-authorship data, these centrality measures 
can be normalised to account for differences in the size of the scientific production of the 
various actors involved. 

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network, weighted by their own degree centrality (Prell, 2012). If a focal actor is 
connected  to  other  actors  with  a  high  degree  centrality,  then  its  eigenvector 
centrality  will  be  high.  Despite  the  advantage  of  eigenvector  centrality  over 
degree  centrality,  this  measure  of  centrality  has  the  disadvantage  that,  in 
disconnected  networks,  it  gives  zeros  to  (i.e.  ignores)  all  actors  of  the  smaller 
components  of  the  network.  In  the  case  of  a  network  showing  a  bow-tie 
structure, this measure of centrality  does not properly consider the importance 
of  actors  in  the  smaller  group  as  these  actors  will  show  a  lower  degree  of 
centrality compared to those in the larger group (Borgatti, Everett et al., 2013). 

  Betweenness  centrality:  The  concept  of  betweenness  centrality  is  closely 
related to that of eigenvector centrality as it takes into account the position of 
other actors in the network. However, it differs in the sense that it considers the 
position  of  a  focal  actor  in  the  network  by  measuring  how  often  the  latter  is 
placed  between  two  other  disconnected  actors  (Prell,  2012)  or  falls  under  the 
shortest  path  between  them  (Borgatti,  Everett  et  al.,  2013).  In  other  words, 
actors with a high degree of betweenness centrality play an important role in a 
network  by  acting  as  potential  gatekeepers  among  other  actors.  For  example, 
an  actor  with  high  betweenness  centrality  can  bridge  information  between 
otherwise  disconnected  subgroups  in  a  highly  clustered  network.  This  actor 
therefore consists of a point of failure or structural hole, which is to its benefit 
as it can control the flow of information. An actor playing such a role is usually 
referred to as a ‘broker’. 

  Closeness  centrality:  Like  the  above  two  concepts  of  centrality,  closeness 
centrality takes into account the rest of the network to measure the centrality of 
a  focal  actor.  It  measures  the  distances  between  actors  in  a  network,  with 
actors  that  are  closest  to  other  actors  being  more  central  than  those  who  are 
further away. In other words, the former do not need as many intermediaries to 
reach other actors in the network. The disadvantage of this concept of centrality 
is  that  it  does  not  apply  well  in  the  case  of  disconnected  networks  since  the 
distance  between  actors  belonging  to  different  disconnected  sub-networks  is 
indefinite (Borgatti, Everett et al., 2013). 

The different concepts of centrality have been used extensively in the peer-reviewed 
and grey literature on collaborative research networks. 

Guan  and  Zhao  investigate  the  effects  of  multiplicative  interactions  between 
clustering  and  reach  on  members’  knowledge  creation  and  patent  value  based  on 
complex  network  analyses  in  the  field  of  nanobiopharmaceuticals.  To  do  so,  they 
use  data on patents co-invented by at least one  university  and  one  firm.  They use 
the concept of betweenness centrality, together with other SNA concepts, to explain 
its effect  on  members’  patent  value  in  the  field  (Guan  and  Zhao,  2013).They  have 
found  that  betweenness  centrality  has  a  statistically  significant  and  positive  effect 
on members’ patent value. 

Abbasi,  Hossain  et  al.  analyse  whether  preferential  attachment  in  scientific  co-
authorship networks is different for authors with different forms of centrality. Using 
a  complete  database  for  research  on  ‘steel  structures’,  they  show  that  the 
betweenness  centrality  of  an  existing  actor  is  a  significantly  better  predictor  of 
preferential  attachment  by  new  entrants  than  degree  or  closeness  centrality 
(Abbasi, Hossain et al., 2012). 

Cantner  and  Rake  study  the  dynamic  properties  of  cross-country  networks  in 
pharmaceuticals  using  bibliometric  data  on  co-authorship  in  OECD  and  non-OECD 
countries. Using different concepts of centrality—including but not limited to degree 
centrality  and  betweenness  centrality—they  find  that  average  betweenness 

133 

 
 
centrality scores increase over time for both groups of countries, while the scores of 
OECD  countries  remain  considerably  larger  than  those  of  non-OECD  countries 
(Cantner and Rake, 2014). 

Beaudry  and  Allaoui  measure  the  impact  of  public  grants,  private  contracts  and 
collaboration  on  the  scientific  production  of  Canadian  nanotechnology  academics. 
They  estimate  a  time-related  model  of  the  impact  of  academic  research  financing 
and network structure on the research output of individual academics measured by 
number  of  papers.  In  doing  so,  they  rely  on  several  SNA  concepts,  including  the 
betweenness  centrality  of  individuals,  a  concept  introduced  in  their  model  as  an 
independent  variable.  They  find  that  betweenness  centrality  has  a  positive  and 
significant effect on the number of articles published (Beaudry and Allaoui, 2012). 

Heller-Schuh,  Barber  et  al.  analyse  the  properties  of  networks  and  their  actors  in 
European  framework  programmes  between  1984  and  2006  using  the  CORDIS 
projects and EUPRO databases. Building on a composite indicator of centrality based 
on several centrality measures (degree centrality, closeness centrality, betweenness 
centrality,  and  eigenvector  centrality),  they  identify  the  core  organisations  in  the 
networks  by  organisation type  using  the  results of  the  composite  indicator  (Heller-
Schuh, Barber et al., 2011). 

In  their  study  about  the  structuring  effects  of  community  research,  AVEDAS  AG, 
CWTS  et  al.  also  use  different  concepts  of  centrality—including  betweenness  and 
closeness—to  identify  core  institutions  and  projects  in  the  Sixth  Framework 
Programme (AVEDAS AG, CWTS et al., 2009). 

Individual actors in ego networks 

Another important theme in SNA is associated with the analysis of individual actors 
in  ego  networks  defined  as  networks  that  are  self-reported  by  a  focal  actor  (ego) 
(Prell, 2012)—that is, their immediate networks by opposition to complete networks. 
In ego networks, ties are between the focal actor and adjacent actors, and between 
adjacent  actors.  Other  ties  are  ignored.  The  SNA  literature  has  examined  the 
characteristics of ego networks through four main concepts (ibid.), the ego network 
size  and  density,  structural  holes,  brokerage,  and  homophily.  All  these  concepts 
with  the  exception  of  brokerage  are  described  below,  the  concept  of  brokerage19 
relying on directed graphs only. 

  Ego network size and density: The ego network size refers to the number of 
actors (alters) that are adjacent to the focal actor in the ego network, while the 
measure  of  density  is  calculated  as  the  proportion  of  all  possible  edges  in  the 
ego network that effectively  exist.  Some  authors consider ego networks with a 
high density of ties linking ego to  alters to  be  advantageous for the  former. In 
turn,  this  type  of  social  structure  is  seen  as  being  beneficial to  the extent that 
the networks are closed (Coleman, 1988). Yet a high degree of density in an ego 
network  and  a  closed  network  are  not  often  desirable  for  ego,  as  revealed  by 
the  structural  holes  (Burt,  1995)  and  the  strength  of  weak  ties  (Granovetter, 
1973) theories, which put emphasis on the importance of brokerage rather than 
cohesion. 

19  Structural  holes  point  to  the  existence  of  brokerage  in  ego  networks.  The  concept  of 
brokerage  has  been  extended  by  the  identification  of  different  broker  roles  in  the  SNA 
literature  (Prell,  2012):  within-group  broker  roles  (i.e.  ‘coordinator  broker’,  ‘consultant 
broker’ and between-groups broker roles (‘representative broker, ‘gatekeeper broker’, and 
‘liaison broker’). 

134 

 
 
                                                
Although  each  collaborative  project  under  FP7  can  be  viewed  as  a  small 
complete  network,  the  size  (total  number  of  actors  without  differentiation 
between ego and alters [i.e. FP7 participants]) of each project will be computed 
as  a  descriptive  statistic  that  will  then  be  used  to  answer  specific  evaluation 
questions using, for  example, regression  analysis (see Updated  Methods of the 
Inception Report). 

  Structural  holes:  The  structural  holes  theory  developed  by  Burt  (1995)  is 
closely  related  to  the  concept  of  betweenness  centrality  in  complete  networks 
because  it  rests  upon  the  idea  of  a  potential  gatekeeper  or  broker  in  an  ego 
network.  A  structural  hole  is  defined  by  the  lack  of  ties  between  two  adjacent 
actors  (alters)  in  an  ego  network  (Borgatti,  Everett  et  al.,  2013).  Burt  argues 
that unconnected adjacent actors (alters) are more likely to offer ego a strategic 
advantage  over  them  because  ego  can  benefit  from  a  greater  diversity  of 
information  (i.e.  non-redundant  information)  from  them  (Prell,  2012),  which  is 
not the case if the ego network is highly dense. The concept of structural  holes 
is closely associated with the concept of network constraint, the latter capturing 
redundancies in networks. Lower redundancies or network constraint imply that 
ego occupies a less constrained position in the network, thereby brokering more 
intensively within the network. Structural holes are computed using complicated 
mathematical equations. 

Note  that  in  the  current  study,  structural  holes  will  be  analysed  in  complete 
networks.  Thus,  the  distribution  of  betweenness  centrality  scores  across  actors 
will  be  used to  analyse  structural  holes.  In  a  network  with  no  structural  holes, 
the  distribution  of  betweenness  scores  will  tend  to  be  uniform  across  actors. A 
strong  concentration  of  high  betweenness  scores  for  a  few  actors  with  most 
other  actors  having  relatively  small  scores  is  indicative  of  the  presence  of 
structural holes, especially in highly clustered networks (i.e. high modularity). In 
turn,  actors  with  high  betweenness  scores  (the  brokers)  potentially  ‘benefit’ 
from  these  structural  holes.  By  colour-coding  (gradient)  the  nodes  based  on 
their betweenness scores, the position of structural holes (missing links between 
low  betweenness  nodes)  and  brokers  in  a  network  can  be  identified.  In  this 
study, the average and/or maximum betweenness of participants in each project 
will  be  used  as  a  proxy  for  measuring  structural  holes  and  their  impact  on 
positive  outcomes  of  networking  through  regression  analysis  (see  Updated 
Methods of the Inception Report). 

  Homophily: The concept of homophily rests upon the idea that some actors in 
networks  prefer  to  have  ties  with  others  who  are  comparable to themselves  in 
terms of certain characteristics (Prell, 2012). For instance, researchers working 
in similar fields of science and technology would be more likely to have ties than 
those working in different fields. Homophily can be measured by calculating the 
proportion  of  ties  between  alters  and  ego  in  an  ego  network  based  on  similar 
characteristics  of  the  total  number  of  ties  in  the  network.  A  limitation  of 
homophily  in  social  networks  is  the  causality  direction  because  it  is  difficult  to 
understand  whether  actors  develop  ties  first  and  then  increasingly  share 
common characteristics or the contrary (ibid.).  

In  the  FP7  participation  complete  networks  to  be  analysed  in  this  study, 
homophily  will  be  measured  by  quantifying  the  extent  to  which  the  same 
disciplines  cluster  together  using  two-mode  networks  linking  FP7  projects  with 
their  participants  (disciplines  will  be  identified  based  on  the  specialty  in  which 
each  participant  publishes  the  most).  The  concept  of  network  size  described 
above  for  ego  and  complete  (in  the  context  in  which  it  will  be  used  for  this 
study)  networks  can  be  also  be  computed  by  categorising  actors  (i.e. 
participants in a FP7 project) by their organisational sectors or fields of research 
(to  compute  an  interdisciplinarity  index).  The  number  of  fields  and  sectors  will 

135 

 
 
therefore  supplement  information  on  homophily  at  the  individual  project  level 
and  make  it  possible  to  assess  the  impact  of  homophily  on  the  positive 
outcomes  of  networking  (e.g.  scientific  production  and  innovation)  to  be 
measured  using  regression  analysis  (see  Updated  Methods  of  the  Inception 
Report). 

The  analysis  of  individual  actors  in  ego  networks,  especially  ego  network  density 
and  structural  holes,  has  been  tackled  in  the  peer-reviewed  and  grey  literature  on 
collaborative research networks. 

Building upon Coleman’s argument that closed social structures are beneficial, some 
studies  underline  the  importance  of  collaborative  networks  with  high  density  in 
building  mutual  trust  and  transferring  tacit,  complex,  or  proprietary  knowledge 
(Uzzi,  1997;  Hansen,  1999;  Fleming,  Mingo  et  al.,  2007),  thereby  contributing  to 
the  creation  of  novel  knowledge  and  innovations.  In  order  to  further  explore  how 
embeddedness  affects  the  publication  output  and  impact  of  scientists,  Gonzalez-
Brambila,  Veleso  et  al.  built  a  panel  data  model  with  different  SNA  concepts  as 
independent  variables,  including  local  density,  measured  using  co-authorship  data. 
They found that local density had a negative impact on both measures of publication 
performance,  implying  that  non-redundant  information  that  comes  from  brokerage 
is  more  beneficial  than  the  coordination  that  is  obtained  in  dense  networks 
(Gonzalez-Brambila, Veloso et al., 2013). 

Other  studies  focus  on  the  role  of  structural  holes  in  collaborative  research 
networks. For instance, Abbasi, Chung et al. explore the association between the co-
authorship  network  properties  of  scholars  and  their  citation-based  research 
performance using structural holes theory. They find that the research performance 
of  scholars  is  significantly  correlated  with  scholars’  ego-network  measures.  In 
particular, researchers with efficient collaboration networks (i.e. ego networks with a 
low  number  of  redundant  contacts)  who  maintain  a  strong  co-authorship 
relationship with one primary co-author within a group of linked co-authors (i.e. co-
authors  that  have  joint  publications)  show  a  higher  performance  than  researchers 
with many ties to the same group of linked co-authors (Abbasi, Chung et al., 2012). 

Forti, Franzoni, et al. examine the acquaintances of a sample of academic inventors 
and  their  paired  controls  to  investigate  the  contribution  of  social  networks  to  the 
generation  of  inventive  ideas.  Using  data  on  patents,  they  show  that,  prior  to 
patenting, inventors are involved in networks of a similar structure to those of their 
colleagues  who  do  not  invent,  although  they  are  more  peripheral.  However,  over 
time,  both  inventors  and  non-inventors  extend  their  networks,  and  patenting 
inventors  end  up  having  networks  with  fewer  redundancies  than  non-inventors 
(Forti, Franzoni et al., 2013). 

Ahuja examines the effects of a firm’s network of ties on innovation in the chemicals 
industry  using  the  structural  hole  concept.  The  results  show  that  increasing 
structural holes has a negative effect on innovation (Ahuja, 2000). 

The literature on the analysis of individual actors in ego networks has also used the 
concept of homophily to examine the preference of actors to collaborate with others 
in collaborative research networks. 

Dunn, Galleho et al. measure the relative influence that industry researchers in the 
pharmaceutical industry have on collaborative research communities using different 
measures from the SNA literature and data on co-authorship and citations, including 
homophily.  Their  network  analysis  reveals  that  industry  researchers  tend  to 
collaborate  more  often  with  non-industry  researchers  despite  a  preference  toward 

136 

 
 
their own group (Dunn, Gallego et al., 2012). Kegen examines the relations between 
formal and informal science networks and focuses on gender homophily as a driver 
for  structural  embeddedness  in  German  collaborative  research  networks  supported 
by  the  Clusters  of  Excellence  (CoE)  initiative.  The  author  finds  that  gender 
homophily  has  a  small  influence  on  involvement  in  research  cooperation  (Kegen, 
2013).  

Dyad and triad levels 

The  previous  levels  of  analysis  focused  on  actors  in  complete  and  ego  networks. 
Other common levels of analysis in the SNA literature are the dyad and triad levels. 
A dyad consists of a pair of actors and the possible ties between them, while a triad 
consists  of  a  subset  of  tree  actors  and  the  possible  ties  among  them  (Wasserman 
and  Faust,  1994).  The  use  of  the  term  ‘possible’  is  justified  by  the  existence  of 
directed  or  undirected  links  among  actors.  Most  of  the  SNA  literature  reviewed  on 
this  subject  offers  an  extensive  discussion  of  dyads  and  triads  in  the  context  of 
directed graphs. For this reason, these concepts are not discussed comprehensively 
here. 

  Dyads:  In  the  context  of  undirected  and  valued  graphs,  the  frequency  of 
interaction in dyads can provide information on the similarity in attitudes of two 
actors,  with  frequency  of  interaction  often  being  positively  correlated  with 
similarity  in  attitudes,  while  strength  of  a  tie  can  provide  information  on  the 
degree of agreement between two actors (Prell, 2012). 

In  this  study,  changes  in  the  number  of  dyads  between  FP6  and  FP7  (e.g. 
number  of  lasting  dyads)  will  be  used  to  address  the  following  questions:  To 
what  extent  has  the  FP  contributed  to  new  collaboration  and the integration of 
new  organisations?  How  many  new  collaborations  emerged  due  to  the  FP?  To 
what extent are FP project results used as an input for new FP projects, and to 
what  extent  has  knowledge  generated  in  previous  FPs  been  transformed  into 
new knowledge? (See Updated Methods of the Inception Report.)  

  Triads: In the context of undirected graphs, the identification of triadic relations 
among  actors  can  be  used  to  count  the  types  of  relations  that  prevail  within  a 
network. In these graphs, triadic relations occur in four states: ‘isolation’ (i.e. no 
ties),  ‘couples  only’  (i.e.  one  tie  only),  ‘structural  holes’  (i.e.  two  ties),  or 
‘clusters’  (i.e.  three  ties)  (Hanneman  and  Riddle,  2005).  An  important  related 
concept is that of transitivity,  under  which, when  an  actor A has a relationship 
with  an  actor  B  and  actor  B  has  a  relationship  with  an  actor  C,  there  is  a 
relationship between actors A and C. The triad is therefore said to be transitive. 
The  measure  of  the  transitivity  of  a  network  is  used  particularly  to  depict  the 
properties of small worlds in large networks. 

A  number  of  studies  on  collaborative  research  networks  based  on  undirected  and 
unvalued  data  have  examined  triads  through  clustering  coefficients  (transitivity). 
This literature is discussed later on in the review. 

Subgroup level 

A  fourth  level  of  analysis  in  the  SNA  literature  is  the  subgroup  level,  where 
subgroups  are  areas  of  networks  larger  than  dyads  or  triads  but  smaller  than 
complete  networks  (Prell,  2012).  Depending  on  the  concepts  used,  this  level  of 
analysis  can  be  applied  to  undirected  and  directed  graphs.  Only  the  concepts 
applicable  to  undirected  graphs  are  presented  below  (many  of  the  following 
concepts will be used in this study; see Updated Methods of the Inception Report). 

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  Components: In SNA, a component is a subgroup of actors, whereby all actors 
are linked by at least one path. Actors in the component are also disconnected 
from other actors in the complete network. The concept of component is closely 
related  to  the  notion  of  cohesion  because  it  is  a  measure  of  the  degree  of 
connectedness  in  networks.  Put  differently,  the  number  of  components  in  a 
network reveals to which extent the network is cohesive. The higher the number 
of components in a network is, the more a network is fragmented. 

  Cliques  and  n-cliques:  Another  concept  used  in  the  study  of  subgroups  in  a 
network  is  the  concept  of  clique.  A  clique  can  be  defined  as  the  maximal 
complete sub-network of three or more actors. In such a maximal complete sub-
network, all actors are adjacent to each other (Wasserman and Faust, 1994). It 
is important to  note  that an actor  can  belong  to different cliques in a network. 
This  co-membership  can  allow  them  to  play  a  bridging  role  in  the  network 
(Granovetter,  1973).  Because  a  clique  is  a  restrictive  definition  of  a  cohesive 
subgroup, other related concepts such as  n-cliques have been suggested in the 
SNA  literature.  An  n-clique  is  a  sub-graph  in  which  each  pair  of  nodes  is 
connected  by  a  path  of  length  n  or  less.  It  is  nevertheless  advisable  to  retain 
values  equal  or  smaller  to  three  for  n  in  order  to  obtain  interpretable  results 
(Prell, 2012). 

  K-cores: A k-core is a sub-network in which each actor is adjacent to at least a 
minimum number, k, of other actors in the sub-network. This concept is closely 
related to that of degree centrality  since  an  actor  is  a member of a  k-core if it 
has at least a degree centrality of  k in the group (ibid.). The concept of k-core 
can  be  useful  to  identify  the  more  important  actors  in  ensuring  cohesion  in  a 
network, namely those actors that belong in higher-level k-cores. Indeed, these 
actors  are  also  members  of  lower-level  k-cores  as  k-cores  are  ‘nested’  in  one 
another (ibid.). 

  Modularity:  Actors  in  a  complete  network  can  be  partitioned  into  a  varying 
number  of  subgroups  called  clusters  (or  communities)  based  on  the  degree  to 
which ties between actors are homogeneously distributed among all actors; the 
more ties are  heterogeneously  distributed, the  more  clusters there will  be.  The 
optimal partition is considered to be the one that maximises ‘modularity’, which 
measures  the  density  of  ties  within  clusters  as  compared  to  ties  between 
clusters.  The  higher  the  maximal  modularity  for  a  given  network,  the  more 
clustered the network with a lesser density of ties between clusters than within 
them.  Numerous  algorithms  have  been  developed  to  solve  this  optimisation 
problem and to find the maximum modularity score of a network together with 
its optimal subdivision into clusters (e.g. Clauset et al., 2004). In this study, the 
Louvain method for community detection will be used (Blondel et al., 2008). 

  Clusters: See above description of modularity. 

The  peer-reviewed  and  grey  literature  on  collaborative  research  networks  has 
analysed  subgroups  in  larger  networks  using,  in  particular,  the  concepts  of 
component and clique. 

In their study on the properties of networks and their actors in European framework 
programmes  between  1984  and  2006,  Heller-Schuh,  Barber  et  al.  (2011)  use  the 
concept  of  component  to  examine  to  which  extent  collaborative  research  networks 
are  fragmented,  on  the  assumption  that  a  higher  number  of  components  is 
associated  with  higher  fragmentation.  Examining  the  structure  and  dynamics  of 
international  research  networks  in  pharmaceuticals  using  co-publication  data, 
Cantner and Rake (2014) calculate the number of components and the absolute and 
relative  size  of  the  largest  component  to  gauge  multi-connectivity  in  these 
networks.  Newman  (2001)  uses  the  concept  of  component  to  study  the 
characteristics  of  scientific  collaboration  networks  in  different  fields  of  science  and 

138 

 
 
technology.  Based  on  co-authorship  data,  Newman  shows  that  the  largest 
component connects more than 90% of authors in biomedical research and less than 
60% of them in computer science. 

Some  exploratory  studies  have  examined  the  effects  of  the  largest  component  on 
innovation.  Fleming,  King  et  al.  find  that  the  size  of  the  largest  component  has  a 
positive impact on innovation at the regional level, using patent data (Fleming, King 
et al.,  2007); however, the results of this study  are not  corroborated by Chen and 
Guan in their investigation of the properties of patent  collaboration networks of 16 
main innovative countries during 1975–2006 (Chen and Guan, 2010). 

In their study on collaborative scientific networks in coronary heart disease based on 
co-authorship data, Yu, Shao et al. (2013) use the concept of clique, together with 
other  SNA  metrics,  to  characterise  these  networks  and  their  actors  over  the  past 
decades  and  to  extract  cohesive  research  groups  from  them.  In  another  study  on 
the  evolution  of  scientific  collaboration  networks  in  physics  and  biology  over  time, 
Newman  (2001)  finds  that  the  probability  of  a  particular  scientist  acquiring  new 
collaborators  rises  with  the  number  of  his  or  her  past  collaborators,  and  that  the 
probability  of  a  pair  of  scientists  collaborating  rises  with  the  number  of  other 
collaborators they have in common, as analysed through the creation of cliques. 

As  noted  by  Beaudry  and  Allaoui  (2012),  several  studies  have  also  suggested  that 
sustained  collaboration  over  time  has  a  positive  impact  on  knowledge  productivity 
(Cowan,  Jonard  et  al.,  2004;  Fleming,  King  et  al.,  2007).  Drawing  upon  this  idea, 
Beaudry  and  Allaoui  introduce  the  concept  of  cliquishness  in  their  model,  reported 
earlier, as an independent variable. The latter is measured by the egocentric density 
of  an  actor,  which  is  defined  as  the  fraction  of  all  pairs  among  the  immediate 
neighbours of an actor that are also directly connected to each other. They find that 
scientific  productivity  exhibits  diminishing  returns  to  increased  cliquishness  over 
time. 

The  other  SNA  concepts  developed  at  the  subgroup  level  and  discussed  above, 
namely  k-core  and  modularity,  seem  to  be  used  less  frequently  in  the  literature 
aimed  at  understanding  how  characteristics  of  a  network  influence  the  positive 
outcomes of a network on research and innovation output. 

Huang, Dong et al. (2012) examine the globalisation of collaborative creativity using 
data  on  co-inventor  patents  at  the  country  level.  Drawing  upon  the  concept  of  k-
core, they find that the  size  of  the  k-core component increased between  1990 and 
2009. Leydesdorff and Wagner report similar results in  their study on  international 
collaboration in science using data on co-authorship (2008). 

Network level 

A fifth level of analysis in the SNA literature is the network itself. Its properties are 
defined using a few concepts introduced in the following paragraphs.  

  Density: Density is certainly the most popular concept in the SNA literature. It 
is defined as the ratio between the number of effective ties between actors in a 
network  to  the  number  of  all  possible  ties  between  them.  In  other  words,  the 
higher this ratio is, the denser is the network. Despite its popularity, the concept 
of density has some weaknesses (Prell, 2012). A high degree of density does not 
necessarily  imply  that  a  network  is  highly  cohesive  since  this  degree  can  be 
explained by the high number of ties of  a few  actors in a  network.  In order to 
overcome  this  weakness,  the  concept  of  centralisation  has  been  proposed  in 
the  literature.  This  concept  measures to  which  extent  a  high  degree  of  density 

139 

 
 
can be attributable to one single actor. Moreover, the degree of density depends 
on  the  size  of  the  network.  For  instance,  a  large  network  can  show  more 
effective  ties  than  a  smaller  one  but  a  lower  degree  of  density  because  it  has 
more actors. Therefore, comparisons between networks need to take the size of 
networks into account. 

  Centralisation: The dispersion of centrality scores is important in assessing the 
level  of  centralisation  of  a  network—i.e.  the  degree  to  which  centrality  is 
concentrated  on  a  few  actors.  It  is  quite  common  that  centrality  is  highly 
concentrated in a network of scientific collaboration, with a few highly important 
actors  having  high  centrality  scores  and  a  majority  of  relatively  unimportant 
actors  having  low  centrality  scores.  When  the  centrality  of  a  majority  of 
countries  is  increased  (as  revealed  by  the  mean/median  centrality  score)  and 
the  dispersion  or  concentration  of  centrality  scores  (as  revealed  by  measures 
such  as  the  Gini  coefficient)  is  reduced,  implying  a  lower  degree  of 
centralisation,  actors  are,  on  the  whole,  more  integrated,  and  the  network  is 
more  balanced,  with  smaller  inequalities  between  nodes.  In  this  study,  this 
concept will be used to assess to what extent framework programmes contribute 
to  integrate  countries  and  regions.  (See  Updated  Methods  of  the  Inception 
Report.) 

  Diameter  and  average  path  length:  In  order  to overcome  the  limitations of 
the concept of density, other concepts to measure cohesion at the network level 
have  been  developed:  diameter  and  average  path  length.  The  diameter  of  a 
connected  network  can  be  defined  as  ‘the  length  of  the  largest  geodesic 
between  any  pair  of  actors’  (Wasserman  and  Faust,  1994),  geodesic  being  the 
shortest  path  between  two  actors.  The  higher  the  diameter  is,  the  more 
cohesive the network. Likewise, the average path length (also called reach) of a 
connected network is the average of the geodesic in that network. 

  Small  world  networks  and  large  networks:  An  increasing  popular  research 
area in the SNA literature has been the characterisation of network properties of 
small  worlds  in  large  networks  (Watts  and  Strogatz,  1998).  These  networks 
share  common  properties:  low  density,  high  average  clustering,  and  short 
average  path  length.  They  contain  a  large  number  of  actors.  These  actors  are 
tied  to  all  the  other  actors  through  a  small  number  of  intermediary  relations 
(Prell, 2012). Proposed by Watts and Strogatz, the clustering coefficient is used 
as  a  measure  in  the  context  of  small  world  networks  to  capture  the  extent  to 
which a network has areas of high and low density. It is measured in two steps. 
First, the individual clustering coefficient is calculated to measure the density of 
ties in each actor’s ego network. Then all individual clustering coefficients in the 
network  are  averaged  in  order  to  obtain  the  overall  clustering  coefficient 
(Borgatti,  Everett  et  al.,  2013).  Such  coefficients  can  indicate  the  probability 
that two adjacent actors of a focal actor have direct ties themselves (Newman, 
2001). 

  Scale-free networks and large networks:  Another  popular research  area  in 
the  SNA  literature  has  been  the  identification  of  scale-free  networks  in  large 
networks. Scale-free networks are networks in which ‘a new node creates ties to 
existing nodes in proportion to the existing nodes’ degree, so that nodes of high 
degree attract edges and so their degree increases even more (often  expressed 
as  “the  rich  get  richer”)’  (Borgatti,  Everett  et  al.,  2013).  Barabási  and  Albert 
proposed  a  model  to  identify  such  properties  in  large  networks  (Barabási  and 
Albert, 1999). 

A  large  body  of  the  peer-reviewed  and  grey  literature  on  collaborative  research 
networks  has  used  SNA  concepts  developed  at  the  network  level,  in  particular 
network density. 

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For  instance,  in  order  to  better  understand  the  innovation  potential  of  a  firm’s 
collaborative  network,  Gilsing,  Nooteboom  et  al.  (2008)  examine  the  role  of 
collaborative networks in the pharmaceutical, chemical, and automotive industries in 
terms of technological distance between its actors and SNA concepts, including total 
network density. They find that success in novelty creation and efficient absorption 
depends  notably on the  density  of  the network  and  technological distance.  In their 
study  on  the  impact  of  university–industry  collaboration  networks  on  innovation 
(measured  by  an  indicator  of  weighted  patent  value)  in  nanobiopharmaceuticals 
using university–industry patent data, Guan and Zhao include network density as a 
control  variable  in  their  model  as  they  assume  that  the  rate  and  extent  to  which 
information  diffuses  increases  with  density  (2013).  In  their  study  on  the 
globalisation  of  collaborative  creativity  through  cross-border  patent  activities, 
Huang, Dong et al. (2012) show that the total network density of the collaborative 
research  networks  significantly  increased  from  1990  to  2009.  In  their  2009  study 
about the structuring effects of community research, AVEDAS AG, CWTS et al. also 
use total network density, together with other SNA concepts, to gauge the extent to 
which  collaborative  research  networks  supported  by  the  Sixth  Framework 
Programme under different themes and funding schemes are dense. 

Likewise,  the  peer-reviewed  and  grey  literature  has  relied  on  the  concepts  of 
average  diameter  and  average  path  length  to  examine  the  properties  of 
collaborative research networks, especially from a dynamic perspective. 

Using  co-authorship  data,  Yin,  Kretschmer  et  al.  (2006)  analyse  connection  and 
stratification  in  an  international  collaboration  network  called  COLLNET.  Among  the 
SNA  concepts  used  in  their  study  is  the  concept  of  diameter.  This  is  used  to 
measure how ‘big’ the network is. Perc (2010) examines the evolution of Slovenia’s 
scientific  collaboration  from  1960,  using  different  SNA  metrics,  including  diameter. 
The  author  shows  that  the  diameter  increased  sharply  until  1985  and  decreased 
slowly afterwards, suggesting an increasingly dense network after this date. In their 
above-cited  study,  Huang,  Dong  et  al.  reveal  a  low  diameter  of  the  international 
collaborative research network through cross-border patent activities between 1990 
and  2009,  although  it  increased  from  2  to  3.  They  also  highlight  how  the  average 
path length in the network decreased during the period, from 2.15 in 1990–1994 to 
2.04  in  2005–2009  (Huang,  Dong  et  al.,  2012).  Leydesdorff  and  Wagner  (2008) 
calculate  the  diameter  of  the  global  scientific  network  based  on  co-authorship, 
showing  its  low  level  between  1990  and  2005,  thereby  having  a  high  degree  of 
density of the network. They also measure the average path length, the latter being 
low  and  decreasing  from  2  in  1990  to  1.8  in  2005.  In  their  2011  study  on  the 
characteristics  of  networks  and  their  organisations  in  European  framework 
programmes  between  1984  and  2006,  Heller-Schuh,  Barber  et  al.  show  that  the 
diameter  of  the  largest  component  decreased  from  9  during  the  First  Framework 
Programme to 7 during FP6. 

In addition to these descriptive studies, some exploratory studies have investigated 
the effects of the average path length on the performance of collaborative research 
networks.  In  their  study  on  patent  collaborative  networks  at  the  regional  level, 
Fleming,  King  et  al.  find  that  shorter  path  lengths  correlate  with  increased 
innovation  (2007).  Chen  and  Guan  reach  the  same  results  in  their  paper  on  the 
impact  of  patent  collaboration  networks  of  16  main  innovation  countries  over  the 
1975–2006  period  (2010).  These  findings  are  corroborated  by  Guan  and  Zhao  in 
their study on the impact of university-industry collaboration networks on innovation 
in  nanobiopharmaceuticals  (2013).  One  explanation  for  this  correlation  is  that 
shorter  path 
information  diffusion  and  novel 
influence 
recombination (ibid.). 

lengths  positively 

141 

 
 
The ‘small world’ properties attached to large collaborative research networks have 
been examined by the peer-reviewed and grey literature. 

In their descriptive analysis of the attributes of collaborative networks supported by 
the  European  framework  programmes  between  1984  and  2006,  Heller-Schuh, 
Barber et al. (2011) reveal that small world networks (with high clustering) emerge 
in FP5 and FP6 sub-programmes that are strongly oriented towards applied research 
and development. 

In their study on the impact  of  collaborative  patent  networks at the regional level, 
Fleming,  King  et  al.  (2007)  fail  to  find  a  significant  statistical  effect  of  small  work 
properties  on  innovation  performance.  In  their  study  on  the  impact  of  university–
industry collaboration networks on innovation in nanobiopharmaceuticals, Guan and 
Zhao  (2013)  investigate  the  effects  of  small  world  properties  on  patent  value. 
Building on the assumption of Uzzi and Spiro (2005), they find that the small world 
properties of the networks have an effect on patent value at the firm level as they 
enhance  creativity  and  innovation.  This  effect  is  parabolic,  however:  below  a 
medium  level,  small  world  properties  correlate  positively  with  patent  value,  while 
the  correlation  becomes  negative  above  that  level.  Indeed,  beyond  this  level, 
excessive  structural  connectivity  has  a  negative  impact  on  the  creativeness  of 
clusters,  the  latter  becoming  too  interlinked  and  less  distinctive  in  terms  of 
creativity (Guan and Zhao, 2013). Chen and Guan explore the impact of small world 
properties on innovation at  the  national level  with  an empirical investigation of the 
patent  collaboration  networks  of  16  main  innovative  countries  during  1975–2006. 
They  found  no  evidence  that  the  size  of  the  largest  component  in  a  network 
significantly  enhances  innovative  productivity,  but  their  results  show  that  small-
world  structures  benefit  innovation.  However,  this  is  limited  to  a  specific  range, 
outside of which the effects are inversed, with shorter path length always correlating 
with increased innovation output (Chen and Guan, 2010). 

The ‘scale-free’ properties associated with large collaborative research networks and 
their evolution have been investigated by several scholars. 

Using data on co-authorship in the fields of mathematics and neuroscience over the 
1991–1998  period,  Barabási,  Jeong  et  al.  (2002)  find  that  the  evolution  of  the 
collaborative research networks in these fields is governed by scale-free properties, 
where  preferential  attachment  in  terms  of  collaboration  affect  both  internal  and 
external  collaboration  ties.  Lemarchand  analyses  the  long-term  dynamics  of 
scientific  co-authorship  networks  in  Ibero-American  countries  between  1973  and 
2010. His findings reveal that  co-authorship  follows a power law  and  behaves as a 
self-organising  scale-free  network,  where  each  country  appears  as  an  actor  and 
each  co-publication  as  a tie  (Lemarchand,  2012).  Examining  collaborative  research 
networks  among  organisations  in  the  context  of  the  Fifth  European  Framework 
Programme  using  administrative  data  from  CORDIS,  Almendral,  Oliveira  et  al. 
suggest  the  presence  of  a  scale-free  network,  implying  that  the  creation  of  new 
collaborations is encouraged (Almendral, Oliveira et al., 2007). 

Advantages and limitations of social network analysis 

The preceding paragraphs have underlined the most frequently used concepts in the 
literature  on  applied  SNA  and  shown  how  these  concepts  have  been  used  by 
researchers to investigate collaborative research networks. 

Overall, social network analysis is a powerful method to examine many properties of 
collaborative  research  networks  at  different  levels  of  analysis.  Yet  the  concepts 
developed  in  the  SNA  literature  should  be  used  with  care  when  analysing 

142 

 
 
collaborative  research  networks  because  some  of  them  (such  as  clique  and degree 
centrality)  have  limitations.  Furthermore,  the  validity  of  the  theories  and 
assumptions  behind  them  are  still  subject  to  debate.  For  instance,  it  is  not  clear 
whether  ego  networks  characterised  by  structural  holes  or  networks  with  small 
world properties are more desirable than highly cohesive ones. 

Linked to this limitation,  the literature review also  pointed out that the use  of SNA 
concepts to investigate collaborative networks is often insufficient when the derived 
measures are calculated for the purpose of descriptive analysis only. 

A  number  of  the  reported  empirical  studies  have  used  network  variables  as 
explanatory  variables,  together  with  other  control  variables,  in  regression  models 
based  on  multivariate  statistics  to  explain  the  impact  of  the  properties  of 
collaborative  research  networks  and  their  actors  on  certain  outcomes  such  as 
scientific  publications  and  patents.  Therefore,  SNA  metrics  are  not  appropriate  by 
themselves  to  assess  the  outcome  of  these  networks.  They  must  be  used  in 
conjunction with other data and metrics. 

The  literature  review  has  also  shown  that  most  of  the  empirical  studies  on 
collaborative research networks relying on multivariate statistics have used network 
variables  as  independent  variables  rather  than  as  dependent  variables.  Yet  studies 
explaining  the  antecedents  of  network  variables  (e.g.  homophily)  in  the  context  of 
collaborative  research  networks  are  highly  desirable  to  better  understand  the 
formation of these networks and the determinants of their properties. 

However,  bibliometric  data—whether  publications  or  patents—and  administrative 
data  on  publicly  funded  collaborative  R&D  projects  cannot  capture  all  the 
determinants  and  outcomes  of  collaborative  research  projects.  For  instance,  the 
outcomes of these networks can be multiple, including not only the creation of new 
scientific  and  technological  knowledge  but  also  the  development  of  new  scientific 
and  technological  tools,  research  training,  and  the  introduction  of  product  and 
process  innovations.  Likewise,  the  determinants  of  collaborative  networks  can  be 
diverse, including variables that are  not  covered  by bibliometric and administrative 
variables  such  the  motivations  and  aspirations  of  partners  and  the  type  of  R&D 
executed (e.g. pure basic research, applied research). This type of information can 
only be gathered through ad-hoc surveys. 

To  provide  an  in-depth  analysis  of  the  determinants  of  collaborative  research 
networks, their attributes and those of their actors, as well as their outcomes, case 
studies  are  required  to  supplement  the  findings  by  describing  how  and  why  the 
developments of interest have arisen. 

Finally,  and  more  fundamentally,  it  is  important  to  take  into  account  the  fact  that 
scientific  and  technological  knowledge  has  the  properties  of  a  public  good  as  its 
benefits can be enjoyed by many actors both concurrently and sequentially without 
being  diminished  and  it  is  costly  for  the  provider  to  exclude  unauthorised  users 
(Foray,  2004).  This  implies  that  two  actors  do  not  need  to  have  effective 
relationships 
identified  via  various  data—including  bibliometric  data  and 
administrative  data  on  publicly  supported  collaborative  research  networks—to 
exchange knowledge since its production is subject to spillovers. 

References 

Abbasi,  A.,  et  al.  (2012).  Egocentric  analysis  of  co-authorship  network  structure, 
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150 

 
 
 
 
Appendix II: Overview  of networking activities under FP7 and its  different 
specific programmes and thematic priorities (Task 2) 

This section provides a broad overview of the networking activities that took place in 
FP7. It investigates three levels of networking: 

 

 

 

participant level; 

regional level; and 

national level. 

It  also  describes  network  dynamics  over  time  through,  for  example,  comparison 
with  the  previous  framework  programme  (FP6),  and  provides  a  broad  comparative 
analysis of FP7 specific programmes and their underlying thematic areas. 

R1—Headline overall figures (i.e. number of participants, sectors, countries, regions 

in  total  and  per  project)  by  programme,  specific  programme  and 

broad/thematic area 

Key findings 

  When  the  non-collaborative  components  of  FP6  and  FP7  are  excluded,  both 
framework  programmes  appear  highly  collaborative,  linking  organisations  from 
multiple sectors of activity, regions and countries.  

 

They  are  also  comparable  in  terms  of  the  extent  to  which  they  supported  co-
participation: each with, on average, about 11 organisations, six countries, nine 
NUTS 3 regions and three sectors of activity per project. 

  When all components are included (i.e. even those not supporting collaboration 
by design), FP7 projects appeared, on average, less collaborative than those of 
FP6. 

  Results are skewed in that direction by the large number of projects in the 
Ideas and People specific programmes, which are not focused on fostering 
collaboration and represent roughly 60% of the total number of FP7 
projects. In comparison, projects in the Marie Curie Actions programme 
(also not focused on cooperation) represent only 44% of all FP6 projects. 

  Indeed, the specific programmes of FP7 that aimed to be highly collaborative 
by design are also those that appear as such when looking at the average 
co-participation level of projects, namely Cooperation and Capacities. The 
other two specific programmes (i.e. People and Ideas), appear to be only 
marginally collaborative. 

 

 

The  thematic  priorities  showing  the  highest  levels  of  international,  inter-
regional,  intersectorial  and  inter-organisational  co-participation  are:  Research 
Infrastructures  (Capacities  specific  programme),  Environment  (Cooperation 
specific programme) and Food, Agriculture and Biotechnology (Cooperation). 

The  same  countries  have  the  largest  number  of  distinct  organisations 
participating in  both  FP6 and  FP7: Germany,  the  United  Kingdom, Italy, Spain, 
and France. 

  Among Non-ERA countries, the only change within the top five countries 
from FP6 to FP7 was the inclusion of Ukraine, as the number of distinct 
organisations from Brazil that participated in FP7 diminished markedly from 
FP6. 

151 

 
 
 

 

Two  of  the  top  five  regions  (NUTS  3)  with  the  largest  number  of  distinct 
organisations  participating  in  either  FP6  or  FP7  are  in  Spain:  Madrid  and 
Barcelona. 

Paris  was  a  strong  regional  player  in  both  framework  programmes,  having 
participated  in  the  largest  number  of  projects  and  ranking  in  the  top  five  in 
terms  of  the  number  of  distinct  organisations  having  participated  in  the 
framework programmes. 

Co-participation level by specific programmes and broad/thematic areas 

FP7  is  composed  of  four  specific  programmes:  Cooperation,  Ideas,  People,  and 
Capacities.  Cooperation  comprises  a  number  of  thematic  areas,  while  Capacities 
comprises  a  number  of  broad  areas.  Table  XXII  presents  statistics  on  the  average 
number of actors on FP7-funded projects for the programmes, specific programmes 
and  the  areas  of  specific  support,  as  well  as  for  FP6-funded  projects  (aggregated) 
for  comparative  purposes  with  FP7.  The  term  actors  can  refer  to  organisations, 
countries,  regions  (NUTS  3)  or  activity  sectors,  depending  on  the  level  of  analysis 
required  (international,  regional,  organisational,  etc.).  This  table  allows  for  a 
comparison  of  the  levels  of  participation  and  relative  collaborations  within  a  given 
thematic area, specific programme and framework programme. 

These data, like that of several analyses used in the present study, are drawn from 
the  E-CORDA  database,  commonly  referred  to  as  CORDA.  The  CORDA  database 
contains  data  pertaining  to  framework  programme  projects  and  also  contains  data 
on  project  participants  (organisations  or  individuals)  for  each  project.  For  each 
participant  the  database  contains  self-declared  information  such  as  country  of 
origin, region and sector. From there it is possible to cross-reference each project to 
its participants and measure the average number of actors for each given indicator. 

as 

on 

levels 

there  were 

co-participation 

Comparing FP7  to FP6 leads to  the observation  that, overall, FP6 seemed to foster 
greater 
average  more 
organisations/countries/regions/sectors  participating  in  each  project.  It  should  be 
noted,  however,  that  these  results  are  not  comparable,  mainly  because  the  two 
specific  programmes  that  are  not  collaborative  by  design  (i.e.  People  and  Ideas) 
represent a larger share of all FP7 projects (about 60%) than the non-collaborative 
component of FP6 (i.e. Marie Curie Actions), which roughly represent 44% of all FP6 
projects (data not shown). When these specific programmes are excluded from FP6 
and  FP7,  it  is  quite  obvious  that  both  framework  programmes  are  highly 
collaborative,  linking  organisations  from  multiple  sectors  of  activity,  regions  and 
countries. They also do so to a comparable extent: each with, on average, about 11 
organisations,  six  countries,  nine  NUTS  3  regions  and  three  sectors  of  activity  per 
project. 

Within FP7, the specific programmes that aimed to be highly collaborative by design 
are  also  those  that  appear  as  such  when  looking  at  the  average  co-participation 
level  of  projects  based  on  the  dimensions  listed  above,  namely  Cooperation  and 
Capacities.  Unsurprisingly,  the  People  and  Ideas  specific  programmes  show  the 
lowest  levels  of  co-participation,  generally  having  the  smallest  averages  of  the 
number  of  organisations,  countries,  NUTS  3  regions  and  sectors  per  project.  In 
total, only two thematic areas from the Cooperation and Capacities sub-programmes 
had  comparatively  small  scores  in  terms  of  the  average  number  of  distinct 
organisations  per  project  (less  than  five  per  project)—namely,  General  Activities 
(under  Cooperation)  and  Research  Potential  (under  Capacities).  Yet,  the  scores 
observed  for  Capacities  and  Cooperation  do  not  mean  these  specific  programmes 
performed better in terms of collaboration; this was a core aspect in their design, in 
contrast to People and Ideas, which aim to support research excellence. 

152 

 
 
In  FP7,  three  broad/thematic  areas  stand  out  for  their  relatively  high  co-
participation  level—namely,  Environment  (including  Climate  Change)  and  Food, 
Agriculture  and  Biotechnology  under  Cooperation,  and  Research  Infrastructures 
under Capacities. These three areas only represent 5.5% of the total number of FP7 
projects, but on average their projects involve more than 13 different organisations 
coming from three different sectors of activity. Projects stemming from those areas 
are  also  generally  associated  with  higher  international  integration  (i.e.  number  of 
countries  per  project)  even  beyond  the  ERA;  for  instance,  there  is  on  average  at 
least one country from outside the ERA participating in each project in Environment 
(including Climate Change) and in Food, Agriculture and Biotechnology. 

Table XXII 

Average number of actors per project by specific programme 
and broad/thematic area (FP6 and FP7)  

Note:  

Source:  

‡ Excluding the Marie Curie Actions (equivalent of People under FP7) under FP6 
and  †excluding  People  and  Ideas  under  FP7  since  they  are  not  specifically 
designed  to  formally  support  cooperation.  The  colour  gradient  indicates 
whether  the  score  of  a  given  thematic  (under  Cooperation)  or  broad  (under 
Capacities)  area  is  above  (green)  or  below  (red)  the  average  across  projects 
inside  a  given  specific  programme  for  each  indicator.  Only  Cooperation  and 
Capacities  were  analysed  in  this  manner  as  they  are  the  only  specific 
programmes with more than one underlying area. 
Computed by Science-Metrix using CORDA (European Commission) data 

Core participating regions and countries 

As  can  be  seen  in  Table  XXIII,  the  core  countries  in  both  framework  programmes 
are  Germany,  the  United  Kingdom,  Italy,  Spain,  and  France,  all  of  which  both 
participated  in  the  largest  number  of  projects  and  had  the  largest  numbers  of 
unique  organisations  participating  in  the  framework  programmes.  The  Non-ERA 
countries  with  the  largest  number  of  unique  organisations  participating  in  FP7  are 
Russia, the US, China, India and Ukraine. This is a change from FP6 when Brazil was 
found  among  the  five  countries  with  the  largest  number  of  organisations.  In  fact, 
the  number  of  distinct  organisations  from  Brazil  that  participated in  FP7  decreased 
markedly  compared  to  FP6.  These  Non-ERA  countries  participated  to  a  similar  or 

153 

No. of ProjectsAvg. No. of Organi-sationsAvg. No. of CountriesAvg. No. of ERA CountriesAvg. No. of Non-ERA CountriesAvg. No. of NUTS3 RegionsAvg. No. of SectorsFP69,7817.14.24.00.36.12.2FP6 excluding Marie-Curie Actions‡5,33111.56.56.00.59.73.0FP721,9635.23.33.20.24.61.9FP7 excluding People & Ideas†8,86710.56.25.80.49.12.9COOPERATION7,01710.86.35.90.49.43.0Info. and Comm. Tech.2,0659.35.75.40.28.12.9Health97710.96.66.00.79.53.1Nanosci., Nanotech., Mat. and new Prod. Tech.76112.36.66.40.210.73.1Transport (including Aeronautics)68912.36.46.20.310.33.2Joint Tech. Initiatives (Annex IV-SP1)6267.33.43.30.036.02.1Environment (including Climate Change)44313.98.67.51.112.23.3Food, Agriculture, and Biotechnology43714.68.97.81.112.83.4Energy33311.36.46.00.39.63.1Space2409.96.25.70.58.62.9Security22912.07.27.10.110.13.5Socio-economic sciences and Humanities20910.58.27.50.89.92.6General Activities (Annex IV)81.01.01.001.01.0CAPACITIES1,8509.35.85.40.48.02.7Research for the benefit of SMEs9058.85.15.10.027.62.7Research Infrastructures33415.19.58.80.713.43.2Research Potential1941.51.41.40.11.51.1Science in Society1629.57.36.70.58.72.8Activities of International Cooperation1498.46.44.32.17.03.1Regions of Knowledge8111.74.44.40.027.14.1Coherent development of research policies255.13.83.804.62.0PEOPLE9,2931.91.51.50.021.81.2IDEAS3,8031.21.11.10.011.11.1 
 
 
lesser  extent  than  some  of  the  smaller  associated  countries  of  the  ERA.  Large 
countries  such  as  Russia,  China  and  the  US  have  participated  less  frequently  than 
small  ERA  countries  such  as  Romania  and  Ireland.  These  results  illustrate  the 
framework programme’s aim to target a stronger integration of European countries 
towards  realising  the  ERA.  In  terms  of  project  participation,  three  of  the  non-ERA 
countries  with  the  largest  number  of  distinct  organisations  also  participated  in  the 
largest  number  of  projects  across  both  framework  programmes,  namely  the  US, 
Russia  and  China,  along  with  Canada  and  Morocco  in  FP6,  and  South  Africa  and 
India in FP7. 

Table XXIII  Number  of  projects  in  which  a  country  participated  alongside 
the number of distinct organisations that participated from the 
given country for the 25 most participating countries under FP7 
(comparison with FP6 is provided) 

Note: 
Source: 

Projects and organisations are aggregated for the 2007–2014 period. 
Computed by Science-Metrix using CORDA (European Commission) data 

Looking at the number of projects, the region with the highest level of participation 
was Paris in both FP6 and FP7. In FP6, Paris was the city with the largest number of 
distinct organisations having participated in at least one project. In FP7, it has been 

154 

FP6FP7CountryNo. of ProjectsNo. of OrganisationsNo. of ProjectsNo. of OrganisationsTop 20 ERAUnited Kingdom4,32515,1928,85320,816Germany4,16416,0507,75126,704France3,55810,7346,25417,272Italy3,04810,5485,50317,664Spain2,64110,1085,44817,760Netherlands2,3095,8504,33410,536Belgium1,7204,4463,1408,184Switzerland1,3052,8922,9675,648Sweden1,5574,1512,6685,920Greece1,4112,8322,1974,192Austria1,2303,5102,1315,344Denmark1,0423,5681,7474,024Poland1,3033,5761,5034,360Finland9121,7851,4983,680Portugal7833,0481,4283,840Israel5481,2781,4152,160Norway7812,3941,2583,560Ireland6801,9111,2513,024Hungary8412,1301,0182,640Czech Republic7941,7459762,824Top 20 Non-ERAUnited States3711,2423752,104Russia2891,9882982,104China2071,6382361,560South Africa99245174441India915461671,224Brazil100777160616Australia88276149371Canada105408135664Ukraine85432126777Argentina7341392318Morocco10022487282Egypt6930686264Japan2510083308Mexico4820480342Tunisia7922076282Kenya3210862210Chile5319255180Korea (Republic of)165545162Colombia155241138Ghana184441126 
 
 
 
overtaken  by  two  Spanish  regions;  namely,  Madrid  and  Barcelona.  In  total,  five 
regions  made  the  top  five  for  having  the  largest  number  of  unique  organisations 
participating  in  the  framework  programme  (either  FP6  or  FP7);  namely,  Madrid, 
Brussels, Barcelona, Paris and Attiki.  

Table XXIV  Number  of  projects  in  which  a  NUTS  3  region  participated 
that 
alongside 
for  the  25  most 
participated 
participating  regions  under  FP7  (comparison  with  FP6  is 
provided) 

the  number  of  distinct  organisations 

from  the  given  region 

Note: 
Source: 

Projects and organisations are aggregated for the 2007–2014 period. 
Computed by Science-Metrix using CORDA (European Commission) data 

Munich, Germany, was a very highly participating region under FP7. It ranks second 
in the number of projects, thereby having a similar contribution to those of Madrid, 
London  and  Barcelona,  despite  having  a  much  smaller  number  of  participating 
organisations  (i.e.  distinct  ones).  Munich  also  showed  a  greater  participation  level 
than  Berlin  in  both  framework  programmes,  showing  that the  core  presence  is  not 
necessarily  matching  every  country’s  capital.  Among  the  core  countries  (Germany, 
the UK, Spain, France  and Italy), most countries have one (France  and the  UK)  or 
two (Germany, Spain and Italy) highly participating regions. 

R2  &  R3—Network  structure  and  its  characteristics  by  level  of  analysis  (i.e. 

organisational,  regional  and  national)  as  well  as  by  programme,  specific 

programme and broad/thematic area 

Key findings 

 

In all levels of analysis (organisations, NUTS 3 regions and countries), networks 
appeared  to  be  closely  knit  among  a  small  number  of  actors  at  the  centre 
(core),  with  the  rest  of  the  actors  distributed  around  it  (periphery)  and 
connected to the core but not so much between themselves. 

155 

FP6FP7NUTS-3NameNo. of ProjectsNo. of OrganisationsNo. of ProjectsNo. of OrganisationsFR101Paris2,2222,3163,8273,328DE212München, Kreisfreie Stadt1,2958162,4301,256ES300Madrid1,2102,1212,2663,720UKI11Inner London - West1,1861,3982,1812,232ITE43Roma1,1441,5662,0122,536ES511Barcelona8071,9981,7273,552EL300Attiki9431,9261,4332,728IL000Israel5481,2781,4152,160BE100Arr. de Bruxelles-Capitale / Arr. van Brussel-Hoof7722,0641,3503,688ITC45Milano7381,5961,2982,640AT130Wien7841,2051,2732,296SE110Stockholms län7241,2181,2092,160FI181Uusimaa6749501,0961,800DE300Berlin5861,0741,0652,224CH040Zürich4605761,0581,248NL326Groot-Amsterdam4935859751,304UKJ14Oxfordshire395462872735FR105Hauts-de-Seine5717708371,368NL333Delft en Westland371192807504BE242Arr. Leuven384230805728UKH12Cambridgeshire CC425570792728HU101Budapest6361,2207491,656PT171Grande Lisboa4621,2887311,384IE021Dublin3838287121,344ITC11Torino3856106981,440 
 
 
 

 

 

FP7  projects  are  mainly  coordinated  by  one  group  of  central  actors,  mainly 
composed  of  large  universities  and  national  research  organisations.  Firms 
(mostly SMEs) are generally located on the periphery. 

To increase the span of the core, one could put some constraints on the nature 
of collaboration within projects. It has been shown that having connections to at 
least  two  core  players  who  know  each  other  increases  the  integration  of  a 
newcomer to the core of the network. 

In  FP7,  increased  collaboration  between  the  actors  lying  at  the  periphery  with 
the actors at the core of the network was visible compared to FP6. 

  All  co-participation  networks  (by 

framework  programme  and  specific 
programme)  are  characterised  by  a  highly  uneven  distribution  of  centrality 
scores  typical  of  core-periphery  structures,  and  the  level  of  inequality  between 
actors is similar between FP6 and FP7 networks for all levels of analysis. 

 

Interestingly, the density of the network decreased only slightly from FP6 to FP7 
at the organisational level, despite a sizeable increase in the number of actors. 
This  indicates  that  the  number  of  partnerships  increased  by  an  appreciable 
margin  in  FP7  relative  to  FP6  given  that  it  is  difficult  to  maintain  density  in  a 
large network as the number of actors increases. 

  At  the  regional  (NUTS  3)  and  national  levels,  network  density  increased  while 
keeping  constant  the  number  of  actors  to  maximise  comparability  across 
framework  programmes;  such  an  approach  was  not 
the 
organisational level. If this approach had not been used at the national/regional 
levels,  one  would  have  expected  to  observe  a  decrease  in  density  even  in  the 
presence  of  a  densification  of  the  core  of  the  networks,  as  is  the  case  with 
organisations.  

feasible  at 

Network structure of FP6 and FP7 

For  all  levels  of  analysis—organisational,  regional,  and  national—and  for  all 
programmes  (i.e.  FP6,  FP7,  Cooperation,  Capacities,  People  and  Ideas),  networks 
are  characterised  by  a  core-periphery  structure.  This  structure  can  be  detected 
through  a  visual  inspection  of  a  collaboration  network,  whereby  a  few  influential 
actors  (i.e.  hubs)  located  at  the  centre  of  the  graphic  representation  establish 
strong ties in between themselves and weaker connections with the periphery where 
the density of ties—and consequently the flow of knowledge—is reduced in between 
the  peripheral  players.  This  means  that  actors  are  unequally  represented  in  the 
network, some of them participating in a large  number of  projects (i.e.  the central 
players  or  hubs)  while  others  are  participating  rather  infrequently.  Figure  8  and 
Figure  9  illustrate  this  at  the  NUTS  3  regional  level  for  FP6  and  FP7,  respectively; 
note  that  similar  structures  are  observed  for  the  other  levels  of  analysis  (i.e.  local 
and national) as well as for the specific programmes of FP7. 

The presence of only one core suggests that FP7 projects are mainly coordinated by 
one  group  of  central  actors.  Large  universities  and  national  research  organisations 
usually play the part of the core actors, whereas firms (mostly SMEs) are generally 
located  on  the  periphery  (see  later  in  Section  0).  Although  this  network  trait 
remained  unchanged,  an  increase  in  collaboration  both  between  the  core  players 
and  between  the  core  and  peripheral  players  is  noticeable  from  FP6  to  FP7  (see 
Figure 8 and Figure 9). An important aspect of the framework programme networks 
is  the  presence  of  recurring  participants  who  tend  to  be  the  core  players,  and 
newcomers who are usually positioned at the periphery. To increase the span of the 
core, one could put some constraints on the nature of collaboration within projects. 
For example, it has been shown that having connections to at least two core players 

156 

 
 
who know each other increases the probability of a newcomer being integrated into 
the core of the network.20 

Figure 8 

Overall FP6 network at the NUTS 3 level 

Note: 

Source: 

Ties  representing  at  least one  hundred  collaborations  are shown.  The  NUTS 3 
network  is  shown  as  a  representative  example  of  the  structure  of  all  three 
levels of analysis. 
Computed by Science-Metrix using CORDA (European Commission) data 

20 Collier, B., & Kraut, R. (2012). Leading the Collective: Social Capital and the Development of 
Leaders  in  Core-Periphery  Organizations.  Paper  presented  at  the  Collective  Intelligence 
conference, Cambridge, MA. 

157 

 
 
 
                                                
Figure 9 

Overall FP7 network at the NUTS 3 level 

Note: 

Source: 

Ties  representing  at  least one  hundred  collaborations  are shown.  The  NUTS 3 
network  is  shown  as  a  representative  example  of  the  structure  of  all  three 
levels of analysis. 
Computed by Science-Metrix using CORDA (European Commission) data 

This  type  of  structure  can  also  be  detected  using  various  network  statistics  (Table 
XXV). In this regard, the betweenness centrality of actors (i.e. organisations, NUTS 
3  regions  or  countries)  is  highly  informative  as  it  reflects  the  number  of  shortest 
paths from all actors to all others that pass through a given actor, normalised by the 
total number of shortest paths in a network. In other words, the higher the number 
of shortest paths on which an actor appears, the higher its centrality in the network 
as it falls in between all others to the greatest extent. 

In a core-periphery structure, central actors—those with high centrality scores—are 
therefore  crucial  to  the  network  structure,  as  knowledge  exchange  in  between 
peripheral  actors—those  with  low  centrality  scores—must  often  flow through  them. 
Thus,  by  looking  at  the  distribution  of  betweenness  centrality  scores,  one  can 
appreciate  the  extent  to  which  a  network  is  characterised  by  a  core-periphery 
structure. The Gini coefficient of the betweenness centrality scores is shown for each 
network  in  Table  XXV  to  indicate  whether  the  underlying  distribution  of  the  scores 
across  actors  is  uniform  (i.e.  projects  having  relatively  similar  scores)  or  non-
uniform  (i.e.  a  few  projects  having  large  scores  with  the  remaining  majority  of 
projects  having  relatively  small  scores)  as  would  be  expected  for  a  core-periphery 
structure.  The  Gini  coefficient  of  any  distribution  ranges  from  0  indicating  that  all 
scores  are  equal,  in  which  case  the  score  of  each  actor  is  equal  to  the  average 
centrality across actors, to 1 indicating that one actor is highly central with all ties in 
between any two actors in the network including this highly central player (extreme 
case  of  core-periphery  structure;  as  in  a  star  network).  As  can  be  seen  in  Table 
XXV,  the  Gini  coefficient  of  all  networks  examined  ranges  from  0.82  to  0.98 
indicating  that  all  networks  are  characterised  by  a  highly  uneven  distribution  of 
centrality  scores  typical  of  core-periphery  structures.  For  instance,  the  level  of 
inequality between  actors is similar between FP6  and FP7 networks  for all levels of 
analysis (see the Gini coefficients in Table XXV). 

158 

 
 
 
Table XXV 

Network  statistics  depicting  the  structure  of 
the  co-
participation  networks  of  various  programmes  at  the  level  of 
organisations, regions (NUTS 3) and countries 

Note: 

Source: 

When  comparing  individual  FP7  specific  programmes,  a  higher  density  is 
observed for People and Ideas compared to Capacities and Cooperation at the 
organisational  level.  This  can  be  explained  by  the  fact  that  organisations 
participating  on  a  recurring  basis  (i.e.  large  universities  and  research 
organisations  situated  in  the  core  of  the  network)  are  more  frequent  in  the 
former two specific programmes (as seen in Section 0). A higher proportion of 
recurring  participants  generally  improve  the  overall  connectivity  of  a  network 
by increasing its density. 

* The number of actors at the national and regional levels was artificially kept 
constant  to  maintain  the  comparability  of  the  network  density  across 
framework  programmes  and  specific  programmes.  Thus,  note  that  the  actual 
number  of  actors  at  these  levels  of  analysis  might  diverge  from  the  numbers 
displayed  in  the  table.  This  was  not  feasible  at  the  organisational  level  since 
organisations  are  more  prone  to  structural  changes  (e.g.  merger,  acquisition, 
re-branding, and bankruptcy). 
Computed by Science-Metrix using CORDA (European Commission) data 

On  the  other  hand,  it  is  worth  nothing  that  the  density  of  the  co-participation 
network,  which  measures  how  well  the  actors  of  a  network  are  interconnected, 
declined slightly from FP6 to FP7 at the organisational level (Table XXV). This is not 
unexpected  since  the  number  of  actors  (i.e.  organisations)  increased  by  an 
appreciable  margin  in  FP7  making  it  difficult  for  any  set  of  actors  to  maintain 
density.  Indeed,  since  the  number  of  possible  ties  increases  much  faster  than  the 
number of actors in a network, it is usually difficult to maintain density—that is, the 
number  of  actual  ties  over  the  total  possible  number  of  ties—when  the  number  of 
actors increases.  Despite this slight  decrease in  density, it is worth noting that the 
actual number of partnerships increased by an appreciable margin in FP7 relative to 
FP6. 

159 

FP6FP7CooperationCapacitiesPeopleIdeasCountry-levelNodes*183183183183183183Edges4,1594,8274,1492,991782131No Connected Components2871845119142Density*0.24970.28990.24910.17960.04700.0079Modularity0.0520.0480.0420.0680.0250.092Clustering CoefAvg.0.6840.7760.7250.6370.3250.135Gini0.2470.1450.2030.3230.6490.854Betweeness CentralityAvg.0.002640.003600.003200.002190.000480.00031Gini0.8310.8160.8250.8880.9030.969NUTS3-levelNodes*1,4721,4721,4721,4721,4721,472Edges101,141110,77995,41135,99115,146581No Connected Components175781783947611,255Density*0.09340.10230.08810.03320.01400.0005Modularity0.0780.0720.0800.1420.1460.476Clustering CoefAvg.0.6480.6970.6560.5100.3200.049Gini0.2420.1790.2420.4000.6240.937Betweeness CentralityAvg.0.000490.000570.000480.000400.000200.00003Gini0.8910.8910.9030.9240.9300.975Organisation-levelNodes*20,79426,01419,8738,0133,391664Edges458,427525,702426,24094,56228,576781No Connected Components1842135768381254Density*0.00210.00160.00220.00290.00500.0035Modularity0.4350.4000.4100.5640.4040.701Clustering CoefAvg.0.8320.8060.8080.8590.6770.192Gini0.1520.1700.1670.1300.3020.785Betweeness CentralityAvg.0.000090.000070.000090.000260.000450.00170Gini0.9720.9740.9720.9620.9370.897 
 
 
 
Compared  to  organisations,  regions  (NUTS  3)  and  countries  are  less  prone  to 
structural changes (e.g. merger, acquisition, re-branding, and bankruptcy) and their 
existence  is less ephemeral.  As such,  when  analysing density at these  aggregation 
levels,  it  makes  sense,  to  some  extent,  to  consider  that  the  networks  include  all 
actors who could have participated in either FP6 or FP7 even if they did not. In that 
case,  they  can  therefore  be  regarded  as  isolates  in  the  networks.  That  is,  they  do 
not contribute to any linkages in the networks.  

This  approach  allows  one  to  appreciate  the  increased  level  of  partnerships  in  FP7 
since  the  networks  being  compared  are  of  the  same  size  and  are  thus  more 
amenable  to  an  analysis  of  change  in  network  density  over  time—that  is,  the 
theoretical maximum number of ties is the same in each network since the number 
of actors (regions or countries) is held constant across FP6 and FP7. The number of 
actors was set to the pooled set of actors in FP6 and FP7. Thus, note that the actual 
number  of  countries/regions  that  indeed  participated  in  either  FP6  or  FP7  might 
diverge from the numbers displayed in Table XXV.  

Additionally, it must be pointed out that the framework programmes aim to realise 
the ERA. Consequently, the core of the co-participation network of any edition of the 
framework  programme  is  expected  to  comprise  ERA-associated  countries  (mostly 
European  countries)  at  the  national  level  and  most  of  the  participations  are  from 
those  countries.  Thus,  the  expansion  in  the  number  of  actors  between  framework 
programmes  at  the  national  level  mostly  comes  from  the  addition  of  Non-ERA 
countries  with  low  levels  of  participation  (note  that  this  is  not  the  case  at  the 
organisational  level).  In  other  words,  new  actors  are  added,  but  they  contribute 
very  few  ties  to  the  network  structure  relative  to  the  increase  in  the  theoretical 
maximum number of ties that result from their inclusion. It results that a reduction 
in density would be almost inevitable in spite of an increased number of ties in the 
core of the network if the size of the networks being compared is not held constant. 
Using  this  approach,  the  density  increased  at  the  regional  (NUTS  3)  and  national 
levels indicating that the number of ties increased from FP6 to FP7. 

R4 & R6—Key actors (i.e. participants, regions and countries) by different positions, 

their  roles  and  their  prestige  in  the  network:  central  actors/brokers  and 

structural holes by programme and specific programme 

Key findings 

  At the country level, Germany, the United Kingdom, France, Italy and Spain are 

the main hubs in the co-participation networks of FP6 and FP7.  

 

 

 

The  five  leading  countries  in  the  Capacities,  Cooperation  and  People  specific 
programmes  of  FP7  are  very  similar  (generally  including  Germany,  the  United 
Kingdom,  France,  Italy  and  Spain),  while  some  minor  changes  are  observed  in 
the Ideas specific programme, notably the Netherlands and Switzerland replaced 
Italy and Spain among the main hubs. 

The main brokers are similar to the main hubs at all levels of analysis (i.e. local, 
regional and national). 

In  the  Ideas  specific  programme,  Norway,  Poland  and  Hungary  are  among  the 
most important brokers despite their smaller participation level, while the same 
can be said of Greece, Ireland and Turkey in the People specific programme. In 
the  larger  specific  programmes,  Portugal  (Capacities  and  Cooperation),  Estonia 
(Capacities) and Norway (Cooperation) show the same pattern. 

160 

 
 
  Removing  the  effect  of  the  size  of  countries  in  studying  trends  in  the 
centrality/integration  of  countries  from  FP6  to  FP7,  it  appears  that  the  former 
Yugoslav  countries—and  in  particular  the  Former  Yugoslav  Republic  of 
Macedonia,  Serbia,  Croatia,  and  Bosnia  and  Herzegovina—were  the  ones  that 
found  new  fame  in  FP7  relative  to  FP6  (Slovenia's  attractiveness  changed 
comparatively little). 

 

FR101 (Paris) is the main hub in FP6, FP7 and the specific programmes of FP7. 
Other  important  hubs  include  ES300  (Madrid),  DE212  (München,  Kreisfreie 
Stadt), ITE43 (Roma) and UKI11 (Inner London – West). 

  Many UK regions (UKI11, UKH12, UKM25 and UKJ14) are among the main hubs 

under the Ideas specific programme. 

 

 

 

 

 

Important  regional  brokers  with  low  levels  of  participation  (regions  having  a 
capacity to bridge other regions to a greater extent than is usually observed for 
regions having participated in as few projects as they have) are: NL221 (under 
FP7  overall,  Capacities  and  Cooperation),  EL221  (FP7  overall  and  Capacities), 
CZ010 (Cooperation), ITC45 (Ideas), UKM25 (Ideas), PL127 (Ideas and People) 
and HU101 (People). 

The main organisational hubs are  the large national research centres scattered 
across the main national hubs (e.g. CNRS, Fraunhofer Society, CNR, Max-Planck 
Society and CSIC), as well as top UK universities (e.g. Cambridge, Oxford). 

The  main  hubs  include  more  universities  under  the  Ideas  and  People  specific 
programmes, which is not surprising as they are focused on promoting research 
excellence.  

Four  private  companies  (TWI  limited,  Inspiralia  SL,  UK  Intelligent  Systems 
Research Institute Limited and Ateknea Solutions Catalonia SA)  are  among the 
main  brokers  under  the  Capacities  specific  programme.  These  companies 
provide  technical  support  and  offer  R&D  services  to  SMEs.  Therefore,  it  is  not 
surprising  that  they  bridge  other  actors  in  the  network,  especially  under 
Capacity, which includes a broad area titled Research for the benefit of SMEs. 

The University of Edinburgh, Durham University, and the Manchester Centre for 
Healthcare Management, although they participate in a relatively small number 
of  projects,  are  found  among  the  top  10  brokers  under  the  Ideas  specific 
programme. Under the People specific programme, the Foundation for Research 
&  Technology  –  Hellas  Institute  of  Molecular  Biology  &  Biotechnology  shows  a 
similar pattern. 

Structural holes and central actors/brokers 

Networks  with  a  core-periphery  structure,  such  as  those  described  above,  are 
characterised by the presence of multiple structural holes between peripheral actors 
where  the  lack  or  sporadic  presence  of  ties  hampers  the  direct  circulation  of 
knowledge. According to the scientific literature, centrally located players derive an 
advantage  from  the  presence  of  structural  holes  in  networks  with  a  marked  core-
periphery  structure  because  the  exchange  of  knowledge  between  peripheral  actors 
often has to travel through them. In other words, actors occupying this key position 
can  act  as  brokers  bridging  over  structural  holes  and  potentially  controlling  the 
information flow to their advantage, as they have a privileged access to knowledge. 
However,  actors  are  usually  found  in  this  position  for  a  reason.  For  instance,  they 
are often involved in a considerable number of projects by virtue of their size.  

The study of central actors/brokers in a network can be performed at various levels 
(i.e.  organisational,  regional,  and  national).  However,  the  most  interesting  level  of 
analysis  in  this  regard  is  the  organisational  level,  since  it  is  closest  to  the  actual 

161 

 
 
researcher controlling or accessing knowledge in the  conduct of research activities. 
Organisations  were  used  in  place  of  the  actual  scientists  since  such  data  was  not 
available  for  FP6  in  CORDA,  thereby  preventing  a  comparative  analysis  over  time 
with  FP7.  Additionally,  the  effect  of  the  sheer  size  of  entities  being  examined  is 
amplified as one moves to higher aggregation levels (e.g. at the level of countries). 
Note  that  this  section  strictly  focuses  on  identifying  (i.e.  naming)  the  key  actors—
that  is,  the  central  actors/brokers—by  the  different  levels  of  analysis  (i.e.  local, 
regional,  and  national).  For  countries,  it  also  investigates  the  integration  of  actors 
using a method that partly removes the effect of their sheer size. Later, in Section 
0,  a  deeper  analysis  of  the  groups  (i.e.  private  firms,  universities,  etc.)  of 
organisations that benefit the most from the structural holes in the co-participation 
networks of FP6 and FP7 is presented.  

Central  actors/brokers  can  be  identified  using  various  centrality  measures  such  as 
the  eigenvector  centrality  and  betweenness  centrality.  The  former  metrics  places 
more  emphasis  on  the  quality  of  an  actor’s  ties  since  connections  to  high-scoring 
actors  contribute  more  to  the  score  of  that  actor  than  equal  connections  to  low-
scoring  actors.  As  such,  it  can  be  viewed  as  a  good  measure  of  network 
integration/importance  and  suitable  for  identifying  hubs  in  the  network.  The  latter 
metrics  places  more  emphasis  on  the  role  of  an  actor  in  bridging  the  flow  of 
knowledge/information  in  between  every  pair  of  actors  in  the  network.  Indeed,  it 
reflects the number of shortest paths from all actors to all others that pass through 
a given actor, normalised by  the  total  number of  shortest paths in a network. It is 
therefore  suitable  to  identify  brokers  in  the  network.  Note  that  the  most-central 
actors usually play the role of  hubs as well as of  brokers, and the eigenvector and 
betweenness  centralities  of  actors  are  expected  to  correlate  strongly  with  one 
another.  

National and regional levels 

At the country level, the hubs were essentially identical between FP6 and FP7 (see 
Table  XXVI).  Germany,  the  United  Kingdom,  France,  Italy,  Spain,  the  Netherlands, 
Belgium,  Sweden,  Switzerland  and  Greece  were  usually  ranked  highest,  with some 
variation  in  their  order,  based  on  eigenvector  centrality  scores.  Looking  at  the 
specific  programmes  individually,  the  same  leaders  usually  stand  out.  There  are, 
however, some exceptions, especially in the Ideas and People specific programmes. 
In Ideas, Israel and Ireland place in the top 10 based on eigenvector centrality, with 
Israel  participating  in  a  similar  number  of  projects  to  the  usual  leaders  (i.e.  228 
projects) in Ideas, and with Ireland taking part in only 36 projects, suggesting that 
Ireland is likely co-participating with the usual leaders to obtain such a high score. 
Austria is also worth mentioning as it appears in the top 10 in three out of the four 
specific  programmes  (i.e.  in  Cooperation,  Ideas  and  People),  although  it  is  not 
among the top 10 overall leaders in either FP6 or FP7. 

Unsurprisingly,  the  key  hubs  on  a  regional  level  consist  of the  main  regions  of  the 
leading  countries.  The  most  important  NUTS  3  regions  under  FP7  (all  specific 
programmes  combined)  are,  in  order,  FR101  (Paris),  ES300  (Madrid),  DE212 
(München,  Kreisfreie  Stadt),  ITE43  (Roma),  UKI11  (Inner  London  –  West),  BE100 
(Arr.  de  Bruxelles-Capitale/Arr.  van  Brussel-Hoofdstad),  ES511  (Barcelona),  EL300 
(Aττική  (Attiki)),  ITC45  (Milano)  and  SE110  (Stockholms  län).  As  can  be  seen  in 
Table  XXVI,  this  order  is  very  similar  to  that  observed  in  FP6.  It  is  interesting  to 
note that Italy and Spain are the only countries with two regions (ITE43 and ITC45, 
ES300  and  ES  511,  respectively)  in  the  top  10.  HU101  (Budapest)  comes  in  10th 
place in the Capacities specific programme, which is quite surprising given the fact 
that Hungary never appears among the top 10 most central actors. Moreover, no UK 
region  is  among  the  top  10  within  that  same  specific  programme.  However,  many 
regions from the UK are in the top 10 in the Ideas specific programme. Particularly, 

162 

 
 
apart  from  UKI11  (Inner  London  –  West),  which  was  already  among  the  top  10  in 
FP7 overall, UKJ14 (Oxfordshire), UKH12 (Cambridgeshire CC) and UKD31 (Greater 
Manchester South) appear as hubs under  Ideas. Notably, some uncommon leaders 
that  show  up  in  the  top  10  of  the  People  specific  programme  are  CH040  (Zurich), 
NL326 (Groot-Amsterdam) and UKJ14 (Oxfordshire). 

Table XXVI  Hubs  at  the  national  and  regional levels in  FP6,  FP7  and  FP7 

specific programmes 

Note: 
Source: 

Centralities were computed using a one-mode network. 
Computed by Science-Metrix using CORDA (European Commission) data 

As  expected,  the  central  countries  that  convey  the  bulk  of  the  information  (i.e. 
brokers  based  on  betweenness  centrality)  are  generally  identical  to  the  hubs 
identified above (with Portugal replacing Switzerland in the FP7 overall top 10), but 
in  a  slightly  different  order  (see  Table  XXVII).  Norway  (who  participated  in  38 
projects)  and  Poland  (16  projects)  appear  in  the  top  10  brokers  for  the  Ideas 
specific programme but the number of projects in which they took part is unusually 
small  compared  to  the  other  top  10  countries.  These  countries  therefore  likely 
participated  in  key  projects  involving  many  partners  to  occupy  such  strategic 
positions  without  having  the  sheer  size  usually  observed  for  brokers.  The  same 
situation  applies  for  Ireland,  Turkey  and  Greece  under  the  People  specific 
programme,  as  well  as  for  Portugal,  Estonia  and  Bulgaria in  the  Capacities  specific 
programme. 

Looking at the brokers on a regional basis, it can be observed that these are often 
very  similar  to  the  regional  hubs  identified  above.  The  new  entries  in  the  top  10 
brokers are FI181 (Uusimaa) under the Cooperation specific programme and overall 
in  FP7,  as  well  as  AT130  (Vienna)  under  both  Capacities  and  Cooperation.  Several 
UK  regions  are  again  among  the  top  10  in  the  Ideas  specific  programme,  namely 
UKI11, UKH12, UKM25 and UKJ14, along with a new region from Switzerland CH040 
(Zurich).  Some  regions  positioned  within  the  leading  brokers  despite  having 
unusually  low  levels  of  participation  when  compared  to  other  actors  with  similar 
betweenness  centrality  scores  include  NL221  (under  FP7  overall,  Capacities  and 

163 

CountriesRankFP6FP7CapacitiesCooperationIdeasPeople1GermanyGermanyUnited KingdomGermanyUnited KingdomUnited Kingdom2United KingdomUnited KingdomGermanyUnited KingdomGermanyGermany3FranceFranceSpainFranceFranceFrance4ItalyItalyItalyItalyNetherlandsItaly5SpainSpainFranceSpainSwitzerlandSpain6NetherlandsNetherlandsNetherlandsNetherlandsItalyNetherlands7BelgiumBelgiumGreeceBelgiumSpainSwitzerland8SwedenSwedenSwedenSwedenIsraelSweden9GreeceSwitzerlandBelgiumSwtizerlandAustriaBelgium10PolandGreecePolandAustriaIrelandAustriaNUTS-3RankFP6FP7CapacitiesCooperationIdeasPeople1FR101FR101FR101FR101FR101FR1012DE212ES300ITE43DE212UKI11DE2123ES300DE212ES300ES300UKJ14UKI114ITE43ITE43EL300ITE43DE212ES3005UKI11UKI11BE100UKI11UKH12ITE436EL300BE100ES511BE100UKD31ES5117BE100ES511AT130ITC45NL326CH0408AT130EL300DE212ES511ITE43SE1109ITC45ITC45FI181EL300IL000NL32610FI181SE110HU101FR105ITC45UKJ14 
 
 
Cooperation),  EL221  (FP7  overall  and  Capacities),  CZ010  (Cooperation),  ITC45 
(Ideas), UKM25 (Ideas), PL127 (Ideas and People) and HU101 (People).  

Table XXVII  Brokers  at  the  national  and  regional  levels  in  FP6,  FP7  and 

FP7 specific programmes 

Note: 
Source: 

Centralities were computed using a one-mode network. 
Computed by Science-Metrix using CORDA (European Commission) data 

Note  that  the  countries  that  stood  out  as  the  main  hubs/brokers  in  the  co-
participation networks of FP6  and  FP7 were usually the larger European  economies 
with  a  strong  tradition  of  performing  scientific  research.  This  is  because  most 
centrality  measures  in  collaboration  networks  are  sensitive  to  scale;  that  is,  in  the 
present  case,  to  differences  in  the  number  of  projects  in  which  countries 
participated.  Indeed,  one  would  expect  that  the  larger  European  countries  would 
participate in the framework programmes to a larger extent than the smaller ones, 
thereby having a profound impact on the network structure. Although it is useful to 
analyse integration by accounting for such differences, it is also of interest to study 
trends in the integration of countries by removing size effects. This can be achieved 
by  computing  the  affinity  of  a  country  to  partner  with  others  based  on  a  random 
assortment of countries, which accounts for the respective participation level of each 
country  in  a  network.  The  probabilistic  affinity  index  (PAI)  that  is  obtained  in  this 
manner  compares  the  number  of  co-participations  between  any  two  countries  to 
their  expected  number  of  co-participations  based  on  their  respective  shares  of  all 
participations  in  the  network.  A  score  above  one  therefore  indicates  that  two 
countries preferentially partner with one another, whereas a score below one means 
the  opposite.  Note  that  the  scores  are  transformed  so  that  they  are  symmetrically 
distributed  between  -1  (maximal  negative  affinity)  and  +1  (maximal  positive 
affinity). By looking at how the affinities of country pairs changed from FP6 to FP7, 
it  is  therefore  possible  to  appreciate  whether  integration  generally  progressed  in 
between  both  editions  to  assess  whether  or  not  the  framework  programme  filled 
structural holes in the network. 

164 

CountriesRankFP6FP7CapacitiesCooperationIdeasPeople1United KingdomFranceFranceFranceUnited KingdomUnited Kingdom2FranceItalyItalyUnited KingdomGermanyItaly3GermanyUnited KingdomUnited KingdomGermanyFranceGermany4SpainGermanySpainItalyItalyFrance5ItalySpainPortugalNetherlandsNorwayGreece6NetherlandsNetherlandsGermanySpainSpainSwitzerland7BelgiumPortugalGreeceBelgiumBelgiumIreland8PortugalBelgiumNetherlandsSwedenAustriaTurkey9DenmarkSwedenAustriaSwitzerlandPolandBelgium10SwedenGreeceBelgiumGreeceSwitzerlandSpainNUTS-3RankFP6FP7CapacitiesCooperationIdeasPeople1FR101FR101DE212FR101FR101FR1012DE212DE212ES511DE212UKI11UKI113ES300ITE43ITE43ITE43DE212DE2124EL300ES300FR101ES300ITE43ES3005ES511EL300ES300DBE100NL326ITE436ITE43ES511BE100EL300ITC45CH0407AT130BE100EL300UKI11UKH12AT1308BE100UKI11UKJ14ES511CH040ES5119UKI11FI181AT130FI181UKM25EL30010ITC45AT130ITC45ITC45UKJ14SE110 
 
 
In  this  case,  structural  holes  under  FP6  consist  of  the  country  pairs  with  negative 
affinities.  Thus,  in  a  chart  where  the  affinity  of  each  country  pair  under  FP6  is 
presented  on  the  x-axis  and  the  corresponding  affinity  under  FP7  is  presented  on 
the y-axis, if most of the data points lying to the left of the y-axis are above the x-
axis  (i.e.  in  the  upper  left  quadrant),  then  one  could  assume  that  the  framework 
programme  filled  most  of  the  structural  holes  (i.e.  negative  affinities  under  FP6 
became positive under FP7). On the other hand, if most data points lay on a quasi-
perfect regression line whose regression coefficient is nearly equal to one, then one 
could  assume  that  FP7  generally  reproduced  the  pattern  of  affinities  between 
countries found in FP6. This is what the data actually shows in Figure 10. Therefore, 
to a large extent, FP7 did not fill structural holes at the country level relative to FP6. 

Figure 10  Relationship  between  the  affinities  of  country  pairs  under  the 

co-participation networks of FP6 and FP7 

Note: 

Source: 

Each point represents a pair of countries; its probabilistic affinity index (PAI) in 
FP6 is charted on the abscissa, and its PAI in FP7 is charted on the ordinate.  
Computed by Science-Metrix using CORDA (European Commission) data 

Note that there are nevertheless some outliers in the above charts that may point to 
some countries whose integration improved from FP6 to FP7. By looking at how the 
affinities  of  a  country  (i.e.  its  affinities  for  each  of  the  other  countries  in  the 

165 

-1.0-0.8-0.6-0.4-0.20.00.20.40.60.81.0-1.0-0.8-0.6-0.4-0.20.00.20.40.60.81.0Probabilistic Affinity Index (PAI) of Country Pairs in FP7 ParticipationProbabilistic Affinity Index (PAI) of Country Pairs in FP6 ParticipationNew Dyads: 365Vanishing Dyads:57Common Dyads: 3,042PearsonR: 0.89R2: 0.78 
 
 
network)  changed  from  FP6  to  FP7,  it  is  also  possible  to  appreciate  whether  it 
became  a  more  prominent  actor  in  the  network  taking  account  of  its  size  (i.e.  its 
number of participations). 

Figure  11  presents  evidence  on  the  countries  that  benefitted  more  systematically 
than  others  from  changes  in  affinity—that  is,  which  countries  became  more 
preferred  as  collaborators  in  FP7  compared  to  where  they  stood  in  FP6.  It  is  very 
noteworthy  that  former  Yugoslav  countries,  and  in  particular  the  Former  Yugoslav 
Republic of Macedonia, Serbia, Croatia, and Bosnia and Herzegovina, were the ones 
that  found  new  fame  in  FP7  relative  to  FP6  (Slovenia's  attractiveness  changed 
comparatively little). This is likely to reflect at least in part Croatia’s application for 
EU  membership.  This  application  started  in  2003,  with  Croatia  becoming  an  EU 
member state in 2013.  

Many  eastern  EU  Member  States  increased  their  attractiveness  in  FP7  including 
Bulgaria, Estonia, and Slovakia. In Asia, China gained some ground while India was 
not  in  favour  as  much  as  it  was  in  FP6.  Poland,  Austria,  Spain  and  Italy  are  ERA 
countries  that  lost  more  of  their  appeal  in  FP7,  while  Canada,  South  Africa, 
Australia,  Chile,  Russia,  Brazil,  and  Argentina  are  those  outside  the  ERA  that  lost 
more of their lustre in the new framework programme. These data demonstrate that 
FP7  has  facilitated  not  only  the  integration  of  new  EU  Member  States,  it  has  also 
been  able  to  provide  greater  integration  for  countries  that  were  on  the  ERA 
periphery.  It  shows  that  countries  outside the  ERA  are,  with  a  few  exceptions,  not 
as attractive as they used to be. 

166 

 
 
Figure 11  Country change in attractiveness between FP7 and FP6 

Source: 

Computed by Science-Metrix using CORDA (European Commission) data 

Organisation level 

The  main  organisational  hubs  dominating  the  network  are  the  large  national 
research  centres  and  the  leading  research  universities,  mostly  from  the  countries 
identified  as  the  main  hubs/brokers  above  (especially  from  the  UK  and  Germany). 
Among the national research centres, the most notable are the Centre National de la 
Recherche  Scientifique  (CNRS)  and  the  Commissariat  à  l'énergie  atomique  in 
France,  the  Fraunhofer  Society  and  Max  Planck  Society  in  Germany,  the  Consiglio 
Nazionale delle Ricerche (CNR) in Italy and the Consejo Superior de Investigaciones 
Cientificas  (CSIC)  in  Spain.  Among  universities,  Cambridge  University,  the 
University of Oxford,  Imperial  College  London and ETH  Zürich stand out. The main 
hubs were very similar in FP6, with the only differences being that Imperial College 
London  and  ETH  Zürich  replaced  the  Department  of  Chemical  Physics  of  Lund 
University  and  the  Technical  University  of  Denmark  respectively  (Table  XXVIII). 
When  considering  the  specific  programmes,  changes  in  the  main  institutional  hubs 
can  be  observed.  The  hubs  of  the  Capacities  specific  programme  are  exclusively 
research  centres.  Only  two  universities  are  found  in  the  Cooperation  specific 
programme:  the  Catholic  University  of  Leuven  and  the  Technical  University  of 
Denmark. For the two other specific programmes the main hubs are predominantly 
universities, although these differ between each specific programme. This is not so 
surprising since these programmes aim to support excellence in research. In Ideas, 
the  top  ranking  universities  are  Cambridge  University,  the  University  of  Oxford, 
University  College  London,  EPFL,  ETH  Zürich,  the  Hebrew  University  of  Jerusalem, 
and Imperial College London. Under the People specific programme, the universities 

167 

-0.15-0.1-0.0500.050.10.150.20.25Macedonia (FYR)SerbiaCroatiaBosnia and HerzegovinaCyprusMexicoLithuaniaMaltaIsraelMoroccoBulgariaEstoniaAlbaniaTunisiaSlovakiaTurkeyIcelandLatviaSwedenUnited KingdomCzech RepublicNorwayIrelandChinaUkraineRomaniaGreecePortugalDenmarkLuxembourgSwitzerlandSloveniaFranceFinlandGermanyHungaryNetherlandsItalySpainBelgiumEgyptAustriaPolandUnited StatesIndiaArgentinaBrazilRussiaChileAustraliaSouth AfricaCanadaCountries that are moreattractive in FP7 than they were in FP6Countries that are lessattractive in FP7 than they were in FP6 
 
 
that rank in the top 10 are Cambridge University, the University of Oxford, Imperial 
College London, ETH Zurich, University College London, Copenhagen University, and 
the Catholic University of Leuven. 

Table XXVIII  Hubs  at  the  organisational  level  in  FP6,  FP7  and  FP7  specific 

programmes 

Note: 

Source: 

Centralities  were  computed  using  a  two-mode  network  linking  organisations 
and their projects. 
Computed by Science-Metrix using CORDA (European Commission) data 

Unsurprisingly, the main hubs identified above within each programme and specific 
programme are very similar to the main brokers identified in Table XXIX. Still, there 
are  four  private  companies  that  appear  in  the  top  10  under  the  Capacities  specific 
programme,  namely  TWI  limited,  Inspiralia  SL,  UK  Intelligent  Systems  Research 
Institute  Limited,  and  Ateknea  Solutions  Catalonia  SA.  These  companies  provide 
technical support and offer R&D services to SMEs. Therefore, it is not surprising that 
they bridge other actors in the network, especially under Capacity, which includes a 
broad  area  titled  Research  for  the  benefit  of  SMEs.  In  the  smaller  specific 
programme  of  Ideas,  the  Manchester  Centre  for  Healthcare  Management,  the 
University of Bristol, the University of Edinburgh, and Durham University  appear in 
the top 10, along with the usual leaders. 

168 

RankFP6FP7CapacitiesCooperationIdeasPeople1Centre national de la recherche scientifiqueCentre national de la recherche scientifiqueCentre national de la recherche scientifiqueFraunhofer-gesellschaft zur foerderung der angewandtenCentre national de la recherche scientifiqueCentre national de la recherche scientifique2Fraunhofer-gesellschaft zur foerderung der angewandtenFraunhofer-gesellschaft zur foerderung der angewandtenFraunhofer-gesellschaft zur foerderung der angewandtenCentre national de la recherche scientifiqueChancellor masters and scholars of the university of oxfordMax Planck gesellschaft zur foerderung der wissenschaften e.v.3Consiglio nazionale delle ricercheCommissariat a l'energie atomiqueConsiglio nazionale delle ricercheCommissariat a l'energie atomiqueCambridge universityChancellor masters and scholars of the university of oxford4Max Planck gesellschaft zur foerderung der wissenschaften e.v.Consiglio nazionale delle ricercheAgencia estal consejo superior de investigaciones cientificasConsiglio nazionale delle ricercheMax Planck gesellschaft zur foerderung der wissenschaften e.v.Cambridge university5Agencia estal consejo superior de investigaciones cientificasAgencia estal consejo superior de investigaciones cientificasCommissariat a l'energie atomiqueTechnical research centre of finlandUniversity college londonAgencia estal consejo superior de investigaciones cientificas6Commissariat a l'energie atomiqueMax Planck gesellschaft zur foerderung der wissenschaften e.v.Council for the central laboratories of research councilsNederlandse organisatie voor toegepast-natuurwetenschappeEcole polytechnique federale de LausanneImperial college for science technology and medicine7Cambridge universityCambridge universityMax Planck gesellschaft zur foerderung der wissenschaften e.v.Deutsche zentrum fur luft- und raumfahrt e.v. (german aeroscpace center)Eidgenoessische technische hochschule zurichEidgenoessische technische hochschule zurich8Chancellor masters and scholars of the university of oxfordChancellor masters and scholars of the university of oxfordDeutsche zentrum fur luft- und raumfahrt e.v. (german aeroscpace center)Catholic university of leuvenThe Weizmann institute of scienceUniversity college london9department of chemical physics lund universityImperial college for science technology and medicineMarmara research centerAgencia estal consejo superior de investigaciones cientificasthe Hebrew universityCopenhagen university10Danmarks tekniske universitetEidgenoessische technische hochschule zurichInstituto nazionale di fisica nucleareDanmarks tekniske universitetImperial college for science technology and medicineCatholic university of leuven 
 
 
Table XXIX  Brokers  at  the  organisational  level  in  FP6,  FP7  and  FP7 

specific programmes 

Note: 

Source: 

Centralities  were  computed  using  a  two-mode  network  linking  organisations 
and their projects. 
Computed by Science-Metrix using CORDA (European Commission) data 

Structural holes in the co-participation networks of FP6 and FP7 are found between 
the  peripheral  actors  who  rely  on  brokers  (central  actors)  to  participate  in  the 
framework  programmes.  In  order  to  form  a  more  complete  and  balanced  network, 
further  ties  could  be  created  in  between  the  peripheral  actors  around  the  core 
brokers  either  at  the  local,  regional  or  national  level.  This  could  be  achieved  by 
having at least two peripheral actors participating on a common project with central 
actors.  However,  the  peripheral  actors  mostly  consist  of  private  firms  (SMEs;  see 
Section  0)  that  often  have  competing  interests,  thereby  reducing  the  potential  for 
partnerships.  Whether  more  equality  is  needed  across  actors  in  terms  of 
centrality/integration to boost EU research competitiveness as a whole is a question 
that warrants further exploration. While, indeed, the scientific output would likely be 
more  evenly  distributed  across  the  network,  an  orientation  away  from  the  most 
productive  knowledge  brokers  could,  in  parallel,  result  in  a  reduction  in  scientific 
productivity.  Potentially,  a  certain  level  of  inequality  and  structural  holes  may  be 
inevitable and perhaps is even beneficial for complex research systems. 

169 

RankFP6FP7CapacitiesCooperationIdeasPeople1Fraunhofer-gesellschaft zur foerderung der angewandtenFraunhofer-gesellschaft zur foerderung der angewandtenFraunhofer-gesellschaft zur foerderung der angewandtenFraunhofer-gesellschaft zur foerderung der angewandtenCentre national de la recherche scientifiqueCentre national de la recherche scientifique2Centre national de la recherche scientifiqueCentre national de la recherche scientifiqueCentre national de la recherche scientifiqueCentre national de la recherche scientifiqueMax Planck gesellschaft zur foerderung der wissenschaften e.v.Agencia estal consejo superior de investigaciones cientificas3Consiglio nazionale delle ricercheConsiglio nazionale delle ricercheConsiglio nazionale delle ricercheConsiglio nazionale delle ricercheChancellor masters and scholars of the university of oxfordChancellor masters and scholars of the university of oxford4Nederlandse organisatie voor toegepast-natuurwetenschappeAgencia estal consejo superior de investigaciones cientificasAgencia estal consejo superior de investigaciones cientificasCommissariat a l'energie atomiqueCambridge universityMax Planck gesellschaft zur foerderung der wissenschaften e.v.5Agencia estal consejo superior de investigaciones cientificasCommissariat a l'energie atomiqueTWI limitedNederlandse organisatie voor toegepast-natuurwetenschappethe university of EdinburghCambridge university6Commissariat a l'energie atomiqueTechnical research centre of finlandFundacin LabeinTechnical research centre of finlandUniversity college londonImperial college for science technology and medicine7Cambridge universityCambridge universityInspiralia SLCatholic university of leuvenManchester center for healthcare managementCatholic university of leuven8Max Planck gesellschaft zur foerderung der wissenschaften e.v.Catholic university of leuvenCommissariat a l'energie atomiqueDeutsche zentrum fur luft- und raumfahrt e.v. (german aeroscpace center)the university of BristolEidgenoessische technische hochschule zurich9Technical research centre of finlandChancellor masters and scholars of the university of oxfordUK intelligent systems research institute limitedDanmarks tekniske universitetDurham universityUniversity college london10Catholic university of leuvenNederlandse organisatie voor toegepast-natuurwetenschappeAteknea solutions Catalonia SAFundacin LabeinInstitut national de la sante et de la recherche medicale (inserm)Copenhagen university 
 
 
R5—Key groups (e.g. clusters, components and k-cores) and their compositions (i.e. 

participants, regions and countries) by programme, specific programme and 

broad/thematic area 

Key findings 

  Clusters provide the best results for the identification of sub-groups within well-
connected  networks  such  as  the  co-participation  networks  of  FP6  and  FP7, 
especially  at  the  country  level.  Compared  to  components  and  k-cores,  they 
divide the actors (i.e. countries) in a greater number of smaller sub-groups.  

  Components and k-cores are useful to assess the completeness of 

networks—that is, how well connected they are. 

  Based  on  an  analysis  of  components,  most  of  the  networks  analysed  (i.e.  FP6 
overall,  FP7  overall,  the  Cooperation  specific  programme  [and  all  its  thematic 
areas]  and  the  Capacities  specific  programme  [nearly  all  its  broad  areas])  are 
highly complete. 

 

The  broad  areas  of  Coherent  Development  of  Research  Policies  and  Research 
Potential  under  the  Capacities  specific  programme,  as  well  as  the  People  and 
Ideas specific programmes are the less complete networks based on an analysis 
of components. 

  Based  on  an  analysis  of  k-cores,  the  size  of  the  network  nucleus  (i.e.  highest 
order  k-core)  increased  from  FP6  to  FP7  in  all  levels  of  analysis  (i.e. 
organisational,  regional  and  national)  and  the  number  of  ties  by  actors  within 
the nucleus increased. 

  Within  FP7,  the  nucleus  of  the  co-participation  network  is  larger  and  more 
strongly  connected  for  the  Cooperation  specific  programme  followed  by 
Capacities,  People  and  Ideas.  This  is  in  line  with  the  emphasis  placed  on  co-
participation in the design of the specific programmes.  

  A low level of aggregation of the actors (i.e. organisational rather than national 
level  of  analysis)  provides  more  resolution—that  is,  finer  grain  division  of  the 
actors—to examine the clustering patterns of networks, especially in the case of 
the co-participation networks of FP6 and FP7 that are well connected. 

  High aggregation at the country level leads to large groups that include the 

vast majority of the actors and whose levels of interaction are established by 
the lower levels of aggregation (i.e. by organisations). 

  On  the  whole,  ERA  countries  constitute  the  heart  of  the  co-participation 

networks of FP6 and FP7; they generally group within the same clusters. 

  In FP7 the majority of EU-15 is found grouped within one cluster, along with 
strong Non-ERA partners (US and Canada). The rest of the ERA is found in a 
second cluster, closely linked to the first, along other Non-ERA countries 
from Eastern Europe (Russia, Ukraine, etc.). Most of the remaining Non-ERA 
countries are found in a third and peripheral cluster. 

  Organisations  show  a  slightly  stronger  tendency  to  agglomerate  with  actors 
from  their  own  or  neighbouring  countries  than  with  actors  from  distant 
countries.  Thus,  geographic  proximity  has  a  strong  influence  on  the  clustering 
pattern  found  at  the  higher  aggregation  levels  (e.g.  NUTS  3  regions  and 
countries);  for  instance,  NUTS  3  regions  from  the  same  country  tend  to  group 
together. 

The  structure  of  the  co-participation  networks  under  the  framework  programmes 
(both  FP6  and  FP7)  can  be  investigated  by  studying  the  clustering/grouping  of 

170 

 
 
actors  (either  organisations,  regions  or  countries)  into  sub-networks  (or  sub-
groups), which can be defined using various concepts, such as components, k-cores, 
clusters, and cliques. Because  cliques  are defined as  complete sub-networks within 
which all actors are connected to every other actor, the application of this definition 
to detect communities in large and dense networks as those of FP6 and FP7 would 
lead to a very large number of cliques and data overload. As such, the efforts were 
concentrated  on  other  types  of  sub-networks  that  are  more  appropriate  for 
synthesising  the  structure  of  large  networks  to  uncover  the  main  underlying 
communities (e.g. components, k-cores and clusters).  

Each  of  these  types  of  sub-networks  has  a  defined  set  of  characteristics.  Firstly,  a 
weak  component is  the  maximal  sub-network  in  which  a  direct/indirect  path  exists 
from every actor to every other actor (i.e. they are all reachable with direct [A—C] 
or  indirect  [A—B—C]  ties).  In  a  strong  component,  a  direct  path  must  exist  from 
every  actor  to  every  other  actor.  A  connected  network  has  only  one  weak 
component.  Secondly,  a  k-core  is  a  maximal  sub-network  within  which  each  actor 
has at least a degree of k (i.e. k incident ties); it therefore informs on the strength 
of  the  connectivity  within  a  sub-network,  satisfying  the  above  condition  or  the 
nucleus  (or  nuclei)  of  the  network  when  looking  at  the  highest  order  k-core  of  a 
network (i.e. the core of a network in which the minimal degree of actors is highest; 
highest  k).  Finally,  clusters  are  identified  by  maximising  the  modularity  of  the 
network—that is, by optimising the fragmentation of the networks into sub-networks 
(i.e.  clusters)  in  a  way  that  maximises  the  density  of  ties  within  clusters  and 
minimises the density of ties between them. The larger the modularity of a network, 
the larger its fragmentation into coherent clusters is. 

Figure  12  gives  an  overview  of  the  largest  sub-groups  found  within  the  various 
networks  analysed  for  all  levels  of  analysis  (i.e.  organisational,  regional  and 
national) using weak components. The size of the largest sub-group is reported as a 
percentage of the theoretical maximum for a given network—that is, the number of 
distinct actors (i.e.  organisations, NUTS 3 regions,  countries) that  participated in a 
given network. For nearly all of the networks analysed, components do not provide 
any  resolution  towards  identifying  the  underlying  communities  in  the  network;  the 
largest component is nearly always approaching 100% of the theoretical maximum. 
On the other hand, they inform us on the completeness of the networks analysed—a 
complete network does not have any isolated actors or isolated sub-groups; in other 
words, there is always a path to reach any actor in the network. Thus, the networks 
for  FP6  overall,  FP7  overall,  the  Cooperation  specific  programme  (and  all  its 
thematic  areas)  and  the  Capacities  specific  programme  (nearly  all  its  broad  areas) 
are  nearly  complete,  with  the  largest  component  in  each  of  them  including  nearly 
100%  of  all  actors  regardless  of  the  level  of  analysis  (i.e.  organisational,  regional, 
and national). 

The  broad  areas  of  Coherent  Development  of  Research  Policies  and  Research 
Potential under the Capacities specific programme, as well as the People and Ideas 
specific  programmes  are  clearly  characterised  by  the  less  complete  networks.  The 
results for People and Ideas are not surprising since these two specific programmes 
were  not  specifically  designed  to  promote  co-participation.  As  would  be  expected, 
the incompleteness level for these specific programmes and areas usually increases 
as  the  aggregation  level  at  which  the  actors  are  considered  decreases  (i.e.  going 
from  countries  to  organisations);  for  example,  when  two  organisations  (one 
connected  to  an  Italian  organisation  and  the  other  connected  to  a  Swedish 
organisation)  are  pooled  because  they  are  in the  same  country  (France),  their  ties 
are  aggregated and, all of a sudden, Italy and Sweden are  connected even though 
there is no direct tie between Italian and Swedish organisations. It also shows that 
Research  Potential  is,  at  the  level  of  organisations,  potentially  very  fragmented, 

171 

 
 
since fewer than 30% of the organisations are found within the largest component, 
meaning  that  there  are  many  small  components  that  constitute  this  network. 
Overall,  components  are  useful  to  assess  the  completeness  of  a  network,  but  are 
less so in trying to uncover the smaller communities within large networks. 

Figure 12  Size  of  the  largest  sub-network  in  each  programme,  specific 
programme, and broad/thematic areas using weak components 

Note:  

Source: 

The Y-axis displays the size  of the largest component inside a given network. 
The  size  is  expressed  in  terms  of  the  percentage  of  the  theoretical  maximum 
size for the corresponding type of sub-network. The General Activities (Annex 
IV)  area  has  been  removed  from  the  graph  since  it  only  consisted  of  isolated 
actors. 
Computed by Science-Metrix using CORDA (European Commission) data 

To deepen the analysis of the network structure for framework programmes, specific 
programmes  and  broad/thematic  areas  in  terms  of  their  completeness  level,  the 
highest  order  k-core  was  identified  in  each  network.  This  informs  on  the  set  of 
actors, showing the strongest connectivity within the maximal sub-network in which 
the degree  k  (i.e.  the number of incident  ties) of each actor is highest.  Indeed,  k-
cores  aim  to  find  sub-groups  with  the  largest  number  of  actors  having  an  equal 
number (k) of ties within it. Table XXX presents the degree k and number of actors 
present in the highest order k-core of the co-participation network of FP6 and FP7 to 
study  the  dynamics  of  the  framework  programme  co-participation  network  over 
time.  

Firstly, the results show that the highest order k-core is composed of a single weak 
component  in  both  FP6  and  FP7.  This  means  that  it  forms  a  single  cohesive  sub-
group,  which  can  therefore  be  viewed  as  the  network  nucleus  in  both  framework 
programmes.  The  results  also  show  that  FP7  on  the  whole  has  a  stronger  nucleus 
than FP6. Indeed, its nucleus is formed of 290 organisations, each with at least 127 
ties  to  other  organisations  in  the  nucleus.  In  comparison,  the  nucleus  of  FP6  was 
formed of 263 organisations, each with at least 119 ties. Similar findings hold at the 
national and regional level.  

A similar assessment can be done at the level of FP7 specific programmes through 
lateral  comparisons  of  the  highest  order  k-core  of  the  Cooperation,  Capacities, 
People  and  Ideas  specific  programmes.  For  example,  Cooperation  has  a  stronger 
nucleus  at  the  organisational  level  (K-Max  of  111  with  269  actors  within  the  core) 
than Capacities (K-Max of 53 with 54  actors within  the  core).  This finding  holds at 

172 

0%10%20%30%40%50%60%70%80%90%100%CountriesNUTS3OrganisationsComponents 
 
 
the  regional  level  but  not  at  the  national  level.  It  reflects  the  stronger  emphasis 
placed on co-participation under Cooperation compared to Capacities. The nuclei of 
the  People  and  Ideas  specific  programmes  are  clearly  not  as  strong,  which  is  not 
surprising since they were not specifically designed to support cooperation. 

Table XXX  Size of the highest order k-core in the co-participation network 
of  each  programme,  specific  programme,  and  broad/thematic 
area 

Note:  

Source: 

represents 

K-Max represents the highest order k-core found within each network. The No. 
of  Actors 
the  number  of  distinct  Countries/NUTS  3 
regions/Organisations found within the highest order k-core. 
*The projects found under that thematic area were non collaborative. As such, 
no  ties  exist  between  the  organisations,  which  puts  them  in  the  k-core  0, 
representing the absence of ties. 
Computed by Science-Metrix using CORDA (European Commission) data 

Where  components  and  k-cores  fail  is  in  identifying  smaller  sets  of  cohesive 
communities within the co-participation networks of FP6 and FP7, as well as within 
the  specific  programmes  and  broad/thematic  areas  of  FP7.  This  is  where  the 
identification  of  clusters  is  interesting  as  they  usually  provide  a  more  fragmented 
view  of  a  network’s  communities  and  therefore  provide  better  resolution  for 
studying sub-groups. As stated previously in regards to components, an analysis at 
the  level  of  countries  often  provides  less  resolution,  with  the  largest  cluster 
encompassing a much larger  fraction  of  all  actors at  the country level, followed  by 
the  regional  level,  and  finally  the  organisational  level  (Figure  13).  Indeed,  as  the 
aggregation  level  at  which  the  actors  are  considered  increases  (i.e.  going  from 
countries  to  organisations),  the  ties  of  lower  level  actors  belonging  to  a  common 
actor  from  a  higher  level  are  merged,  creating  bridges  between  actors  (e.g. 
countries)  that  might  not  otherwise  be  directly  connected  through  the  actual 
research  performers  (e.g.  organisations).  As  in  the  previous  example,  when  two 
organisations (one connected to an Italian organisation and the other connected to a 
Swedish  organisation)  are  pooled  because  they  are  in  the  same  country  (France), 
their ties are aggregated and Italy and Sweden are connected even though there is 
no  direct  tie  between  Italian  and  Swedish  organisations.  For  instance,  the  largest 
cluster across the networks analysed (Figure 13) generally remains quite large with 
potentially many very small clusters/isolates at the country level. For example, 123 

173 

K-MaxNo. of ActorsK-MaxNo. of ActorsK-MaxNo. of ActorsFP64849174253119263FP75363186255127290COOPERATION4547172223111269Energy273243833233Environment (including Climate Change)3542671165253Food, Agriculture, and Biotechnology3545641383872General Activities (Annex IV)07080*8Health30357412058148Info. and Comm. Tech.3435891729495Joint Tech. Initiatives (Annex IV-SP1)192055985657Nanosci., Nanotech., Mat. and new Prod. Tech.2729671266566Security293436803940Socio-economic sciences and Humanities363737824243Space313547485960Transport (including Aeronautics)263166976667CAPACITIES4647861385354Activities of International Cooperation234024473058Coherent development of research policies101812131430Regions of Knowledge142415164142Research for the benefit of SMEs3233411073839Research Infrastructures4546641205354Research Potential151615161516Science in Society333433343738PEOPLE24315211233119IDEAS710626614CountryNUTS 3Organisations 
 
 
distinct  actors  at  the  country  level  appear  to  be  distributed  across  63  clusters  in 
Health  with  more  than  50%  of  actors  being  concentrated  in  the  largest  cluster.  In 
comparison, 3,301 distinct actors at the organisational level appear to be distributed 
across  21  clusters  in  Health  with  only  15%  of  actors  being  concentrated  in  the 
largest  cluster  (Table  XXXI).  Thus,  the  country  level  is  characterised  by  very  large 
groups with very little clustering. 

Figure 13  Size  of  the  largest  sub-network  in  each  programme,  specific 
programme, and broad/thematic area using clusters 

Note:  

Source: 

The Y-axis displays the size of the largest cluster inside a given network. The 
size  is  expressed  in  terms of  the  percentage  of  the  theoretical  maximum  size 
for  the  corresponding  type  of  sub-network.  The  General  Activities  (Annex  IV) 
area  has  been  removed  from  the  graph  since  it  only  consisted  of  isolated 
actors. 
Computed by Science-Metrix using CORDA (European Commission) data 

174 

0%10%20%30%40%50%60%70%80%90%CountriesNUTS3OrganisationsClusters 
 
 
 
Table XXXI  Dispersion  of  actors  in  the  largest  sub-group  of  the  co-
participation  networks  of  the  framework  programmes  as 
revealed through the analysis of clusters 

Note:  

Source: 

Dispersion  ratio  of  the  network  =  the  ratio  of  the  expected  number  of  actors 
calculated  by  removing  the  actors  from  the  largest  sub-group  over  the 
expected number of actors calculated with all actors. The expected number of 
actors  by  sub-group  is  calculated  by  splitting  the  number of  actors  in  a given 
framework  programme,  specific  programme  and  thematic/broad  area  equally 
across the sub-groups found.  
Computed by Science-Metrix using CORDA (European Commission) data 

This can also be appreciated by looking at  the  dispersion ratio of the network; the 
lower  the  ratio,  the  more  the  actors  are  concentrated  within  the  largest  cluster 
(Table  XXXI).  On  the  whole,  countries  were  found  to  have  a  lower  dispersion  ratio 
compared  to  organisations.  For  example,  when  removing  the  largest  cluster,  the 
number  of  countries  per  cluster  falls  to  17.8%  of  the  expected  value,  whereas  for 
organisations  it  only  drops  to  75.7%  of  the  expected  value.  This  means  that  most 
countries  are  concentrated  within  one  cluster  while  organisations  are  more 
dispersed. 

Careful analysis of the composition of clusters identified at the country level reveals 
that  most  of  the  EU-15  countries  are  grouped  together,  along  with  Norway  and 
Switzerland  (Figure  14).  The  heart  of  this  cluster  is  formed  by  Germany,  the  UK, 
France, Spain and Italy. Grouped  with them  are  also  a few non-ERA countries that 
have  strong  historical  ties  to  this  nucleus,  namely  Canada  and  the  US.  This 
important  cluster  (blue  nodes)  is  highly  important  to  the  network  structure  since 
they are very central; they have some of the highest centrality values. The second 
cluster (green nodes) is mostly composed of the remaining Member States from EU-
28.  It  also  includes other associated  countries  that  are  part of  the  ERA,  as  well as 
Eastern European countries, such as Russia and Ukraine. Alongside these countries 
are  clustered  others  that  have  cultural  proximity,  such  as  Azerbaijan,  Kazakhstan 
and  Uzbekistan.  Members  of  both  the  first  and  second  clusters  have  strong 
connections within and between them.  

The  last  cluster  (red  nodes)  is  composed  of  the  remaining  Non-ERA  countries  that 
participated  to  a  lesser  extent  in  the  framework  programme.  These  countries  are 

175 

CountryOrg.CountryOrg.CountryOrg.CountryOrg.FP6 (overall)3022812041061572079424.4%80.6%FP7 (overall)923611365721772601440.7%74.7%COOPERATION197413848231661987317.8%75.7%Energy117252824669204559.9%88.0%Environment (including climate change)632136360124262972.1%86.3%Nanosci., Nanotech., Mat. and new Prod. Tech.114204079172427144.8%81.5%Food, Agriculture, and Biotechnology781764453107278540.7%83.7%Health632166516123330147.1%84.4%Security139212517346147146.0%88.2%Info. and Comm. Tech.6922541373118577055.0%76.2%Joint Technology Initiatives (Annex IV-SP1)1441022951441197329.5%73.9%Socio-economic sciences and Humanities97164517391104251.1%83.4%Space114152631172102264.5%69.6%Transport (including Aeronautics)113252573574322966.8%77.2%CAPACITIES4896641130139801355.1%85.9%Activities of International Cooperation61253811612761971.2%81.3%Coherent development of research policies1551812223411265.1%80.4%Regions of Knowledge1512918923582448.9%88.8%Research for the benefit of SMEs133402634652501050.4%93.1%Research Infrastructures95104130892160556.0%80.8%Research Potential15312216193521754.6%91.2%Science in Society11635251027198265.4%89.6%PEOPLE1204094076277339148.5%77.5%IDEAS14526829474766438.6%92.9%No. of ClustersLargest GroupNo. of ActorsConcentration ratio of the network 
 
 
mostly positioned on the periphery of the network, with most of their ties being with 
countries in the two main clusters (the strength of their ties is often weaker than it 
is for countries in the other two clusters). This group of Non-ERA countries includes 
China, India, the Republic of Korea, and others. A few oddities can also be observed, 
such  as  Somalia  and  Haiti  (among  others)  being  grouped  with  the  EU-15  cluster. 
This probably stems from the  fact that  their collaborations were completely limited 
to the EU-15, which puts  them  in  the same  sub-group even  if  they stand more on 
the  periphery  of  the  network.  In  the  end,  clusters  show  that  the  geographical 
location  of  the  various  actors  involved  continues  to  be  a  determinant  of  the 
propensity with which ties are established between them; there is a form of isolation 
by distance (either physical and/or cultural). 

The  extent  to  which  the  geographical  location  of  actors  is  a  determinant  of  the 
propensity with which ties are established between them was investigated using an 
indicator of homophily at the organisational level. A visual inspection of clusters on a 
network  graph  was  not  adequate  to  appreciate this at  the organisational  analytical 
level due to the much larger number of actors. The homophily indicator (Figure 15) 
that  was  developed  for  this  purpose  reflects  the  extent  to  which  organisations  are 
linked given a shared characteristic, which in this case is their respective geographic 
location  (i.e.  country  of  affiliation).  Homophily  ranges  from  -1  (highly  heterophilic) 
to  1  (highly  homophilic),  with  0  representing  a  neutral  network  whereby  the 
observed occurrences of homophilic (i.e. two organisations from the same country) 
and  heterophilic  (i.e.  two  organisations  from  different  country)  pairs  are  equal  to 
their  expected  occurrences  based  on  a  random  network  sharing  the  same 
characteristics  as  the  network  analysed  (i.e.  with  the  same  number  of  actors  and 
ties as well as the same frequency distribution of actors across countries). 

176 

 
 
 
Figure 14  Co-participation network of countries in FP7 projects 

Note:  

Note 2: 

Source: 

Links between nodes representing less than 10 co-participations are not shown 
to  prevent  visual  cluttering.  Size  of  the  nodes  is  based  on  the  eigenvector 
centrality of the nodes, the larger the node the more central the corresponding 
actor  in  the  network.  Grey  nodes  represent  isolated  countries  that  did  not 
participate in FP7 or participated alone. 
The ‘EU’ node represents the European Union (JRC) as represented in CORDA 
as a country. 
Computed by Science-Metrix using CORDA (European Commission) data 

When  looking  at  the  calculated  homophily  score  for  both  framework  programmes 
(FP6 = 0.23; FP7 = 0.26), there is a slight tendency towards homophily in both FP6 
and FP7 and it is also similar between them. This seems to corroborate the previous 
finding at the country level. This is also to be expected since the clustering at higher 
levels  of  aggregation  (countries  and  NUTS  3)  is  greatly  driven  by  the  aggregation 
patterns observed at lowest level examined (organisations). 

177 

 
 
 
 
Figure 15  Network  graphs  showing,  to  the  left,  a  highly  homophilic 

network and, to the right, a somewhat heterophilic network 

Note: 

Actors  (i.e.  nodes)  are  represented  by  bubbles,  and  ties  by  lines  between 
actors. There are two sets of nodes each sharing a common characteristic (i.e. 
their colour or, alternatively, a given country of affiliation).  

R7—Role  of  industry  (SMEs  in  particular),  universities,  public  authorities,  national 

and  European  agencies  relevant  to  the  particular  programme,  specific 

programme and broad/thematic area 

Key findings 

  Compared  to  FP6,  there  was  a  clear  increase  in  the  number  of  distinct 
organisations  from  the  Private  sector  (including  SMEs)  that  participated  in  the 
framework  programme  in  FP7;  their  share  among  all  organisations  increased 
from 44% to 65% (share of SMEs among all firms went from 57% to 69%). 

 

 

 

 

In terms of the number of unique organisations that participated in FP7, private 
firms  (including  SMEs)  therefore  dominate,  well  ahead  of  the  Higher  Education 
sector (8%). 

In  terms  of  the  actual  participation  level  of  each  sector  in  FP7—that  is,  the 
actual  number  of  times  organisations  from  a  given  sector  participated  in  FP7 
projects—the  picture  is  completely  different,  with  the  Higher  Education  sector 
dominating the co-participation network (37% share). 

In this regard, the Private sector comes in second place with a 30% share of all 
participations  in  FP7.  An  increase  is  still  visible  from  FP6  to  FP7  (from  23%  to 
30%) and the SMEs share in all firms’ participation increased from 54% to 60%. 

The  prevalence  of  academic  and  research  organisations  in  FP7  participations—
despite their small shares among all participating organisations—is due to their 
much more frequent and recurring participation in FP7 projects in comparison to 
firms (and SMEs). 

  Consequently,  it  is  generally  the  large  institutions  from  the  Research 
Organisations  and  Higher  Education  sectors  that  are  the  most  central  to  the 
network  structure,  acting  as  knowledge  brokers  between  the  more  peripheral 
actors,  which  mostly  included  firms  (and  SMEs)  as well  as organisations  in the 
Public Body & Others sector. 

 

In  FP7,  the  main  brokers  in  the  Research  Organisations  sector  include  the 
Fraunhofer Society in Germany, the Consiglio Nazionale Delle Ricerche in Italy, 
and  the  Centre  National  de  la  Recherche  Scientifique  in  France.  In  the  Higher 
Education sector, they include the Technical University of Denmark, the Catholic 
University of Leuven in Belgium, and the Technical University of Delft (Dept. of 
Applied Hearth Sciences) in the Netherlands. 

178 

 
 
 
 

 

 

 

For  both  the  Capacities  and  the  Cooperation  specific  programmes,  the  most 
central  actors  were  chiefly 
from  the  Higher  Education  and  Research 
Organisations sectors. 

In the Ideas and the People specific programmes, the most central actors were 
chiefly composed of participants from the Higher Education sector. 

The participation level of the Private sector (excluding SMEs) was highest in the 
Cooperation  specific  programme  (especially  the  Joint  Technology  Initiatives, 
Transport,  and  Energy  thematic  areas).  For  SMEs,  it  was  highest  in  the 
Capacities specific programme due to the Research for the benefit of SMEs area. 

The  participation  level  of  the  Public  Body  &  Others  sector  was  highest  in  the 
Capacities  specific  programme  under  the  Coherent  development  of  research 
policies area.  

Distribution of organisations and participations by sector 

Given  the important role that business enterprises  (including SMEs) appear to play 
in  fostering  innovation  within  the  collaborative  projects  supported  through  the 
framework  programme,  it  is  of  interest  to  characterise  whether  or  not  their 
integration  progressed  in  FP7  relative  to  FP6.  This  was  first  achieved  through  an 
analysis of framework programme participation based on CORDA data. 

As  shown  in  Figure  16,  private  firms  represent  the  largest  group  of  distinct 
organisations  among  all  participating  organisations  under  FP7  (65%).  They  are 
followed  by  public  bodies  and  other  organisations  (14%),  research  organisations 
(13%)  and  the  higher  education  sector  (8%).  Comparing  the  distribution  of  the 
distinct number of participating organisations across sectors from FP6 to FP7 reveals 
a substantial increase in the diversity of participating firms, with their share among 
all participating organisations having increased from 44% to 65%. Also noticeable is 
an increase in the share of SMEs among participating firms going from 57% in FP6 
to 69% in FP7 (Figure 16).  

179 

 
 
Figure 16  Distribution of the number of distinct organisations per sector: 

comparison between FP6 and FP7 

Source: 

Computed by Science-Metrix using CORDA (European Commission) data 

When  looking  at  the  actual  participation  level  of  each  sector  in  FP7—that  is,  the 
actual  number  of  times  organisations  from  a  given  sector  participated  in  FP7 
projects (Figure 17), as opposed to the number of unique organisations from a given 
sector  having  participated  in  FP7  projects  (Figure  16)—the  picture  is  different.  For 
example, the Higher  Education sector dominates the network, having a 37% share 
of  all  FP7  participations,  slightly  ahead  of  the  Private  sector  (30%)  and  Research 
Organisations  (25%).  Finally,  Public  Body  &  Other  organisations  (8%)  appear  to 
participate on an occasional basis in FP7. Here again, an increase in the share of the 
Private sector, from 23% to 30%, is visible from FP6 to FP7, as well as in the share 
of  SMEs  within  the  firms’  participations  (54%  to  60%).  The  strong  prevalence  of 
academic  and  research  organisations  in  FP7  participations—despite  their  small 
shares  among  all  participating  organisations—is  due  to  the  fact  that,  compared  to 
firms (and SMEs), they represent large organisations performing research in a broad 
range of scientific disciplines. Consequently, they participate much more frequently 
on  a  recurring  basis  in  a variety  of  FP7  projects  than  firms  (and  SMEs).  As  will  be 
seen later in this section, this has a sizeable effect on the structure of the network 
of  FP7  participation  with  the  large  academic  (e.g.  University  College  London, 
Université  Paris  VI)  and  research  organisations  (e.g.  Centre  National  de  la 
Recherche  Scientifique  [CNRS]  and  the  Max  Planck  Institutes)  being  centrally 
located, and with the majority of firms (and SMEs) being located at the periphery of 
the network. 

180 

Higher Education10%Private43%Public Body & Others27%Research Organisations16%Undefined4%Distribution of participating organisations per sector for FP6Higher Education9%Private64%Public Body & Others14%Research Organisations13%Distribution of participating organisations per sector for FP7Private35%SME65%Proportions of SMEs amongst firms for FP6Private31%SME69%Proportions of SMEs amongst firms for FP7 
 
 
Figure 17  Distribution of in the number of projects in which organisations 

participated per sector: comparison between FP6 and FP7 

Source: 

Computed by Science-Metrix using CORDA (European Commission) data 

Additional data on framework programme participation broken down by programme, 
specific programme, thematic area, and broad area are shown in Table XXXII. From 
FP6 to FP7 the share of participations by SMEs increased from nearly 13% to about 
18%. In Capacities, SMEs make  up nearly a third of  all participating organisations. 
This  is,  in  part,  due  to  the  inclusion  of  the  broad  area  Research  for  the  benefit  of 
SMEs  in  which  SMEs  account  for  58%  of  participations.  Under  Cooperation,  it  is 
noteworthy that SMEs account for  the  largest share of all participations in industry 
driven-sectors such as Nanosciences, Joint Technology Initiatives and Security.  

181 

Higher Education35%Private23%Public Body & Others15%Research Organisations26%Undefined1%Distribution of participations per sector for FP6Higher Education37%Private30%Public Body & Others8%Research Organisations25%Distribution of participations per sector for FP7Private46%SME54%Share of SMEs' within the private sector participations for FP6Private40%SME60%Share of SMEs' within the private sector participations for FP7 
 
 
Table XXXII  Share  of  participations  by  sector  for  FP6  and  FP7  as  well  as 
for the specific programmes, thematic areas and broad areas 
of FP7 

Note: 

Source: 

HES  =  Higher  education  sector;  REC  =  Research  organisations;  PUB  =  Public 
body; PRC = Private; OTH = Others; UND = Undefined. 
Computed by Science-Metrix using CORDA (European Commission) data 

Role  of  sectors  (e.g.  brokers)  in  the  co-participation  network  of  FP6  and 

FP7 

As  was  shown  in  Section  0,  the  co-participation  network  of  FP6  and  FP7are 
characterised  by  a  core-periphery  structure  whereby  the  lack  or  sporadic  presence 
of  ties  in  between  peripheral  actors  hampers  the  direct  circulation  of  knowledge 
among them. In such networks, centrally located players derive an advantage from 
the presence of these structural holes because the exchange of knowledge between 
peripheral actors often has to travel through them. At the local level—that is, at the 
level of organisations—the betweenness centrality of actors can be used to identify 
the  types  of  organisations  (i.e.  universities,  research  organisations,  public  bodies, 
private  firms  [including  SMEs])  in  between  which  structural  holes  exists  (i.e.  in 
between the peripheral actors) as well as to identify the types of organisations that 
derive a benefit from those structural holes (i.e. the central actors/brokers bridging 
the structural holes). 

This was first done by establishing bins—for each network—corresponding to deciles 
of  the  distribution  of  the  centrality  scores  of  all  organisations  regardless  of  their 
sector;  the  first  decile  corresponds  to  the  10%  highest  scores  and  the  10th  decile 
corresponds to the 10% lowest scores. Due to equalities in the scores across some 
bins,  some  of  them  were  grouped  together  (e.g.  decile  4  to  10  merged  together). 
Assuming a uniform distribution of centrality scores across sectors (i.e. universities, 
research  organisations,  public  bodies,  private  firms  [including  SMEs]),  10%  of  the 
organisations  in  each  sector  would  be  expected  to  fall  in  each  decile  (or  60%  if 
deciles 4 to 10 are merged). For a given sector, a positive departure (highlighted in 
green  in  Figure  18)  from  expectation  in  the  deciles  corresponding  to  low 
betweenness scores (i.e. deciles 3–10 or 4–10), combined with a negative departure 
(highlighted  in  red  in  Figure  18)  in  the  deciles  corresponding  to  the  high 

182 

Total ParticipationsHESRECPUB & OTHSMEsPRC (other than SMEs)UNDFP6 (overall)70,09634.9%26.0%14.5%12.7%10.8%1.2%FP6 excluding Marie-Curie Actions61,86730.9%25.8%16.3%14.1%11.9%1.2%FP7 (overall)115,95437.5%25.3%7.5%17.9%11.9%N/AFP7 excluding People and Ideas93,84531.0%25.6%8.6%21.0%13.8%N/ACOOPERATION76,51932.8%25.3%4.0%18.8%15.8%3.3%Energy3,78423.4%24.0%4.5%17.9%26.3%4.0%Environment (including Climate Change)6,19235.2%36.8%6.7%13.7%4.0%3.5%Food, Agriculture, and Biotechnology6,41735.6%31.7%4.2%19.2%5.7%3.7%General Activities (Annex IV)825.0%62.5%N/AN/AN/A12.5%Health10,66148.2%25.6%4.4%15.7%3.2%2.9%Information and Communication Technologies19,46635.1%21.5%3.3%18.1%18.8%3.1%Joint Technology Initiatives (Annex IV-SP1)4,62723.4%19.6%1.4%22.1%31.6%1.9%Nanosciences, nanotech., mat. & new prod. tech.9,45726.2%24.5%1.1%28.1%17.0%3.1%Security2,79219.9%23.6%10.5%21.6%20.4%4.0%Socio-economic sciences and Humanities2,21859.3%26.6%4.9%3.6%1.2%4.4%Space2,38923.2%38.0%6.2%18.3%12.2%2.1%Transport (including Aeronautics)8,50820.8%21.7%4.0%19.5%29.3%4.8%CAPACITIES17,32623.2%27.0%7.2%30.3%5.2%7.2%Activities of International Cooperation1,26618.7%37.4%24.4%6.9%2.9%9.6%Coherent development of research policies1289.4%20.3%45.3%10.2%1.6%13.3%Regions of Knowledge95417.5%14.4%21.1%17.0%11.9%18.1%Research for the benefit of SMEs8,02411.3%16.3%1.8%58.0%6.7%5.9%Research Infrastructures5,11536.4%44.6%7.5%4.5%2.8%4.2%Research Potential29445.9%43.9%6.5%1.4%0.7%1.7%Science in Society1,54545.2%21.2%8.4%6.1%4.1%15.1%PEOPLE17,65963.1%23.0%1.7%6.1%4.5%1.6%IDEAS4,45071.8%26.9%0.3%0.4%0.6%0.1% 
 
 
betweenness scores (i.e. decile 1), would therefore be indicative that organisations 
in this sector are often surrounded  by  structural holes. The  opposite pattern would 
be  that  organisations  in  the  corresponding  sector  are  often  those  deriving  an 
advantage from structural holes (acting as brokers). 

The  analysis  shows  that  the  key  sectors  in  terms  of  position  within  the  co-
participation  network  of  the  framework  programmes  remained  largely  unchanged 
from FP6 to FP7 (Figure 18). In both framework programmes, participants from the 
Higher Education and Research Organisations sectors were mostly found in the bins 
corresponding  to  the  largest  values  of  the  distribution  of  betweenness  centrality 
scores (i.e. deciles 1 and 2) and appeared less frequently than expected in the bins 
corresponding  to  the  smallest  scores  (i.e.  deciles  4–10).  They  are  therefore  the 
sectors  that  derive  an  advantage  from  the  presence  of  structural  holes  in  the 
networks. Broadly speaking, the opposite pattern is observed for the Private sector 
(Business Enterprise), including SMEs, and to a lesser  extent for the Public Body & 
Others sector. This indicates that organisations in  these  sectors are  surrounded by 
structural  holes  and  are  therefore  in  a  less  privileged  position  compared  to  the 
Higher  Education  sector  or  Research  Organisations  when  requiring  access  to 
information  from  various  parts  of  the  network.  Similar  findings  are  observed  for 
Cooperation and Capacities. For Ideas and People, it is mostly the Higher Education 
sector  that  benefits  from  the  presence  of  structural  holes,  with  the  remaining 
sectors being mostly affected by the presence of structural holes to various degrees.  

Figure 18  Heat  map  of  expected  to  observed  ratio  of  the  number  of 
organisations  in  betweenness  centrality  bins  (deciles)  as  an 
indicator of dominance in co-participation networks 

Note: 

Source: 

The lower the decile numbering, the higher the betweenness scores. Scores in 
the table (i.e. ratio of observed to expected proportion of organisations from a 
given  sector  in  a  given  decile)  are  coloured  according  to  the  extent  of 
departure  from  expectation  (red  =  negative  departure  and  green  =  positive 
departure;  grey  =  merged  with  next  deciles).  Deciles  containing  low 
betweenness  centrality  values  (4–10  or  3–10  in  the  cases  of  Capacities  and 
Ideas) were merged together. 
Computed by Science-Metrix using CORDA (European Commission) data 

It  is  noteworthy  that  although  organisations  from  the  private  sector  dominate  the 
network  in  terms  of  their  share  of  the  total  number  of  unique/distinct  participants 
(i.e.  65%  of  which  69%  are  SMEs,  see  Figure  16),  they  were  largely 
underrepresented  within  the  10%  most  central  organisations  in  the  network  (i.e. 
decile  1).  This  is  true  in  all  specific  programmes  and  most  broad/thematic  areas 

183 

Decile1234-10Decile1234-10Higher EducationHigher EducationResearch OrganisationsResearch OrganisationsPublic Body & OthersPublic Body & OthersPrivatePrivateSMESMEDecile1234-10Decile12N/A3-10Higher EducationHigher EducationOthersOthersPublic BodyPublic BodyResearch OrganisationsResearch OrganisationsPrivatePrivateSMESMEDecile12N/A3-10Decile1234-10Higher EducationHigher EducationOthersOthersPublic BodyPublic BodyResearch OrganisationsResearch OrganisationsPrivatePrivateSMESMEFP6 overallFP7 overallCooperationCapacitiesIdeasPeople 
 
 
(Table  XXXIII).  This  is  explained  by  the  fact  that  private  firms,  although  very 
common  among  participants,  took  part  in  comparatively  fewer  projects,  whereas 
higher  education  and  research  organisations  took  part  in  multiple  projects  (see 
Figure  17).  This  participation  in  multiple  projects  necessarily  translates  into  more 
integration  within  the  network  (higher 
co-participation  and  better  overall 
betweenness centrality).  

This is in line with findings in the literature, which indicate that business enterprises 
traditionally prefer to have exclusivity in a market  and either protect with a patent 
or  keep  their  recipe  as  secret  as  possible.  In  other  words,  firms  do  not  want  to 
cooperate  much,  especially  with  other  firms  having  competing  interests,  at  least 
compared  to  universities  and  other  public  organisations.  However,  this  may  be 
changing.  Cooperation  in  R&D,  or  more  generally  in  innovation  activities,  is  often 
pursued by firms to benefit from complementarities, to reduce risk or to save costs, 
to internalise knowledge spillovers, and to increase their standing. 

Table XXXIII  Expected to observed  ratio of the proportion of organisations 
in  the  1st  decile  of  the  betweenness  centrality  distribution 
(i.e. in the 10% most central organisation) by sector for each 
programme, specific programmes and broad/thematic area 

Note: 

Source: 

Scores  in  the  table  (i.e.  ratio  of  observed  to  expected  proportion  of 
organisations  in  the  1st  decile)  are  coloured  according  to  the  extent  of 
departure from expectation (dark red = no participants found, red = negative 
departure,  green  =  positive  departure).  Coherent  Development  of  Research 
Policies  (Capacities),  Research  Potential  (Capacities)  and  General  Activities 
(Cooperation)  were  not  included  in  this  table  since  the  decile  included 
organisations  with  betweenness  centrality  scores  of  0.00,  such  that  it  is  not 
efficient  at  discriminating  organisations  based  on  their  centrality  in  the 
corresponding network. 
Computed by Science-Metrix using CORDA (European Commission) data 

Table  XXXIV  below  lists  the  most  central  organisations  (based  on  betweenness 
centrality) by sector for the global co-participation network of FP7. Note that those 
in  the  Research  Organisations  and  Higher  Education  sectors  are  the  main  brokers 
bridging  the  flow  of  information  in  between  the  more  peripheral  actors.  For 
example,  they  include  the  Fraunhofer  Society  (normalised  score  of  0.119),  the 

184 

Programme/ thematicsHigher EducationResearch OrganisationsPublic Body & OthersPrivateSMEFP6 overall2.971.740.700.520.40FP7 overall3.402.100.810.510.42Programme/ thematicsHigher EducationResearch OrganisationsPublic BodyPrivateSMEOthersCapacities3.481.890.910.330.350.62Activities of international cooperation0.931.230.861.332.220.60Regions of knowledge1.101.811.130.470.601.06Research for the benefit of SMEs3.882.900.860.550.590.88Research infrastructures1.671.060.620.120.130.19Science in society1.671.080.500.150.130.44Cooperation3.331.850.970.510.380.56Energy2.521.830.450.470.250.65Environment (including climate change)2.091.520.400.140.150.12Food, agriculture and biotechnology2.291.640.460.170.170.41Health2.811.080.610.130.140.31Information and communication technologies3.121.660.580.440.290.59Joint technology initiatives2.341.850.420.540.330.34Nanosciences, nanotechnology, materials and new prod. tech.3.192.370.250.360.320.87Security1.741.721.290.500.380.36Socio-economic sciences and humanities1.540.690.700.120.170.00Space1.071.630.350.750.560.25Transport (including aeronautics)2.382.090.650.590.380.67Ideas1.280.630.000.000.000.00People2.780.660.070.040.020.00 
 
 
Consiglio  Nazionale  Delle  Ricerche  (CNR;  0.032)  and  the  Centre  National  de  la 
Recherche Scientifique  (CNRS; 0.031)  in the Research  Organisations sector. In the 
Higher Education sector, they include the Technical University of Denmark (0.018), 
the  Catholic  University  of  Leuven  (0.018)  and  the  Technical  University  of  Delft 
(Dept. of Applied Hearth Sciences; 0.013).  

Table XXXIV  The 10 most central actors (based on betweenness centrality) 

by sector for FP7 overall 

Source: 

Computed by Science-Metrix using CORDA (European Commission) data 

185 

SectorActor (legal name)CountryCountry codeNormalised betweenness centralityDecileDANMARKS TEKNISKE UNIVERSITETDenmarkDK0.01831CATHOLIC UNIVERSITY OF LEUVENBelgiumBE0.01821DELFT TECHNICAL UNIVERSITY, DEPARTMENT OF APPLIED EARTH SCIENCESNetherlandsNL0.01301EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICHSwitzerlandCH0.01161IMPERIAL COLLEGE FOR SCIENCE, TECHNOLOGY AND MEDICINEUnited KingdomUK0.01051ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNESwitzerlandCH0.01031AACHEN UNIVERSITY OF TECHNOLOGYGermanyDE0.01011UNIVERSITY COLLEGE LONDONUnited KingdomUK0.01001CAMBRIDGE UNIVERSITYUnited KingdomUK0.00991MANCHESTER CENTRE FOR HEALTHCARE MANAGEMENT, UNIVERSITY OF MANCHESTERUnited KingdomUK0.00981ACCIONA INFRAESTRUCTURAS S.A.SpainES0.00501ATOS ORIGIN SOCIEDAD ANONIMA ESPA?OLASpainES0.00451SIEMENS AGGermanyDE0.00381PHILIPS ELECTRONICS NEDERLANDNetherlandsNL0.00291EADS DEUTSCHLAND GMBHGermanyDE0.00261THALES COMMUNICATIONS & SECURITY SASFranceFR0.00191ST MICROELECTRONICS SRL.ItalyIT0.00181ELSAG DATAMAT S.P.A.ItalyIT0.00181SAP AGGermanyDE0.00151INFINEON TECHNOLOGIES AGGermanyDE0.00131THE SECRETARY OF STATE FOR ENVIRONMENT, FOOD AND RURAL AFFAIRSUnited KingdomUK0.00211TELEFONICA INTERNATIONAL WHOLESALE SERVICES SLSpainES0.00171COPENHAGEN UNIVERSITY HOSPITAL RIGSHOSPITALETDenmarkDK0.00081BUREAU DE RECHERCHE GEOLOGIQUES ET MINIERESFranceFR0.00101ALMA  CONSULTING GROUP S.A.SFranceFR0.00151SERVICIO MADRILENO DE SALUDSpainES0.00091MINISTERIE VAN ECONOMISCHE ZAKENNetherlandsNL0.00101HOSPITAL UNIVERSITARI VALL D'HEBRONSpainES0.00081MET OFFICEUnited KingdomUK0.00011AGROSCOPE FAL RECKENHOLZ; SWISS FEDERAL RESEARCH STATION FOR AGROECOLOGY AND AGRICULTURE (FAL)SwitzerlandCH0.00041FRAUNHOFER-GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.VGermanyDE0.11921CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUEFranceFR0.03101CONSIGLIO NAZIONALE DELLE RICERCHEItalyIT0.03221COMMISARIAT  A L'ENERGIE ATOMIQUEFranceFR0.02361TECHNICAL RESEARCH CENTRE OF FINLANDFinlandFI0.03011DE NEDERLANDSE ORGANISATIE VOOR TOEGEPAST-NATUURWETENSCHAPPELIJK NetherlandsNL0.02741AGENCIA ESTAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICASSpainES0.02171FUNDACIN LABEINSpainES0.02201DEUTSCHE ZENTRUM FUR LUFT- UND RAUMFAHRT E.V. (GERMAN AEROSPACE CENTER)GermanyDE0.01561COMMISSION OF THE EUROPEAN COMMUNITIES - DG JOINT RESEARCH CENTREEuropean Union (JRC)EU0.01221D'APPOLONIA SPAItalyIT0.00631THE UK INTELLIGENT SYSTEMS RESEARCH INSTITUTE LIMITEDUnited KingdomUK0.00441ATEKNEA SOLUTIONS CATALONIA, SASpainES0.00401THE UK MATERIALS TECHNOLOGY RESEARCH INSTITUTE LIMITEDUnited KingdomUK0.00281THE UK HEALTH & ENVIRONMENT RESEARCH INSTITUTEUnited KingdomUK0.00201ARTTICFranceFR0.00151OSAUHING EESTI INNOVATSIOONI INSTITUUTEstoniaEE0.00131ACREO ABSwedenSE0.00101NOVAMINA CENTAR INOVATIVNIH TEHNOLOGIJA DOOCroatiaHR0.00091GABO GESELLSCHAFT FUER ABLAUFORGANISATION, INFORMATIONSVERARBEITUNG UND KOMM. GMBH & CO. KGGermanyDE0.00071Higher EducationPrivatePublic Body & OthersResearch OrganisationsSME 
 
 
R8—Key  findings  from  the  above  points  pertaining  to  network  dynamics  over  time 

(mainly through a comparison at the programme level; i.e. FP6 vs. FP7) 

Summary of remarkable key findings from the above points 

  When  the  non-collaborative  components  of  FP6  and  FP7  are  excluded,  both 
framework programmes appear to be  highly  collaborative, linking organisations 
from multiple sectors of activity, regions and countries.  

 

They  are  also  comparable  in  terms  of  the  extent  to  which  they  supported  co-
participation: each with, on average, about 11 organisations, six countries, nine 
NUTS 3 regions and three sectors of activity per project. 

  When all components are included (i.e. even those not supporting collaboration 
by  design),  FP7  projects  appeared,  on  average,  less  collaborative  than  in  FP6; 
this  is  because  the  share  of  non-collaborative  projects  in  FP7  (from  Ideas  and 
People) is greater than in FP6 (from Marie Curie Actions). 

 

 

 

 

The  same  countries  have  the  largest  number  of  distinct  organisations 
participating in  both  FP6 and  FP7: Germany, the United Kingdom, Italy, Spain, 
and  France.  Among  Non-ERA  countries,  the  only  change  within  the  top  five 
countries from FP6 to FP7 was the inclusion of Ukraine at the expense of Brazil. 

There  are  no  major  changes  between  FP6  and  FP7  in  terms  of  the  most 
participating  NUTS  3  regions.  Paris  was  a  strong  regional  player  in  both 
framework  programmes,  having  participated  in  the  largest  number  of  projects 
and  ranking  in  the  top  five  in  terms  of  the  number  of  distinct  organisations 
having  participated  in  either  FP6  or  FP7.  Two  of  the  top  five  regions  with  the 
largest number of distinct organisations participating in either FP6 or FP7 are in 
Spain: Madrid and Barcelona. 

For  both  FP6  and  FP7,  the  co-participation  network  in  all  levels  of  analysis—
organisational,  regional,  and  national—are  characterised  by  a  core-periphery 
structure whereby a few influential actors (i.e. hubs) located at the centre of the 
graphic representation establish  strong  ties in  between themselves and weaker 
connections with the periphery, where the density of ties—and consequently the 
flow of knowledge—is reduced in between the peripheral players. 

Interestingly, the density of the network decreased only slightly from FP6 to FP7 
at  the  organisational level  despite  a  sizeable  increase  in  the  number  of  actors. 
This  indicates  that  the  number  of  partnerships  increased  by  an  appreciable 
margin  in  FP7  relative  to  FP6  given  that  it  is  difficult  to  maintain  density  in  a 
large network as the number of actors increases.  

  At  the  regional  (NUTS  3)  and  national  levels,  network  density  increased  while 
keeping  constant  the  number  of  actors  to  maximise  comparability  across 
framework  programmes;  such  an  approach  was  not 
the 
organisational level. If this approach had not been used at the national/regional 
levels,  one  would  have  expected  to  observe  a  decrease  in  density  even  in  the 
presence  of  a  densification  of  the  core  of  the  networks,  as  is  the  case  with 
organisations.  

feasible  at 

  Clusters provide the best results for the identification of sub-groups within well-
connected  networks  such  as  the  co-participation  networks  of  FP6  and  FP7, 
especially  at  the  country  level.  Compared  to  components  and  k-cores,  they 
divide the actors (i.e. countries) into a greater number of smaller sub-groups.  

  Components and k-cores are useful to assess the completeness of 

networks—that is, how well connected they are. 

186 

 
 
  Based  on  an  analysis  of  components,  most  of  the  networks  analysed  (i.e.  FP6 
overall,  FP7  overall,  the  Cooperation  specific  programme  [and  all  its  thematic 
areas]  and  the  Capacities  specific  programme  [nearly  all  its  broad  areas])  are 
highly complete. 

 

 

 

In  fact,  the  size  of  the  network  nucleus  (i.e.  highest  order  k-core)  increased 
from  FP6  to  FP7  in  all  levels  of  analysis  (i.e.  organisational,  regional  and 
national) and the number of ties by actors within the nucleus increased. 

ERA  countries  constitute  the  heart  of  the  co-participation  networks  of  FP6  and 
FP7  and  the  main  hubs  bridging  the  structural  holes  in  between  the  peripheral 
actors (i.e. the brokers) are similar between FP6 and FP7 at all levels of analysis 
(i.e. local, regional and national). 

The  main  brokers  at  the national  level  include:  Germany,  the  United  Kingdom, 
France, Italy and Spain. Paris (FR101) is the main broker at the regional level. 
At  the  organisational  level,  the  main  brokers  are  from  the  Research 
Organisations  (e.g.  Fraunhofer  Society)  and  Higher  Education  sectors  (e.g. 
Technical University of Denmark). This is in line with findings based on the sheer 
number of participations of the various actors, which is not surprising. The more 
an  entity  participates,  the  more  ties it  creates,  thus  moving  it to the  centre of 
the network. 

  Removing  the  effect  of  the  size  of  countries  in  studying  trends  in  the 
centrality/integration  of  countries  from  FP6  to  FP7,  it  appears  that  the  former 
Yugoslav  countries—and  in  particular  the  Former  Yugoslav  Republic  of 
Macedonia,  Serbia,  Croatia,  and  Bosnia  and  Herzegovina—were  the  ones  that 
found  new  fame  in  FP7  relative  to  FP6  (Slovenia's  attractiveness  changed 
comparatively little). 

  Although the Private sector (including SMEs) are mostly located in the periphery 
of the network, their share among all participating organisations and their share 
of all participations increased from FP6 to FP7. 

R9—Highlight of remarkable specificities per specific programme and broad/thematic 

area 

Summary of remarkable key findings from the above points 

 

 

The specific programmes of FP7 that were intended to be highly collaborative by 
design  are  also  those  that  appear  as  such  when  looking  at  the  levels  of 
international, 
co-
participation;  namely,  Cooperation  and  Capacities.  The  other  two  specific 
programmes (i.e. People and Ideas), appear to be only marginally collaborative. 
As  such,  there  is  a  greater  share  of  isolated  actors  in  the  co-participation 
network of People and Ideas. 

inter-organisational 

intersectorial  and 

inter-regional, 

The  FP7  areas  showing  the  highest  levels  of  international,  inter-regional, 
intersectorial 
are:  Research 
Infrastructures  (Capacities  specific  programme),  Environment  (Cooperation 
specific programme) and Food, Agriculture and Biotechnology (Cooperation). 

inter-organisational 

co-participation 

and 

  All co-participation networks (for FP7 overall and each of its underlying specific 
programme)  are  characterised  by  a  highly  uneven  distribution  of  centrality 
scores typical of core-periphery structures. 

 

The top five hubs at the national level in the Capacities, Cooperation and People 
specific  programmes  of  FP7  are  very  similar  (generally  including  Germany,  the 
United  Kingdom,  France,  Italy  and  Spain),  while  some  minor  changes  are 

187 

 
 
observed  in  the  Ideas  specific  programme,  notably  the  Netherlands  and 
Switzerland replaced Italy and Spain among the main hubs. The top five brokers 
are similar with slight variations. 

In  the  Ideas  specific  programme,  Norway,  Poland  and  Hungary  are  among  the 
most important brokers despite their smaller participation level, while the same 
can be said of Greece, Ireland and Turkey in the People specific programme. In 
the  larger  specific  programmes,  Portugal  (Capacities  and  Cooperation),  Estonia 
(Capacities) and Norway (Cooperation) show the same pattern. 

FR101 (Paris) is the main hub in FP6, FP7 and the specific programmes of FP7. 
Other  important  hubs  include  ES300  (Madrid),  DE212  (München,  Kreisfreie 
Stadt), ITE43 (Roma) and UKI11 (Inner London – West). 

 

 

  Many  UK  regions  (UKI11  [Inner  London  –  West],  UKH12  [Cambridgeshire  CC], 
UKM25 [City of Edinburgh] and UKJ14 [Oxfordshire]) are among the main hubs 
under the Ideas specific programme. 

 

 

 

 

 

 

 

 

 

Important  regional  brokers  with  low  levels  of  participation  (regions  having  a 
capacity to bridge other regions to a greater extent than is usually observed for 
regions  having  participated  in  as  few  projects  as  they  have)  are:  NL221 
[Veluwe]  (under  FP7  overall,  Capacities  and  Cooperation),  EL221[Zakynthos] 
(FP7 overall and Capacities), CZ010 [Hlavní město Praha ] (Cooperation), ITC45 
[Milano] (Ideas), UKM25 [City of Edinburgh] (Ideas), PL127 [Miasto Warszawa ] 
(Ideas and People) and HU101 [Budapest] (People). 

For  both  the  Capacities  and  the  Cooperation  specific  programmes,  the  most 
central  actors  (i.e.  brokers)  were  mostly  from  the  Higher  Education  and 
Research Organisations sectors. 

In  the  Ideas  and  the  People  specific  programmes, the  most  central  actors  (i.e. 
brokers)  were  mostly  composed  of  participants  from  the  Higher  Education 
sector. 

The participation level of the Private sector (excluding SMEs) was highest in the 
Cooperation  specific  programme  (especially  the  Joint  Technology  Initiatives, 
Transport,  and  Energy  thematic  areas).  For  SMEs,  it  was  highest  in  the 
Capacities specific programme due to the Research for the benefit of SMEs area. 

The  participation  level  of  the  Public  Body  &  Others  sector  was  highest  in  the 
Capacities  specific  programme  under  the  Coherent  development  of  research 
policies area.  

The main organisational hubs are  the large national research centres scattered 
across the main national hubs (e.g. CNRS, Fraunhofer Society, CNR, Max-Planck 
Society and CSIC), as well as top UK universities (e.g. Cambridge, Oxford). 

The  main  hubs  include  more  universities  under  the  Ideas  and  People  specific 
programmes, which is not surprising as they are focused on promoting research 
excellence.  

Four  private  companies  (TWI  limited,  Inspiralia  SL,  UK  Intelligent  Systems 
Research Institute Limited  and  Ateknea Solutions Catalonia SA) are  among the 
main  brokers  under  the  Capacities  specific  programme.  These  companies 
provide  technical  support  and  offer  R&D  services  to  SMEs.  Therefore,  it  is  not 
surprising  that  they  bridge  other  actors  in  the  network,  especially  under 
Capacity, which includes a broad area titled Research for the benefit of SMEs. 

The University of Edinburgh, Durham University, and the Manchester Centre for 
Healthcare Management, although they participated in a relatively small number 
of  projects,  are  found  among  the  top  10  brokers  under  the  Ideas  specific 
programme. Under the People specific programme, the Foundation for Research 

188 

 
 
&  Technology  –  Hellas  Institute  of  Molecular  Biology  &  Biotechnology  shows  a 
similar pattern. 

 

The  broad  areas  of  Coherent  development  of  research  policies  and  Research 
potential  under  the  Capacities  specific  programme,  as  well  as  the  People  and 
Ideas specific programmes are the less complete networks based on an analysis 
of components. 

  Within  FP7,  the  nucleus  of  the  co-participation  network  is  larger  and  more 
strongly  connected  for  the  Cooperation  specific  programme  followed  by 
Capacities,  People  and  Ideas.  This  is  in  line  with  the  emphasis  placed  on  co-
participation in the design of the specific programmes.  

  Organisations  show  a  slightly  stronger  tendency  to  agglomerate  with  actors 
from  their  own  or  neighbouring  countries  than  with  actors  from  distant 
countries.  Thus,  geographic  proximity  has  a  strong  influence  on  the  clustering 
pattern  found  at  the  higher  aggregation  levels  (e.g.  NUTS  3  regions  and 
countries);  for  instance,  NUTS  3  regions  from  the  same  country  tend  to  group 
together. 

189 

 
 
 
 
Appendix III: Cross-case analysis and synthesis (Task 4) 

Why Case studies? 

Within  the  Network  Analysis  of  the  FP7  Participation,  12  case  studies  were  carried 
out by Oxford Research in consortium  with Science-Metrix  and  Fraunhofer ISI.  The 
purpose of the case studies was to integrate the results gathered in the bibliometric 
and  survey  phases  of  the evaluation  and to  provide  a  more  qualitative perspective 
and  contribute  to  a  deeper  understanding  of  the  results  that  were  found  in  the 
earlier  phases.  The  case  studies  were  focused  on  revealing  the  factors  that 
contributed to the success of FP7 funded projects. 

The purpose of the case studies 

Rather  than  to  describe  the  process  within  each  project,  the  purpose  of  the  case 
studies  was  to  reveal  how  collaboration  within  projects  occurred.  While 
understanding  how  knowledge  is  created  in  networks  and  through  cooperation  is 
important, attention also needs to be paid to where the transition from invention to 
innovation  occurs  within  and  outside  networks.  Therefore,  the  12  case  studies 
illustrate  factors  that  contributed  to  successful  processes  including  impacts  on 
networking  and  collaboration  as  well  as  on  the  outcome  of  each  project,  based  on 
success at the project level and at the network level. 

The 12 cases that were selected reflect a broad sample of FP7 funded projects. The 
case  studies  were  conducted  based  on  the  Generalised  Success  Case  Method 
(GSCM)  approach,  which  is  characterised  by  its  purposive  rather  than  random 
sampling,  focuses  on  learning,  includes  successful  and  less  successful  cases,  and 
places  emphasis  on  the  conditions  and  variables  that  are  critical  to  success  rather 
than on providing a description of a case. This approach used in conducting the 12 
case studies assisted in identifying the actors and factors that led to these projects’ 
success.  

Although  similar  success  factors  were  identified  across  the  12  cases,  the  approach 
also emphasises  the importance  of environmental,  cultural and system  factors that 
interplay  with  learning  to  contribute  to  the  success  of  a  project.  There  are, 
therefore,  some  limitations  to  the  case  studies  as  well.  For  example,  there  are 
several  success  factors  that  are  shared  across  the  12  cases  but  some  success 
factors may only be specific to one case. 

Furthermore,  cases  were  conducted  based  on  interviews  with  project  coordinators 
and  project  partners  that  they  recommended  to  the  analysts  at  Oxford  Research. 
The  interview  participants  have  their  own  bias  in  how  they  describe,  present  and 
evaluate the project. As the case studies were primarily based on interview material, 
important aspects of the project may have been missed if the right questions were 
not asked or if the interviewee left out details in their answers. 

Case study approach 

The  case  studies  were  selected  based  on  the  results  from  the  bibliometric  and 
survey  phases  of  the  network  analysis.  First,  the  chosen  cases  were  distributed 
across  three  project  sizes:  small,  medium-sized,  and  large  projects.  Some  of  the 
cases selected represent individual researchers who have received funding from the 
FP7  programme,  other  cases  involve  much  larger  consortiums  with  over  50 
partners.  Secondly,  the  cases  selected  fall  under  four  broad  categories  as  well  as 
being  selected  based  on  a  predetermined  set  of  qualities  determined  by  the 

190 

 
 
bibliometric  study  and  the  survey.  All  12  of  the  cases  fall  into  one  of  these  four 
categories: 

  Networking/cooperation success and successful outcomes 

  Networking/cooperation success and less successful outcomes 

 

 

Less networking/cooperation success and successful outcomes 

Less networking/cooperation success and less successful outcomes 

As  a  part  of  the  GSCM  approach,  less  successful  cases  have  also  been  included  in 
the  12  case  studies.  Although  most  of  the  projects  and  interview  participants 
included in the case  studies considered their own  projects to be  successful, factors 
were identified that hindered collaboration or that made it more challenging for the 
project to reach the desired outcomes.  

After  selecting  the  12  cases,  with  each  case  being  selected  based  on  specific 
parameters such as degree of  interdisciplinarity  and number of institutional  sectors 
among  the  participants  of  a  given  FP7  project,21  the  interviews  began.  Desk 
research was conducted on each of the 12 cases to identify project coordinators and 
to  gain  a  basic  understanding  of  the  project  before  beginning  the  interviews. 
Invitations to participate in the case studies were sent to the 12 project coordinators 
through  email.  After  a  preliminary  phone  call  with  the  coordinators,  references  for 
three  or  four  other  project  partners  were  requested.  From  there,  interviews  were 
scheduled  and  conducted  over  the  telephone  or  through  Skype  with  the  project 
coordinators  and  their  recommended  partners.  Oxford  Research  conducted  55 
interviews to complete the 12 case studies. 

Semi-structured interviews with questions based on  a standardised interview guide 
were  conducted.  The  interview  guide  was  used  to  structure  the  interviews  but  as 
each  case  was  different,  the  semi-structured  interview  approach  allowed  the 
analysts  at  Oxford  Research  to  adapt  to  each  situation.  Combined  with  data  from 
the  interviews  and  information  gathered  through  desk  research,  analyses  were 
undertaken across the 12 cases based on the same format to allow for comparisons 
between the cases. This standardised data collection format also provided space for 
description and diversity in the cases presented with emphasis placed on explaining 
success  factors.  In  each  of  the  12  case  studies,  a  description  of  the  project’s 
background, how the project was implemented, its impact on the network level, and 
its outcomes and outputs are highlighted. 

Main conclusions of the case studies and cross-case analyses and synthesis 

The case studies provide a glimpse into how FP7 funded projects have been able to 
strengthen existing networks and develop new networks, how fundamental research 
has  been  furthered  and  how  innovation  has  occurred,  as  well  demonstrating  how 
different  disciplines  and  organisations  across  and  beyond  Europe  can  collaborate 
and work together successfully. 

The  case  studies  have  identified  potentials  as  well  as  limits  to  the  network 
approach.  It is important to stress the paradoxical nature  of  the networks, where 
observed limits  of  the  networks  can  be  viewed  as  strengths  at  the  same time,  i.e. 

21  The  case  selection  is  based  on  data  from  a  comprehensive  network  analysis  and  survey 
conducted  by  Science-Metrix  and  Fraunhofer  ISI  (see  appendix  1  for  a  methodological 
description). 

191 

 
 
                                                
setting  up  a  large  network  largely  strengthens  the  European  innovation  and 
research  capacity  by  involving  researchers  and  industry  partners  from  different 
scientific fields and countries.  However, at  the  same time, the limitation of a large 
network is that it is difficult to manage and involves a high degree of administrative 
work.  The  main  conclusions  of  the  case  studies  are  presented  in  the  following 
sections  with a primary  focus on the potentials and limits to the network approach 
promoted and implemented in relation to FP7. 

Potentials associated with the network approach 

The  findings  from  the  case  studies generally  support  that  the  network  approach 
has  a  huge  potential  for  generating  direct  outputs  such  as  peer-reviewed 
articles,  presentations  of  results  at  conferences,  patent  applications,  and  spin-off 
projects. On the longer term, the networks can potentially generate outcomes such 
as stronger knowledge transfer from research to industry, enhancement of research 
performance  in  Europe,  improvement  of  European  research  capacities  and 
competencies. 

impacts.  The  direct  network 

A  key  finding  from  the  case  studies  is  that  networks  generate  direct  and 
indirect 
the  measurable 
scientific/commercial  output  and  outcomes  attributed  to  the  network,  while  the 
indirect impacts are the skills and knowledge diffused through the network, as well 
as the relations and trust built between the partners. 

impacts  are 

  Networks 

inspire  researchers  to  broaden  their  perspective.  An 
interdisciplinary and intersectoral approach brings people together from different 
disciplines  and  different  institutional  sectors  who  would  otherwise  not  work 
together. As researchers work together with academics from different disciplines 
and/or  partners  from  other  institutional  sectors,  they  become  aware  of  what 
others  in  their  field  are  doing  as  well  as  how  other  partners  are  thinking  and 
working, which stimulates creativity and innovation. 

  Complementarity  is  a  central  driver  in  the  formation  of  the  networks. 
When  complex  projects  are  split  into  assignments  that  are  more  manageable 
and  well  defined,  the  partners  are  able  to  limit  their  research  focus  to  a  few 
aspects while relying on other partners for other aspects. This reduces the level 
of research effort spent on academic fields outside the partners’ core fields.  

  Networks  lead  to  increased  mobility  of  researchers.  All  the  case  studies 
are  examples  of  projects  in  which  partners  from  different  countries  work 
together  and  have  to  travel  across  borders  to  meet  face-to-face  at  meetings, 
seminars and conferences, or for longer stays to do research at a university or 
research  institute  in  a  foreign  country.  Some  projects  have  created  small 
networks  with  partners  from  a  few  countries  and,  conversely,  other  project 
networks comprise partners from many EU countries and non-EU countries.  

  Networks  stimulate 

innovation  by 

research-related 
knowledge  from  the universities to the industry  and  vice  versa. The networks 
cover  the  range  from  ‘pure  basic  research’,  ‘use-inspired  basic  research’  to 
‘applied  research’.  Two  thirds  of  the  cases  studies  are  examples  of  research 
projects where private sector businesses are part of the project network. 

transferring 

Limits associated with the network approach 

A major component in the networks is the concept of multi-  and interdisciplinarity. 
This  can  be  a  limitation  in  projects  where  the  partners  have  not  managed  to 
develop a mutual understanding of the project’s objective and thus cannot value 
the  diversity  in  the  project.  Furthermore,  it  takes  a  lot  of  time  and  effort  for 

192 

 
 
researchers  from  different  disciplines  to  get  to  understand  each  other’s  way  of 
thinking  and  to  obtain  the  mutual  benefits  and  synergies  from  taking  a  multi-  and 
interdisciplinary approach. 

Different  motivations  can  be  a  limit  to  a  constructive  cooperation  between 
partners from different institutional sectors. Put in simple terms, academic research 
is  driven  by  the  impact  it  will  have  on  the  global  research  community,  where 
measures  of  success  are  largely  based  on  peer-reviewed  articles  and  publications. 
Research  in  industry,  on  the  other  hand,  is  driven  by  commercial  impact,  and  the 
measures of success are largely based on the long- and short-term value created for 
the  company.  Another  limitation  or  dilemma  in  relation  to  cooperation  between 
public and private organisations is that the EU demands that all research produced 
within  a  FP  financed  project  network  is  published,  which  most  enterprises  are  not 
interested  in  due  to  confidentiality  and  Intellectual  Property  Right  (IPR) 
issues. 

In  projects  with  EU  and  third-country  collaborations,  it  has  been  identified  that  a 
barrier  to  international  collaboration  is  to  obtain  travel  visas  for  foreign  partners. 
EU-level immigration laws can hinder foreign partners to enter the EU—even for 
meetings and conferences with a scientific purpose. In one of the cases studies, EU 
visa  obstacles  were  highlighted  as  a  hindrance  to  an  effective  international 
cooperation. 

Long geographical distances make communication harder. The main reason is 
that the frequency of personal face-to-face meetings is lowered, as the geographical 
distances  increase.  Face-to-face  meetings  are  costly,  but  necessary  for 
partners to get to know each other, to align expectations and in order to understand 
one’s  role in a project,  even though partners can handle  most  communication with 
different ICT tools such as Skype. 

There are some limitations to a large network: one is the dynamics of the network, 
where  there  is  a  core  of  partners  and  a  periphery  of  partners,  meaning  that  the 
benefits gained vary according to the partners’ role in the network. Another 
limit is the practicalities involved with running a large network—it takes up many 
resources on administration and management, but can also be extremely cost 
and resource intensive—for example, when partners from different countries have to 
meet  face-to-face.  Having  a  large  number  of  partners  also  creates  challenges  in 
regards  to  network  sustainability  and  different  working  styles  due  to  different 
working cultures and norms as well as discrepancies in expertise. 

SQ1:  How  do  the  FP  projects  link  separate  fields  of  knowledge,  bridge  different 

fields of science and facilitate new scientific disciplines? 

Gaining access to complementary expertise is a major driver for participation in FP7 
projects  for  most  participants,  including  research,  industry  and  public  authority 
participants  alike.  Almost  all  projects  require  multidisciplinary  solutions  and  it  has 
been  emphasised  that  a  multidisciplinary  approach  is  fundamental  for  innovation. 
The  differences  in  expertise  amongst  project  partners,  however,  has  been 
highlighted  both  as  an  important  success  factor  in  projects,  but  also  as  one  of  its 
biggest challenges. 

The  following  factors  are  considered  vital  for  linking  separate  fields  of  knowledge, 
bridging different fields of science and facilitating new scientific disciplines: 

193 

 
 
face-to-face  meetings, 

  On the project level, the project manager  needs to ensure that there is  space 
for partners to share information as well as setting aside enough time for 
partners  to learn from  one another  and establish a common understanding 
of what each partner does in their field before collaboration can occur. Based on 
the  case  studies,  space  for  consortium  members  to  learn  from  and  come  to  a 
shared understanding of how they all contribute to a project can take the form 
of 
forms  of  mediated 
communication,  or  through  establishing  an  online  platform  or  forum  for  the 
exchange  of  information,  for  example.  In  all  of  the  cases  that  were 
multidisciplinary  in  nature,  regular  meetings  or  communication  throughout  the 
life  of  a  project  were  necessary  as  different  phases  of  a  project  required 
different  knowledge  and  meeting  to  ensure  that  all  partners  shared  the  same 
understanding was  necessary to continue.  One coordinator highlighted how the 
interdisciplinary nature of their project was its success and its biggest challenge, 
leading them to focus on and prioritise time on engaging participants  with each 
other across their scientific fields so that the entire group would understand the 
inputs that each partner was contributing to the project. 

teleconferences,  other 

  On  the  organisational  level,  previous  collaboration  between  project 
partners  may  mitigate  obstacles  that  arise  from  a  multidisciplinary  project. 
Previous experience and a past history of working together can bridge diversity, 
build  trust  and  strengthen  personal  relationships,  facilitating  the  flow  of 
information  within  the  network.  In  almost  all  of  the  cases,  the  project 
coordinators  and  participants  who  were  interviewed  all  had  a  connection  to  at 
least  one  other  partner  within  their  FP7  project  consortium.  Some  level  of 
familiarity and an understanding of other partners and what their expertise and 
way  of  working  is,  is  beneficial  in  overcoming  differences  and  obstacles  to 
multidisciplinary  collaboration.  However,  not  all  partners  have  a  relationship 
with one another prior to entering a project and their relationship will need to be 
developed throughout the course of their collaboration. 

SQ2:  How  do  the  FP  projects  link  different  types  of  organisations  (universities, 

industry, SMEs, etc.) and bridge the flow of innovation ideas between them? 

There  are  fundamental  differences  between  academic  and  industrial  partners  and 
despite their different interests, they are able to collaborate and innovate together. 
When  working  together  these  two  sectors  are  able  to  cover  the  entire  spectrum, 
from  conducting  fundamental  research  to  applying  and  disseminating  research  in 
society.  On  an  organisational  level  there  are  challenges  such  as  different  work 
cultures,  different  expertise  and  different  end  goals  that  project  partners  need  to 
address and overcome for a project to achieve its goals. Despite their organisational 
differences, academia and industry can work together and succeed when they share 
the same  objectives and  when  each partner benefits from  their involvement in the 
collaboration. 

Factors  for  successful  collaboration  between  different  organisations  are  similar  to 
those identified in SQ1, with a few additional dimensions:  

  On  the  project 

level,  there 

for  strong  but  transparent 
is  a  need 
management.  Besides  the  project  objectives,  the project manager  also  needs 
to  ensure  that  each  partner’s  goal  in  participating  in  the  project  is 
communicated  with  all  partners  and  then  brought  together  in  a  way  that  the 
groups can work together, to ensure transparency and facilitate synergy within 
the  group.  In  several  cases,  project  coordinators  prioritised  balancing  the 
expectations  of  project  partners.  Four  of  the  projects  that  involved  different 
organisational  partners  included  in  this  evaluation  held  kick-off  or  cooperation 

194 

 
 
meetings  at  the  initiation  of  the  project  to  bring  partners  together  and 
coordinate  how  to  proceed  in  the  project.  These  meetings  are  particularly 
important  in  the  projects  that  involved  different  types  of  organisations  or 
involved industry and SME partners as they may have different ways of working 
and have different reasons for participating in these collaborations. Expectations 
amongst  different  organisational  partners  needed  to  be  aligned.  In  two  of  the 
largest  projects  that  involved  various  types  of  organisations,  the  coordinators 
and consortiums spent the first two years of the project devoted to networking 
and  engaging  all  partners,  or  they  devoted  time  to  coordinating  and  aligning 
industry and academia’s wishes for the project. The time spent at the beginning 
of these projects consortium members to a common understanding of what role 
each partner would have in the project and how the partners would benefit from 
their collaboration.  

  On  the  personal  level,  good  personal  and  professional  relationships  need 
to  be  established  amongst  members  of  the  group  to  alleviate  any  challenges 
that  they  may  encounter  due  to  their  different  ways  of  working  and  their 
different aims. In one of the cases, a project encountered challenges due to the 
different organisational backgrounds that the participants came from. The group 
encountered  different  work  practices,  different  types  of  expertise,  and 
differences 
in  procedures.  Even  though  the  group  encountered  these 
fundamental differences, the consortium was able to overcome these challenges 
and  produce  results  together  due  to  the  networking  opportunities  they  had  to 
get  to  know  each  other  and  form  stronger  personal  relationships.  In  another 
project,  industry  and  academia  do  not  always  collaborate  with  each  other,  but 
through  the  trust  that  they  had  built  within  their  project  and  the  network  that 
has  been  established  to  bridge  these  two  sectors,  the  consortium  has  not 
encountered any problems between its key actors since the project started.  

SQ3:  How  have  the  FP  projects  contributed  to  new  collaborations  and  the 

integration of new organisations? 

Collaborations  and  the  integration  of  new  organisations  and  new  Member  States 
have  been  strengthened.  As  the  European  Union  continues  to  grow  with  new 
members  and  with  other  countries  expressing  their  desire  and  taking  actions 
towards  integrating  themselves  with  the  EU,  there  has  been  a  demand  for 
knowledge and research in different areas to align the policies of New and Candidate 
Member States with those of the EU. Not only does international collaboration have 
an impact on EU Members, New Members, and Potential Members, collaboration also 
strengthens  relationships  between  organisations  within  these  states.  Through 
working  with  more  established  organisations  and  older  Members  within  the  Union, 
newer  and  candidate  states  have  been  able  to  gain  experience  on  a  more 
international  level,  further  their  research  capabilities,  and  strengthen  their  own 
networks. 

Aside  from  working  together  with  organisations  in  New,  Candidate  and  Potential 
Member  States,  there  has  been  increased  collaboration  in  FP7  projects  with  new 
organisations and expanding already existing networks or creating new ones. On the 
organisational  level,  mature  networks  have  the  ability  to  integrate  new  members 
into their  networks  more  easily  and  have  the  desire  to  strengthen  and  continue  to 
build their network. As innovation often requires a multidisciplinary and cross-sector 
approach,  more  networks  are  being  developed  and  further  strengthened  by  the 
inclusion and addition of new organisations and partners.  

The  following  factors  contributed  to  new  collaborations  and  the  integration  of  new 
organisations within the FP7 programme: 

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  On  the  personal  level,  project  partners  entering  into  new  networks  or  projects 
integrating  new  partners  need  to  establish  their  relationships  and 
familiarity  with  one  another  through  face-to-face  meetings.  The  social 
aspects  of  networks  are  fundamental  to  building  trust  amongst  new  partners 
and  to  develop  professional  relationships.  As  found  in  two  of  the  case  studies 
where  new  Member  States  and  new  organisations  were  a  part  of  the 
consortiums, project meetings were often held over several days. This provided 
ample  time  for  project  partners  to  connect  and  network  in  between  sessions, 
over dinners, and in more relaxed social settings. Networking at these meetings 
facilitated  the  integration  of  new  partners  into  a  group  where  some  of  the 
members already had an established relationship with one another. 

  On  the  project  level,  a  common  goal  needs  to  be  established  with  each 
partner’s role defined within the collaboration in order for each partner to see 
the benefit in their participation. Drawing from one case, partners in this project 
were highly motivated because they recognised that the outputs and outcomes 
from  the  project  were  useful  for  them.  As  each  partner  represented  a  specific 
country,  they  all  had  their  own  knowledge  and  expertise  to  contribute  to  the 
group.  In  another  case,  partners  in  this  network  each  had  a  specific  expertise 
within  the  research  infrastructure,  ensuring  that  each  partner  had  a  specific 
role, eliminating competition  and  promoting collaboration  amongst members of 
the network. 

SQ5:  How  have  FP  project  results  been  used  as  an  input  for  new  FP  projects,  and 

how  has  knowledge  generated  in  previous  FPs  been  transformed  into  new 

knowledge? 

It  is  very  positive  that  FP  funded  projects  develop  new  research,  tools  and 
techniques,  however,  it  is  equally  important  that  the  project  results  are 
communicated,  disseminated,  spread,  and  transferred  to  others  or  lead  to  new 
projects  where  knowledge  can  be  further  developed  and  used  in  new  ways.  Some 
types  of  knowledge  are  relatively  easy  to  disseminate  and  transfer  to  others 
whereas  other  types  of  knowledge  are  very  difficult  to  transfer.  Explicit  knowledge 
can be articulated, codified and stored in different kinds of media such as manuals, 
documents, and procedures ready to transmit to others. Conversely, tacit knowledge 
or  know-how  cannot  be  transferred  and  shared  directly  to  another  person  in  text 
documents or verbally. Observation, imitation, and practice are needed. The key to 
acquire  tacit  knowledge  is  experience.  The  ability  to  speak  a  certain  foreign  or 
‘scientific’  language  or  to  use  a  technique  or  a  tool  to  perfection  are  examples  of 
tacit  knowledge.  It  is  always  important  that  a  project  team  has  been  adept  at 
communicating  and  transmitting  knowledge  and  results  to  others—for  instance,  by 
producing  peer-reviewed  articles,  by 
implementing  a  communication  and 
dissemination  plan,  developing  promotional  material  and  creating  a  dedicated 
website. However, a certain technique and knowledge developed in relation to a FP 
supported project may be so closely connected to a specific person—an expert—that 
it cannot be transferred in a text document or verbally. The expert needs to carry on 
the knowledge herself/himself or to teach it to someone else. 

The  following  factors  are  considered  vital  when  results  are  transferred  from 
concluded FP supported research projects to new FP projects, and to transform the 
results into new knowledge, new methods and new tools: 

  On the personal level, one or more participants on the current FP7 project also 
worked  together  on  a  concluded  FP6  project.  One  can  say  that  one  or  more 
experts  from  one  project  carried  on  tacit  knowledge  they  managed  to 
develop to use in another project. In order to continue working on a certain tool 

196 

 
 
or  technique  they  chose  to  apply  for  new  funding  while  working  on  the  former 
project  due  to  a  desire  to  use  their  obtained  knowledge,  new  methods, 
techniques, and tools in new ways and with new partners. Less than half of the 
cases  in  the  case  study  are  projects  that  build  on  results  from  former  FP 
supported  projects.  With  a  few  exceptions,  those  projects  that  do  not  build  on 
past  project  results  generated  in  connection  to  former  FPs  are  typically  also 
examples  of  projects  where  a  new  network  was  created  in  relation  to  the 
respective  FP7  project.  On  the  contrary,  the  FP7  projects  that  build  on  results 
generated  in  connection  to  an  FP6  project  are  usually  more  established 
networks. That does not  mean  that the whole network of partners is the same 
from one project to another, but that the core of the network has brought some 
of the project results from a concluded FP6 project to a FP7 project in order to 
continue their work and collaboration. Most of the cases of projects that build on 
project results from concluded FP projects are examples of the latter. However, 
in one of the cases, almost the whole network of partners from the FP6 project 
are  included  in  the  FP7  project  network;  however,  a  few  new  partners  have 
been added to the network in order to develop the network and to spread their 
research and knowledge. 

  Also  on  the  personal  level,  the  transformation  of  knowledge  developed  in 
former  projects  seem  to  happen  when  applied  in  new  ways  and  when 
‘confronted’ with perspectives from other people or by taking a new approach to 
an  old  or  new  topic/problem  combining  techniques  from  different  academic 
disciplines  and  different  sectors.  It  appears that  a cross-disciplinary  and  cross-
sectorial project focus is essential to the transformation of knowledge. In almost 
all  of  the  cases,  partners  from  the  respective  FP7  funded  project  want  to 
continue  to  work  together  in  relation  to  other  collaborative  projects  both 
bilaterally  and  unilaterally.  Furthermore,  there  are  also  examples  of  project 
partners who express a wish to continue to work together on new FP supported 
projects and have therefore chosen to apply for new funding or are planning to 
apply. A central reason for applying for new funding is that a new FP supported 
project will enable the partners to continue their collaboration and work on their 
developed tools, techniques and/or theories. Some of the cases are examples of 
networks  where  partners  have  worked  together  during  several  FP  supported 
projects making it possible for them to ‘spread knowledge’ to others as well as 
bring in new perspectives when inviting in new partners to their projects. 

SQ6 and SQ7: How do the FP projects contribute to international research relations 

between  the  EU  and  third  countries  and  foster  a  wider  participation  of 

relevant actors and stakeholders within the ERA? 

A motivation for EU researchers and industries to engage in research collaborations 
with third countries is to access and acquire  complementary know-how and  to find 
solutions  for  scientific  and  technical  problems  that  could  not  be  solved  with  the 
resources found in the EU. The cultural factors on the national level are particularly 
crucial  for  research  relations  between  the  EU  and  third  countries;  differences 
between  research  traditions  of  different  states  seem  to  be  the  main  contextual 
factor in this type of project. Language barriers and legislative barriers should also 
not  be  underestimated.  Additionally,  differences  of  geography,  including  the 
practical  difficulties  of  long  distances  or  simply  time  zone  differences,  cause 
additional  problems  on  the  project  level,  making  project  management  a  huge 
challenge.  

The  following  factors  contribute  to  international  research  relations  and  to  foster  a 
wider participation of relevant actors and stakeholders within the ERA: 

197 

 
 
  On  the  project  level,  a  strong,  but  transparent  management  structure  is 
important  because  of  the  potential  issues  arising  from  culture  differences, 
geographical  distances,  and  language  barriers.  In  one  of  the  12  cases 
highlighted in the case studies, international partners from third countries were 
actively included in the formation and the completion of the project. This active 
inclusion  included  establishing  discussion  forums  for  partners  in  the  EU  and  in 
the  third  country  to  communicate  through  because  telephone  conversations 
were  difficult  for  partners  due  to  language  barriers.  However,  cultural 
differences rooted in national traditions are a contextual factor that is extremely 
difficult  to  control.  Referring  again  to  the  project  mentioned  above,  the 
consortium encountered differences in how academics in the EU and in the third 
country receive funding, affecting the quality and the priorities that were placed 
by partners in the third country on the outputs of the project. These engrained 
academic practices were one challenge that could not be solved through project 
management,  as  it  was  based  on  national  academic  contexts  outside  of  the 
project’s control. 

  On  the  personal  level,  personal  relationships  and  a  mutual  cultural 
understanding  mitigate  the  barriers  associated  with  differences  in  culture.  A 
contextual factor that seems to have a positive effect on building up trust in the 
network is experience with previous collaborations in the same type of projects. 
Similar to multidisciplinary projects or projects involving partners from different 
sectors,  good  personal  and  professional  relationships  build 
trust  and 
understanding that assist in overcoming cultural differences. 

FQ1: How does the network approach promoted and implemented by FP7 contribute 

to the achievement of EU Research Policy objectives? (cross-case synthesis) 

The broad objectives of FP7 are grouped into four categories: Cooperation, Ideas, 
People  and  Capacities.  For  each  of  the  four  objectives,  there  is  a  specific 
programme  corresponding  to  the  main  areas  of  EU  research  policy.  All  specific 
programmes  work  together  to  promote  and  encourage  the  creation  of  European 
poles of (scientific) excellence: 

  Cooperation: The objective is to gain leadership in key science and technology 
areas  by  supporting  cooperation  between  the  main  institutional  actors  of 
research and innovation across the EU and with the rest of the world; 

 

Ideas:  The  objective  is  to  stimulate  creativity  and  excellence  in  Europe  by 
supporting frontier research undertaken by individual researchers in Europe, on 
a competitive basis; 

  People:  The  objective  is  to  develop  and  strengthen  high-skilled  and  mobile 

human capital in Europe; and 

  Capacities:  The  objective  is  to  enhance  research  and  innovation  capacity 

throughout Europe, particularly by supporting research infrastructures. 

Based  on  the  findings  of  the  case  studies,  it  is  clear  that  the  networks  in  the  FP7 
projects contribute to the achievement of EU  Research Policy objectives across the 
four  main  objectives.  This  means  that  one  project  done  under  the  ‘Cooperation’ 
programme  not  only  meets  the  objectives  under  ‘Cooperation’,  but  potentially  also 
the objectives specified under ‘Ideas’, ‘People’ and ‘Capacities’. Another key finding 
from  the  case  studies  is  that  networks  generate  direct  and  indirect  impacts.  The 
direct  network  impact  is  the  measurable  scientific/commercial  output  and  outcome 
attributed  to  the  network,  while  the  indirect  impact  is  the  skills  and  knowledge 
diffused  through  the  network,  as  well  as  the  relations  and  trust  built  between  the 
partners. 

198 

 
 
The  specific  findings  from  the  case  studies  illustrate  how  the  network  approach 
contributes to the achievement of the EU Research Policy objectives: 

  Networks 

inspire  researchers  to  broaden  their  perspective.  An 
interdisciplinary and intersectoral approach brings people together from different 
disciplines  and  different  institutional  sectors  who  would  otherwise  not  work 
together. As researchers work together with academics from different disciplines 
and/or  partners  from  other  institutional  sectors,  they  become  aware  of  what 
others  in  their  field  are  doing  as  well  as  how  other  partners  are  thinking  and 
working, which stimulates creativity and innovation. In the majority of the case 
studies,  there  are  examples  of  collaboration  between  partners  from  different 
academic disciplines and/or sectors, which contribute to knowledge sharing and 
knowledge  creation  by  approaching  a  complex  research  question  or  practical 
problem from different angles and perspectives. 

  Complementarity  is  a  central  driver  in  the  formation  of  the  networks. 
When  complex  projects  are  split  into  assignments  that  are  more  manageable 
and  well  defined,  the  partners  are  able  to  limit  their  research  focus  on  a  few 
aspects while relying on other partners for other aspects. This reduces the level 
of research effort spent on academic fields outside the partners’ core fields. The 
cases  studies  shows  how  a  network—small  as  well  as  large  networks—can 
potentially  lead  to  a  more  efficient  and  effective  use  of  resources  in  complex 
projects  when  managed  properly  by  creating  work  packages  and  delegating 
tasks to specific partner teams with the right competencies. 

  Networks lead to increased mobility of researchers. All the case studies are 
examples of projects where partners from different countries work together and 
have  to  travel  across  borders  to  meet  face-to-face  at  meetings,  seminars  and 
conferences,  or  for  longer  stays  to  do  research  at  a  university  or  research 
institute  in  a  foreign  country.  Some  projects  have  created  small  networks  with 
partners  from  few  countries  and,  conversely,  other  project  networks  comprise 
partners  from  many  EU  countries  and  non-EU  countries.  In  some  of  the  case 
study  projects,  PhD  student  and  junior  researcher  exchanges  are  created  and 
thereby younger scholars are able to build international networks that they can 
leverage in the future. Other cases show how participation in academia–industry 
collaboration  can  encourage  young  researchers  to  be  more  industry  focused. 
Through  collaboration  with  industry,  opportunities  to  translate  laboratory 
research into practice are facilitated and accelerated.  

  Networks  stimulate  innovation  by  transferring  research-related  knowledge 
from universities to industry and vice versa. The networks cover the range 
from  ‘pure  basic  research’,  ‘use-inspired  basic  research’  to  ‘applied  research’. 
Two thirds of the cases studies are examples of research projects where private 
sector  businesses  are  part  of  the  project  network.  All  these  cases  show  that 
even though it does take an effort from all parties to achieve a positive outcome 
when  partners  from  different  institutional  sectors  with  unlike  motivations 
(academic  vs.  industrial  motivation)  work  together,  knowledge  is  shared  and 
transferred across sectors, which fosters creativity and innovation.  

FQ2:  Potentials  and  limits  of  the  network  approach  promoted  and  implemented  by 

FP7 approach? (cross-case synthesis) 

The case studies have identified potentials as well as limits to the network approach. 
It  is  important  to  stress  the  paradoxical  nature  of  the  networks,  where  observed 
limits  of the  networks  can  be  viewed  as  strengths  at  the  same  time.  For example, 
setting  up  a  large  network  largely  strengthens  European  innovation  and  research 
capacity  by  involving  researchers  and  industry  partners  from  different  scientific 

199 

 
 
fields and countries. However, at the same time, the limitation of a large network is 
that it is difficult to manage and involves a high degree of administrative work. 

The findings from the case studies generally support that the network approach has 
a  huge  potential  for  generating  direct  outputs  such  as  peer-reviewed  articles, 
presentations  of  results  at  conferences,  patent  applications,  and  spin-off  projects. 
On  the  longer  term,  the  networks  can  potentially  generate  outcomes  such  as 
stronger  knowledge  transfer  from  research  to  industry,  enhancement  of  research 
performance  in  Europe,  improvement  of  European  research  capacities  and 
competencies. 

However,  a  number  of  limits  are  associated  with  the  network  approach  as  well. 
These are explained in more detail below: 

  The  concept  of  multi-  and  interdisciplinarity:  A  major  component  in  the 
networks is the concept of multi- and interdisciplinarity. This can be a limitation 
in  projects  where  the  partners  have  not  managed  to  develop  a  mutual 
understanding  of  the  project’s  objective  and  thus  cannot  value  the  diversity  in 
the  project.  Furthermore, it  takes  a lot  of  time  and effort  for  researchers  from 
different  disciplines  to  get  to  understand  each  other’s  way  of  thinking  and  to 
obtain  the  mutual  benefits  and  synergies 
from  taking  a  multi-  and 
interdisciplinary approach. 

  Academic vs. industrial motivation: Put in simple terms, academic research 
is  driven  by  the  impact  it  will  have  on  the  global  research  community,  where 
measures  of  success  are  largely  based  on  peer-reviewed  articles  and 
publications.  Research  in  the  industry,  on  the  other  hand,  is  driven  by  
commercial impact, and the measures of success are largely based on the long- 
and short-term value created for the company. Another limitation or dilemma in 
relation  to  cooperation  between  public  and  private  organisations  is  that the  EU 
demands  that  all  research  produced  within  a  FP  financed  project  network  is 
published,  which  most  enterprises  are  not  interested  in  due  to  confidentiality 
and Intellectual Property Right (IPR) issues. In one of the cases, the latter has 
been  an  issue,  which  has  limited  the  integration  of  private  sector  partners  in 
what is a mature and well-functioning network. 

  Legal  issues:  In  projects  with  EU  and  third-country  collaboration,  it  has  been 
identified that a barrier to international collaboration is to obtain travel visas for 
foreign partners. EU-level immigration laws can hinder foreign partners to enter 
the EU—even for meetings and conferences with a scientific purpose. In one of 
the  cases  studies,  EU  visa  obstacles  were  highlighted  as  a  hindrance  to  an 
effective international cooperation. 

  Geographical  distances:  Long  geographical  distances  make  communication 
harder. The main reason is that the frequency of personal face-to-face meetings 
is  lowered  as  the  geographical  distances  increase.  One  of  the  cases  is  an 
example of project where a new network was created with many partners from 
different countries as well as from different academic disciplines and institutional 
sectors. When writing the proposal the project coordinators underestimated the 
need to meet face-to-face in order to break down barriers and therefore they did 
not apply for enough money to cover the travelling costs. Face-to-face meetings 
are necessary for partners to get to know each other, to align expectations and 
in order to understand one’s role in a project, even though partners can handle 
most communication with different ICT tools such as Skype. 

  Large  number  of  project  partners:  There  are  some  limitations  to  a  large 
network—one is the dynamics of the network, where there is a core of partners 
and a periphery of partners, meaning that the benefits gained vary according to 

200 

 
 
the partners’ role in the network. Another limit is the practicalities involved with 
running  a  large  network—it  takes  up  a  lot  of  administrative  and  management 
resources, but can also be extremely cost and resource intensive—for example, 
when  partners  from  different  countries  have  to  meet  face-to-face.  Having  a 
large  number  of  partners  also  creates  challenges  in  regards  to  network 
sustainability and different working styles due to different working cultures and 
norms, as well as discrepancies in expertise. 

201 

 
 
 
 
Appendix IV: Case studies (Task 4) 

Case study I: AgriPolicy, Enlargement Network for AgriPolicy Analysis 

Summary of the case 

The  AgriPolicy  project  illustrates  a  case  where  strong  networks  have  been  created 
with and between new Member States, Candidate countries and Potential Candidate 
countries.  The  aim  of  the  project  was  to  support  the  application  of  EU  agricultural 
policies  in  recent  EU  Member  States  and  Candidate  and  Potential  Candidate 
countries  and  produce  country  reports  on  contemporary  topics  for  DG  Agriculture 
and  Rural  Development.  Creating  networks  between  researchers  focused  on 
agricultural economic analyses was also one of the prime objectives of the project. 

The  project is  a  rather  unusual  FP7  project in  that  the  call  was  identical  to  an  FP6 
call except for the inclusion of a wider network of participants. A key element of the 
project was the development of country reports on contemporary agricultural policy 
topics. The topics were not decided in advanced but as the project was rolled out. In 
this  way,  the  project  was  similar  to  a  framework  contract  where  the  Commission 
could request different pieces of research during the project. 

Strong  networks  have  been  created  among  partners  in  the  project  and  they  have 
continued to work together both bilaterally and unilaterally in several  constellations 
after the conclusion of the AgriPolicy project. 

Among  the  key  factors  behind  the  success  of  the  project  were  highly  motivated 
partners,  a  well  organised  and  managed  project,  aligned  interest  between  the 
project  manager  and  the  EU  Commission,  demand  for  the  output  from  the 
Commission  and  face-to-face  meetings  over  several  days  to  facilitate  networking 
and learning. 

Introduction 

The  AgriPolicy  project  has  been  selected  as  an  example  of  a  project  that  has 
contributed to new collaboration and to integrating new organisations and countries. 
According  to  the  network  analysis  and  survey  results,  AgriPolicy  has  collaborated 
and  worked  with  partners  from  new  Member  States  as  well  as  Candidate  and 
Potential Candidate countries. In addition to this, the project was built on knowledge 
generated  in  previous  framework  programmes,  transforming  the  earlier  work  done 
into  new  knowledge.  Previous  work  has  also  contributed  to  lasting  networks  and 
partnerships  that  continued  into  the  FP7  funded  AgriPolicy  project.  Also  of 
importance  is  that  this  project  established  international  research  relations  between 
the EU and third countries.  

The case is built on interviews with the project leader and four project participants 
from  the  Netherlands,  Slovenia,  Serbia,  and  Bosnia  and  Herzegovina.  Responses 
from the participant interviews have been anonymised in this case study.  

202 

 
 
 
 
Description of the case 

As 

France 

Total cost 

€998,219  

€1,141,128  

Project period 

Coordinated in 

EU contribution 

01.06.2008 – 31.05.2010 

Background data for AgriPolicy 

Agriculture  and  farming  is 
an integral part of European 
EU 
the 
society. 
welcomes 
new  Member 
States,  there  has  been  a 
need to refine these policies 
for  the  more  recent  EU 
Member  States,  upcoming 
members 
potential 
and 
members,  specifically  those 
in  Central  and  Eastern 
AgriPolicy 
The 
Europe. 
project  started  on  June  1, 
2008,  and  concluded  on 
May  31,  2010.  AgriPolicy 
was  funded  with  €998,219 
through the funding scheme 
‘CSA-SA  –  Support  actions’ 
and  under 
the  scientific 
'Food,  agriculture 
theme 
and 
fisheries, 
and 
biotechnology'.  The  aim  of 
the  AgriPolicy  project  was 
to support the application of 
EU  agricultural  policies  in 
recent  EU  Member  States, 
upcoming  Members  and  Potential  Members  by  establishing  a  space  where  insight 
and analyses of agricultural developments in the different countries could be shared 
in order to strengthen policy formulation in the countries involved. 

KBBE-2007-1-4-05 – 
Enlargement network – agro-
economic policy analysis of the 
accession and the candidate 
states and the Western Balkan 
countries 

‘Food, agriculture and fisheries, 
and biotechnology' 

CSA-SA - Support actions 

Funding Scheme 

Sub-programme 

Scientific fields 

The project provided analytical tools to increase the effectiveness of EC measures in 
support  of  agriculture  and  rural  development  within  the  accession  process.  The 
project has produced studies of the agricultural and rural sectors in these regions to 
help the countries upgrade and harmonise their policies with the EU. The project has 
supported  the  application  of  EU  agricultural  policies  by  sharing  information  and 
strengthening policy formulation in 25 concerned countries. 

The objectives of the AgriPolicy project were to firstly provide data and analysis on 
market issues and rural development issues in the 12 new Member States and the 8 
Candidates  and  Potential  Candidate  countries.  Secondly,  the  project  sought  to 
stimulate  networking  and  reinforce  analytical  capacities  of  researchers  in  the 
network. 

Within their overall aims, AgriPolicy had four specific objectives: 

1.  Networking  and  information  sharing:  The  objective  was  to  stimulate  the 
networking  and  the  sharing  of  information  between  organisations  involved  in 
agri-economics analysis. Networking was stimulated through the organisation of 
3 symposia and 6 workshops and the development of a dynamic web site, which 
amongst others, included an updated directory of experts and organisations. 

2.  Provide scientific input for policymaking: The goal was to provide analyses on a 

number of specific topics (8-monthly as well as reports on demand). 

203 

 
 
3.  Prepare  future  analysis:  This  objective  set  out  to  prepare  for  future  policy  and 
sectorial  analyses  and  research  by  collecting  quantitative  and  qualitative 
information. In the new Member States, three monitoring reports of agricultural 
and  rural  were  to  be  prepared.  In  the  Western  Balkan  countries,  the  project 
sought  to  conduct  a  study  on  the  existing  availability  of  key  agricultural  and 
rural statistics. 

4.  Develop  analytical  capacities:  The  aim  was  to  strengthen  the  analytical 
capabilities  of  the  research  organisations  involved  in  the  project  in  the  field  of 
policy  evaluation  and  foresight  analysis.  This  project’s  goal  was  to  implement 
training sessions as well as conduct pilot studies. 

Detailed  country  reports  were  written  to  assist  policymakers  and  stakeholders  to 
streamline and upgrade agricultural policy and harmonise them with the rest of the 
EU.  

Formation of the partnership 

AgriPolicy  builds  on  the  results  of  the  previous  FP6  project  ‘CEEC  AGRI  POLICY’, 
which  had  been  awarded  financial  support  under  FP6  (call-2003-SSP-3  -  task  15: 
Agro-economic  policy  analysis  of  the  accession  and  the  candidate  states  and  the 
countries of the Western Balkans). 

Figure 19  Map of the participating partners 

Source: 

Cordis, website: http://cordis.europa.eu/home_en.html 

The formation of the project was rather different from most FP7 projects in that the 
decision  to  create  the  project  came  from  Agriculture  and  Rural  Development.  It  is 
very  rare  that  the  European  Commission  initiates  a  project  in  this  way.  DG 
Agriculture  and  Rural  Development  published  a  call  with  the  exact  same  text  as  a 
previous FP6 project with the exception that the new project should also include the 

204 

 
 
 
Western  Balkan  countries.  The  FP6  project  only  included  new  Member  States  but 
had  been  highly  useful  for  both  the  participants  and  the  EU  Commission  so  DG 
Agriculture and Rural Development wanted to continue a similar project.  

The  French  consultancy  company  Euroquality  had  the  overall  responsibility  for 
gathering  partners  and  submitting  the  proposal.  Euroquality  was  also  the  project 
leader  in  the  previous  FP6  project  and  most  of  the  partners  from  the  FP6  project 
were also included in the FP7 project. The Slovenian partner in the FP6 project had 
connections  to  relevant  organisations  in  the  Western  Balkans  and  they  assisted  in 
enlarging the network for the FP7 call. The survey conducted in earlier phases of the 
evaluation  also  shows  that  most  partners  had  worked  with  a  large  share  of  the 
participants before the FP7 project was initiated. For the partners who had not taken 
part  in  the  FP6  project  the  situation  was  reversed,  they  had  only  worked  with  a 
small share of the partners previously. 

Process and Implementation 

The project was original in its design. Topics of the reports to be produced were not 
decided in advance but were decided as the projected unfolded. DG Agriculture and 
Rural  Development  called  it  ‘rolling  programming’.  DG  Agriculture  initiated  the 
project  in  order  to  have  a  network  at  hand  that  could  produce  information  and 
reports  on  current  topics  they  needed  insight  into.  In  this  way,  the  project  was 
similar to a framework contract. 

The  work  in  the  project  centred  on  the  compilation  of  statistics  and  agricultural 
economic  analyses  of  a  number  of  specific  topics  as  requested  by  the  Commission 
and  the  production  of  country  reports.  The  project  partners  peer  reviewed  each 
other’s  reports  and  met  face-to-face  to present  and  discuss  the  reports on  specific 
topics.  The  meetings  lasted  for  two  days  to  provide  space  for  networking  and 
socialising.  Most  of  the  participants  were  large  research  organisations  and  in  most 
instances  had  expertise  within  the  topics  selected  for  analysis  by  the  Commission. 
The research was not scientific in nature and was not intended for academic journals 
but for practical applied research.  

Impact at network level 

The project is different from most other FP7 projects as the research carried out was 
applied research and where networking and creating strong networks was one of the 
primary objectives of the project. Networking was mainly promoted through face-to-
face meetings and the creation of a directory of experts on the AgriPolicy website. 

The networking aspect of the project was very successful and partners from Western 
Europe  and  the  Western  Balkans  in  particular  have  continued  to  work  together  in 
several  projects  following  AgriPolicy.  The  partners  from  the  new  Member  States  in 
Eastern  Europe,  who  were  more  advanced  in  their  adoption  of  EU  agricultural 
policies  than  those  in  the  Western  Balkans  and  who  also  took  part  in  the  FP6 
project, were less active in the project and networking related to the FP7 AgriPolicy 
project. 

Following  the  AgriPolicy  project,  Euroquality  has  initiated  a  similar  project  focused 
on Russia, Ukraine and the neighbouring countries called ‘Streamlining of agriculture 
and  rural  development  policies  of  SEE  countries  for  EU  accession’.  The  project 
includes eight partners from the FP7 Agripolicy project and their experience and the 
architecture from the AgriPolicy project was used in the formulation of the project. 

205 

 
 
 
 
Other projects following the FP7 project include: 

 

 

 

The  Joint  Research  Centre  project  ‘Facilitating  an  area-based  development 
approach in rural regions in the Western Balkans’, which includes four Agripolicy 
partners. 

The  FAO,  SWG  RR  (The  Food  and  Agriculture  Organization  Of  the  United 
Nations,  The  Regional  Rural  Development  Standing  Working  Group)  project 
‘Streamlining of agriculture  and rural development policies of SEE  countries for 
EU accession’, which include eight Agropolicy partners. 

‘The  impact  of  socio-economic  structure  of  rural  population  on  success  of  rural 
development policy’ for the Swiss Agency for Development and Cooperation also 
involves three AgriPolicy partners.  

Several  partners  have  also  continued  to  cooperate  bilaterally  on  projects.  The 
Serbian  and  the  Croatian  partners  cooperated  on  the  project  ‘Opportunities  to 
increase marketing of agricultural and food products of family farms’ funded by the 
Ministry of Science in Serbia and the Ministry of Science in Croatia. 

Likewise, the Dutch and the Serbian partners have  cooperated on a project  for the 
Dutch  and  Serbian  governments  investigating  the  implications  for  the  agricultural 
sector  in  Serbia  if  Serbia  joins  EU.  The  Dutch  partner  has  also  worked  with  the 
partner from Lithuania and, through the partner in the Czech Republic, has a visiting 
researcher for two years from the Czech Republic. 

The  partners  from  the  Balkans  have  also  continued  the  network  by  taking  part  in 
and presenting at each other’s conferences. For example, the 14th Congress of the 
European  Association  of  Agricultural  Economists  was  held  in  Ljubljana  in  2014  and 
included  a  section  on  ‘Western  Balkan  agriculture  and  European  integration  of 
participation’,  organised  by  a  participant  in  the  Agripolicy  project  and  with 
presentations from several AgriPolicy partners. 

The  survey  conducted  in  earlier  phases  of  the  evaluation  confirms  that  the  project 
has allowed partners in the project to strengthen linkages with researchers working 
in similar fields of science and technology to a high degree. The interviewed project 
partners also agree that the creation of networks and cooperation facilitated by the 
project  would  not  have  been  possible  without  the  FP7  funding  of  the  AgriPolicy 
project. No other funding stream could have financed a project like AgriPolicy. 

Outputs, outcomes, and impact 

Even  though  the  project  was  not  focused  on  scientific  research,  some  important 
outputs and outcomes have been produced. Several project partners have furthered 
the research compiled in AgriPolicy and distributed it in different ways. For example, 
the  Dutch  partner  conducted  a  comparable  analysis  of  the  partner  countries  in 
AgriPolicy,  which  was  published  in  an  academic  journal.  The  partner  has  also 
presented to international audiences on several occasions, including representatives 
for  the  European  Commission  and  the  Dairy  Industry.  The  German  partner  IAMO 
published  a  book,  ‘Studies  on  the  Agricultural  and  Food  Sector  in  Central  and 
Eastern Europe’, based on the findings from the project. The book was edited by the 
Slovenian  partner  and  included  country  chapters  by  many  project  partners.  As 
mentioned  earlier,  the  partners  from  the  Western  Balkans  have  also  helped 
organise,  participate  in,  presented  at  and  produced  papers  for  each  other’s 
conferences. The project also hired two native English-speaking economists to work 
on  revising  and  improving  the  country  reports,  which  provided  important 
competency building for the researchers from the Western Balkans in particular. The 

206 

 
 
building and collection of statistical indicators has also been an important output for 
several countries due to the general lack of agricultural statistics in many countries. 
These  countries  have  continued  to  update  and  use  the  indicators  developed  under 
the project. 

Reflection on the success factors 

According  to  the  interview  participants,  several  factors  have  contributed  to  the 
success of the project. Firstly, the partners in the AgriPolicy project were motivated. 
The partners could clearly see that the outputs and outcomes from the project were 
useful  for  them,  motivating  them  to  produce  deliverables  of  a  high  quality. 
Furthermore,  the  face-to-face  meetings  facilitated  networking.  The  meetings  held 
during  the  project  were  always  held  over  several  days,  including  overnight  stays, 
dinners  and  different  networking  opportunities  for  the  partners.  Another  success 
factor  for  this  project  was  that  the  interests  of  the  partners  were  aligned.  The 
project was led by a private consultancy company that was focused on providing a 
service  to  DG  Agriculture  and  Rural  Development.  This  is  not  always  the  case  in 
projects  focused  on  research  and  that  are  more  complex.  In  such  projects, 
researchers  may  have  strong  academic  interests  that  may  not  align  with  the 
interests of the Commission or the project description.  

The  demand  for  the  AgriPolicy  project  also  contributed  to  its  success.  The  project 
had  a  good  relationship  with  DG  Agriculture  and  Rural  Development,  who  showed 
interest  in  the  project  and  its  outputs  and  outcomes.  DG  Agriculture  and  Rural 
Development  also  acted  as  a  liaison  between  AgriPolicy  and  DG  Research.  DG 
Research  and  DG  Agriculture  and  Rural  Development  in  particular  also  came  to 
project meetings, illustrating that they had an interest in the project and its output, 
which was motivating for the participants. The demand was extra important in this 
project because country reports were fairly simple and not suitable for publishing as 
academic papers.  Lastly, AgriPolicy was a well-designed and managed project.  The 
project  design  in  the  proposal  struck  a  balance  between  being  clear  about  the 
deliverables and results on the one hand, and allowing flexibility to adopt the project 
as  knowledge  was  generated  on  the  other.  Furthering  this,  the  project  coordinator 
always informed the partners of upcoming tasks well ahead of time and gave them 
sufficient time to produce the requested reports.  

207 

 
 
 
 
Case  study  II:  ATLAS,  Development  of  laser-based  technologies  and  prototype 

instruments for genome-wide chromatin immunoprecipitation analyses 

Summary of the case 

The  overall  objective  of  the  ATLAS  project  has  been  to  develop  innovative 
technologies that will overcome the current limitations of the applicative research in 
the field of molecular biology.  The  project  aimed to  achieve this by defining a new 
laser-based  high-throughput  technology  for  analysing  protein  bindings  in  DNA  for 
use  in  both  research  and  clinical  contexts.  The  project  coordinator  is  connected  to 
the  Second  University  of  Naples.  The  development  of  the  new  technologies  was 
dependent  on  applied  and  basic  research  advances  within  a  variety  of  scientific 
fields. 

The key findings of the case study are: 

 

 

 

The project consortium has been able to develop a novel laser-based technology 
that can be applied in research and in hospital and clinical analysis departments. 
This  has  also  lead  to  the  establishment  of  a  new  field  of  research  overlapping 
various scientific disciplines.  

The  ATLAS  project  has  served  as  a  highly  multi-disciplinary  and  fruitful 
collaboration  platform  that  has  managed  to  balance  the  basic  and  applied 
aspects of the individual scientific fields. 

The outcomes of the ATLAS project have served as the base for future academic 
and commercial collaboration. 

  Some  of  the  research  subtasks  were  dependent  on  each  other  to  be  finished. 
This meant that some of the partners had to wait before they could commence 
their individual tasks.  

 

The research team would have liked for the project period to be extended as it 
was not possible to fully complete the prototype within the FP7 funded period.  

Introduction 

‘ATLAS’  is  an  example  of  a  FP7  funded  project  that  is  based  on  contributions  from 
both  academia  and  industry.  In  addition,  it  consists  of  advances  within  a  range  of 
scientific  fields  that  has  been  represented  by  molecular  biologists,  MDs,  chemists, 
physicists,  IT  experts  and  mathematicians.  Due  to  the  complexity  of  the  line  of 
research  and  to  the  many  technologies  involved  in  the  development  of  the  final 
product,  the  objectives  of  the  ATLAS  project  have  been  reached  through  a  multi-
disciplinary approach.22  

The ATLAS project has been selected as a case that is ‘visibly linking separate fields 
of  knowledge,  bridging  different  fields  of  science’.  It  has  been  funded  through  the 
HEALTH  sub-programme  and  under  the  research  theme  ‘Health’.  The  case  will  be 
evaluated  in  relation  to  the  overall  focal  points  of  the  funding  scheme,  which  are 
‘basic  discoveries  [that]  can  be  translated  into  clinical  applications  and  new 
therapies for the promotion of  health [of the  European citizens]’ by ‘increasing the 

22  The  case  selection  is  based  on  data  from  a  comprehensive  network  analysis  and  survey 
conducted  by  Science-Metrix  and  Fraunhofer  ISI  (see  appendix  I  for  a  methodological 
description). 

208 

 
 
                                                
competitiveness  and  boosting  the  innovative  capacity  of  European  health-related 
industries and businesses’.23  

The  case  study  builds  on  survey  data24  conducted  in  earlier  phases  in  the 
evaluation,  on  relevant  project  material  and  on  four  interviews  with  project 
participants  including  an  interview  with  the  project  coordinator,  who  represents  a 
university.  The  partners,  all  representing  universities,  were  selected  after 
consultation with the project coordinator. Responses have been anonymised. 

The  primary  objective  of  this  case  study  is  to  outline  how  knowledge  is  created  in 
the networks and through cooperation. 

Description of the case 

The  ATLAS  project  has  run 
for a three-year period from 
April 1, 2009, to September 
30,  2012,  with  a  total  cost 
of  €3,841,277.  The  survey 
answers  indicate  that  the 
ATLAS  project  has  been 
realised  through  the  FP7 
‘to 
in  order 
programme 
critical  mass  by 
reach 
sufficient 
accumulating 
financial 
resources, 
equipment, and researchers 
to  achieve 
the  project’s 
objectives’.  The  project  has 
also  been  realised  in  the 
FP7 
context 
to 
programme 
linkages  with 
‘establish 
excellent 
international 
research  partners’  and  to 
‘enhance  the  international 
visibility  of  an  existing  line 
of research’. 

the 
in  order 

of 

Background data for ATLAS 

Project period 

01.04.2009 – 30.09.2012 

Total cost 

€3,841,277  

EU contribution 

€2,930,599  

Coordinated in 

Italy 

Sub-programme 

Funding Scheme 

HEALTH-2007-1.1-4 – SME – 
driven collaborative research 
projects for developing tools 
and technologies for high-
throughput research 

CP-TP - Collaborative Project 
targeted to a special group 
(such as SMEs)25 

Scientific fields 

‘Health’ 

The primary aim of the ATLAS project has been to develop innovative technologies 
that will overcome the current limitations of  the  applicative research in  the field of 
molecular  biology  by  defining  a  new  laser-based  high-throughput  technology  for 
analysing  protein  bindings  in  DNA.  Conventionally,  this  has  been  done  through  a 
chemical  crosslinking  chromatin  immunopreciptation  technology  (ChIP).  This 
technique  has  a  number  of  intrinsic  technical  limitations,  including  selectivity, 
accuracy, sensitivity, and efficiency. The partners of the ATLAS project have shown 
that these limitations can be overcome through the application and integration of a 

23 The Cordis website: http://cordis.europa.eu/programme/rcn/852_en.html 

24 The survey data is based on the answers from two survey respondents 

25 The Cordis website: http://cordis.europa.eu/programme/rcn/852_en.html 

209 

 
 
                                                
tuneable ultraviolet laser. As listed in the survey answers, generating contributions 
to fundamental science is of high relevance for the ATLAS project.  

The  knowledge  of  mechanisms  leading  the  interactions  among  bio-molecules  in 
living cells represents one of the main goals in molecular biology in order to define 
the  dynamics  of  (direct  and  indirect)  bindings.  Understanding  the  mechanisms  as 
well as the spatio-temporal patterns of binding in a single cell or in small groups of 
cells is critical to a full understanding of gene expression. The understanding of this 
genome-encoded  information  will  act  as  a  resource  to  study  pathology.  Normally, 
the  establishment  of  a  stable  interplay  between  nucleic  acid  and  proteins  (in 
particular  DNA  proteins)  is  obtained  through  chemical  crosslinking  chromatine 
immunoprecipitation  technologies  (XChIP)  involving  the  use  of  a  bi-functional 
reagent,  for  instance,  formaldehyde.  However,  XChIP  does  not  allow  factor-DNA 
interaction studies on a temporal scale less than a few minutes. In addition, it does 
not  facilitate  the  study  of  <106  cells  or  cell  populations  within  complex  biological 
samples.  

By  applying  a  novel  type  of  tuneable  femtosecond  laser  pulse  of  high  energy  and 
extremely  short  duration  (LChIP)  and  by  connecting  the  laser  with  a  microfluidic 
system,  which  may  sort  out  specific  cell  populations,  ATLAS  aims  to  overcome  the 
limitations of XChIP in terms of selectivity, accuracy, efficiency and sensitivity. The 
technology  had  been  successfully  utilised  in  vitro  beforehand  but  the  goal  was  to 
apply  it  to  living  human  cells.  In  addition,  the  project  partners  wanted  to  develop 
software  to  automate  the  laser  and  microfluidic  station  system  and  by  that, 
construct  a  new  versatile  and  user-friendly  dedicated  machine  of  high  commercial 
interest  available  to  the  scientific  community.  In  the  survey,  this  point  is  also 
emphasised  as  preparing  the  ground  for  applications  outside  the  academic  sector 
and was selected as being highly relevant for the ATLAS project. 

Formation of the partnership 

Since  2008,  researchers  in  general  pathology  and  applied  physics  from  the 
University of Naples and from the Second University of Naples have been dedicated 
to  developing  laser-based  chromatin  immunoprecipitation  technology.  It  was  clear 
from the beginning that a significant development of the technology would need to 
include  expertise  from  a  range  of  different  scientific  fields.  Thus,  it  needed  to  be 
executed on a larger scale than the local funding in  Italy, and Naples in particular, 
would  allow.  With  the  multidisciplinary  scope  in  mind,  this  line  of  research  was 
realised through funding from the FP7 programme.  

The  coordinator  was  in  charge  of  the  application  for  the  FP7  programme  and  the 
organisation of the ATLAS project and all its activities. Several SMEs and academic 
institutions  that  the  project  coordinator  knew,  as  well  as  some  participants  from 
former  research  projects,  joined  the  network  as  partners.  Therefore  it  was  not  a 
completely  but  partly  new  network.  All  the  participants  were  divided  into  work 
packages (WPs) based on their scientific expectations, contributions, and expertise. 
The industrial partners were integrated from the beginning of the project since their 
role was mainly based on their potential to reach the overall target. The coordinator 
was  in  charge  of  a  WP  to  set  up  the  biological  conditions  for  the  LChIP.  Their 
connections, both with academic and industrial partners, have been central nodes in 
the network in realising this new technology.  

The list of SMEs covers: 

 

Light  Conversion  Ltd.,  Lithuania  (specialised  in  making  the  dedicated  laser 
prototype) 

210 

 
 
  Diagenode SA, Belgium (provision and development of bioproducts) 

  Sigolis AB, Sweden (development of microfluidics station) 

  Net Brinel SA, Romania (software development) 

Besides  the  Second  University  of  Naples,  the  following  universities  and  scientific 
institutions have been involved in the project: 

  Department of Physics, the  University  of  Naples, Italy (concerned in setting up 
and integrating the new laser and the optical apparatus in order to prepare the 
UV-crosslink in cells, physics) 

 

The French National Center for Scientific Research, France 

  Department  of  Molecular  Biology,  Radboud  University  Nijmegen,  Holland 
(engaged with the genome-wide research and sequencing, molecular biology)  

 

Laboratory  for  Molecular  and  Biomolecular  Physics,  INCDTIM  Cluj-Napoca, 
Romania (concerned with the theoretical modelling of the interaction of the laser 
with the biological systems) 

  Department  of  Organic  Chemistry,  University  of  Vigo,  Spain  (focused  on  the 

chemical reactions and mechanisms behind crosslinking, chemistry) 

 

Institute of Genetics and Cellular Biology, University of Strasbourg, France 

The academic partners from Holland and Italy also provided PhDs for the laboratory 
tasks in the project. Figure 20 maps the partners across Europe. 

Figure 20  Map of the participating partners 

Source: 

Cordis, website: http://cordis.europa.eu/home_en.html 

211 

 
 
 
Process and Implementation 

All the partners attended a kick-off, a mid-term, and a final meeting in Naples, Italy, 
in  Vigo,  Spain,  and  in  Cluj,  Romania,  respectively.  Aside  from  these  three 
consortium meetings, the participants were divided into workgroups that kept their 
own  schedule  of  meetings.  Throughout  the  project  period,  the  participants  held 
regular  Skype  meetings,  and  general  communication  through  emails  was 
maintained. 

With  focus  on  the  interdisciplinary  group  and  the  various  geographical  locations  of 
the  participants,  the  coordinator  made  an  effort  to  organise  the  project  in  a  way 
that allowed an unrestrained flow of knowledge sharing despite the obvious barriers. 
The  coordinator  has  made  an  effort  to  include  a  range  of  social  activities  in  the 
project period to create an atmosphere in which participants from distinct scientific 
fields can ask each  other to ‘explain it  like you would to a child’.  Judging from  the 
interviews, this transparency of the collaborative process has heightened the overall 
productivity and efficiency of the workgroups. This has been an essential element in 
the  organisation  of  the  project,  as  many  challenges  arose  from  the  range  of 
expertise  in  different  scientific  fields,  of  industry  partners,  and  of  nationalities 
together  in  one  network.  This  can  be  seen  in  the  answers  from  the  survey  where 
‘discrepancies in scientific expertise’ ranks  as one  of the three biggest obstacles  in 
the project collaboration. The second obstacle concerns ‘discrepancies with regard to 
the  ability  to  transfer  scientific  expertise  into  practice’.  The  interviews  have  also 
given  insights  to  this  obstacle  by  pointing  out  the  difficulties  of  conducting 
comprehensive  basic  research  on  one  hand,  and  being  able  to  develop  specific 
instruments on the other hand.  

The interdisciplinary aspect of the project also played a major role in the course of 
the  project.  Some  of  the  research  tasks  were  dependent  on  each  other  to  be 
finished.  For  example,  the  Swedish  partners  who  were  in  charge  of  designing  the 
microfluidics system had to wait for the completion of the first laser system before 
being  able  to  begin  their  tasks.  The  Lithuanian  partners  were  delayed  around  six 
months in delivering the laser prototype, but according to one interviewee, this may 
have been due to the level of specialisation required to design the dedicated laser. 
Generally,  this  meant  that  delays  in  one  work  package  caused  a  shift  in  another 
work package. 

The  collaboration  between  the  industry  and  the  research  institutions  has  shown 
some  differences  of  interest  and  approach.  One  academic  participant  stated  that 
some  of  the  industry  partners  have  been  oriented  towards  fulfilling  their  specific 
task within the project and then concluded the collaboration more or less right after. 
The  academic  institutions,  on  the  other  hand,  are  still  engaged  in  the  ongoing 
process  of  research  within  the  field.  The  development  of  the  technologies  has  also 
demanded  a  certain  level of  basic  research  in  order  to  understand  the  workings  of 
proteins in DNA. Still, the general focus of the project has been the development of 
the  prototype  itself.  The  principal  researcher  of  the  chemistry  work  package  found 
themselves  under  prioritised  and  restrained  economically  as  opposed  to  the  laser 
work  package  and  the  more  clinically  oriented  research  teams  and  industry 
partners.  Nonetheless,  the  same  interviewee  stated  that  the  unequal  division  of 
tasks and resources was necessary for the completion of the objectives. The money 
should thus be divided according to the cost of the specific experiment.  

Contextual factors 

One  of  the  central  contextual  factors  of  the  ATLAS  project  was  the  pre-existing 
network  of  the  coordinator  and  some  of  the  partners.  The  chemistry  team  from 

212 

 
 
Spain had also taken part in former collaborations with the Italian participants. They 
were  therefore  chosen  to  join  the  project  because  the  coordinator  knew  their 
expertise  and  competences  beforehand.  The  same  goes  for  some  of  the  other 
partners,  such  as  the  physics  team  from  the  University  of  Naples  who  had 
collaborated  with  the  Lithuanian  laser  developers.  The  Swedish  company  and  the 
Romanian  company  had  not  been  collaborating  with  any  of  the  other  partners 
before. 

It  is  also  important  to  underline  that  the  project  coordinator  has  been  part  of  the 
line  of  research  from  the  very  start.  According  to  the  interviews,  the  coordinator 
took part in the earliest research on the subject and has had a central role in both 
the initial research and the organisation aspect of the project. 

Impact at network level 

Some of the academic participants from Italy and the Netherlands, together with the 
Swedish  SME,  have  continued  collaborating  in  BLUEPRINT,  another  large-scale 
research  project  with  41  leading  European  universities,  research  institutes  and 
industry  entrepreneurs  funded  by  the  FP7  programme.  This  project  continues  the 
line of epigenomic research that was presented in ATLAS by trying to uncover how 
genes  are  activated  or  repressed  in  both  healthy  and  diseased  human  cells.  This 
may  lead  to  targeted  diagnostics,  new  treatments  and  preventive  measures  for 
specific diseases in individual patients. This point is supported by the data from the 
survey  in  which  it  is  stated  that  the  ATLAS  project  has  allowed  the  participants  to 
strengthen  their  collaboration  with  researchers  from  the  EU  and  FP7  associated 
countries to a very high degree. Furthermore, according to the survey data, ATLAS 
has  contributed  to  the  new  establishment  and  the  further  development  of  large-
scale joint research  infrastructures to a high degree.  This is also emphasised in an 
interview with the coordinator where it is stated that the ATLAS project has inspired 
a  new  project  (BLUEPRINT)  with  a  more  focused  laser  application  based  on  the 
researchers’ experience from ATLAS.  

Outputs, outcomes, and impact 

The  ATLAS  project  has  been  successful  in  establishing  a  solid  connection  between 
the  academic  world  and  the  industry  in  the  development  of  laser-based  molecular 
research. The case study shows that the project has been successful in fulfilling the 
objectives of this theme stating that ‘basic discoveries can be translated into clinical 
applications  and  new  therapies  for  the  promotion  of  health  [of  the  European 
citizens]’.  The  collaborative  effort  of  the  project  has  also  helped  in  ‘increasing  the 
competitiveness  and  boosting  the  innovative  capacity  of  European  health-related 
industries and businesses’ and must be evaluated.26 

The central outcome of the ATLAS project has been the development of a new laser-
based  technology  to  use  in  molecular  biology.  This  prototype  is  meant  to  be 
applicable  in  both  research  and  clinical  settings  and  is  thus  a  contribution  to  both 
basic  and  applied  science.  One  participant  stated  that  ‘the  ATLAS  project  was  a 
highly interactive project and a very  fruitful collaboration that managed to balance 
the  basic  and  applied  aspects’.  This  echoes  the  objectives  of  the  HEALTH  sub-
programme,  which  emphasise  ‘whereby  basic  discoveries  can  be  translated  into 

26 The Cordis website: http://cordis.europa.eu/programme/rcn/852_en.html 

213 

 
 
                                                
 
clinical applications’. The development of the prototype has been made possible by 
individual  and  collaborative  efforts  of  various  scientific  fields  represented  both  by 
academic  and  industrial  institutions.  Therefore,  the  output  of  ATLAS  has  also 
contributed  to  the  advancement  of  specific  new  technologies  and  innovations, 
namely  the  dedicated  laser  developed  by  the  Lithuanian  company  and  the 
microfluidic system  developed by the Swedish  company.  This corresponds with the 
objectives  of  the  HEALTH  sub-programme  where  ‘increasing  the  competitiveness 
and  boosting  the  innovative  capacity  of  European  health-related  industries  and 
businesses’ is key. 

Moreover, ATLAS has brought with it the emergence of new lines of research within 
the represented scientific fields. The survey found that there was a high satisfaction 
with the long-term outcomes of ATLAS. This is further supported by another point in 
the  survey  where  it  was  assessed  that  the  project  has  initiated  a  new  line  of 
research to a high degree. In an interview, the coordinator stated that some of the 
discoveries have changed the standard views of biomedicine. 

Another specific outcome of  the  ATLAS project has  been the construction of a new 
lab  at  the  Department  of  Physics  at  the  University  of  Naples.  This  was  due  to  the 
fact  that  even  though  there  was  a  relatively  short  distance  to  transport  cell 
populations  between  the  two  participating  institutions  in  Naples,  transporting  the 
cells had a significant influence on their condition. Therefore, adding a new section 
to the lab at the physics department made it possible to keep the bioproducts close 
to the laser and therefore decrease the number of influential factors.  

According  to  one  interviewee,  the  project  has  not  influenced  the  scientific  field  as 
much  as  it  could  have.  If  the  funding  had  been  greater  and  the  project  period 
longer,  they  could  have  made  a  bigger  impact  as  they  still  had  not  solved  all  the 
problems connected to the technology at the end of the project. In this researcher’s 
opinion,  at  the  conclusion  of  the  project  period  the  project  team  could  not  yet 
demonstrate that they were at a point where the technology was going to work in a 
routine  setting.  According  to  them,  this  could  have  been  solved  by  dividing  the 
project  into  phases  from  the  beginning  so  that  the  building  and  designing  of  the 
machines would be complete before conducting the biological experiments. In their 
opinion, the total period should have been longer since three years was a short time 
to  organise  and  integrate  activities  in  interdisciplinary  projects.  The  project  team 
would  end  up  under  pressure  to  fulfil  the  objectives  because  money  would  be 
running out.  

From  the  survey  data,  the  ATLAS  project  has  resulted  in  the  production  of  the 
following types of scientific and technological knowledge: 

 

Peer-reviewed articles 

  Other academic publications (e.g. books, proceedings) 

 

Presentation of results at conferences or workshops  

  Development of new models or approaches 

  Awarded recognition for contribution to fields 

The survey data also shows that ATLAS  has resulted in the development of a ‘new 
instrument type, new software, new hardware, and new datasets’.  

214 

 
 
 
 
 
 
Reflection on the success factors 

According  to  the  project  coordinator,  one  of  the  greatest  success  factors  of  the 
ATLAS  project,  its  multi-disciplinary  aspect,  had  also  been  one  of  its  greatest 
challenges.  To  meet  this  challenge,  the  coordinator  was  very  focused  on  engaging 
the  participants  with  each  other  across  scientific  fields—both  concerning  social 
interaction  and  concerning  professional  sharing  of  knowledge.  In  this  way,  the 
participants  had  been  better  equipped  to  understand  the  overall  progress  and 
process  the  inputs  from  each  individual  scientific  field.  This  was  elaborated  in  an 
interview  with  a  principal  researcher  who  said  that  interdisciplinary  groups  in 
particular  need  all  the  internal  communication  they  can  get  since  people  with 
different  backgrounds  need  time  to  understand  the  other  researchers  and  experts. 
In  their  opinion,  the  consortium  has  been  very  open  and  no  questions  were 
regarded as stupid. 

One participant stated in the interview that another defining success factor was the 
strong  relations  between  some  of  the  partners.  As  mentioned  earlier  in  the  case 
study,  many  of  the  partners  had  worked  together  in  previous  projects.  This  was  a 
very important factor in the project for two reasons: 

1.  The  application  and  formation  process  was  eased  since  the  coordinator  and 
some of the participants already knew who would have the proper expertise and 
capacity to deliver. 

2.  The  joint  experience  from  earlier  collaborations  allowed  the  partners  to 

commence the tasks quickly.  

Another  success  factor  mentioned  in  the  interviews  is  the  proof  of  principle  that 
motivated  the  project  team  to  keep  working  on  the  project.  Knowing  that  the 
objective  was  achievable  through  the  completion  of  individual  tasks  was  a  major 
motivational factor that has laid the foundation for next generation sequencing, and 
which is being further developed in the BLUEPRINT project. 

215 

 
 
 
 
Case Study III: Climate for Culture, Damage risk assessment, economic impact and 

mitigation strategies for  sustainable  preservation of cultural heritage in the 

times of climate change 

Summary of the case 

The  Climate  for  Culture  project  is  an  example  of  a  project  where  the  project 
coordinator has managed to build a large network consisting of partners—some pre-
existing partners and several new partners—from many different countries (EU and 
non-EU countries), from different sectors, and from different academic disciplines. It 
is  also  a  good  example  of  a  project  that  has  been  built  on  results  generated  in 
concluded  research  projects  supported  by  former  framework  programmes  (such  as 
Noah’s Ark), which have then been transformed into new knowledge, new methods 
and new tools. 

The  main  conclusion  from  this  case  study  is  that  effective  coordination  of  work, 
active project leadership and delegation of work are necessary to secure satisfactory 
progress  in  large  projects,  especially  when  they  contain  many  smaller  projects 
(work  packages)  and  are  dependent  on  many  parties.  These  factors  have  been 
central  regarding  network  development,  collaboration  and  knowledge  creation.  In 
the  case of  the  Climate  for  Culture  project, two  success  factors  are  pointed  out  as 
decisive. 

First was project management. Across partners, the management of this project was 
rated high. Key characteristics of how this project was managed include: 

 

 

Impartiality and respect of each partner 

Technical insight in the different work packages 

  Coordination and facilitation of work progress 

  Delegation of work 

Secondly, face-to-face meetings between partners also contributed to the success of 
the  project.  Some  meetings  were  mandatory  for  all  partners  and  others  were  in 
connection  to  the  work  packages.  There  is  a  consensus  that  the  consortium  would 
not  have  achieved  what  they  did  without  face-to-face  meetings  where  knowledge 
was shared and networks were formed for future collaboration. 

Introduction 

The  FP7  project  ‘Climate  for  Culture  –  Damage  risk  assessment,  economic  impact 
and  mitigation  strategies  for  sustainable  preservation  of  cultural  heritage  in  the 
times  of  climate  change’  is  an  example  of  a  project  that  has  been  built  on  results 
from  concluded  EU  research  projects  with  the  goal  of  ensuring  an  efficient  use  of 
resources. The case was selected in order to study a project that was strongly built 
on  knowledge  generated  in  former  Framework  Programme  supported  projects, 
amongst others, ‘NOAH’s ARK – Global Climate Change Impact on Built Heritage and 
Cultural Landscapes’, which has been transformed into new knowledge.27 The output 
and outcomes of the Climate for Culture project should be assessed in the context of 

27  The  case  selection  is  based  on  data  from  a  comprehensive  network  analysis  and  survey 
conducted  by  Science-Metrix  and  Fraunhofer  ISI  (see  appendix  I  for  a  methodological 
description). 

216 

 
 
                                                
the FP7-ENVIRONMENT funding scheme. The overall objective of FP7-ENVIRONMENT 
is to 

support  research  activities  carried  out  in  different  forms  of  trans-national 
cooperation  across  the  European  Union  and  beyond.  Furthermore,  to  address 
environment  issues  including  climate  change  and  to  advance  knowledge  on  the 
interactions  between  the  climate,  the  biosphere,  ecosystems  and  human  activities 
and  to  develop  new  technologies,  tools  and  services  that  will  ensure  a  more 
sustainable management of the environment and its resources.28 

The  case  study  builds  on  survey  data29  conducted  in  earlier  phases  of  the 
evaluation,  on  relevant  project  materials  and  on  five  interviews  with  project 
participants including an interview with the project coordinator. The other partners 
were  selected  in  consultation  with  the  project  coordinator  and  are  all  academic 
researchers. Responses from the participants have been anonymised. 

The primary objective of the case studies is to outline how knowledge is created in 
networks and through cooperation. 

28 http://cordis.europa.eu/programme/rcn/855_en.html  

29 The survey data is based on the answers from one survey respondent 

217 

 
 
 
 
                                                
Description of the case 

from 

period 

The  Climate 
for  Culture 
project  has  run  for  a  five-
1 
year 
November  2009 
to  31 
October  2014,  and  had  a 
total  cost of  €6,556,647,  of 
which  the  EU  contributed 
€4,964,866.  The  overall 
to  generate 
objective 
is 
contributions 
to 
fundamental 
science,  as 
well  as  outside  academia, 
through 
the 
impact of climate change on 
cultural heritage. 

studying 

the 

reasons 

According  to  the  survey 
data, 
for 
undertaking  the  Climate  for 
in 
Culture  project 
the 
the  Seventh 
context  of 
Framework 
Programme 
rather  than  in  the  context 
of  a  national  or  regional 
funding programme are: 

Background data for Climate for Culture 

Project period 

01.11.2009 – 31.10.2014 

Total cost 

€6,556,647 

EU contribution 

€4,964,866 

Coordinated in 

Germany 

Sub-programme 

ENV.2008.3.2.1.1. 
Development and 
application of 
methodologies, 
technologies, models and 
tools for damage 
assessment, monitoring and 
adaptation to climate 
change impacts 

Funding Scheme  CP-IP - Large-scale 
integrating project 

 

 

 

 

Scientific fields 

To 
improve 
complementarities  and 
diversity  in  research  by 
using information, data, 
techniques, 
concepts, 
and/or theories from several disciplines 

To better connect science and society 

Built Environment & Design; 
Engineering; General Art, 
Humanities & Social Science 

To establish linkages with excellent research partners 

To enhance the international visibility of an existing line of research 

In 

the 

interviews, 

The  Climate  for  Culture  project  had  a  multidisciplinary  approach.  The  institutions 
working on the project came from different scientific fields such as economics (one 
partner),  mathematics,  engineering,  and  design. 
the 
multidisciplinary  approach  is  described  as  a  challenge,  but  at  the  same  as  a 
necessity due to the cross-disciplinary project focus. Climate for Culture focused on 
damage risk assessment, economic impact and mitigation strategies for sustainable 
preservation  of  cultural  heritage  in  the  times  of  climate  change.  The  aim  of  the 
project was to study the impact of climate change on historic buildings by choosing 
a new approach combining climate modelling and building simulation. The objective 
of the project was to improve the assessment of the climatic functioning of historic 
buildings and their future energy demand. In order to improve the assessment, they 
used whole building simulation with regard to the dangers for the interior equipment 
or  works  of  art  and  resulting  measures  to  improve  conditions.  For  these  research 
tasks, the newest available technology from European projects (AMECP, LASERACT, 
Noah's  Ark,  Friendly  Heating,  EUROCARE  EU-1383  PREVENT,  OnSiteforMasonry, 

218 

 
 
SMooHS)  were  applied.30  The  Climate  for  Culture  project  team  tested  and  further 
developed  a  variety  of  tools  and  models  to  study  the  impact  of  climate  change  on 
the  interior  of  historic  buildings  using  different  types  of  technologies  from  these 
former framework programme projects. 

Formation of the partnership 

Fraunhofer-Gesellschaft  zur  Förderung  der  angewandten  Forschung  e.  V. 
(Frauenhofer), Germany, was the coordinator of the project and was responsible for 
scientific,  technical,  financial  and  administrative  coordination  in  close  cooperation 
with the steering committee. The coordination of the Climate for Culture project was 
divided into the following three fields: Overall Coordination, Scientific Coordination, 
and  Financial  and  Administrative  Coordination.  The  team  consisted  of  27 
multidisciplinary  partners  plus  associated  partners  and  stakeholders  such  as 
museums from all over Europe and Egypt, including the world’s leading institutes in 
climate modelling and whole-building simulation. Even though some of the partners 
had  collaborated  on  past  EU  supported  projects,  many  of  the  partners  had  never 
collaborated before becoming a part of the Climate for Culture project, meaning that 
a  new  network  was  formed  in  the  context  of  the  Seventh  Framework  Programme. 
Figure 21 shows a map of the partners in the project. 

Figure 21  Map of the participating partners 

Source: 

Cordis, website: http://cordis.europa.eu/home_en.html 

Within  Climate  for  Culture  there  was  a  strong  focus  on  cross-sector  collaboration 
between  research  institutions,  public  sector  organisations  and  industrial  partners 

30 http://www.climateforculture.eu/index.php?inhalt=project.overview  

219 

 
 
 
                                                
including  SMEs  to  ensure  the  transfer  of  knowledge,  tools  and  technologies  into 
practice  and market.  The goal  was that cross-sector collaboration  would  contribute 
to European competitiveness, innovation and job creation. Besides the partners and 
associated  partners,  there  were  also  18  PhD  students  from  different  disciplines 
involved in the project. 

Furthermore,  there  was  a  Political  Dissemination  Board  attached  to  the  project. 
Their  function  as  an  advisory  board  was  to  ensure  the  dissemination  of  politically 
relevant project results. The Political Dissemination Board included members of the 
EU  Parliament,  National  Ministries,  Local  Authorities  as  well  as  persons  involved  in 
the wider range of decision making. 

Climate  for  Culture  was  divided  into  11  work  packages.  For  each  work  package,  a 
key  partner  was  assigned  as  a  work  package  leader.  The  responsibility  for  the 
implementation  of  activities  concerning  the  respective  work  packages  were 
delegated  by  the  overall  project  coordinator  to  the  work  package  leaders.  Still  the 
project coordinator played a large role in terms of making sure each work package 
progressed as planned. 

Process and Implementation 

The  Climate  for  Culture  project  had  three  phases:  the  application  phase,  the 
planning and networking phase, and the research and implementation phase. 

The  project  coordinator  at  Frauenhofer,  who  is  based  in  Munich,  Germany,  was  in 
charge  of  writing  the  proposal,  which  led  to  the  Climate  for  Culture  project.  The 
project,  which  is  partly  based  on  a  former  proposal  that  was  not  accepted,  reuses 
some  of  the  work  packages  from  the  original  proposal.  Climate  for  Culture  is 
therefore  also  an  example  of  a  project  developed  over  many  years.  Frauenhofer 
developed  the  project  structure,  assembled  the  multidisciplinary  project  team  and 
coordinated  the  proposal  writing  process.  Input  from  the  different  partners  was 
taken into account when several of the partners met at a conference in Prague and 
it  was  then  that  the  main  themes  of  the  project  were  defined.  The  work  package 
leaders  in  particular,  played  a  key  role  in  defining  the  content  and  scope  of  the 
specific work packages. 

The  planning  and  networking  phase  took  place  over  the  first  year  or  two  of  the 
project period. As many of the partners did not know one another before becoming 
a partner in the Climate for Culture project, a lot of time and effort was devoted to 
engage all partners and involving researchers.  

At the beginning of the project period, the project managers thought the network of 
partners was too big. Some of the partners found it difficult to find their role in the 
project, which as described was very complex. According to the project coordinator, 
it  took  at  least  one  year  for  partners  to  become  familiar  with  their  role  in  the 
project. Besides the networking issues, a minor redesign of the project was carried 
out  to  ensure  a  better  outcome  of  the  project.  Some  of  the  work  packages  were 
split-up into sub-work packages in order to distribute tasks more efficiently. 

After  a  year  or  two,  the  partners  found  their  roles  in  the  project  and  things 
developed more or less according to the work plan. The work package leaders were 
in  charge  of  the  progress  of  their  respective  work  package  and  together  with  the 
project  coordinator,  they  directed  the  partners  who  contributed  to  each  work 
package. According to the technical review report written 30 months into the project 
period,  the  overall  objectives  of  the  project  were  achieved  and  they  were  making 

220 

 
 
satisfactory  progress  in  relation  to  the  work  plan,  even  though  one  of  the  work 
packages was delayed. 

In order to secure project progress, different meetings were organised regularly in 
collaboration  with  the  Project  Steering  Committee  (PSC)  and  the  Work  Package 
Leaders (WPL). Official project meetings were held every 12 months and each Work 
Package  (WP)  met  additionally  if  scientific  results  required  further  discussion.  The 
Mid-Term Assessment meeting, aimed at all partners, the Advisory Panel Members, 
the European Commission's representative and independent reviewers appointed by 
the  EC,  served  the  purpose  of  reporting  on  the  progress  to  date  and  of  managing 
the exploitation of results.31 Across partners, the face-to-face meetings are regarded 
as essential to the progress and implementation of the project objectives. There is a 
consensus that they would not have achieved what they did without prioritising the 
face-to-face  meetings,  even  though  some  partners  are  critical  of  the  mandatory 
five-day  Annual  Meetings  with  the  entire  consortium,  which  took  a  significant 
amount  of  much  time.  Others  believe  it  would  have  had  a  positive  effect  if  the 
partners  had  met  face-to-face  more  often  and  for  more  days  in  a  row  at  the 
beginning of the project in order to get to know one another. 

In terms of securing project progress, the coordinating institution, Frauenhofer, and 
the  assigned  project  coordinator  were  regarded  as  highly  competent  by  the 
interviewees. For example, the project coordinator travelled around Europe to meet 
the  partners  and  arranged  meetings  in  order  to  ensure  things  were  progressing. 
Other important and key characteristics of the project management were: 

 

 

Impartiality and respect for each partner 

Technical insight in the different work packages 

  Coordination and facilitation of work progress 

  Delegation of work 

Another  important  factor  in  terms  of  satisfactory  project  progress  was  that  the 
consortium  consisted  of  highly  motivated  and,  in  general,  skilled  partners.  Despite 
this,  there  were  examples  of  problems  in  terms  of  aligning  expectations  when 
partners from different backgrounds worked together. Especially when partners are 
depending on other people’s work, it is important that expectations are aligned and 
deadlines are met. 

Contextual factors 

One of the key contextual factors was a lack of knowledge on a certain area in the 
research on climate  change and its impact on humanity and its environment. Little 
was known about whether or how climate change influences cultural heritage before 
the Climate for Culture project was initiated, even though much research has been 
done  to  study  climate  change  and  to  explore  the  impact  of  climate  change  on 
different  aspects  of  humanity  and  our  environment,  such  as  agriculture  and 
biodiversity.  This  ‘knowledge  gap’  paired  with  the  necessary  skills  and  building 
blocks developed in former  framework  programme projects and a former  proposal, 
which was not accepted, created the necessary platform for the Climate  for Culture 
project. 

31 http://www.climateforculture.eu/index.php?inhalt=meetings.projectmeetings  

221 

 
 
                                                
An  important  reason  for  the  lack  of  information  on  how  climate  change  influences 
cultural heritage—in this case, the  indoor environments in historic buildings—is the 
lack  of  sponsors  and  funding  possibilities  in  the  cultural  field.  According  to  the 
interviews, it is normally very difficult to get financing for research projects focused 
on cultural heritage. 

Impact at network level 

The Climate for Culture project seems to have made a large impact at the network 
level despite the fact that the network of partners is characterised as: 

 

 

Large in terms of the number of partners and associated partners 

International 

  Multidisciplinary 

  Cross-sectoral 

  Many partners did not know each other before the Climate for Culture project 

According  to  interviews  as  well  as  the  survey  data,  all  of  the  characteristics 
mentioned  above  acted  as  barriers  in  regards  to  collaboration  between  partners  in 
the Climate for Culture network. 

According to the interviews, the number of partners and scope of the project made 
it  difficult  to  get  an  overview  of  all  the  different  partners  and  their  competences, 
which was not made easier by the fact that many partners did not know each other 
prior  to  the  project.  Only  the  project  coordinator  and  some  of  the  key  partners 
(work package leaders) seem to have had the complete overview. On the one hand, 
this is regarded as positive  in the sense  that the single partners could concentrate 
on their own part of the project (work package) together with the partners assigned 
to  work  on  the  same  work  package  as  themselves.  On  the  other  hand,  potential 
synergies from collaboration between partners across work packages did not always 
occur as some partners found it overwhelming to get to know all the partners in the 
network  besides  those  assigned  to  the  same  work  package.  Two  interviewees 
explain  that  they  mostly  collaborated  with  partners  within  each  work  package  and 
with the project coordinator, but very little across work packages. 

Another barrier to collaboration is the international and multidisciplinary character of 
the network, which created cultural and language barriers. The cultural barriers and 
the language barriers are both related to that fact that partners come from different 
countries  and  related  to  the  fact  that  many  partners  are  rooted  within  different 
academic  disciplines  and  institutional  sectors.  The  aligning  of  expectations  and 
understanding each other was a big challenge according to interviews. 

With the hope of overcoming these barriers, each year all the partners met for five 
days  at  the  ‘Annual  Meeting’  in  order  to  get  to  know  one  another,  to  exchange 
knowledge  and  to  ensure  that  the  project  was  progressing  as  planned.  Generally, 
the Annual Meetings are described as very important as meeting each other face-to-
face helped to break down barriers. 

Cross-sector  collaboration  in  relation  to  the  Climate  for  Culture  project  between 
researchers,  SMEs,  cultural  heritage 
institutions  and  public  administrative 
institutions  focusing  on  cultural  heritage  is  generally  described  as  a  positive 
experience by interviewees concerning future use of the results in society developed 
in  relation  to  the  project  as  well  as  future  collaboration.  One  partner  for  example, 
explains how the models and methods they participated in developing is going to be 

222 

 
 
integrated  as  part  of  the  planning  and  restoration  of  certain  cultural  heritage 
buildings in their respective country. 

Another  important  impact  at  the  network  level  is  that  several  partners  have 
expressed that they will continue to work together with partners from the network. 
Interviewees  explained  that  they  are  working  on  new  project  proposals  with 
partners  from  the  network.  According  to  the  survey  data,  the  Climate  for  Culture 
team also managed to establish links between partners from organisations that were 
not part of the project such as: 

  Museums 

 

Policy makers 

  Government research organisations 

  Higher education institutions and research universities 

 

Private non-profit research performing organisations 

  Civil society organisations 

Outputs, outcomes, and impact 

The aim of the Climate for Culture project was to contribute to fundamental science 
as well as prepare ground for applications outside the academic sector especially in 
the public research sector and private non-profit sector. 

New research, methods  and models in different  research  fields were developed. As 
an  example,  mathematical  models  in  connection  to  indoor  climate  control  were 
developed  targeting  the  need  to  develop  low-cost  indoor  climate  control  models  in 
an area where there is not a lot of money. Another example of new research was on 
the  assessment  of  the  economic  impact  of  climate  change  under  different  climate 
scenarios (cost-benefit). 

The  main  innovation  of  the  project  was  to  use  simulation  and  modelling  tools  to 
better predict the influence of the changing outdoor climate on the indoor climate in 
historic  buildings  until  the  year  2100.  Secondly,  it  helped  to  assess  the  damage 
potential of these future indoor climates on art collections in various climate zones. 
Around  120  in  depth  case  studies  on  various  historic  buildings  in  different  climate 
zones were conducted during the project period to provide new knowledge. 

According to the  survey  data,  interviews and project materials, Climate for Culture 
produced both scientific and technological knowledge as well as the development of 
new tools including: 

 

Peer-reviewed  articles,  academic  publications  e.g.  books  and  presentations  of 
results at conferences and workshops 

  Spin-off projects 

  Awarded recognition for contributions to fields 

  Development of new models, approaches, software, datasets, etc. 

Reflection on the success factors 

The Climate for Culture project is described as a highly complex project due to the 
many  partners  coming  from  different  countries,  sectors  and  academic  disciplines. 

223 

 
 
According  to  the  interviews,  an  important  success  factor  has  been  strong  project 
management  including  the  delegation  of  tasks,  active  facilitation  and  a  large  effort 
from  management  to  promote  internal  communication,  face-to-face  meetings  as 
well as exchange of information between partners. The latter, is described as crucial 
in a project as multidisciplinary  and  complex  as the  Climate for Culture project. In 
terms  of  delegation  of  work  and  to  ensure  project  objectives  were  reached,  the 
project  management  appointed  task  leaders  who  reported  to  the  work  package 
leaders.  Furthermore,  some  of  the  work  packages  were  divided  into  sub  work 
packages. 

224 

 
 
 
 
Case Study IV: DELTA-MIN, Mechanisms of Mineral Replacement Reactions 

Summary of the case 

The  FP7  project  ‘DELTA-MIN’  was  a  European  based  research  and  training 
programme  in  the  scientific  fields  of 
‘Earth  and  Environmental  Sciences’, 
‘Chemistry’,  and  ‘Physics’.  Twelve  early  stage  researchers,  two  experienced 
researchers  and  two  visiting  scientists  were  engaged  in  the  network.  All  the 
researchers  were  given  a  personalised  training  programme  within  the  DELTA-MIN 
project.  With  the  personalised  training  programme,  each  of  the  early  stage 
researchers  had  a  supervisor  at  the  host  institution  and  a  second  supervisor  from 
one of the other partner institutions that took part in the network of the DELTA-MIN 
project.  

Scientifically,  DELTA-MIN  is  concerned  with  mechanisms  of  re-equilibration  of 
minerals under changing physical and chemical conditions, primarily in the presence 
of an aqueous fluid phase. As such, it includes research projects that cover a broad 
range  of  complementary  methodologies:  experimental  work  on  fluidmineral 
interaction, field-based studies of fluidmineral interaction in nature, thermodynamic 
and kinetic modelling. 

The key findings of the case study are: 

 

It was a  high priority  in DELTA-MIN to  create synergies between the individual 
research  projects  so  that  each  project  could  take  advantage  of  the  insights 
gained in other projects, even if the projects were not obviously related.  

  Because of the awareness of interaction in the network, the cultural differences 
between  institutions,  academic  disciplines  and  the  fact  that  the  network 
consisted  of  researchers  and  partners  from  different  Member  States  of  the  EU 
had  not  been  considered  a  restriction  to  the  way  of  working  and  conducting 
research.  

  By  including  industrial  partners,  the  project  raised  awareness  among  the 

researchers of the possibilities of working in the industry.  

 

 

The project reached  its goal as all  the  students that engaged in  DELTA-MIN as 
early  stage  researchers  went  on  to  secure  positions  as  researchers  at different 
European universities or in industry following their participation in DELTA-MIN. 

In the network, the general opinion is that the project was successful in forming 
the  right  team  of  researchers.  However,  it  was  suggested  that  the  early  stage 
researchers  could  have  benefitted  by  the  inclusion  of  more  experienced 
researchers,  i.e.  a  distribution  of  10  early  stage  researchers  and  four 
experienced researchers.  

Introduction 

The  FP7  project  DELTA-MIN  is  an  example  of  a  collaborative  research  and  training 
programme that  has  had  a  major  impact on  networking.  The  case  was  selected  to 
study a project with ‘intense networking and positive outputs and/or outcomes’ that 
has  received  funding  either  through  the  IDEAS  or  through  the  PEOPLE  sub-
programme.32 As this case study shows, the project has succeeded in strengthening 

32  The  case  selection  is  based  on  data  from  a  comprehensive  network  analysis  and  survey 
conducted  by  Science-Metrix  and  Fraunhofer  ISI  (see  appendix  I  for  a  methodological 
description) 

225 

 
 
                                                
relations  between  researchers  in  the  EU  and  has  had  positive  outputs  and/or 
outcomes.  The  DELTA-MIN  project  received  financial  support  from  the  funding 
scheme  ‘MC-ITN  -  Networks  for  Initial  Training  (ITN)’  that  focuses  on  training 
researchers  and  promoting  a  circulation  of  researchers  and  research-based 
knowledge  within  Europe  and  globally.  Seen  in  the  framework  of  the  funding 
scheme,  the  outputs  and  outcomes  of  this  project  were  primarily  about 
strengthening the network and to a lesser extent, about generating contributions to 
fundamental science.  

The  primary  objective  of  this  case  study  is  to  outline  how  knowledge  is  created  in 
the network and through cooperation. 

Description of the case 

The  DELTA-MIN  project  ran 
for  a  four-year  period  from 
September  1,  2008, 
to 
August  31,  2012,  with  a 
total  cost  of  €3,109,822.  It 
is  a  Marie  Curie  Initial 
Training  Network,  which  is 
a  part  of  the  ‘Marie  Curie 
Action' sub-programme that 
focuses  on  responding  to 
the  needs  of  Europe's 
scientific 
in 
terms  of  training,  mobility 
and career development.  

community 

Background data for DELTA-MIN 

Project period 

01.09.2008 – 31.08.2012 

Total cost 

€3,109,822  

EU contribution 

€3,109,822  

Coordinated in 

Germany 

Sub-
programme 

PEOPLE-2007-1-1-ITN - Marie 
Curie Action: ‘Networks for Initial 
Training’ 

Funding 
Scheme 

MC-ITN - Networks for Initial 
Training (ITN) 

The  idea  for  the  project 
came  as  a  continuation  of 
coordinator  Andrew 
the 
Putnis’s  academic  interest. 
His research focuses on two 
primary areas. One is phase 
transformations in minerals, 
and the other is mineral surface science. He brought the two together and sought to 
find  answers  to  the  more  general  question  of  how  minerals  equilibrate  in  the 
presence of a fluid phase arise.  

‘Earth and Environmental 
Sciences’, ‘Chemistry’, ‘Physics’ 

Scientific fields 

According  to  the  survey  data,  there  are  many  reasons  behind  the  decision  to 
undertake the project in the context of FP7 rather than in the context of a national 
or  regional  funding  programme:  (1)  is  to  reach  critical  mass  by  accumulating 
sufficient  financial  resources,  equipment,  and  researchers  to  achieve  the  project’s 
objectives,  (2) is to improve  complementarities and  diversity by using information, 
data, techniques, concepts, and/or theories form several disciplines, (3) is to better 
connect science and society, (4) is to establish linkages with excellent international 
research partners, (5) is to enhance the international visibility of an existing line of 
research. 

The  network  has  provided  training  in  research  for  12  three-year  early  stage 
researchers  (doctoral  students)  and  2 
researchers 
(postdoctoral level). The general objective of the network level training courses and 
the  involvement  of  the  industry  was  to  provide  the  young  researchers  with  a  wide 
multi-faceted training to underpin their professional development. An appreciation of 
the  different  requirements  of  industry  and  academia  as  a  career  was  also  a 

two-year  experienced 

226 

 
 
recurrent  theme  reinforced  by  training  contributions  from  industry  partners  related 
to  the  sub-projects.  In  order  to  meet  the  overall  objective  of  the  project,  the 
mobility  between  the  participating teams  for  the  researchers  was  a  central  feature 
of the project.  

The  DELTA-MIN  project  contributed  to  the  academic  research  by  investigating  the 
mechanism  of  mineral  re-equilibration  (phase  transformation)  in  the  presence  of  a 
fluid  phase  in  a  wide  range  of  minerals  and  rocks,  under  a  range  of  chemical  and 
physical conditions, using both natural and experimental samples. The principles of 
interface-coupled  dissolution-reprecipitation  were  applied  to 
the 
mechanisms  of  processes  important  in  earth  sciences  and  in  industry,  including 
metasomatic  reactions 
in  rocks,  chemical  weathering,  mineral  replacement 
mechanisms  in  CO2  sequestration,  the  aqueous  durability  of  nuclear  waste 
materials,  remediation  of  contaminated  water  by  mineral  reaction,  and  the 
preservation  of  stone-based  cultural  heritage.  The  research  methods  brought 
together  a  range  of  complementary  expertise,  from  field-related  studies  to  nano-
scale  investigations  of  reaction  interfaces  using  state-of-the-art  high  resolution 
analytical methods.  

investigate 

Formation of the partnership 

Institut  für  Mineralogie  at  the  University  of  Münster,  Germany  coordinated  the 
project  with  teams  from  nine  universities  and  consultancies  from  five  Member 
States. Figure 22 shows DELTA-MIN’s distribution across Europe.  

Figure 22  Map of the participating partners 

Source: 

Cordis, website: http://cordis.europa.eu/home_en.html 

The  network  was  formed  based  on  bringing  together  partners  that  were  already 
working scientifically within the research area, but form different perspectives. The 

227 

 
 
 
coordinator  was  the  driver  behind  finding  the  partners.  Their  strategy  for  selecting 
partners  was  to  balance  the  different  scientific  fields  and  in  this  way  use 
interdisciplinarity to reach the project’s objectives.  

At  the  time  of  its  formation,  there  was  already  an  existing  network  of  researchers 
that  knew  each  other  from  conferences  around  Europe.  The  coordinator  was  also 
acquainted  with  some  of  the  industrial  companies  that  worked  with  geological  and 
environmental  issues.  There  was  a  core  team  of  partners  that  already  knew  each 
other well, and the new partners were identified based on the core partners’ inputs 
and recommendations.  

Process and implementation 

The DELTA-MIN network consisted of individual research projects, but an important 
element  was  to  create  synergies  between  the  individual  projects  by  enhancing 
networking. For creating synergies, two methods were applied - one method was to 
have  regular  network  meetings,  where  all  researchers  could  present their  research 
results and the progress made in their individual projects. The other method was to 
involve at least two partners in each of the research projects. This opened up room 
for providing inter-host collaboration and multi-site training of the researchers.  

A  kick-off  meeting,  five  network-level  workshops  and  a  final  conference  were  held 
over  the  duration  of  the  project.  These  were  hosted  by  the  different  network 
participants:  

  November 2008: Kick-off meeting in Münster, Germany 

  October 2009: The first network meeting in Granada, Spain  

  March 2010: The second network meeting in Seefeld, Austria 

  October 2010: The third network meeting in Santorini, Greece 

  March 2011: The fourth network meeting in Taormina, Sicily 

  October 2011: The fifth network meeting in La Palma, Spain 

  March 2012: Final conference in Oviedo, Spain 

The DELTA-MIN project started with a kick-off meeting. The purpose of this meeting 
was to define the aim of the project as well as to lay the foundation for procedures 
in  recruiting  the  researchers.  The  range  of  methods  chosen  for  recruitment  varied 
from  formal  advertising  in  international  science  magazines  (Elements),  internet 
advertising  (Earthworks,  Euraxess),  scientific  society  email  listings  and  individual 
emails to university departments. The recruitment is highlighted as the most difficult 
part of the project, because it in some ways determines the success of the project, 
i.e. the better the students, the better the project. A lot of the recruitment was done 
on  Skype  meetings,  which  has  some  communicative  limitations  compared  to  face-
to-face meetings.  

The  process  of  recruitment  was  completed  within  the  initial  12-month  period,  with 
12 early stage researchers (six  male,  six  female)  and  two experienced researchers 
(both  female).  In  each  of  the  participating  institutions  a  plan  for  the  individual 
projects  was  defined,  taking  the  scientific  background  of  the  researcher  as  well  as 
the researcher’s training needs into account. 

The five network meetings that were held in a two-year period acted as forums for 
discussing a range of relevant issues in the project, including:  

228 

 
 
  Changes, problems or solutions in research priorities 

 

Experimental or technological issues 

  Coordination and management issues 

 

Future career planning 

The  first  network  meeting  served  as  a  forum  for  defining  the  responsibilities  in 
research  and  training  for  each  of  the  DELTA-MIN  teams,  while  the  subsequent 
network  meetings  consisted  of  presentations  by  the  young  scientists,  round  table 
discussions with industrial partners and board members, and network management 
or business. 

Besides  the  network  meetings,  another  key  element  of  the  project  was  to  provide 
inter-host collaboration  and multi-site training by involving at least two  partners in 
each  of  the  research  projects.  The  researchers  had  an  exchange  between  the  nine 
institutions involved in the network. The exact distribution of these exchange visits 
varied between the individual collaborative research projects in the network. Almost 
all  of  the  researchers  of  the  network  spent  three  to  six  months  working  at  the 
laboratory  of  another  network  team.  According  to  the  interviews  carried  out,  the 
time  spent  at  one  or  several  other  DELTA-MIN  partners  provided  the  researchers 
with  an  opportunity  to  learn  complementary  skills  and  perform  additional  research 
using  techniques  that  were  not  available  at  their  own  institution.  The  exchanges 
provided  access  to  and  training  in  experimental  and  analytical  techniques  not 
available in their own institution.  

Contextual factors 

One  contextual  factor  that  had  an  impact  on  the  project,  were  the  students’ 
different  academic  backgrounds.  This  was  a  challenge  for  putting  together  a 
programme  that  suited  all  the  students.  The  solution  was  to  create  training 
programmes that balanced individual needs with the overall objective of the project.  

Another  factor  was  the  previous  collaboration  between  the  partners  that  helped  in 
bridging cultural barriers and established a common language between researchers, 
promoting  openness  and  greater  use  of  informal  channels.  According  to  the 
interviews,  the  informality  was  particularly  strong  where  partners  had  worked 
together  before.  Where  there  was  no  prior  experience,  face-to-face  meetings, 
shared objectives and common ground through  shared membership of professional 
networks all helped to build trust. 

Impact at network level 

The  institutional  exchange  and  the  regular  network  meetings  contributed  to 
developing a close connection between the researchers socially, and because of the 
tight personal relationships built up during the project, the information flowed more 
smoothly in the professional networks.  According  to the interviews carried out, the 
effectiveness  of  personal  relationships  in  maximising  information  exchange  was 
recognised  and  actively  promoted  by  the  coordinator.  With  their  awareness  of 
interaction  in  the  network,  the  cultural  differences  between  institutions,  academic 
disciplines and the fact that the network consisted of researchers and partners from 
different  Member  States  of  the  EU  were  not  considered  a  restriction  to  the  way  of 
working  and  conducting  research.  In  none  of  the  interviews  was  it  suggested  that 
the  contrasting  cultures  defined  by  the  mix  of  partners  involved  have  been  an 
obstacle in the project.  

229 

 
 
Generally,  there  was  a  positive  view  on  the  size  of  the  network.  There  was  an 
understanding  that  the  partners  and  the  scientific  fields  complemented  each  other 
well and that it has had a positive influence on the formality of information handling 
processes.  Even  though  it  was  a  large  and  complex  collaboration,  the  logistics  of 
communication  were  considered  quite  informal.  The  use  of  exchange  and  transfers 
with  the  industrial  partners  moderated  the  potential  barriers  to  information  flows 
associated  with  physical  distances  spread  across  multiple  sites.  The  interviewed 
researcher’s  emphasise  that  the  transfers  to  the  industry  have  been  specifically 
favourable  for  creating  greater  tacit  knowledge  exchange  and  to  broaden 
perspectives  on  employment.  As  one  of  the  interviewees  highlights,  it  raised  an 
awareness of working in the industry by one of the industrial partners AMPHOS XXI 
Consulting, S.L and the associated partners Reykjavik Energy, IFE Norway and SKB 
Sweden. The presence  of industry extended  the  research-based network to include 
an industrial perspective. 

The survey results support the perception that the project has contributed positively 
to  networking.  In  the  survey,  one  of  the  university  partners  has  strengthened 
linkages with researchers in similar fields of science  and  technology  to a very high 
degree.  In  addition,  the  survey  indicates  that  the  project  has  succeeded  in 
promoting  interdisciplinarity  and  multidisciplinarity  by  strengthening  linkages  with 
researchers  that  are  not  at  the  core  of  the  organisations  usual  research  activities. 
The  DELTA-MIN  project  was  also  an  international  project  with  the  participation  of 
researchers and institutions in different EU countries, which according to the survey, 
allowed one university partner to strengthen collaboration with researchers from the 
European Union and FP7 associated countries to a very high degree.  

The  interdisciplinary  ambition  evident  in  the  inclusion  of  physicists,  mineralogists, 
hydrologists  and  chemical  engineers  was  an  important  factor  in  the  DELTA-MIN 
project. The project also hosted two different schools of mineralogy that differed in 
fundamental  scientific  questions  and  approaches.  The  early  stage  researchers 
cherished  the  opportunity  to  learn  from  the  senior  researchers’  discussions  on 
fundamental  mineralogy  topics,  which  was  enriching  for  their  own  understanding. 
There was the fear that the large amount of different disciplines required too much 
of the early stage researchers. As a strong interdisciplinary understanding required 
deep  understanding  in  several  disciplines,  the  project  may  have  been  asking  the 
students to master too much. Since  the  project  ended, the DELTA-MIN project has 
been  evaluated  positively,  when  it  comes  to  creating  the  right  balance  between 
nurturing and overloading the researchers with interdisciplinary topics.  

Outputs, outcomes, and impact 

The  DELTA-MIN  project  focused  on  training  researchers  and  promoting  the 
circulation of researchers and research-based knowledge within Europe and globally. 
According to these success criteria, the project reached its goal, as all the students 
that engaged in DELTA-MIN as early stage researchers went on to secure positions 
as  researchers  later  on  at  different  European  universities  or  the  industry.  Some  of 
the  early  stage  researchers  had  success  with  publishing  their  work  in  high-rated 
geological  journals  as  well.  This  was  also  an  indication  that  the  recruitment  of  the 
researchers was successful. 

According  to  the  survey  results,  the  DELTA-MIN  project  has  resulted  in  the 
production of the following types of scientific and technological knowledge: 

 

Peer-reviewed articles 

  Other academic publications (e.g. books, proceedings) 

230 

 
 
 

Presentation of results at conferences or workshops 

  Development of a new models or approaches 

The survey data also showed a high level of satisfaction with the project regarding 
direct outputs and long-term outcomes.  However, the  survey  results also indicated 
that  the  DELTA-MIN  project  had  enhanced  knowledge  transfer  from  science  to 
market and from science to society to a low degree. It is a reflection of the purpose 
of  the  funding  scheme,  which  was  primarily  aimed  at  promoting  circulation  of 
researchers  and  research-based  knowledge  and  to  a  less  extent  at  transforming 
knowledge  to  innovation.  The  project  has,  however,  enhanced  knowledge  transfer 
by  the  inclusion  of  the  industry  and  thus  given  the  researchers  an  industrial 
perspective  on  their  career  opportunities.  Through  the  DELTA-MIN  project,  several 
of the researchers have moved into positions in the industry.  

Reflection on the success factors 

Generally,  the  project  has  been  evaluated  positively  and  regarded  a  success. 
According to the survey results, the benefits gained from the project have exceeded 
the costs. According to the coordinator, primarily two factors were important for the 
success of the project:  

 

It  was  a  priority  in  the  project  to  have  network  meetings  in  exciting  and  nice 
places like Santorini and Sicily instead of holding the meetings in a dark lecture 
room  at  a  university.  The  effect  was  that  the  students  were  excited,  more 
focused and paid greater attention in them. 

  Another  important  factor  for  success  was  that  the  network  aimed  at  breaking 
the  barriers  and  hierarchy  between  young  researchers  and  top  scientists.  The 
informality between the two promoted dialogue and knowledge sharing.  

The recruitment of the researchers was a success  taking into consideration that all 
the  researchers  have  obtained  a  position  in  research  or  industry.  Largely,  the 
recruitment  was  done  on  Skype,  and  it  could  have  strengthened  the  recruitment 
process, had the recruitment been decentralised with local networks and partners as 
the responsible actors. It would have provided room for more personal meetings.  

While the project is considered successful, one of the interviewed researchers points 
out that the benefits gained from the project would have been even greater if more 
senior  scientists  were  involved,  as  this  would  have  increased  learning  among  the 
students.  Another  concern  expressed  in  one  interview  is  that  the  intense  focus  on 
networking may have had a negative effect on the scientific outcomes.  

231 

 
 
 
 
 
Case Study V: E3ARTHS, Exoplanets and early earth atmospheric research: theories 

and simulations 

Summary of the case 

The  E3ARTHS  project  is  an  example  of  a  project  designed  by  one  key  participant; 
the project initiator (PI). The PI wrote the application and after receiving the grant, 
the PI played the role as both the principal researcher and the project coordinator. 
Based  on  the  grant  from  the  European  Research  Council  (ERC)  the  PI  managed  to 
build a multidisciplinary  and  international research  team and together the research 
team achieved positive results. Scientifically, the research team developed new tools 
and models to study the nature of exoplanetary atmospheres and developed a new 
line  of  research.  Another  and  perhaps  more  lasting  project  output  is  the  fact  that 
there  is  now  a  consolidated  research  group  at  the  University  of  Bordeaux  doing 
research  on  exoplanets,  which  would  not  have  happened  without  the  E3ARTHS 
project receiving the ERC grant. However, the  E3ARTHS project is also an example 
of  a  FP7  project  with  low  levels  of  networking  between  partners  from  different 
sectors,  but  with  a  high  degree  of  collaboration  between  researchers  within 
academia  both  the  local  environment  and  other  regions  of  France  as  well  as 
researchers  from  other  countries  in  the  European  Union  and  outside  the  European 
Union. 

A  key  factor  to  the  project’s  success,  identified  through  the  qualitative  interviews 
with  project  partners,  was  the  careful  composition  of  a  dedicated  multidisciplinary 
research  team  with  talented  academic  researchers.  The  multidisciplinary  research 
approach  working  at  the  interface  between  disciplines  such  as  (astro)  physics, 
(astro) biology and chemistry is described as one of the key factors to the success 
of the E3ARTHS project. 

Secondly,  good  project  management  is  regarded  as  another  success  factor  by  the 
interviewees.  The  project  coordinator  managed  to  create  a  very  attractive  and 
flexible working environment with great focus on knowledge sharing and discussing 
new ideas at meetings every week at the University of Bordeaux. On top of that, the 
University of Bordeaux’s willingness to  provide administrative and financial support 
made  it  possible  for  the  core  research  team  to  focus  on  building  a  research  team 
and conduct research. 

Introduction 

The  FP7  project  ‘E3ARTHS  -  Exoplanets  and  early  earth  atmospheric  research: 
theories  and  simulations’  is  an  example  of  a  project  where  a  strong  network 
between researchers from academia was formed. The case was selected for studying 
a  project  characterised  by  low  levels  of  collaboration  and  networking  with 
organisations  from  other  institutional  sectors  than  research  institutions  such  as 
private enterprises, but which achieved positive outputs and outcomes.33 The output 
and  outcomes  should  be  assessed  in  the  context  of  the  funding  scheme  ‘ERC 
Starting Grant’, which aims at supporting basic research. 34 Thus, the project should 

33  The  case  selection  is  based  on  data  from  a  comprehensive  network  analysis  and  survey 
conducted  by  Science-Metrix  and  Fraunhofer  ISI  (see  appendix  I  for  a  methodological 
description) 

34 http://cordis.europa.eu/programme/rcn/845_en.html 

232 

 
 
                                                
be  studied  as  an  initial  basic  research  project  that  in  the  long  run  can  lead  to 
commercialisation. 

The  case  study  builds  on  survey  data35  conducted  in  earlier  phases  in  the 
evaluation,  on  relevant  project  material  and  on  five  interviews  with  project 
participants including an interview with the  project coordinator.  The  other partners 
were  selected  in  consultation  with  the  project  coordinator  and  are  all  academic 
researchers. Their responses have been anonymised. 

Generally  the  primary  objective  of  the  case  studies  is  to  outline  how  knowledge  is 
created in networks and through cooperation. 

Description of the case 

E3ARTHS 

the  host 

The  E3ARTHS  project  took 
place  from  October 1, 2008 
to  September  30,  2013. 
The 
project 
received €719,759 from the 
ERC  Starting  Grant. 
In 
addition,  some  of  the  PhDs 
and  postdocs  working  on 
the  E3ARTHS  project  were 
funded  or  co-funded  either 
by 
institution, 
University  of  Bordeaux,  or 
other  universities,  research 
public-
institutions 
bodies.  The  project 
is 
funded by the ‘ERC Starting 
Grant’ 
sub-programme, 
which  supports  ‘individual 
projects,  which  may  be 
carried  out  in  any  field  of 
basic 
and 
scientific 
technological research’.36 

and 

Background data for E3ARTHS 

Project period 

01.10.2008 – 30.09.2013 

Total cost 

€719,759  

EU contribution 

€719,759  

Coordinated in 

France 

Sub-programme 

ERC-SG-PE7 - ERC Starting 
Grant - Systems and 
communication engineering 

Funding Scheme 

ERC-SG - ERC Starting Grant 

Scientific fields 

Astrobiology , Astrophysics, 
Astronomy, Aerospace 
Technology, Geology, 
Chemistry 

According  to  the  survey  data,  the  E3ARTHS  project  is  aimed  to  generate 
contributions  to  fundamental  science  and  to  a  lesser  degree  to  prepare  ground  for 
applications  outside  the  academic  sector.  Furthermore,  the  reasons  the  PI  decided 
to  undertake  the  project  in  the  context  of  FP7  rather  than  in  the  context  of  a 
national  or  regional  funding  programme  were  to  reach  a  critical  mass  by 
accumulating  sufficient  financial  resources,  equipment,  and  researchers  to  achieve 
the project’s objectives and to enhance the international visibility of an existing line 
of research. 

The  E3ARTHS  project  builds  on  the  PI’s  own  research  on  ETPs,  which  already  had 
important  implications  for  the  design  of  instruments  for  ETP  search  and 
characterisation.  Based  on  new  generation  codes,  the  E3ARTHS  project  aimed  to 

35 The survey data is based on the answers from one survey respondent 

36 http://cordis.europa.eu/programme/rcn/845_en.html 

233 

 
 
                                                
provide expert knowledge in the key area of astrobiology,37 which is the study of the 
origin,  evolution  and  identification  of  habitable  worlds,  and  to  find  biomarkers  on 
Earth-like  planets.  Secondly,  the  project  team  wanted  to  re-examine  early  Earth 
models  for  a  better  understanding  of  the  context  of  the  origins  of  life,  in  light  of 
recent  work  on  Earth  formation,  impact  history  and  Solar  evolution.  The  overall 
ambition  of  the  E3ARTHS  project  was  to  simulate  and  characterise  Extrasolar 
Terrestrial Planets (ETP) atmospheres. 

The observable signatures of an ETP and its ability to sustain life are determined by 
atmospheric  properties:  chemistry,  radiative  transfer  and  climate.  Although  these 
processes  are  usually  treated  separately,  they  evolve  in  a  tightly  coupled  scheme 
under  the  influence  of  astrophysical,  geophysical  and,  if  present,  biological 
mechanisms.  Eventually,  realistic  planetary  environments  will  thus  have  to  be 
modelled with self-consistent 3D tools, involving a multidisciplinary and international 
approach. Observatories like Darwin/TPF and ELTs would provide direct information 
on ETPs within 10 to 15 years. Ongoing transit searches such as CoRoT and Kepler, 
and  radial-velocity  surveys,  were,  when  the  PI  wrote  the  application  for  an  ERC 
grant,  on  the  verge  of  detecting  ETPs.  In  this  context,  the  objective  was  that  the 
E3ARTHS project could become one of the cores in European theoretical research on 
ETPs, in close interaction with observation programmes. 

The  E3ARTHS  project  had  an  interdisciplinary  and  cross-disciplinary  approach.  The 
staff working on the project came from different backgrounds such as astrophysics, 
geology, biology and chemistry. In the interviews, the team members are described 
as highly competent academics at different levels. 

The  E3ARTHS  project  team  developed  a  variety  of  tools  to  study  and  characterise 
the  nature  of  exoplanets’  atmospheres.  In  order  to  do  so  they  had  to  overcome 
some  challenging  technical  tasks  on  very  different  aspects:  molecular data; 2D/3D 
flexible codes; the influence of orbital dynamics and radiative transfer. 

The peer-reviewed research articles produced in connection to the E3ARTHS project 
are  typically  co-produced  by  more  than  one  researcher,  who  often  work  at 
universities  or research institution located in  different EU  countries as well  as non-
EU countries. 

Formation of the partnership 

The  research  team  working  on  the  E3ARTHS  project  consisted  of  permanent 
researchers, PhDs and postdocs working primarily at Laboratoire d’Astrophysique de 
Bordeaux  (LAB)  at  University  of  Bordeaux.  However,  the  permanent  staff  is 
dependent  on  both  the  French  National  Centre  for  Scientific  Research  (CNRS)  and 
LAB, which are the core institutions in the E3ARTHS project. The project is made up 
of educational and research institutions of which two mentioned above are the core 
institutions, six are funders or co-funders of PhDs and postdocs and five are external 
collaborators. Figure 23 maps the participating partners in the project. 

37  Astrobiology  is  the  study  of  the  origin,  evolution,  distribution,  and  future  of  life  in  the 
universe. This multidisciplinary field encompasses the search for habitable environments in 
our Solar System and habitable planets outside our Solar System, the search for evidence 
of  prebiotic  chemistry  and  life  on  Mars  and  other bodies  in  our  Solar System,  laboratory 
and field research into the origins and early evolution of life  on Earth, and studies of the 
space 
to 
potential 
(http://astrobiology.nasa.gov/about-astrobiology/) 

challenges 

adapt 

Earth 

and 

life 

for 

on 

to 

in 

234 

 
 
                                                
Figure 23  Map of the participating partners 

Source: 

Cordis, website: http://cordis.europa.eu/home_en.html 

The E3ARTHS project and thereby the partnership was largely designed by one key 
participant;  the  project  initiator  (PI).  The  PI  wrote  the  application  and  after 
receiving the grant, the PI played the role as both the principal researcher and the 
project  coordinator.  Based  on  the  grant  from  the  ERC  the  PI  managed  to  build  a 
multidisciplinary and international research team. Before the E3ARTHS project, most 
of the partners had not worked together. 

During the project period, the E3ARTHS group attracted more and more interest at 
the local, national, international level, and both in the scientific community and from 
the public. According to the interviews and relevant project materials, the research 
team  primarily  working  at  LAB  received  strong  support  from  the  University  of 
Bordeaux,  CNES,  CNRS,  The  Conseil  Régional  d’Aquitaine  and  the  Institute  of 
France.  Receiving  the  ERC  grant  created  visibility  and  attracted  interest,  which 
attracted  both  highly  recognised  academic  researchers  within  two  years  of  the 
project, as well as external  funding  from  other  organisations,  making it possible to 
hire more PhD students and postdocs to the research team. 

Process and Implementation 

The  E3ARTHS  project  had  three  phases:  the  application  phase,  the  project 
development  and  planning  phase  where  the  research  team  was  created,  and  the 
research and implementation phase. 

The application phase, before receiving the ERA grant, took  place in 2007 in  Lyon, 
France, where the later project coordinator and principal researcher of the E3ARTHS 
project was working. He wrote the application almost by himself, which meant that 

235 

 
 
 
he created the project design and decided the overall focus of the research project, 
largely based on the PIs former research on Extrasolar Terrestrial Planets (ETP). 

Phase  two  began  after  the  E3ARTHS  project  was  selected  by  the  ERA  to  receive  a 
grant  and  was  characterised  by  a  period  where  the  PI  moved  to  Bordeaux  and 
began to build a research team at LAB (Laboratoire d’Atrophysique de Bordeaux) in 
order  to  carry  out  the  E3ARTHS  research  objectives.  Besides  the  project 
coordinator,  three  permanent  researchers  were  officially  assigned  to  the  project 
from  its  start  to  assist  in  hiring  new  researchers  and  applying  for  more  research 
funding.  In  this  phase,  a  lot  of  time  and  resources  were  spent  on  hiring  PhDs  and 
postdocs  from  different  research  disciplines  such  as  geology,  astrophysics  and 
chemistry. Some of the PhDs and postdocs were paid by funds from the ERA grant 
whereas  others  were  funded  or  co-funded  from  external  sources.  Based  on  the 
interviews the ERA grant therefore created a ‘snowball effect’ attracting more funds 
from  other  research  applications,  and  created  interest  inside  the  University  of 
Bordeaux as well as outside the university from people who wanted to be part of the 
E3ARTHS  project  team.  According  to  the  interviews,  when  the  team  grew  in 
numbers  researchers  experienced  some  challenges in  terms  of  understanding  each 
other  academically,  as  well  as  language  barriers.  The  project  coordinator  and  the 
team members therefore devoted a lot of time to meeting in-person to discuss and 
exchange  knowledge,  ideas  and  perspectives,  which  is  regarded  as  decisive  for  a 
successful interdisciplinary collaboration by the interviewees. 

The last phase of the E3ARTHS project was the research and implementation phase. 
At this stage, 10–12 researchers were part of the core research team, including PhD 
students and postdocs all working closely together on a daily basis. 

Contextual factors 

According  to  the  interviews  carried  out,  a  central  contextual  factor  was  the 
development within the research on ETPs—new generation codes and tools such as 
3D climate—at the time the PI chose to apply for funding from the ERC. At the time, 
the PI wrote the proposal models existed to study exoplanets, but the models were 
not flexible and each model was only dedicated to studying one specific planet at a 
time.  Based  on  the  new  generation  codes  and  tools  the  PI’s  goal  was  to  take  this 
research to the next level based on his former research on exoplanets. The aim was 
to  go  from  discovering  planets  to  analysing  the  planets  and  to  create  models  that 
are more flexible. The plan was to create a dedicated interdisciplinary team working 
with  the  integration  of  new  tools  such  as  3D  climate,  photochemical  and  radiative 
transfer  codes,  produce  virtual  observations  of  ETPs,  and  study  their  potential  for 
life.  The  project  (PI),  and  later  principal  researcher  and  project  coordinator  of  the 
E3ARTHS  project,  was  therefore  the  one  who  wrote  the  research  application  and 
designed the project. 

Another  contextual  factor  that  is  important  when  trying  to  understand  the  low 
degree  of  collaboration  with,  for  instance,  private  enterprises  in  the  E3ARTHS 
project  is  the  fact  that  it  is  very  rare  for  astrophysicists  to  work  together  with 
anyone other than partners from academia. 

Impact at network level 

The  patterns  identified  in  the  network  analysis  and  survey  are  largely  aligned  with 
the  findings  of  the  qualitative  interviews,  which  generally  supported  the  view  that 
the project is an example of mitigated success in cross-sector networking. However, 
a consolidated network now  exists,  created in the  context of the  E3ARTHS  project. 
However, it is a relatively small network compared to other FP7 projects, and in this 

236 

 
 
case,  just  between  researchers  from  higher  education  institutions  and  research 
universities: most of the team members worked at the University of Bordeaux. 

Due to the ERC grant, the PI moved to the Laboratoire d’Astrophysique de Bordeaux 
(LAB) from The French National Centre for Scientific Research (CNRS) in Lyon. The 
ERC grant allowed the PI to build a research team focusing on exoplanets, gathering 
researchers  from  different  groups  of  the  institute.  The  research  group  still  exists 
after  the  end  of  the  ERC  grant  and  includes  five  permanent  researchers,  three 
postdocs  and  one  PhD  student.  The  consolidated  research  group  is  officially  called 
ECLIPSE.  This  is  an  example  of  how  the  E3ARTHS  project  had  an  impact  on  the 
network level. 

The  E3ARTHS  project  created  new  links  between  academic  disciplines  by  getting 
researchers  from  different  disciplines  to  work  together.  In  the  beginning  of  the 
process  the  team  consisted  mostly  of  astrophysicists,  and  in  terms  of  wanting  to 
create  a  cross-  and  interdisciplinary  environment,  the  team  started  from  scratch. 
The  core  team  searched  for  people  with  expertise  in  different  relevant  areas  who 
were  interested  in  applying.  When  they  managed  to  hire  researchers  (PhDs, 
postdocs and permanent  staff) the cross-  and interdisciplinary collaboration began. 
For instance, some of the PhD projects had a cross- and interdisciplinary perspective 
using methods and theories from different disciplines and through their collaboration 
they  uncovered  new  research  results.  The  team  was  also  successful  in  terms  of 
attracting  internationally  renowned  researchers  from  the  United  States,  outside  of 
the European Union and FP7 Associated Countries.  

The E3ARTHS team also worked together with researchers outside  of the University 
of Bordeaux, both from other research institutions in France (e.g. CNRS in Paris), in 
other  European  countries  (e.g.  England,  Belgium,  Spain  and  Germany)  as  well  as 
outside Europe (e.g. USA). 

Outputs, outcomes, and impact 

According  to  the  interviewees  and  relevant  project  material,  the  E3ARTHS  project 
resulted in output, outcome, and impact on various levels. 

The project team produced scientific and technological knowledge in peer-reviewed 
articles  and  academic  publications.  A  total  of  45  peer-reviewed  articles  have  been 
published in relation to the E3ARTHS project, which have been cited more than 900 
times.  Most  of  the  peer-reviewed  articles  are  co-authored  with  researchers  from 
other  universities  and  research  institutions  from  different  countries,  mostly  in  the 
EU.  This  shows  another  project  output:  the  E3ARTHS  project  participants 
established and strengthened links between researchers from the host organisation, 
University  of  Bordeaux,  and  researchers  from  other  research  and  educational 
institutions—both  within  similar  fields  of  science  and  from  other  fields  of  science 
through collaboration. 

The  E3ARTHS  project  participants  also  developed  new  models  and  tools,  new 
software and datasets, which are now available to the academic community. Due to 
the newly developed tools, it is now possible to study and characterise the nature of 
exoplanetary  atmospheres.  The  team  also  developed  a  set  of  numerical  tools  to 
model  self-consistently  the  physical  and  chemical  structure  of  a  planetary 
atmosphere, for a broad variety of elemental compositions, temperatures and stellar 
irradiation. These tools allowed the researchers to study the many different kinds of 
atmospheres.  Due  to  these  new-generation  models,  the  research  team  has  shed 
new  light  on  extrasolar  planets  and  published  original  research  achievements  on 
many  topics  such  as  the  atmospheric  composition  of  hot  giant  exoplanets  and  the 

237 

 
 
terrestrial  planets, 

climate  of 
their 
characterisation. One problem raised in the interviews regarding the development of 
new  tools,  is  that  because  some  of  the  E3ARTHS  project  researchers  now  work 
elsewhere, some  of  the highly  specialised tools are not  in  use  as the  knowledge of 
how to use them has not been handed over to others. 

to  habitable  objects,  and 

from  hot 

In  order  to  increase  the  impact  on  society,  project  results  were  communicated  to 
the  public  at  conferences  (16  international  conferences),  in  interviews  with 
journalists,  in  radio  broadcasts  and  in  primetime  television  shows.  One  example 
where  the  E3ARTHS  project  garnered  intense  media  attention  was  in  2011,  in 
connection  to  their  work  on  the  exoplanet  GJ581d.  The  team  had  found  the  first 
potentially habitable planet around a star other than the Sun. 

Reflection on the success factors 

According to the interviewees, receiving the ERC grant was the key success factor. 
Without the funding from ERC, which among other things made it possible to hire a 
work force, the project team simply would not have achieved as much as they did. 
The ERC grant itself provides a lot of visibility and attracted interest both from the 
host organisation and from other organisations. In the case of the E3ARTHS project, 
it  also  attracted  interest  from  CNES  and  CNRS.  Partly  due  to  the  visibility  and  the 
seal  of  approval  that  the  ERC  grant  provides,  several  PhDs  and  postdocs  were 
funded and co-funded by organisations other than the ERC, which strengthened the 
E3ARTHS project and allowed the research team to increase their ambitions. 

Secondly,  the  interdisciplinary  approach  was  decisive  as  researchers  from  different 
academic disciplines manage to create new knowledge. 

Thirdly, University  of  Bordeaux’s  willingness to  provide administrative and financial 
support made it possible for the core team to focus on building a research team and 
carry out their research. Finally and importantly, interviewees regarded good project 
management and talented team members as key factors for a successful project. 

238 

 
 
 
 
Case Study VI: EMIF, European Medical Information Framework 

Summary of the case 

The  European  Medical  Information  Framework  (EMIF)  project  began  on  January  1, 
2013, and will run for five years. The purpose of EMIF is to build a data platform to 
enable improved access and use of human health data for research. In order for the 
consortium’s  work  and  results  to  be  applied  immediately,  the  EMIF  project  also 
contains  two  specific  therapeutic  research  topics,  the  onset  of  Alzheimer’s  disease 
(EMIF-AD)  and  metabolic  complications  of  obesity  (EMIF-Metabolics),  which  will 
assist in guiding the development of the Information Framework. 

Even though the project is still young, the project has contributed to strengthening 
networks  between  the  56  partners  and  approximately  250  individuals  as  well  as 
provided  the  foundation  for  further  collaboration  under  the  auspices  of  other 
projects. Data is being uploaded to the platform, researchers are beginning to work 
on them and the first four scientific publications have been published.  

Face-to-face  meetings  are  key  to  building  strong  networks  and  moving  the  project 
forward.  Face-to-face  meetings  are  especially  important  for  a  project  like  EMIF 
where  so  many  people  from  so  many  different  backgrounds  must  work  together. 
Due to the complexity of the project, face-to-face meetings must be complemented 
by  monthly  teleconferences  to  coordinate  on  a  more  regular  basis.  Face-to-face 
meetings cannot be replaced completely but relying solely on face-to-face meetings 
would not be efficient either. 

Another  key  success  factors  is  building  trust  and  competent  project  management. 
The  partners  in  the  project  must  trust  each  other  to  facilitate  open  innovation 
processes and trust must be built with the data owners, whom the project would like 
to upload  their data to the platform.  If  the  data owners do  not trust that  they can 
maintain  control  over  access  to  the  data  and  revenue  from  the  data  they  will  not 
upload over the data. 

Introduction 

EMIF is an example of a project funded indirectly and in part by the FP7 programme, 
which has the capacity to solidly fill structural holes or missing links in the previous 
structure.  This  case  was  selected  due  to  the  strong  collaboration  amongst  a  large 
interdisciplinary  consortium  consisting  of  academics, 
large  pharmaceutical 
companies,  SMEs,  and  a  patient  organisation.  Furthermore,  the  project  has  a  very 
strong  representation  of  both  large  businesses  and  academia  and  the  potential  to 
generate important contributions to fundamental science and has already early on in 
its existence produced several types of scientific and technological knowledge. 

This  case  study  is  based  on  four  interviews.  Interviews  were  conducted  with  both 
the  project  coordinator,  the  co-coordinator  and  two  participants.  The  interviewees 
represent  academia,  the  pharmaceutical  industry  and  a  patient  organisation.  Data 
was also drawn from the network analysis and survey conducted in earlier phases of 
the evaluation. Responses from the participant interviews have been anonymised in 
this case study.  

239 

 
 
 
 
Description of the case 

European 

of  €56,400,000 

 The 
Medical 
Information  Framework  (EMIF) 
project  began  on  January  1, 
2013, and will run for five years. 
The  project  has  received  a  total 
amount 
in 
funding. 
Innovative 
Medicines  Initiative  (IMI)  has 
provided  €24,400,000  and  the 
European 
of 
Industries  and 
Pharmaceutical 
has 
(EFPIA) 
Associations 
supported the programme in kind 
with €24,100,000. 

Federation 

The 

Background data for EMIF 

Project period 

01.01.2013 – 31.12.2017 

Total cost 

€56,400,000  

EU contribution 

€24,400,000 

Coordinated in 

Belgium 

Sub-programme 

IMI 

Scientific fields  Metabolic, Alzheimer’s 

and 

IMI is a partnership between  the 
the 
Union 
European 
European pharmaceutical industry, represented by EFPIA. The IMI initiative aims to 
improve health by speeding up the development of and patient access to innovative 
medicines, especially in areas where there is an unmet medical or social need. EMIF 
was  funded  under  IMI’s  first  phase  where  half  of  the  Initiative’s  €2  billion  budget 
was  financed  equally  through  contributions  from  the  FP7  programme  and  in  kind 
contributions  from  EFPIA  companies.  The  remaining  €7,900,000  in  EMIF’s  budget 
has been funded by other sources.  

In order to advance  medical  research,  increasing  the  quantity and detail of human 
health  data  is  needed  to  find  answers  and  solutions  to  complex  and  detailed 
research questions that society is facing today. Currently, there is a huge amount of 
human  health  data  already  collected  and  stored  electronically  but  these  data 
sources  are  kept  and  used  in  isolated  environments  and  systems  where  privacy, 
legal, and ethical issues are not always properly maintained. The purpose of EMIF is 
to address these challenges by developing a common set of solutions, both technical 
and  in  terms  of  governance,  to  enable  improved  access  and  use  of  human  health 
data.  

The project has three components: 

1.  The EMIF Platform 

2.  Alzheimer’s disease 

3.  Metabolic complications of obesity 

EMIF’s  vision  is  to  become  the  trusted  European  hub  for  health  care  data 
intelligence, enabling new insights into diseases and treatments. To accomplish this, 
the project’s main objective is to create an environment that allows for efficient re-
use of existing health data. The consortium is working towards creating a common 
Information Framework that will link up and facilitate access to different medical and 
research  data  sources.  This  component  of  the  project  will  be  completed  under 
EMIF-Platform.  

In order for the consortium’s work and results to be applied immediately, the EMIF 
project  also  contains  two  specific  therapeutic  research  topics,  the  onset  of 
Alzheimer’s  disease  (EMIF-AD)  and  metabolic  complications  of  obesity 

240 

 
 
(EMIF-Metabolics), which will assist in guiding the development of the Information 
Framework.  

EMIF-AD  aims  to  discover  and  validate  biomarkers  of  the  onset  of  Alzheimer’s 
disease  in  the  preclinical  and  prodromal  phase  as  well  as  for  disease  progression, 
and to identify high-risk individuals for therapeutic trials to prevent the progression 
of the disease.  

The  aim  of  EMIF-Metabolics  is  similar  in  that  it  hopes  to  discover  and  evaluate 
biomarkers  for  the  risk  of  metabolic  complications  in  obesity  and  to  identify  high-
risk populations for intervention.  

Through  developing  and  establishing  sustainable  and  scalable 
information 
frameworks,  EMIF  will  be  able  to  improve  access  to  human  health  data  for  life 
sciences research.  

Formation of the partnership 

Formation of partnerships for IMI projects is different from traditional FP7 projects. 
IMI  projects  are  initiated  by  an  EFPIA  consortium  (pharmaceutical  companies) 
through  ‘Calls  for  research  proposals’.  Interested  parties  from  academia,  SMEs, 
patient  organisations,  regulatory  agencies,  and  large  non-EFPIA  companies,  for 
example, are then invited to form ‘Applicant Consortia’ and to submit an Expression 
of Interest in response to the call. The selection of the winning proposals is based on 
independent  peer  review.  The  fact  that  industry  and  academia  do  not  choose each 
other before applying for funding makes IMI projects unique. Instead, industry has 
to work with the winning applicant consortia. 

Figure 24  Map of the participating partners 

Source: 

Cordis, website: http://cordis.europa.eu/home_en.html 

241 

 
 
 
Nine  pharmaceutical  companies  were  interested in  taking  part  in  the  EMIF  project. 
Instead  of  asking  for  one  public  applicant  consortium  to  be  formed,  the 
pharmaceutical  companies  behind  EMIF  asked  for  three  public  consortia:  one  for 
each topic/work stream.  

The  project  is  based  on  two  separate  ideas  that  were  joined  into  one  project. 
Janssen  Pharmaceutica  NV  had  the  idea  to  create  a  platform  for  gathering  and 
merging existing health data and GlaxoSmithKline had the idea to explore new uses 
of data. 

industry  partners 

EMIF  has  brought  academia,  SMEs, 
in  EFPIA,  patient 
organisations,  and  world-renowned  experts  together  under  a  common  goal.  The 
partners  involved  in  EMIF  are  shown  in  Figure  24.  According  to  the  project 
coordinator,  within  the  IMI  programme,  EMIF  is  one  of  the  largest  projects—if  not 
the largest—in terms of numbers, with 56 partners and around 250 individuals. The 
group  is  especially  complex  because  of  the  different  disciplines  involved.  Results 
from  the  survey  conducted  in  earlier  phases  of  the  evaluation  confirm  that  the 
project has a strong representation from industry and academia. 

In  this  project  and  other  IMI  projects,  only  public  partners,  patient  organisations 
and  SMEs  are  paid.  The  large  partners  from  industry  contribute  through  in-kind 
contributions. 

Process and implementation 

The project is, in reality, run like a programme consisting of three individual projects 
(the  Platform,  Alzheimer’s  disease  and  Metabolic).  Each  of  the  three parts  consists 
of  a  number  of  work  packages—16  in  total—and  each  work  package  has  a  leader 
and  a  number  of  partners.  Even  though  the  programme  is  run  as  three  separate 
projects,  significant  effort  has  been  made  to  coordinate  the  work  in  the  three 
components of the programme, both within and between the projects.  

Overall coordination is carried out through the Programme Board, which defines the 
strategic  focus  of  the  overall  project,  manages  the  overall  integration  of  the  three 
topics and ensures the objectives of EMIF are delivered. It is a cross-topic forum for 
further  in-depth  scientific  debate  of  cross-topic  issues  to  ensure  synergy  and 
optimisation  of  the  overall  project.  Furthering  this,  each  of  the  three  parts  has  its 
own governance structure to secure coordination.38 

The  partners  in  each  of  the  three  projects  meet  face-to-face  once  a  year.  The 
meetings  are  organised  back-to-back  so  that  people  can  meet  each  other  and  to 
make it easier for people who are taking part in more than one meeting. In addition, 
the  project  coordinators  and  participants  in  the  different  work  packages  meet  in 
smaller  forums.  Sometimes  they  are  held  face-to-face  but  most  meetings  are 
organised  as  teleconferences.  The  interviewees  highlight  that  it  is  impossible  to 
meet  face-to-face  all  the  time  and  that  teleconferences  are  an  efficient  alternative 
but that it is during the face-to-face meetings where significant progress is achieved 
and  when  strong  networks  are  being  built.  In  general,  participants  are  becoming 
more familiar with teleconferences but some participants from academia are still not 
used to this form of collaboration. Since  academic partners, but not industry, have 
funding  within  the  project  for  travelling,  it  is  often  the  industrial  partners  who 
promote the use of teleconferences. 

38 See http://www.emif.eu/emif/consortium/governance  

242 

 
 
                                                
Due to the size and complexity of the programme most of the first year was spent 
on  getting  everyone  on-board,  getting  to  know  everyone  and  their  wishes  for  the 
project, forming a shared understanding of the project and getting partners aligned. 

Another  large  task  for  the  programme  has  been  to  describe  the  databases  and 
datasets in  the  platform,  including  what  they  can  be  used  for,  what  variables  they 
contain,  which  years  they  cover,  which  individuals  they  cover,  and  which  age 
groups,  so  that  researchers  can  decide  if  the  data  can  be  used  for  their  research. 
The  ambition  is  to  have  a  ‘Yellow  Pages’  or  a  ‘shopping  mall’  for  data.  The  project 
partners have also spent a lot of their time on talking to data owners to help them 
understand  the  project,  getting  their  permission  to  include  data  and  making  them 
trust  that  they  can  include  their  data  in  the  platform  and  still  have  some  control 
over the information.  

Additionally,  requests  to  use  data  in  the  platform  are  starting  to  come  from  the 
partners  in  EMIF-Alzheimer’s  and  Metabolic  so  the  programme  is  building  the  plan 
on-the-go. 

The complexity of the programme and the autonomy of the individual activities also 
means  that  the  project  coordinator  and  the  co-coordinator  are  making  an  effort  to 
create a holistic view among partners to ensure that that the platform is constructed 
in the best possible way for the researchers who will be using the data. 

Contextual factors 

IMI  has  made  achievements 

IMI  projects  are  a  special  strand  of  FP7  projects.  The  IMI  initiative  creates  an 
environment  where  public  and  private  entities  in  the  pharmaceutical  industry  can 
innovation  among 
collaborate. 
pharmaceutical  companies  who  used  to  be  focused  on  competition.  One  of  the 
interviewees highlight that the collaboration and innovation between pharmaceutical 
companies within IMI was unseen 10 years ago. This is also spurred by the fact that 
the  pharmaceutical  companies  are,  to  an  increasing  extent,  trying  to  tackle  issues 
that no single pharma company can tackle alone. 

in  opening 

The IMI initiative is further facilitating collaboration between academia and pharma 
companies.  Differences  in  work  style  between  academia  and  industry  have 
previously acted as a barrier to collaboration. Through initiatives like IMI, however, 
industry  and  academia  are  increasingly  becoming  familiar  with  each  other  and 
building  trust.  Even  though  some  academics  are  still  sceptical  towards  the  private 
industry and some companies still feel  that academics are  mainly interested in the 
process  and  not  in  the  results,  the  interviewees  do  not  perceive  it  as  barrier  for 
collaboration  in  EMIF.  At  the  same  time  differences  in  working  cultures  and  norms 
between institutional sectors (e.g.  industry, higher education) is pointed out as the 
main  barrier  to  collaboration  between  partners  in  the  survey  conducted  in  earlier 
phases of the evaluation 

All  the  interviewed  project  partners  agree  that  a  project  like  EMIF  would  not  be 
possible without the IMI initiative, and thereby without the funding from FP7.  

Impact at network level 

Although  the  project  is  still  young,  it  has  already  had  significant  impact  on  the 
network level and partners have already benefitted from the increased networking. 
The  fact  that  the  project  is  a  collaboration  between  56  partners  and  over  200 
researchers alone creates many new linkages and brings people together that have 
not worked together before. 

243 

 
 
The  Alzheimer’s  disease  component  within  the  programme  is  also  building  on  a 
previous FP6 project called  ‘AddNeuroMed’,  which  aimed at  finding a biomarker for 
Alzheimer's  disease.  A  biomarker  is  a  test  that  can  be  used  to  help  diagnose  a 
condition or to help monitor a condition or response to treatment. 

Furthermore,  the  project  is  multidisciplinary  and  brings  together  people  from 
different  disciplines  who  would  otherwise  not  work  together.  The  project  tries  to 
bring  together  people  with  different  takes  on  Alzheimer’s  disease  and  metabolic 
complications  of  obesity.  A  concrete  example  of  this  is  that  EMIF  has  brought 
together  epidemiologists  and  bio  disease  historians  who  are  very  different  in  their 
approach and way of working. 

Even if the project is still only one and a half years into a five-year project period, 
the partners have also started working together in new constellations outside EMIF. 
The coordinator for the  Alzheimer’s disease project highlights that he is involved in 
seeking funding for or  has  almost secured  funding  for three  projects related to the 
EMIF project with partners from EMIF.  Either the expertise or the infrastructure for 
all three projects stems from the EMIF project.  

The  main  tool  for  promoting  networking  is  through  face-to-face  meetings,  which 
nothing  can  substitute  according  to  the  four  partners  interviewed.  Face-to-face 
networking  is  especially  important  for  a  project  like  EMIF  where  people  from 
different backgrounds and scientific fields need to work together. 

As mentioned, all participants in the three components meet once a year and some 
of  the  smaller groups  meet  more often.  All  groups and  work  packages  supplement 
face-to-face  meetings  with  teleconference  meetings,  which  are  more  efficient  and 
are  a  good  means  for  monthly  coordination.  At  the  same  time  the  teleconference 
meetings can never fully replace the face-to-face meetings. 

As  mentioned,  the  IMI  project  has  contributed  to  building  trust  between  industry 
competitors and between industry and academia, which has facilitated collaboration 
and lifted research and innovation to a higher level than was previously possible.  

Outputs, outcomes, and impact 

The project is still relatively young and due to  its complexity much of the first year 
was  spent  on  getting  the  project  up  and  running.  Still,  the  project  has  produced 
concrete results and outcomes. 

The  platform  is  up  and  running  and  the  first  datasets  have  been  entered.  All 
functions have not yet been created and many more datasets will be added but the 
researchers have started working on and analysing the data. 

Furthermore,  the  first  four  scientific  publications  have  been  produced:  two  under 
Metabolic  and  two  under  Alzheimer’s.  Results  have  also  been  presented  at 
conferences and workshops and the project is developing fundamental new models 
and  approaches  for  using  health  data  and  researching  Alzheimer’s  and  metabolic 
complications of obesity. 

The project has also given rise to several other research projects, either through the 
networks, the knowledge or the infrastructure created. 

The  survey  conducted  in  earlier  phases  of  the  evaluation  also  shows  that  the 
satisfaction with the direct and long-term outputs is very high. 

244 

 
 
Reflection on the success factors 

The key success factor of EMIF is trust—trust among the partners in the project, and 
both between industry players and between industry and academia. This is a  major 
feature for the IMI project, which has helped build trust between key actors since its 
inception.  

Trust  is  also  essential  between  the  project  partners  and  data  providers.  The  data 
providers need to feel comfortable participating in the platform and uploading their 
data without losing control over access to and revenue from it. EMIF is essential for 
building this trust. If pharmaceutical companies contacted data providers as private 
entities  it  would  be  difficult  to  persuade  the  data  providers  to  upload  their  data  to 
the  platform,  but  the  fact  that  it  is  done  through  an  EU  project  provides  a  neutral 
platform where data providers feel more secure. Thus, a project like EMIF would not 
be possible without the funding from and (indirect) collaboration with the EU. 

Project management and a good project plan are other key success factors. Due to 
the complexity of the programme, effective project management is very important. 
As mentioned, the programme is organised as three separate projects with 16 work 
packages. The work packages have their own governance structure and make their 
own decisions. At the same time, however, the project coordinator must ensure that 
the different projects are aligned and that the data platforms fulfil the needs of the 
researchers.  The  project  plan  must  also  balance  the  need  to  define  upfront  what 
partners  in  the  consortium  will  be  doing  and  provide  overall  guidance  for  the 
project, describe the activities and expected results and, at the same time, allow for 
flexibility to explore interesting features that are discovered as the project unfolds. 
For example,  Alzheimer’s and diabetes  have  some common  links,  a fact  which was 
unknown to the authors of the project plan when it was drafted, However, because 
some  researchers  wanted  to  research  this  further,  the  project  description  was 
amended to accommodate the wish. 

Face-to-face  meetings  are  another  key  to  success.  Even  if  daily  and  monthly 
coordination  has  to  be  conducted  through  teleconferencing,  this  cannot  substitute 
for  face-to-face  meetings.  The  meetings  are  where  substantial  progress  is  often 
achieved,  where  networks  are  solidified  and  where  people  from  different  research 
backgrounds  develop  an  understanding  for  each  other’s  approaches  and  build  the 
foundation for working together. As an extension of this, it is important that people 
from  different  backgrounds  can  work  together:  both  in  terms  of  researchers  from 
different  scientific  backgrounds  and  academia  and  industry.  While  this  has  the 
potential to be a challenge, it has not been so far in EMIF. 

245 

 
 
 
 
Case Study VII: GP-TCM, Good Practice in Traditional Chinese Medicine Research in 

the Post-Genomic Era 

Summary of the case 

‘Good  Practice  in  Traditional  Chinese  Medicine  Research  in  the  Post-Genomic  Era’ 
(GP-TCM)  was  a  project  under  the  FP7  programme  that  sought  to  establish  best 
practice in research on traditional Chinese medicine (TCM).  

GP-TCM  was  a  project  focused  on  collaboration  and  coordination  of  the  leading 
experts in the field of TCM research from Europe and China. The research relations 
between  the  partners  were  successful 
in  terms  of  harmonising  research, 
collaborating amongst partners and networking, despite the great size and scope of 
the network. Emphasis in the GP-TCM project was not on producing new research in 
the scientific field, but more on harmonising research on TCM in its broadest sense. 

The key findings of the case study are:  

 

The  FP7  programme enabled  this  project  requiring  a  multitude  of  partners  and 
ambitious scope to be successful.  

  Collaboration  across  geographical,  scientific,  and  cultural,  as  well  as  language 
barriers  was  a  crucial  part  of  reaching  a  harmonised  approach  to  research  on 
TCM. The knowledge platforms established during the project played a key role 
in  bringing  partners  together,  aligning  interests  and  ultimately  harmonising 
research. These goals were generally achieved within the timeframe. 

  Main  challenges  in  the  project  were  a  result  of  institutional  and  cultural 
differences  in  scientific  customs  and  language  barriers.  These  were  resolved in 
forums within the knowledge platforms. 

 

Future  collaboration  and  knowledge  sharing  in  the  field  of  TCM  research  is 
secured  through  the  ‘Good  Practice  in  Traditional  Chinese  Medicine  Research 
Association’,  established  as  the  GP-TCM  project  ended  to  continue  aligning 
research  on  TCM  and  serve  as  an  advisory  body  to  the  European  Medicines 
Agency. 

Introduction 

The FP7 project ‘Good Practice in Traditional Chinese Medicine Research in the Post-
genomic Era’ (GP-TCM) is an example of a project that developed and strengthened 
research relations between universities, TCM industries and regulators in the EU and 
third countries, in particular China, within the field of best practice in TCM research. 
It was chosen as a case in order to study a project that ‘has strongly contributed to 
international research relations between the EU  and third countries’ in the HEALTH 
sub-programme.39  The  project  consisted  of  10  interactive  work  packages  (WPs) 
each with a distinct objective. Resulting from our interviews, this case will describe 
the project on a general basis, but go into specific details regarding WP3, WP4, WP9 
and WP10. 

39  The  case  selection  is  based  on  data  from  a  comprehensive  network  analysis  and  survey 
conducted  by  Science-Metrix  and  Fraunhofer  ISI  (see  appendix  I  for  a  methodological 
description). 

246 

 
 
                                                
The case study builds on survey data40 conducted in earlier phases of the project, on 
project-relevant  material  and  on  four  interviews  with  project  participants  including 
an  interview  with  the  project  coordinator.  The  interviewed  partners  were  selected 
after  consultation  with  the  project  coordinator—all  four  interviewees  represent 
university partners. Responses have been anonymised. 

Description of the case 

GP-TCM  was  a  FP7  project 
that  started  on  June  1, 
2009,  and  came  to  an  end 
on  October  31,  2012.  The 
project  was  funded  under 
sub-
‘HEALTH’ 
the 
programme, which supports 
research  activities  carried 
out  in  different  forms  of 
trans-national  cooperation 
across  the  European  Union 
and  beyond  in  the  field  of 
health.41  It  is  important  to 
note that GP-TCM was not a 
research  project,  but  an 
assessment  of  current  EU 
research  practice  on  the 
traditional Chinese medicine 
(TCM).  

Background data for GP-TCM 

Project period 

01.06.2009 – 31.10.2012 

Total cost 

€1,117,778  

EU contribution 

€995,100  

Coordinated in 

United Kingdom 

Sub-programme 

HEALTH-2007-2.1.2-7 - 
Traditional Chinese Medicine in 
post-genomic era 

Funding Scheme 

CSA-CA - Coordination (or 
networking) actions 

Scientific fields 

‘Medicine’, ‘Ethno-
Pharmacology, ‘Pharmacology’ 

The  idea  for  the  GP-TCM 
project  was  conceived  by 
Dr. Qihe Xu, Senior Lecturer at King’s College London, based on his observations of 
the fragmented state of genomic research on TCM.  The project was conceptualised 
with colleagues at King’s College London. One of the main tasks in the beginning of 
the project was to form a comprehensive network with participants from the EU and 
China.  Dr.  Qihe  Xu  acted  as  the  main  coordinator  in  the  application  to  the  FP7 
programme.  

The  project  was  undertaken  in  the  context  of  FP7  because  of  the  international 
aspect  possible  within  this  framework  and  the  level  of  complementary  and  diverse 
research that this could bring to the study. FP7 enabled the collaboration with third 
countries,  which  was  a  fundamental  objective  of  the  GP-TCM  project.  The  survey 
data  clearly  shows  that  the  project  strengthened  links  between  internationally 
renowned researchers.  

The  GP-TCM  project  aimed  to  inform  best  practice  and  harmonise  research  on  the 
safety  and  efficacy  of  TCM,  with  a  focus  on  Chinese  herbal  medicines  (CHM)  and 
acupuncture  in  EU  Member  States,  using  a  functional  genomics  approach  through 
exchange  of  opinions,  experience  and  expertise  among  scientists  in  EU  Member 
States  and  China.  In  10  proposed  WPs,  action  was  taken  to  review  the  current 

40  The  survey  data  is  based  on  the  answers  from  one  survey  respondent  from  a  beneficiary 

partner 

41Source: FP7-HEALTH page on CORDIS: http://cordis.europa.eu/programme/rcn/852_en.html  

247 

 
 
                                                
status, identify problems and solutions in the quality control, extraction and analysis 
of  CHM.  While  these  fundamental  issues  were  addressed,  discussion  forums 
emphasising the use of functional genomics methodology in research of the safety, 
efficacy  and  mechanisms  of  CHM  and  acupuncture  was  at  the  core  of  this 
coordination  project.  It  included  the  application  of  the  technique  in  cell-based 
models, animal models and in clinical studies.42 

The 10 WPs in the GP-TCM project each had a distinct objective. They were involved 
in either reviewing, coordinating, organising or managing the project. WP1 and WP2 
were  fundamental  and  centred  on  reviewing  the  techniques,  and  identifying 
problems  and  solutions,  in  the  quality  control  in  literature  and  current  practice  in 
CHM.  WP3  to  WP8  focused  on  the  coordination  of  collaboration  and  knowledge 
sharing among the partners on the use of various functional genomics methodology 
in research on  CHM and  acupuncture. At the end of  the project, a final conference 
was held for the partners in the project as well as a public dissemination event, the 
GP-TCM  Congress.  WP9  was  responsible  for  this  component  of  the  project.  Finally, 
WP10 had  the responsibility  of  general  issues concerning the project, including WP 
appointment  and  leadership,  updates  on  websites  and  newsletters,  drafting 
operating  procedures,  providing  scientific  and  technological  support,  organising 
internal  review  and  quality  assurance,  integration  and  dissemination  of  results,  as 
well as liaison with the Commission.43 

42 Source: GP-TCM page on CORDIS: http://cordis.europa.eu/project/rcn/90960_en.html  

43 Source: Final Report , GP-TCM 

248 

 
 
 
 
                                                
Formation of the partnership 

King’s  College  London  was  the  coordinating  institution  with  partners  from  all  over 
Europe and Asia. Figure 25 maps all the participating partners of the project (except 
those in Asia). 

Figure 25  Map of the participating partners 

Source: 

Cordis, website: http://cordis.europa.eu/home_en.html 

Initiator  Dr.  Qihe  Xu  at  King’s  College  London  formed  the  partnership  around  GP-
TCM.  Professor  Peter  Hylands  of  the  same  institution  was  also  a  central  figure  in 
selecting  the  partners.  The  underlying  idea  behind  the  formation  was  bringing 
together experts and experienced researchers in either Western medicine or TCM to 
enable a process of reaching best practices in TCM research.  

The actual partnership was formed in a pragmatic way by initially selecting partners 
based  on  whether  or  not  they  could  contribute  with  new  insights  on  the  basis  of 
their  distinct  expertise.  The  partnership  consisted  of  29  beneficiary  partners, when 
the application for the FP7 programme was submitted. These included SMEs, public 
research organisations, other public bodies, and private or non-profit organisations, 
but  the  majority  of  partners  represented  higher  or  secondary  education 
establishments.  Of  these,  approximately  two  thirds  were  European  and  one  third 
Chinese.  In  addition,  the  consortium  had  80  non-beneficiary  partner  organisations 
as  well  as  two  independent  experts  from  countries  including  Australia,  Austria, 
Belgium, Burkina Faso, Canada, China, Democratic Republic of the Congo, Denmark, 
Finland,  Germany,  Italy,  Luxembourg,  the  Netherlands,  Norway,  Romania,  Russia, 
Spain, Sweden, Thailand, the UK and the USA.  

249 

 
 
 
It is clear from the interviews that the formation of the partnership was directed by 
the  project’s  scope  as  the  project  was  conceived  as  ‘truly  international’  from  the 
outset  in  the  sense  that  active  inclusion  of  the  international  partners,  especially 
partners  from  Chinese  research  institutions,  was  a  fundamental  part of  the project 
and  vital  to  its  success.  This  was  important  for  two  reasons.  Firstly,  involving 
Chinese partners in the network established a basis for future collaboration between 
European  and  Chinese  research  institutions  in  the  pharmacology  field.  Secondly, 
Chinese universities held specific key knowledge on TCM research as they provided 
access  to  the  latest  articles  on  TCM  research  in  China  and  were  able  to  translate 
these into English. 

In addition to the scientific and industrial partners, a seasoned project manager with 
concrete  experience  from  other  FP7  projects  was  hired  to  facilitate  the  practical 
coordination  of  the  project.  The  inclusion  of  a  dedicated  and  experienced  manager 
was mentioned in the interviews as a great  asset to  the project and a prerequisite 
for reaching the project’s ambition of bringing researchers together.  

Process and Implementation 

The  GP-TCM  project  lasted  for  three  and  a  half  years,  through  which  the  WPs 
gradually  unfolded.  To  get  an  overview  of  the  project  chronology,  it  is  helpful  to 
divide  the  project  into  three  phases:  an  early  phase  of  establishing  research 
subjects  and  techniques  (WP1  and  2),  a  middle  phase  consisting  of  coordinating 
research  on  TCM  (WP3–WP8)  and  a  final  phase  of  discussion  and  dissemination  in 
WP9. 

The first steps within the frame of the GP-TCM project were taken in order to clarify 
what  was  studied  and  how  it  would  be  studied.  WP1  brought  together  partner 
specialists on the quality control of TCM plants from the EU and China in one-to-one 
meetings  and  telephone  calls  to  establish  which  specific  plants  to  study  in  the 
project.  WP2  analysed  preliminary  extractions  from  the  plants  and  reviewed  the 
existing  methods  for  extracting  to  identify  problems  and  solutions  in  order  to 
propose a coordinated  technique of extracting.  As GP-TCM  was a collaboration and 
coordination project with no previous record to build on, much of the project efforts 
had  to be given to the process  of implementing different actors in the partnership. 
The project coordinator initially faced some confusion from some partners in regards 
to  the  project’s  collaborative  and  coordinative  scope  and  its  focus  on  reviews, 
discussions  and  networking  rather  than  direct  laboratory  or  clinical  research.  This 
was  a  result  of  some  partners  not  being  used  to  being  involved  in  a  coordination 
project  and  required  time  and  consultation  with  the  coordinators  to  become 
accustomed  to  the  form  of  work  performed  in  the  GP-TCM  project.  The  initial 
implementation  of  the  study  thus  required  a  period  for  some  partners  to  become 
accustomed to the collaborative and coordinating character of the work. The general 
view emerging  from the interviews conducted  and survey data is that GP-TCM  was 
quite a successful project in terms of international collaboration in its early stages. 

WP3–WP8  had  a  narrower  scope,  focusing  on  functional  genomic  analysis  of  CHM 
and  acupuncture.  These  WPs  represented  the  middle  phase  of  the  project.  During 
this  period  of  the  project,  partners  developed  and  coordinated  methods  for 
assessing  the  efficacy  of  various  TCM-based  approaches.  Based  on  multiple 
interviews,  a  pattern  of  challenges  encountered  during  this  phase  emerged  in  the 
form  of  difficulties  in  collaboration  across  working  cultures,  language  barriers  and 
institutional differences in approaches to the project. These themes also appeared in 
the  survey  answers,  and  the  large  number  of  project  partners  made  it  difficult  to 
manage  the  project’s  progression.  According  to  the  interviews,  the  size  of  the 

250 

 
 
network  made  it  hard  for  individual  partners  to  influence  the  direction  of  the 
network.  

A  specific  difference  in  the  approach  to  the  study  was  evident  between  industry 
partners,  especially  SMEs,  and  the  research  organisations  forming  most  of  the 
partnership,  resulting  from  a  difference  in  priorities.  The  SMEs’  priorities  were  to 
secure some form of results on a shorter timeframe than the research organisations, 
which occasionally arose as an issue when working together.  

After three and a half years of collaboration, the initial European–Chinese network of 
GP-TCM had evolved into an international team of experts working as a collaborative 
super-network involving 110 institutions, approximately 200 scientists and bridging 
24  (beneficiary  and  non-beneficiary)  countries,  while  a  clear  EU–China  focus 
remained.  All  partners  had  proven  track  records  and  international  reputations  in 
TCM-related research and consisted of both the public sphere (approximately 80%) 
and the private sector (approximately 20%). Such a large and dynamic collaboration 
gave  the  consortium  the  privilege  of  accessing  stakeholders  around  the  world,  as 
well  as  broader,  deeper  and  more  authoritative  expert  knowledge  and  resources.44 
This expert knowledge was presented and discussed  at an internal seminar as well 
as a public dissemination as part of WP9 in the final stages of the project.  

Contextual factors 

The  collaboration  between  the  European  and  Chinese  research  institutions  was  an 
area  with  special  challenges.  Two  things  hindered  collaboration  in  particular:  (1) 
different working cultures and scientific ethos and (2) EU visa obstacles for bringing 
Chinese researchers to Europe.  

Interviewees  voiced  that  they  had  to  take  into  account  the  difference  in  working 
scientifically  in  China  and  Europe  during  the  project,  which  for  the  European 
partners  meant  learning  that  the  pressure  on  publishing  in  China  is  greater  than 
European scientists are accustomed to. This resulted in difference of opinions on the 
priority  between  quality  and  quantity  of  scientific  publications  among  the partners, 
as  they  were  rated  according  to  different  criteria.  This  is  a  result  of  two  different 
funding structures: a centralised European structure and a federal Chinese system. 
In  the  Chinese  federal  system,  the  funding  structure  varies  in  different  regions  of 
China.  European  researchers  expressed  in  interviews  that  they  felt  the  Chinese 
scientific  conduct  took  too  much  priority  on  getting  their  work  published  at  the 
expense  of  the  quality  of  the  research.  The  fact  that  the  funding  structure  also 
varied  among  the  Chinese  researchers  presented  an  unpredictable  element  in  the 
relationship  between  EU  and  Chinese  researchers.  Additionally,  a  few  of  the 
interviewees  mentioned  that  some  Chinese  institutes  did  not  deliver  according  to 
what was expected of them, although many (including those non-beneficiaries) did. 

Another contextual barrier to international collaboration appeared when travel visas 
to the EU needed to be obtained when inviting the Chinese partners to meetings and 
seminars in Europe. With a full work package dedicated to two final disseminations 
in Europe and the project revolving around knowledge sharing with third countries, 
it was very hard for the project coordinators and WP leaders to grasp the usefulness 
of  EU-level  immigration  laws  that  hindered  Chinese  partners  in  entering  the  EU. 
Entrance was secured at a charge of €100 in visa fees for each Chinese researcher, 
a fee which continued to puzzle the project coordinators as the view from inside the 

44 Final Report , GP-TCM 

251 

 
 
                                                
project was that these were people working for the EU and should not be subject to 
the  general  immigration  procedures.  This  obstacle  directly  hindered  the  process  of 
organising the platform for knowledge sharing and international collaboration.  

Impact at network level 

The  GP-TCM  project’s  success  in  achieving  their  goal  of  harmonising  research  and 
bringing  experts  together in  the  field  of  TCM  research  meant  that  the  project  as  a 
whole  had  a  huge  impact  on  the  network  level.  Actors  in  the  field  have  come  to 
know  each  other  through  collaboration  and  sharing  of  knowledge  across  sectorial, 
institutional  and  continental  levels.  Most  remarkable  is  the  broadly  established 
connection  between  Europe  and  China.  This  has  recently  led  to  collaboration 
between  the  University  of  Vienna  and  the  Shanghai  Institute  of  Materia  Medica, 
where  these  two  partners  secured  funding  from  the  European  Commission’s  Marie 
Curie Actions scheme to open up a postdoctoral position at the University of Vienna 
for a Chinese fellow.45  

Being  a  collaboration  and  coordination  project,  GP-TCM  had  a  clear  ambition  of 
making  a  strong  impact  on  the  network  level,  and  with  the  project  succeeding  in 
harmonising  research  through  a  multitude  of  partners  this  has  been  achieved.  The 
survey data shows that a  participant in  the  GP-TCM  who  had  no previous research 
relationships  with  the  partners  in  the  GP-TCM  managed  to  establish  30  new 
relationships  by  participating  in  the  FP7  programme.  These  new  links  established 
were  with  both  researchers  working  in  similar  fields  and  researchers  working  in 
other  related  scientific  fields.  According  to  interviews,  the  linkages  between 
researchers  have  strengthened  to  a  very  high  degree  in  all  levels  of  collaboration, 
from  borrowing  ideas  without  personal  interaction  to  occasional  collaboration 
between individuals, as well as systematic collaboration in multidisciplinary teams. 

The  survey  data  also  shows  that  collaboration  has  strengthened  relationships  with 
researchers from countries outside the European Union to a very high degree during 
the  project.  It  translates  into  the  successful  contact  created  between  especially 
European and Chinese research institutions in the field of TCM research, which was 
one of the main aims of the GP-TCM-project. Interviewees also identified these new 
inter-continental research links, adding that they would  like  to work more with the 
Chinese partner institutions in the future. 

In  addition  to  the  GP-TCM-project  itself,  the  GP-TCM  Research  Association  was 
formed  in  the  aftermath  of  GP-TCM  to  ensure  that  the  network  and  knowledge 
obtained  in  GP-TCM  continue  to  guide  and  coordinate  the  involved  partners  and 
beyond  in  European–Chinese  collaboration  and  in  TCM  research.46  The  association 
works independently of the GP-TCM project and is currently under the leadership of 
Professor Rudolf Bauer, head of TCM Research Centre at the University of Graz. It is 
recognised  as  an  advising  body  on  TCM  at  the  European  Medicines  Agency. 
Interviews  stated  that  GP-TCM  had  lowered  the  barriers  for  collaboration  between 
EU  and  China  and  the  GP-TCM  Research  Association  is  instrumental  to  further 
lowering the barriers. 

The only critical remark regarding the GP-TCM network is that the  large number of 
project partners has  hindered the management of the project and  the participants’ 

45 See project TCM-VASC on CORDIS: http://cordis.europa.eu/project/rcn/97998_en.html  

46 See GP-TCM Research Association: http://www.gp-tcm.org/  

252 

 
 
                                                
ability  to  influence  the  direction  of  project.  It  could  be  viewed  as  a paradox in  the 
project  because  it  is  on  the  one  hand  an  aim  of  the  project  to  establish  a  large 
network, but on the other hand a large comprehensive network aiming to bring out 
collective  wisdom  and  consensus  had  some  effects  on  the  inclusion  of  some 
individual  partners’  opinions.  Nonetheless,  the  project  defines  one  aspect  of  good 
practice as ‘striving for consensus while respecting difference in opinions’.47 

Outputs, outcomes, and impact 

The  GP-TCM  project  succeeded  in  contributing  to  international  research  relations 
between  the  EU  and  third  countries  as  well  as  establishing  best  practice  and 
harmonising research on TCM. As discussion platforms were highly prioritised, more 
than  100 deliverable  reports  as  well  as  public  and  scientific publications  containing 
best practice guidelines for TCM research were widely disseminated and the GP-TCM 
project met all of their planned objectives and milestones. The interviews conducted 
with  leading  members  of  the  GP-TCM  project  generally  found  that  networking  and 
collaboration  did  occur  on  a  scale  large  enough  to  have  an  impact  on  the 
establishment  of  best  practice  in  TCM  research.  Additionally,  the  links  generated 
between partner institutions and scientists in the GP-TCM project has led to lasting 
partnerships and further collaboration that has resulted in a number of new grants 
awarded by the EU and China respectively.48  

The  partnership  developed  globally  competitive  comprehensive  good  practice 
guidelines,  handbooks  and  reports,  including  scientific  reporting  of  TCM  studies, 
clinical  trials  and  the  applications  of  genomics  technologies  in  various  aspects  of 
TCM research. According to the survey, the GP-TCM project resulted in development 
of the following new scientific and technological tools or knowledge: 

  Development of new (abstract) methodologies 

  Creation of surveys 

  Generation of new study materials 

 

 

Peer-reviewed articles 

Presentation of results at conferences or workshops 

  Development of new models or approaches 

  Submission of patent applications 

  Spin-off projects 

  Awarded recognition for contribution to fields 

Output  to  the  scientifically  engaged  public  community  took  the  form  of  a  public 
dissemination  seminar  held  at  the  University  of  Leiden  under  WP9.  The 
presentations  and  discussions  on  the  specific  findings  in  the  study  formed  into  20 
the  peer-reviewed  Journal  of 
articles  published 
Ethnopharmacology.49 

in  a  special 

issue  of 

47 http://www.ncbi.nlm.nih.gov/pubmed/22387462  

48 Source: Final Report , GP-TCM 

49 Journal of Ethnopharmacology, Vol. 140, Issue 3 

253 

 
 
                                                
Reflection on the success factors 

One of the factors that contributed to the success of the GP-TCM project was due to 
the  discussion  platforms  that  enabled  the  partners  to  develop  network  links  and 
share  knowledge  on  best  practices  in  TCM  research  without  being  hindered  by 
geographical, cultural or language barriers. The discussion platforms included more 
than 20 face-to-face meetings, over 50 telephone conferences, an intranet service, 
project  newsletters,  BBS  forums  and  email  exchanges.  These  various  platforms  for 
sharing,  discussing  and  harmonising  research  on  TCM  enabled  the  network  to 
develop  and  ultimately  reach  a  consensus  on  best  practice  in  TCM  by  facilitating 
communication between universities, hospitals, industry and government bodies on 
both interregional, interdisciplinary and across sectoral levels.  

Another crucial factor for the success of the GP-TCM project was the active inclusion 
of international partners, especially Chinese research institutes. Interviews attribute 
the successful integration of Chinese partners into the partnership through face-to-
face meetings prioritised on the discussion platforms. Interviewees emphasised that 
telephone 
language  barriers  and  differences 
conversations  so  difficult  that  the  face-to-face  meetings  planned  on  the  discussion 
platforms  became  a  key  aspect  of  the  international  collaboration.  Survey  data 
supports  this  point  by  pointing  to  differences  in  working  cultures  between 
institutions and countries as the main obstacle encountered in the collaboration.  

in  scientific  customs  made 

Lastly, interviewees mentioned that the efficient administration in this particular FP7 
project  was  exemplary.  It  was  acknowledged  in  a  number  of  interviews  that  the 
main coordinators, work package leaders and the employed FP7 experienced project 
manager  conducted  the  project  in  a  professional  and  timely  manner.  This  was 
extremely important in order to secure a practical as well as a communal spirit in a 
project  that  was  characterised  by  great  geographical  extent  and  involved  many 
partners.  

254 

 
 
 
 
Case Study VIII: LASERLAB EUROPE II, The Integrated Initiative of European Laser 

Research Infrastructures 

Summary of the case 

Laserlab Europe, its second phase (Laserlab Europe II), is an example of a large and 
well-functioning network between research organisations including the national laser 
laboratories  of  many  European  countries,  which  has  expanded  in  size  since  the 
founding  of  Laserlab  Europe  in  2004.  Many  of  the  challenges  and  difficulties  that 
new networks encounter such as language barriers, cultural differences and different 
academic  traditions,  the  Laserlab  Europe  consortium  has  managed  to  overcome  in 
its earlier phases. Furthermore, it has been decisive for the network of the original 
partners  to  be  able  to  continue  their  work  year  after  year  through  support  from 
different framework programmes. 

However, it is also an example of a network that has been very focused on creating 
good  framework  conditions  for  transnational  access  and  laser  research  and  to  a 
lesser  degree  on  how  the  network  could  be  more  attractive  to  private  enterprises. 
The  latter  is  a  dilemma  as  the  EU  demands  that  all  research  produced  within  the 
Laserlab Europe network is published, which most enterprises are not interested in 
due to confidentiality and Intellectual Property Right (IPR) issues. 

Introduction 

The  FP7  project  ‘Laserlab  Europe  II  –  The  Integrated  Initiative  of  European  Laser 
Research  Infrastructures’  is  an  example  of  a  project  where  a  strong  network 
between  Laser  Research  Infrastructures  was  further  developed.  The  foundation  of 
the network was formed and developed during former FP-supported Laserlab Europe 
projects.  Laserlab  Europe  I  existed  from  2004  to  2007  and  was  extended  to  2008 
under FP6. Laserlab Europe II, which lasted from 2009 to 2012, is the primary focus 
in  this  case  study  as  it  was  supported  by  FP7.  The  cooperation  is  now  in  its  third 
phase,  Laserlab  Europe  III  (from  2012  to  2015),  also  financed  by  FP7.  The 
consortium  is  applying  for  a  new  grant  under  Horizon  2020  to  continue  Laserlab 
Europe with support from the EU. 

Though  the  Laserlab  Europe  network  is  well  established,  the  case  was  selected  to 
study  a  project  with  mitigated  success  in  networking  with  industrial  partners,  but 
which encountered positive outputs and outcomes in terms of cooperation within the 
scientific  community.50  The  outputs  and  outcomes  of  Laserlab  Europe  II  are 
assessed  in  the  context  of  the  funding  scheme  FP7-INFRASTRUCTURES  with  the 
subtheme  ‘Capacities’,  which  is  aimed  at  optimising  the  use  and  development  of 
research infrastructures.51 

The  case  study  builds  on  survey  data52  conducted  in  earlier  phases  of  the 
evaluation,  on  relevant  project  material  and  on  five  interviews  with  project 
participants  including  an  interview  with  the  project  coordinator  of  Laserlab  Europe 

50  The  case  selection  is  based  on  data  from  a  comprehensive  network  analysis  and  survey 
conducted  by  Science-Metrix  and  Fraunhofer  ISI  (see  appendix  I  for  a  methodological 
description) 

51 http://cordis.europa.eu/programme/rcn/859_en.html 

52 The survey data is based on the answers from one survey respondent 

255 

 
 
                                                
III, which is the third phase of Laserlab Europe. He has been involved with Laserlab 
Europe since it was founded, so he has in-depth knowledge about Laserlab Europe’s 
development  through  the  years  and  phases.  The  other  partners  were  selected  in 
consultation  with  the  project  coordinator  and  are  all  academic  researchers. 
Responses from the participants have been anonymised. 

The primary objective of the case studies is to outline how knowledge is created in 
networks and through cooperation. 

Description of the Case 

a 

cost 

from 

the  use 

took  place 

II 
The  Laserlab  Europe 
project 
from 
March  1,  2009,  to  May  31, 
2012.  Laserlab  Europe  II 
had 
of 
total 
€11,847,178.  Of  the  total 
cost,  the  project  received 
FP7-
€10,000,000 
INFRASTRUCTURES 
(Capacities).  The  objective 
of  the  programme  is  ‘to 
and 
optimise 
development  of  the  best 
research  infrastructures  in 
Europe  and  to  help  create 
research 
new 
pan-
infrastructures 
in  all 
European 
fields 
and 
technology,  thus  providing 
the 
the 
for 
European 
scientific 
community to remain at the 
forefront 
the 
advancement of research’.53 

of 
interest 

conditions 

science 

of 

of 

Background data for LASER LAB EUROPE II 

Project period 

01.03.2009 – 31.05.2012 

Total cost 

€11,847,178 

EU contribution 

€10,000,000 

Coordinated in 

Germany 

Sub-programme 

INFRA-2008-1.1.1 - Bottom-up 
approach: Integrating Activities 
in all scientific and 
technological fields 

Funding Scheme 

CP-CSA-Infra - Combination of 
CP and CSA 

Scientific fields 

Physics, Medicine, Engineering, 
Communications, Materials 
research, Manufacturing 

During the second phase of Laserlab Europe, the consortium also expanded the Joint 
Research  Activities  to  five  of  the  most  important  scientific  challenges  in  laser 
research, while maintaining support of the user community through the coordinated 
and quality-controlled Transnational Access activities.  

According  to  the  survey  data,  the  reasons  for  undertaking  the  Laserlab  Europe 
project in the context of the framework programmes rather than in the context of a 
national or a regional research programme were: 

 

 

To  improve  complementarities  and  diversity  in  research  by  using  information, 
data, techniques, concepts, and/or theories from several disciplines. 

To establish linkages with excellent international research partners. 

Laserlab  Europe  II  was  a  consortium  of  Laser  Research  Infrastructures  from  19 
European  Member  States,  forming  a  FP7  Integrated  Infrastructure  Initiative.  The 

53 http://cordis.europa.eu/programme/rcn/859_en.html  

256 

 
 
                                                
overall objectives of  Laserlab  Europe  II were quite  complementary to the objective 
of  the  FP7  programme  and  sub-programme.  The  FP7  project  Laserlab  Europe  II 
(2009–2012)  was  aimed  at  extending  the  European  dimension  by  reaching  out  to 
new Member States and new scientific communities. The main objectives of Laserlab 
Europe were: 

 

 

 

 

To  form  a  competitive,  interdisciplinary  network  of  European  national  laser 
laboratories; 

To  strengthen  the  European  leading  role  in  laser  research  through  Joint 
Research  Activities  (JRA),  pushing  the  laser  concept  into  new  directions  and 
opening up new applications of key importance; 

To engage in the Transnational  Access  Programme in a coordinated fashion for 
the benefit of the European research community. 

To  increase  the  European  basis  in  laser  research  and  applications  by  reaching 
out to neighbouring scientific communities and by assisting the development of 
Laser Research Infrastructures on both the national and the European level. 

Formation of partnership 

The  Laserlab  Europe  II  project  consisted  of  several  universities,  research 
organisations as well as public bodies and non-profit institutions. The project had a 
wide  stretch  across  Europe,  represented  by  16  Member  States.  The  partners  are 
mapped in Figure 26 below. 

Figure 26  Map of the participating partners 

Source: 

Cordis, website: http://cordis.europa.eu/home_en.html 

In  the  1990s  a  number  of  national  Laser  Research  Infrastructures  (RIs),  European 
Large-Scale  Facilities,  started  to  cooperate  to  make  their  facilities  available  to 

257 

 
 
 
European  users  and  to  perform  joint  research  and  technical  development  (RTD) 
projects  to  improve  the  access  and  research  capabilities  of  the  participating 
infrastructures.  The  FP5  project,  Cooperation  Network  LASERNET  (2001–2004), 
marks  the  beginning  of  structuring  activities  amongst  the  laser  RIs  in  Europe.  The 
FP6 Laserlab Europe (2004–2008) project became a reality on the basis of the first 
joint application, whereas the former  integrated  activities were  based on individual 
contracts  under  FP5.  With  Laserlab  Europe  the  consortium  introduced  the  concept 
and  vision  of  a  unified  ‘European  Distributed  Laser  Infrastructure’  with  ambitious 
structuring elements such as: 

  Sharpening  the  profile  of  the  consortium  and  of  the  individual  participants  and 

enhancing their complementarity 

Filling gaps in the state-of-the-art instrumentation 

Improving European laser research through Joint Research Activities (JRAs) 

Transnational access 

Laying the foundation for a lasting and sustainable networking activity 

 

 

 

 

At  that  time,  the  consortium  consisted  of  17  laser  infrastructures  from  nine 
European countries. Building on the first phase of Laserlab Europe, one of the main 
objectives  of  the  FP7  project  Laserlab  Europe  II  (2009–2012)  was  to  broaden  the 
European  dimension  by  integrating  new  EU  Member  States  and  new  scientific 
communities. At the start of the period, there were 24 beneficiaries (partners) with 
27  individual  laser  infrastructures  in  16  countries,  plus  13  subcontractors  and  six 
associate partners; altogether there were participants from 19 European countries.54 
The 24 partners in The Laserlab Europe II project consisted of several universities, 
research  organisations  as  well  as  public  bodies  and  non-profit  institutions.  The 
project  had  a  wide  stretch  across  Europe,  represented  by  16  Member  States.  The 
partners are mapped in Figure 26 below. 

The  Laserlab  Europe  III  (2012–2015)  consortium  has  increased  to  30  partners  in 
2014.  The  third  phase  of  Laserlab  Europe  focuses  on  internal  consolidation  and 
further scientific integration across Europe. 

The consortium wishes to continue to build on Laserlab Europe. Therefore, they are 
writing a new proposal to Horizon 2020 to move towards a fourth phase of Laserlab 
Europe, which may lead to the integration of even more partners. Due to demands 
from the EU, the consortium will try to reach out to industrial partners in connection 
to Laserlab Europe IV. 

Besides  being  a  partner  of  Laserlab  Europe,  it  is  also  possible  to  apply  and  to 
become  an  associate  member  of  the  network.  The  associated  partners  of  Laserlab 
Europe  are  members;  however,  they  do  not  receive  any  money  from  the  network. 
The positive aspects of being an associate member  are firstly that they gain access 
to  a  network  of  leading  institution  in  laser-based  inter-disciplinary  research. 
Secondly,  being  an  associate  partner  is  seen  as  a  seal  of  approval  that  increase 
one’s international reputation as a research infrastructure, which helps in leveraging 
funding elsewhere. In order to become an associate partner, organisations need to 
meet a set of criteria, amongst others, that the research infrastructure has to have 
a  strategic  and  national  character.  Some  associate  partners  become  partners  with 
time. 

54 http://www.laserlab-europe.eu/about-us/ec-project-documentation  

258 

 
 
                                                
Process and Implementation 

The  aim  of  Laserlab  Europe  II  was  to  strengthen  Europe’s  leading  position  in  laser 
research  through  increased  collaboration  between  laser  research  infrastructures. 
One of the main objectives of Laserlab Europe II was to integrate more laser centres 
as partners from new EU Member States as well as continuing to improve access to 
research facilities across Europe. 

The different phases of Laserlab Europe II are (1) building on Laserlab Europe I, (2) 
including new partners, and (3) preparing for Laserlab Europe III. 

Building  on  Laserlab  Europe  I,  the  objectives  were  to  continue  the  development  of 
the network between laser research infrastructures, to strengthen Europe’s position 
as the leading laser research region by joint research activities, and to spread laser 
science to countries where it is absent. During the transition from Laserlab Europe I 
to Laserlab Europe II many new EU Member States were invited to participate in the 
project.  There  were  24  leading  research  organisations  from  16  different  countries 
that were a part of Laserlab Europe II from the start. 

During  Laserlab  Europe  II,  the  network  further  expanded  to  Eastern  European 
countries and, among others, Spain, with the mission of supporting the development 
of  laser  research  infrastructures  and  laser  research  communities  in  the  new 
countries.  By  the  end  of  the  second  phase  of  Laserlab  Europe  in  2012  (Laserlab 
Europe  II),  the  network  included  28  laser  laboratories  plus  associate  partners  and 
subcontractors  covering  19  countries.55  Furthermore,  the  Laserlab  Europe  network 
managed  to  increase  their  cooperation  with  the  user  community.  By  getting 
feedback from external users of the laser research infrastructures, Laserlab Europe 
was able to increase the quality of access to the facilities and to improve the laser 
facilities. 

During  Laserlab  Europe  II,  the  consortium  prepared  for  its  third  phase.  In  2014, 
‘Laserlab  Europe  –  The  Integrated  Initiative  of  European  Laser  Research 
Infrastructures  III’  was  in  process  and  funded  under  FP7-INFRASTRUCTURES. 
Laserlab  Europe  III  aims  at  consolidating  the  network  and  creating  new 
infrastructures in Member States where it is currently lacking by increasing training 
activities  including  introducing  a  network  of  national  contact  points,  user  training, 
new  approaches  to  exchanging  personal,  staff  mobility  and  training,  and 
transnational access. 

In  order  to  implement  Laserlab  Europe’s  objectives,  they  have  developed  a  well-
functioning  organisation  and  management  structure  including  a  General  Assembly, 
which is held at least once a year with all the partners. Furthermore, they have an 
official  Coordinator,  a  Management  Board,  an  Administrative  Unit  as  well  as  an 
Ethics Advisor, a Data Controller, and a User Committee. Beneath, there is the JRA 
Board, the Networking Board, and Access Board plus the National Contact Points.56 
According to the new project coordinator of Laserlab Europe III, it is important that 
everyone  is  part  of  the  management  and  is  represented  in  at  least  one  of  the 
boards. 

55 http://www.laserlab-europe.eu/about-us/partners 

56 http://www.laserlab-europe.eu/about-us/management-structure  

259 

 
 
 
 
                                                
Contextual factors 

A key contextual factor of Laserlab Europe II is the fact that the network was based 
on  a  pre-existing  network  of  partners.  Many  of  the  partners  have  worked  together 
for  many  years  on  former  FP-supported  projects,  including  the  first  phase  of 
Laserlab Europe. As mentioned by all the interviewees, the ‘bridges have been built’ 
and  ‘they  have  created  strong  and  lasting  relationships’,  which  will  have  a  great 
impact on laser research in the long run. 

Another  contextual  factor is  the  fact  that  laser  research  has  become  an  integrated 
part of the science policy in many nations as well as in the EU, as it is regarded as a 
key  to  developing  new  technologies  with  great  potential.  This  perception  on  the 
possibilities  in  laser  research  seems  to  foster  a  lot  of  interest  and  enhances  the 
possibilities of getting funding. 

Impact at network level 

The overall impression is that Laserlab Europe II’s impact at the network level was 
and  is  very  substantial.  The  original  consortium  of  Laserlab  Europe  partners 
managed to build an even larger network of partners from research institutions and 
research  infrastructures  across  Europe  expanding  to  Eastern  European  countries 
during  the  three-year  period.  Though  Laserlab  Europe’s  impact  at  network  level  is 
impressive,  one  networking  dilemma  has  been  identified,  which  is  described  in  the 
end of the section. The positive dimensions of the consortium’s influence at network 
level are elaborated in the following. 

According to Laserlab Europe’s website and interviews, networking activities are the 
glue  that  binds  Laserlab  Europe  together.  Therefore,  the  Laserlab  Europe 
Consortium  established a Networking  Board consisting of  10  member partners who 
are  in  charge  of  supervising  the  quality  and  operation  of  the  networking  activities 
and co-ordinates the interaction between them. Secondly, they link the Consortium 
to the user community and to the scientific community at large.57 

As  mentioned,  the  Laserlab  Europe  network  is  described  as  a  very  mature  and 
democratic  network  between  research  infrastructures.  Because  the  network  is 
mature and is used to integrating new members, they are capable of adapting when 
new  members  join  the  consortium.  In  the  case  of  Laserlab  Europe  II,  the  network 
had managed to integrate research infrastructures from Eastern European countries 
such as Romania, Czech Republic and Hungary without facing challenges. 

A  key  element  of  Laserlab  Europe  are  the  Joint  Research  Activities  (JRA)  where 
partners  from  the  network  conduct  research  together—for  example,  based  on  an 
identified  common  topic—which  all  participants  in  the  specific  JRA  work  on  from 
different perspectives. According to interviewees, the JRAs have helped in improving 
the  quality  and  quantity  of  the  services  of  the  research  infrastructures  as  well  as 
helping in  the  sharing of  techniques  and  of  ideas—for  instance,  on  how to  develop 
research facilities. In Laserlab  Europe  II, there were five JRAs or sub-topics. Every 
JRA has a coordinator and at least nine partners from all over Europe. 

Another  key  aspect  of  Laserlab  Europe  is  the  transnational  access  to  the  laser 
research infrastructures for external users. Before Laserlab Europe, scientists could 
only  use  the  laser  research  facilities  in  their  own  home  country.  Laserlab  Europe 

57 http://www.laserlab-europe.eu/networking/networking-board  

260 

 
 
                                                
added  a  European  and  transnational  dimension  to  the  national  access  to  the 
research  infrastructures.  In  terms  of  networking  impact,  networking  with  external 
users  of  the  laser  research  infrastructures  at  user  meetings  for  instance  has  given 
facilitators  of  laser  research  infrastructures  the  opportunity  to  exchange  ideas  and 
knowledge with the users. 

As  mentioned  in  the  introduction,  the  reason  for  doing  a  case  study  on  Laserlab 
Europe is due to what seems to be a mitigated success in terms of networking with 
industrial  partners.  Even  though  there  are  examples  of  cooperation  with  private 
enterprises  including  SMEs  through  collaboration  with  users  of  the  research 
infrastructures,  the  interviewees  agree,  however,  that  there  has  not  been  enough 
focus on how to reach out to industry. In their new proposal to Horizon 2020, there 
is  an  increased  focus  on  how  to  integrate  industrial  partners  in  a  fourth  phase  of 
Laserlab  Europe.  The  central  dilemma  concerns  the  fact  that  most  private 
enterprises want to keep their research, inventions and innovations secret, but the 
European Union demands that research done in connection to Laserlab Europe must 
be public. According to interviews, the network welcomes researchers from industry 
and there is a demand from private enterprises to do research on the laser research 
infrastructures, but not to the extent that one would expect because of Intellectual 
Property  Right  (IPR)  issues.  This  is  a  dilemma  that  the  consortium  will try  to cope 
with in the future. 

Outputs, outcomes, and impact 

Besides  the  network  impact,  Laserlab  Europe  II  has  also  been  scientifically 
successful and has promoted the development  of  new laser  research infrastructure 
and facilities. Enhanced recognition and visibility of newly integrated laser research 
infrastructures in the Laserlab Europe network as well as training and education are 
other significant effects of Laserlab Europe II. 

Scientifically, Laserlab Europe helped in creating better conditions for laser research, 
which  as  an  academic  discipline  has  revolutionised  different  fields  of  physics  and 
neighboring sciences’.58 An important element in laser research is the development 
of  laser  research  infrastructures  across  Europe.  The  consortium’s  strategic 
coordination and overview of research infrastructures across Europe helps to ensure 
that  laser  facilities  are  different  from  each  other  and  to  avoid  duplications  of 
facilities.  A  European  and  more  holistic  view  on  research  infrastructures  enhances 
the possibility of a more efficient use of funds to build and develop unique research 
facilities across Europe. In contrast, a more nationally focused perspective on laser 
research  facilities  and  research  may  lead  to  a  fragmented  European  research 
infrastructure  landscape  with  duplications  of  facilities.  Furthermore,  elements  of 
both  collaboration  and  competition  between  members  of  Laserlab  Europe  are 
therefore  important  in  the  sense  that  cooperation  promotes  synergies  and 
complementary activities across countries on the one hand and competition provides 
the  necessary  incentives  to  perform  laser  based  research  on  a  high  level  and 
develop unique laser facilities on the other hand. 

According to survey responses and interviews, Laserlab Europe, including the JRAs, 
has resulted in the development of scientific and technological knowledge and tools 
by participating organisations including: 

58  Pile,  D.  (2012):  Laserlab  Europe  expands  in  third  phase.  Nature  Photonics,  Vol.  6, 

September 2012. 

261 

 
 
                                                
  Several  peer-reviewed  articles  and  other  academic  publications  such  as  books 

and proceedings 

 

Presentation of results at conferences and workshops 

  Submission of patent applications 

  Awarded recognition for contribution to fields 

  Development of new methodologies 

  Development of new tangible instruments 

Concerning research facility development, Laserlab Europe has played a big part in 
the promotion of the first international, pan-European research infrastructures in the 
laser  sector—ELI  (Extreme  Light  Infrastructure  for  basic  science)  and  HiPER  (High 
Power Laser Energy Research Facility). These facilities are implemented with use of 
EU  Structural  Funds  and  ELI  is  the  first  new  research  infrastructures  in  the  Czech 
Republic, Hungary and Romania. 

Training and education is another important aspect of Laserlab Europe. Being a part 
of  Laserlab  Europe,  one  gains  access  to  courses,  lectures,  and  professional 
consultations,  which  are  available  through  Laserlab  Europe  user  training  activities. 
Knowledge  sharing  between  partners  and  external  users  also  provides  continuous 
training and education of both old and new members of Laserlab Europe. 

Another impact from being a part of the Laserlab Europe network is that it brings a 
research institution national and international recognition. At first, it helps the laser 
research  infrastructures’  capacity  to  attract  and  welcome  external  users  of  their 
facilities.  Secondly,  being  a  part  of  Laserlab  Europe  can  also  help  in  receiving 
funding  from  national  sponsors  due  to  enhanced  recognition  when  becoming  a 
member. 

Reflection on the success factors 

As mentioned, Laserlab Europe is a facilitated laser research infrastructure network 
or consortium of partners focused on optimising transnational access to the research 
facilities.  The  network  is  described  as  well  functioning,  mature,  and  democratic. 
Many  of  the  challenges  and  difficulties  that  new  networks  meet,  such  as  language 
barriers,  cultural  differences,  and  different  academic  traditions  for  example,  the 
consortium  has  managed  to  overcome  in  earlier  phases  of  Laserlab  Europe. 
According to the interviews, important success factors in terms of network creation 
are: 

  A  good  coordinator  who  is  capable  of  setting  a  direction  and  who  receives 

democratic support 

 

Transparency in decision-making process 

  Based on bottom-up ideas 

  A consortium agreement which is democratic an fair 

 

Involving relevant partners 

  Meeting regularly 

  An inclusive network, but one need to exclude members 

  Getting things right from the beginning 

262 

 
 
In light of the description of Laserlab Europe, it is obvious that it has been decisive 
for the network of the original partners to be able to continue their work year after 
year, supported by different framework programmes. 

263 

 
 
 
 
Case Study IX: RESPECT,  Relating Expectations and Needs to the Participation and 

Empowerment of Children in Clinical Trials 

Summary of the case 

The  RESPECT  project  was  a  response  to  the  EU  regulation  regarding  Medicinal 
Products  for  Paediatric  Use  that  came  into  effect  in  2007.  The  project  coordinator 
built a consortium consisting of partners from different organisational backgrounds, 
who  all  shared  the  aim  of  raising  awareness  regarding  paediatric  clinical  trials. 
Partners  in  this  project  included  university-based  researchers,  an  NGO,  a  research 
organisation,  and  a  consultancy  group.  The  group  was  especially  interested  in  the 
experiences  of  child  participants  and  their  families,  and  how  they  can  be  better 
included  in  future  paediatric  clinical  trials.  RESPECT  was  funded  as  a  coordination 
action  and  together  the  consortium  completed  an  extensive  literature  review  while 
also  collecting  qualitative  data  through  interviews  and  surveys.  The  project 
coordinator and the four other participants interviewed for this case study were very 
satisfied  with  the  outcomes  of  the  project  and  with  their  collaboration.  The  group 
has  disseminated  its  results,  recommendations  and  insights  to  other  networks 
involved  in  paediatric  clinical  trials,  and  has  been  able  to  raise  awareness  of  this 
topic  through  a  final  conference,  publishing  a  book  and  academic  journal  articles, 
and developing models for future clinical trials.  

Key findings from this case study are: 

  New  networks  can  be  established  under  a  common  interest  and  goal  that  the 

partners want to move forward and advance 

 

 

 

The  purpose  and  aim  for  projects  funded  as  coordination  activities  need  to  be 
better  defined  and  these  projects  cannot  be  expected  to  produce  the  same 
outcomes as research based projects 

The  social  aspect  of  coordination  projects  and  for  newly  developed  networks is 
essential to the success of a project and collaboration amongst its members 

There  are  challenges  to  overcome  in  regards  to  network  sustainability  and 
working  style,  when  a  consortium  consists  of  partners  from  different 
organisations  and  different  disciplines  due  to  different  working  cultures  and 
norms as well as discrepancies in expertise 

Introduction 

RESPECT  is  an  example  of  a  project  that  visibly  linked  different  types  of 
organisations with mitigated success in outcomes. This case was selected due to its 
high  level  of  networking,  its  interdisciplinary  nature  and  the  involvement  of 
participants from numerous European states. This case highlights how a completely 
new network was established to carry out this coordination project. According to the 
network analysis and the survey conducted by Science Metrix and Fraunhofer, it was 
found  that  RESPECT  was  less  successful  in  terms  of  their  outcomes.  As  this  was  a 
coordination  project,  it  was  not  the  group’s  objectives  or  goals  to  produce  new 
innovations  or  contribute  to  a  new  field  of  science,  for  example.  They  were, 
however, successful in sharing  their  findings at  a final conference, producing peer-
reviewed  articles,  publishing  a  book 
results  and 
recommendations  for  those  involved  in  paediatric  clinical  trials,  and  developed  a 
model for how to empower children in clinical trials to name a few.  

to  communicate 

their 

264 

 
 
This case study is based on five interviews in total, one with the project coordinator 
and  four  interviews  with  project  partners.  Data  were  also  drawn  from  the  network 
analysis and survey conducted  in earlier  phases  of the evaluation. Responses  from 
the participant interviews have been anonymised in this case study.  

Description of the case 

trials 

Sweden 

Total cost 

€500,000  

€560,144  

Project period 

Coordinated in 

EU contribution 

Sub-programme 

01.06.2008 – 31.08.2011 

Background data for RESPECT 

The 
three-year  RESPECT 
project  began  on  June  1, 
2008,  and  came  to  a  close 
on  August  31,  2011.  The 
project  received  a  total  of 
€500,000 
the  FP7 
from 
programme and was funded 
as  a  coordination  study 
‘Identifying 
concerning 
in 
patients’  needs 
the 
context 
clinical 
(HEALTH-2007-4.1-4). 
Coordination 
projects 
funded  by  the  European 
Commission  are  meant  to 
that 
support 
stimulate  and  facilitate  the 
participation  of  different 
organisations 
their 
networks in developing new 
research  clusters.  The  RESPECT  consortium’s  work  was  based  primarily  on 
discussions  of  the  issues  paediatric  clinical  trials  in  Europe  are  facing,  along  with 
conducting  a  comprehensive  literature  review  on  the  topic.  Additional  data  and 
insights were gathered by the group through interviews and surveys with paediatric 
patients  and  their  parents,  patient  support  organisations,  paediatricians,  ethical 
committees and representatives of the pharmaceutical industry. The group explored 
issues raised by child participation in medical research.  

- 
HEALTH-2007-4.1-4 
Identifying  patients'  needs  in 
the clinical trials context 

CSA-SA - Support actions 

Funding Scheme 

Scientific fields 

Paediatrics 

actions 

and 

 Differences  between  children  and  adults  are  not  necessarily  taken  into  account 
when  medicinal  products  are  prescribed.  Children  are  physically  growing  and 
developing and their bodies often do not respond to a drug in the same way adults 
do.  Despite  the  fact  that  many  doctors  and  medical  practitioners  know  and 
recognise the physical differences between adults and children, it has been observed 
that over 50% of medicinal products used in children may not have been tested or 
authorised for use in this age group. It is up to health care professionals to decide 
whether or not to prescribe these medicines to children ‘off label’. It is also up to the 
health  care  professional  to  determine  the  suitability  and  the  correct  dosage  to 
administer  to  a  child,  as  there  is  an  absence  of  paediatric  labelling  on  these 
medicines.  Administering  medicine  to  children  ‘off  label’  poses  significant  risks  of 
inefficacy and/or may lead to adverse reactions in them.  

The  EU  regulation  (EC  No.  1901/2006  on  Medicinal  Products  for  Paediatric  Use) 
came  into  effect  in  January  2007  to  ensure  that  medicinal  products  that  are 
researched,  developed  and  authorised  in  Europe  will  also  meet  the  therapeutic 
needs of children. The lack of clinical trials in children has serious consequences as 
they  may  be  given  ineffective  treatment  or  inaccurate  dosages,  they  may  receive 
medicine  without  knowing  its  possible  adverse  effects,  and  they  may  be  denied 
access  to  new  breakthrough  medicines  that  are  available  to  adults  because  trials 
have  not  been  conducted  in  children.  With  this  regulation  in  effect,  the  project 

265 

 
 
RESPECT recognised the need to identify the needs and motivations of children and 
their families who have participated or may participate in clinical trials on children in 
Europe.  

The  acronym  RESPECT  stands  for  Relating  Expectations  and  Needs  to  the 
Participation  and  Empowerment  of  Children  in  Clinical  Trials.  As  suggested  in  the 
project name, the RESPECT project centred around three key questions: 

1.  How can paediatric patients be better mobilised and empowered? 

2.  How can they get the clinical outcomes that really matter to them? 

3.  How can their needs be integrated into clinical trials? 

The RESPECT team aimed to clarify the expectations and needs of children and their 
families  who  have  taken  part  in  or  may  take  part  in  future  clinical  trials  for  new 
drugs  in  Europe.  Furthermore,  the  project  sought  to  identify  ways  in  which  the 
expectations  and  needs  of  the  child  participants  could  be  translated  into 
empowering and motivating participants in future paediatric clinical trials.  

Formation of the partnership 

The  RESPECT  consortium  consisted  of  experts  from  the  fields  of  clinical  research, 
patient  representation,  and  European  paediatric  research  ethics  and  regulation. 
Figure 27 maps the participating partners in the project. 

Figure 27  Map of the participating partners 

Source: 

Cordis, website: http://cordis.europa.eu/home_en.html 

RESPECT  is  a  project  that  highlights  how  a  new  network  would  not  have  formed 
without  funding  from  the  Commission  through  FP7.  The  project  coordinator  was 
aware that paediatric regulation was coming into force in 2007 and could see from 

266 

 
 
 
their  own  organisation  that  the  regulation  could  lead  to  more  clinical  trials  in 
children and saw that there could be potential problems. Upon seeing the call from 
the Commission, the coordinator chose to proceed with setting up a project that was 
focused  on  child  participation  and  ownership  in  clinical  trials  due  to  the  new 
regulation. 

In  one  of  the  interviews,  a  participant  described  the  RESPECT  project  as  a  pilot 
project. The participant  pointed out that  RESPECT  was the first group in Europe to 
develop the themes of child empowerment, expectations and needs in clinical trials. 
When  there  are  specialised  fields  of  science  in  the  research  community,  there  are 
often established networks already in place. For RESPECT, however, there was not a 
cohesive community formed prior to the project.  

The  coordinator  began  the  project  having  only  one  partner,  an  individual  and 
organisation that they had collaborated with on different occasions before RESPECT. 
From  there,  the  coordinator  began  an  Internet  search  and  contacted  relevant 
organisations  and  individuals  whom  they  thought  could  contribute  to  the  project 
objectives and had an interest in this particular subject. Through contacting various 
individuals, the coordinator received more names, references and referrals to people 
and  organisations  who  could  be  an  asset  to  RESPECT.  As  this  was  a  coordination 
project,  the  coordinator  was  particularly  interested  in  who  people  knew  and  what 
possibilities they brought, in regards to getting in contact with other individuals. The 
final  selection  of  partners  was  based  on  the  coordinator’s  judgment  of  how 
interested and enthusiastic people were about this project, and equally importantly, 
their links to other projects and other networks. When the RESPECT project began, 
the  project  partners  did  not  necessarily  know  one  another.  Rather,  the  group  was 
built  on  one-to-one  relationships  where  there  were  connections  between  some 
members of the consortium, but most of the partners were working together for the 
first time. 

Process and implementation 

RESPECT  carried  out  its  coordination  activities  in  three  phases  and  completed  six 
work packages altogether. The project began with a cooperation phase followed by a 
harmonisation  phase,  and  concluded  with  a  dissemination  phase.  The  coordination 
aspect of the project included a comprehensive literature review of literature related 
to  paediatric  clinical  trials.  Additionally,  the  consortium  conducted  interviews, 
surveys, and workshops across Europe with children and their parents participating 
in clinical trials, as well as their support organisations, ethical committees, and with 
professionals who are planning and carrying out paediatric clinical trials. 

One interview participant reflected that at the start of RESPECT, they were not quite 
sure  what  the  ‘red  thread’  in  the  project  was.  The  project  coordinator  also  stated 
that it took the group awhile to fully realise that they were in a coordination project 
and that they did not actually  need  to collect data. As many of the partners in the 
consortium were researchers they wanted to not only coordinate and to disseminate 
information  regarding  paediatric  clinical  trial  practices,  but  to  also  collect  some  of 
their  own  data  on  the  side  as  well.  One  participant  found  that  in  collaborative 
projects, the payment is made for exchange but what that specifically entails is not 
clear.  Instead,  the  group found  themselves  engaged  in  activities  that they  decided 
to do on their own that were not funded by the project.  

Prior  to  the  start  of  the  project,  the  project  coordinator  and  the  partner  they 
brought  into  the  consortium  created  a  general  project  outline.  As  more  partners 
came aboard, their input and ideas were included in the planning process. RESPECT 
started  with  a  cooperation  meeting  amongst  its  partners  at  which  everyone  in  the 

267 

 
 
consortium met for the first time. Although there was a plan in place upon entering 
that  first  meeting,  each  partner  also  shared  their  own  interpretation  of  the  plan. 
Individuals  came  with  their  own  ideas  for  the  project  and  communicated  different 
perceptions and possibilities for what they could achieve in the three-year period of 
the  project.  Once  the  logistics  were  sorted  out  in  the  first  meeting,  the  work  was 
divided  among  the  partners  in  accordance  to  the  work  plan.  Deliverables  and 
milestones  were  detailed  in  the  project  plan  as  well  as  determining  who  would  do 
what and when.  

One factor that contributed to the success of the RESPECT network and project were 
the  face-to-face  meetings  that  were  held  throughout  the  project.  RESPECT  held  at 
least  two  meetings  and  workshops  per  year.  One  event  was  held  in  each  of  the 
partner countries throughout the three year project. First, the meetings provided a 
time for the consortium to come together physically and decide what tasks needed 
to be completed for their next  meeting.  They  also allowed the consortium  to share 
experiences  and  make  exchanges  as  well  as  provided  a  forum  for  them  to  discuss 
and modify their plans if needed. From the coordinator’s experience, things only get 
done  when  there  are  face-to-face  meetings.  Secondly,  the  meetings  could  be 
compared to mini conferences. Partners shared the work that they had done prior to 
the  meeting  in  workshops  and  presentations  to  the  group,  further  ensuring  that 
members  completed  their  tasks  before  attending  the  meetings.  Thirdly,  coming 
together  physically  allowed  the  team  to  work  together  in  a  collaborative  way  and 
produce  outputs  together.  When  the  project  coordinator  was  interviewed,  they 
mentioned how new, unplanned tasks got completed over the course of a meeting. 
For  example,  the  group  was  able  to  write  protocols  and  educational  materials 
together while they were sitting down around the table. Lastly, the social aspect of 
meeting  with  one  another  contributed  to  strengthening  the  relationships  amongst 
the  group.  One  participant  emphasised  how  important  the  social  aspect  of  the 
meetings were in helping the team complete the project. As they emphasised in the 
interview,  collaborative  projects  thrive  on  social  interactions  when  there  is  not  a 
large research agenda.  

As explained, there were only a handful of members in the RESPECT consortium. All 
of the interview participants acknowledged that the size of the group was just right 
for  this  project  and  the  smaller  number  of  members  to  manage  allowed  for  some 
degree of flexibility within the project plan. The room for flexibility within the project 
also  reflects  the  project  coordinator’s  management  style.  When  a  new  opportunity 
came  along  that  fit  into  the  framework  of  their  objectives,  the  coordinator  was 
willing  to  take  advantage  of  those  possibilities.  The  coordinator  stressed  that  the 
group  did  not  deviate  from  their  plans.  Instead,  there  was  room  to  move  in  the 
project allowing them to take advantage of opportunities that they were not aware 
of at the beginning.  

Contextual factors 

The RESPECT project is a response to the EU regulation on researching and testing 
medicinal products for paediatric use that came into effect in 2007. Clinical trials are 
studies that examine the actions of a drug or intervention on human subjects. Trials 
are  conducted  in  several  phases  over  many  years  to  focus  on  the  safety  and 
effectiveness  of  new  medicines  for  specific  health  conditions  and  diseases  before 
they are approved for use in the general public. The RESPECT consortium was aware 
of  the  challenges  clinical  trials  faced  in  recruiting  child  participants  and  wanted  to 
address the reasons and barriers as to why children and their families are reluctant 
to  taking  part  in  trials.  They  aimed  to  provide  actionable  insights  into  how  to 
empower  children  and  youth  in  a  way  that  medical  researchers  understand  and 

268 

 
 
respect  the  young  people’s  interests  and  can  motivate  their  participation  in  future 
clinical trials. 

Impact at network level 

All of the interview participants highlighted the positive and lasting impact on their 
own networks, having participated in the RESPECT project. There are different ways 
in  which  the  partners  have  benefitted  from  their  collaboration.  For  one  interview 
participant, the younger colleagues they brought into the project were able to work 
and gain experience on a more international project. Furthermore, through informal 
discussion  during  lunches  and  breaks  at  their  meetings,  the  group  was  able  to 
expand  their  research  network  for  their  own  institution.  Several  other  participants 
stated  that  they  were  still  in  contact  with  some  of  the  project  partners  and  have 
teamed  up  on  other  projects  after  RESPECT.  One  interviewee  also  suggested  that 
they  would  work  with  the  project  partners  again,  even  if  they  did  not  have  the 
opportunity  from  the  Commission  to  continue  with  this  topic  and  would  consider 
submitting a proposal with these partners on another call, such as Horizon2020.  

Another  positive  aspect  of  this  new  network  was  the  fact  that  it  was  international 
and multidisciplinary. The partners now know people in other EU countries and have 
contacts  in  different  types  of  organisations  involved  in  paediatric  clinical  trials.  Not 
only have the university researchers benefited from meeting and collaborating with 
other  university  researcher  institutes,  they  have  also  had  the  opportunity  to  work 
with  patient  organisations  for  example  who  represent  the  children  that  they  are 
interested 
in  empowering.  Some  consortium  members  shared  a  similar 
organisational  background  but  as  one  interviewee  mentioned,  everyone  had  their 
own  profession  and  specialisation.  Everyone  was  able  to  contribute  to  the  project 
and the network with their own expertise.  

As mentioned earlier in the formation of the RESPECT consortium, this was a brand 
new  group  of  partners,  many  of  whom  were  working  together  for  the  first  time. 
Through  bringing  together  individuals  and  organisations  who  were  nodes  within 
other networks, RESPECT was able to access other networks and share their work in 
other organisations outside of their own consortium. The group was able to interact 
with  different  patient  organisations,  they  were  able  to  access  those  involved  in 
clinical  trials  in  different  EU  countries,  and  the  group  was  able  to  share  their 
information  through  their  networks  to  other  public  health  organisations  and 
institutions throughout Europe.  

Although a new network  was created  and  the  partners who  were interviewed were 
all open to working with members of the RESPECT consortium again, two interview 
participants  as  well  as  answers  from  the  survey  did  suggest  that there  were some 
challenges in the project in regards to working with different organisations in a new 
network.  

One  challenge  is  the  different  working  cultures  and  norms  between  different  fields 
and types of organisations. In one interview, the partner pointed out that they were 
unsure  of  how  sustainable  some  of  the  relationships  within  RESPECT  are.  This 
participant works in a university setting and they suggested that if their contact in a 
non-profit  organisation  or  consultancy  for  example  got  another  task  or  left  their 
organisation,  their  history  would  disappear.  With  universities  on  the  other  hand, 
research groups are always larger and the interview participant suggested that there 
are  always  possibilities  to  get  in  touch  with  them.  Furthering  this,  the  same 
interviewee found that it was difficult at times to interact with organisations that did 
not have the same procedures or ways of working as they did in the academic world 
and those were some of the challenges they needed to overcome in RESPECT.  

269 

 
 
When  working  with  different  types  of  organisations,  there  may  be  discrepancies  in 
scientific  expertise.  When  interviewing  a  partner  in  the  NGO  sector,  they  have 
found, not only through working with the partners in RESPECT, but that in general, 
academia  is  slowly  coming  around  to  recognising  them  as  genuine  partners. 
Although  they  did  not  necessarily  provide  academic  insights  to  the  project,  this 
patient  organisation  was  able  to  contribute  with  their  extensive  knowledge  and 
unique  expertise  in  representing  patients  and  communicating  patient  experiences. 
This  interview  participant  also  expressed  that  their  organisation  had  learnt  a  lot 
from their experience in RESPECT. They were able to gain confidence and expertise 
from their collaboration in RESPECT and are now leading their own project involving 
academic partners.  

Despite some of these  obstacles, the project  coordinator highlighted that he would 
not have changed the consortium partners because each partner was valuable to the 
collaboration.  As  the  partners  got  to  know  each  other  and  their  working  styles,  it 
then became possible to do a lot more work as a group that was more focused and 
clear.  Unlike  a  ‘old  friends  network’,  the  coordinator  found  that  in  a  newly  formed 
network,  it  takes  some  time  for  everyone  to  get  acquainted  on  a  personal  and  a 
working level before they begin to produce results and outcomes.  

Outputs, outcomes, and impact 

For  the  partners  involved,  RESPECT  was  a  very  successful  project.  According  to 
survey data and the interviews, outputs from the project include: 

 

Peer-reviewed articles 

  Academic publications 

 

Presentation of results at conferences or workshops 

  Development of new models or approaches 

 

Initiating a new line of research 

The  results  and  findings  from  the  coordination  project  were  distributed  in  three 
ways. First, the consortium held a final conference to disseminate their findings and 
recommendations  with  the  goal  of  facilitating  collaboration  and  information 
exchange  with  different  actors,  contacted  through  their  individual  networks,  in  the 
clinical  trial  community.  The  final  conference  included  workshops  where  different 
issues  were  discussed  and  data  collected  in  the  project  was  presented.  Secondly, 
the  book  ‘RESPECT  Patient  Needs:  Relating  expectations  and  needs  to  the 
participation  of  and  empowerment  of  children  in  clinical  trials’  was  written  and 
published  by  the  group.  The  book  contains  all  aspects  of  the  project  from  the 
group’s  objectives,  to  what  they  did,  how  they  carried  out  the  research,  and  what 
their results and recommendations are. Furthermore, the book has been available to 
the  public  through  the  project’s  website.  Another  lasting  impact  that  the  RESPECT 
consortium  has  made  is  through  maintaining  open  access  to  their  project 
information  through  their  website.  Their  website  contains  presentations  from  the 
group’s  meetings  and  conferences,  it  contains  links  to  their  publications  based  on 
their work in RESPECT, and it highlights their recommendations to stakeholders.  

One  area  that  the  RESPECT  consortium  had  hoped  to  work  with  and influence  was 
the  pharmaceutical  industry.  However,  the  project  coordinator  and  several  of  the 
other  partners  were  quite  realistic  in  the  interviews  and  stated  that  they  are  not 
sure how much they have actually changed practice in paediatric clinical trials. The 
pharmaceutical  industry  is  impacted  by  this  regulation,  but  there  were  no  industry 
partners included in this project. The consortium had made great efforts  to include 

270 

 
 
discussions  with  pharmaceutical  companies  in  their  project  but  experienced  that  it 
was  very  difficult  to  access  individuals  who  could  assist  them  in  their  work.  The 
coordinator reported for example, that they had a contact in one company who was 
willing to speak to RESPECT and that contact person felt that they could contribute 
to  their  investigations  into  children’s  participation  in  clinical  trials.  Their  contact, 
however, needed to receive permission from their organisation before being able to 
speak  with  them  and  this  process  led  to  RESPECT  being  put  in  contact  with  the 
company’s  legal  team.  The  company’s  legal  department  was  suspicious  about  why 
RESPECT  wanted information from  them  and  how  this information was going to be 
used. After months of emails and telephone conversations and assurances from the 
RESPECT team  that they  would  maintain  the  company’s anonymity, the group was 
forced to move on because they knew that their chance of a successful negotiation 
with  the  company  was  very  small.  The  consortium  was  able  to  speak  with  people 
from  pharmaceutical  companies  about  children  in  clinical  trials  and  was  able  to 
share  ideas  but  influencing  the  companies  and  decision  makers  within  them  was 
very  difficult.  One  other  challenge  that  the  project  coordinator  encountered  when 
working  with  pharmaceutical  companies  was  that  these  companies  were  not 
interested  in  making  real  changes  to  their  practices  regarding  paediatric  clinical 
trials if they did not help them sell products.  

As one interview participant emphasised in  the interview, the tangible  but also  the 
intangible  results  from  RESPECT  need  to  be  focused  on  as  well.  Several  of  the 
project partners pointed out how RESPECT has been able to raise general awareness 
and  develop  themes  on  paediatric  clinical  trials.  The  project  coordinator  made  the 
remark that the project now  seems  to  have a life of  its own even though they are 
not  actively  advocating  for  the  project.  Through  the  project  website  and  book, 
partners in the project have been contacted to share their knowledge on the topic, 
with  the  coordinator  being  asked  to  conduct  a  lecture  on  it  recently  prior  to  their 
interview. Another partner was very pleased to see some of the guides RESPECT put 
together  being  included  in  other  international  paediatric  meetings  and  mentioned 
that  results  from  RESPECT  has  been  referenced  by  other  agencies  on  different 
occasions. As this was one of the first projects to raise the issue of empowerment of 
children  in  paediatric  trials,  this  same  interviewee  saw  the  need  to  enlarge  and 
continue  to  develop  the  topics  that  RESPECT  has  started.  Another  project  partner 
working  with  companies,  regulators,  and  peers  stated  that  paediatric  clinical  trials 
are very much on the agenda, much more now than it was six years ago when the 
paediatric  regulation  came  into  place.  They  acknowledged  that  there  were  other 
factors that contributed to that but is certain that RESPECT has also contributed to 
that awareness. RESPECT has also continued to inspire the project partners in their 
current  and  future  work  and  has  increased  European  society  and  perhaps 
international awareness of how to empower children and better include them in the 
process of conducting clinical trials.  

Reflection on the success factors 

The  project  partners  interviewed  all  expressed  that  RESPECT  was  a  successful 
project for them professionally and personally. They were all proud of the work they 
had  done  and  accomplished  together.  The  project  was  successful  for  several 
reasons.  First,  the  project  coordinator  was  able  to  recruit  partners  who  had  the 
common goal of moving the agenda of paediatric clinical trials further. The level of 
interest amongst members of the consortium was high and is what initially created a 
bond  for  this  new  network.  Secondly,  the  group  invested  time  into  meeting  each 
other in person, establishing a relationship and sharing a common vision and idea of 
how  to  move  forward  and  proceed  in  the  project.  A  new  network,  like  RESPECT, 
took  time  to  develop  in  terms  of  understanding,  trusting,  and  working  with  one 
another. Once that level of familiarity was established, the real work in the project 

271 

 
 
could  begin.  And  lastly,  the  project  team  recognised  the  importance  that  each 
partner  brought  to  the  project.  As  this  was  an  interdisciplinary  team  coming  from 
different  types  of  organisations,  every  partner  was  able  to  contribute  according  to 
their expertise and learn from the other members of the consortiums. Despite some 
of their differences in working style and expertise, the group was able to overcome 
these challenges and work towards the project objectives.  

272 

 
 
 
 
Case Study X: SOMMACT, Self Optimising Measuring Machine Tools 

Summary of the case 

Bridging  different  sectors  and  competences  was  a  key  element  of  the  Self 
Optimising  Measuring  Machine  Tools  (SOMMACT)  project.  The  Italian  SME 
Alesamonti discovered a knowledge gap in  their line  of work, and it was natural to 
bring  in  other  partners  in  the  form  of  universities,  research  laboratories  and 
industry. Collaboration between industry and academia was not a specific challenge 
in  the  project.  On  the  contrary,  the  wish  inside  the  consortium  was  to  enable  the 
academics  and  enterprise  to  influence  each  other.  Synergies  like  these  were 
consciously aimed for in the SOMMACT project. 

The  main  objective  of  the  FP7  project  SOMMACT  was  to  contribute  to  the 
transformation of the  European manufacturing  industries from  a resource  intensive 
to  knowledge  intensive  condition.  For  reaching  this  ambition,  a  new  machine  tool 
concept  was  developed  based  on  self-learning,  adaptive  control  capabilities  and 
embedded traceable measurement capabilities.  

Key findings of the case study are: 

  Complementarity was a strong driver for partner selection and for ensuring that 
the  partners’  competencies  complemented  each  other  in  the  best  possible  way 
for  reaching  the  project’s  objectives.  The  principle  of  complementarity  is 
considered  a  prerequisite  for  composing  a  project  team  that  could  solve  the 
tasks of the project effectively. 

  A  lot  of  emphasis  and  energy  was  put  into  the  formation  of  the  partnership, 
taking approximately one year. The careful selection of partners secured a solid 
partner base. 

 

 

The organisation of the project was based on a governance model. According to 
the interviews carried out, the clear structure had a range of positive effects: A 
clear  and  defined  role  for  each  partner  in  the  project,  a  high  level  of 
transparency and synergies between the partners.  

It  was  suggested  that  the  project  was  too  time  consuming  because  of  the 
ambition of delivering a sufficiently far going innovation instead of looking for a 
more realistic approach.  

Introduction 

The FP7-project SOMMACT is an example of an ambitious project based on synergy 
between  actors  from  different  types  of  sectors.  The  case  was  selected  for 
highlighting a project that ‘visibly links different types of organisations (universities, 
industry,  SMEs)  and  clearly  bridges  the  flow  of  innovation  ideas  between  them’. 
Another  criterion  was  to  select  a  project  that  has  received  funding  through  either 
the  Cooperation  or  the  Capacities  sub-programme.59  As  this  case  study  shows, 
collaboration  between  industry  and  academia  was  not  a  specific  challenge  in  the 
project. On the contrary, the project created synergies between the two. According 
to  the  survey  data  and  interviews  carried  out,  SOMMACT  is  a  project  that  was 

59  The  case  selection  is  based  on  data  from  a  comprehensive  network  analysis  and  survey 
conducted  by  Science-Metrix  and  Fraunhofer  ISI  (see  appendix  I  for  a  methodological 
description). 

273 

 
 
                                                
primarily concerned with preparing the ground for applications outside the academic 
sector, and to a lesser degree it was about generating contributions to fundamental 
science.  This  stems  from  the  fact  that  the  project  was  initiated  on  the  basis  of 
industrial needs and to a lesser extent based on a research gap. 

The case study builds on survey data60 conducted in earlier phases in the project, on 
project relevant material and on three interviews with project participants including 
an  interview  with  the  project  coordinator,  who  represents  an  SME.  The  partners 
were  selected  after  consultation  with  the  project  coordinator—one  represents 
academia, and the others represents an SME. Responses have been anonymised. 

The  primary  objective  of  this  case  study  is  to  outline  how  knowledge  is  created  in 
the networks and through cooperation, but also how the transition from invention to 
innovation occurs within and outside the network. 

Description of the case 

The  SOMMACT  project  ran 
from September 1, 2009, to 
August  31,  2012,  with  a 
total  cost  of  €5,210,324. 
The  aim  of  the  project  was 
to  strengthen  and  increase 
the 
the 
industrial 
of 
machinery.  

application 

efficiency 

of 

Background data for SOMMACT 

Project period 

01.09.2009 – 31.08.2012 

Total cost 

€5,210,324  

EU contribution 

€3,668,700  

Coordinated in 

Italy 

and 

Ottone 

National 

developed 

Sub-programme 

Funding Scheme 

NMP-2008-3.2-2 - Self-learning 
production systems 

The  idea  behind  SOMMACT 
was 
in 
between 
collaboration 
from 
Renato 
Dr. 
Alesamonti 
from 
Alessandro  Balsamo 
the 
Italian 
Metrological 
Institution 
(INRIM).  Alesamonti  faced 
in 
increasing  competition 
the 
field  of  producing 
machine tools and realised that they had to innovate to be successful and asked Dr. 
Balsamo for help.  Together  they  developed the concept behind SOMMACT. Overall, 
they  applied  for  funding  from  the  Framework  Programme  twice  and  succeed  in 
getting  funding  for  the  project  the  second  time  they  applied.  From  the  first  to  the 
second  application,  the  content  and  objective  of  the  project  was  revised,  and  the 
new focus areas in the project gave rise to a need for new partners as well. 

CP-FP - Small or medium-scale 
focused research project 

‘Enabling & Strategic 
Technologies’ 

Scientific fields 

According to survey data, the reasons for undertaking the project in the context of 
FP7  rather  than  in  the  context  of  a  national  or  regional  funding  programme  was 
firstly  to  reach  critical  mass  by  accumulating  sufficient  financial  resources, 
equipment,  and  researchers  to  achieve  the  project’s  objects,  and  secondly  to 
improve  complementarities  and  diversity  in  research  by  using  information,  data, 
techniques,  concepts,  and/or  theories  from  several  disciplines.  Lastly,  the  group 

60 The survey data is based on the answers from one survey respondent 

274 

 
 
                                                
hoped  to  establish  linkages  with  excellent  international  research  partners  through 
participating in the FP7 programme. 

The  technological  aim  of  SOMMACT  was  to  develop  and  validate  an  innovative 
production hardware and control system founded on understanding, evaluating and 
controlling large machine tools production performances. The SOMMACT project was 
based on three pillars: 

1.  A  new  metrological  concept  to  enhance  the  measuring  capabilities  of  machine 
tools,  to  monitor  their  geometrical  deformations  reliably  and  to  inspect 
machined parts characteristics traceably.  

2.  Enhanced sensor systems, measuring  the  six  degrees of  freedom  (dof) of  each 
machine  component,  and  a  control  system  integrating  machine  and  workpiece 
data with environment and load conditions, and adapting machining accordingly. 

3.  A  self-learning  model  of  the  system  performance,  accumulating  knowledge  on 
the  machine  behaviour  based  on  calibration  and  real-time  measurement  data, 
and on their relationship with workpiece characteristics (e.g. mass). 

Formation of the partnership 

Alesamonti in Italy was the coordinating institution. The partners were mainly from 
Italy,  but  the  project  also  included  partners  from  the  United  Kingdom,  the 
Netherlands,  Spain  and  Germany.  Figure  28  maps  all  the  participating  partners  in 
the project. 

Figure 28  Map of the participating partners 

Source: 

Cordis, website: http://cordis.europa.eu/home_en.html 

275 

 
 
 
Once  the  objectives  of  the  project  (ISM-3D  was  also  involved)  were  defined,  the 
next step was to select the partners that were the best suited for doing the project. 
It took approximately a year to form the partnership. Renato Ottone and Alessandro 
Balsamo  shared  the  task  of  assigning  partners.  They  prioritised  inviting  partners 
that they knew beforehand and already had a working relationship with. Alesamonti 
had  collaborated  with  six  of  the  11  partners,  and  INRIM  had  collaborated  with  the 
remaining four. In the initial  formation of the project,  the role of technical director 
was  assigned  to  Dr.  Gianfranco  Malagolas  to  attract  more  European  SMEs  from 
outside  of  Italy.  It  was  hoped  that,  in  addition  to  the  funding,  the  SMEs  would  be 
attracted by Malagola’s name.  

Complementarity  was  a  strong  driver  for  partner  selection  to  ensure  that  the 
partners’  competences  complemented  each  other  in  the  best  possible  way  for 
reaching the project’s objectives. Room for overlapping competences was made if it 
was considered useful for reducing risks associated to the successful implementation 
of  the  project.  According  to  the  interviews  carried  out,  the  principle  of 
complementarity  is  considered  a  prerequisite  for  composing  a  project  team  that 
could solve the tasks at hand effectively. 

According  to  the  interviews,  the  emphasis  put  on  the  formation  of  the  partnership 
mitigated  any  obstacles  related  to  the  geographic  distance.  By  selecting  the  right 
project  team,  regular  meetings—both  personal  and  on  Skype—were  sufficient  to 
align the partners’ expectations and ensure progress in the project.  

Process and Implementation 

The  SOMMACT  project  lasted  for  three  years  through  which  the  seven  work 
packages  gradually  unfolded.  The  project  was  divided  into  seven  work  packages 
(WPs)  with  a  work  package  leader  assigned  to  each  WP.  Each  of  the  partners  was 
assigned  a  very  specific  role  to  fill  and  complete  in  each  WP.  The  WP  leader  was 
responsible  for  the  detailed  technical  management  of  the  WP,  but  for  each  single 
task  within  a  WP,  a  task  leader  was  designated.  The  tasks  were  assigned  to  the 
partners  according  to  their  relevant  expertise,  but  also  derived  from  their  evident 
interest  in  the  exploitation  of  the  project’s  results.  The  main  characteristic  of  the 
project was that for solving the specific tasks, the partners worked together in many 
different ways and constellations filling a specific role in each task. 

A  governance  model  was  designed  taking  an  integrated  cooperation  between 
industry  and  academia  into  account.  In  regards  to  the  organisational  structure,  it 
was a priority to ensure that the project management was efficient and consistent in 
the  interest  of  all  partners.  In  the  implementation  of  the  SOMMACT  project, 
emphasis  was  put  on  having  a  clear  and  visible  decision-making  structure.  The 
Italian  SME  Alesamonti  was  the  main  coordinating  organisation  of  the  FP7  project, 
and Renato Ottone was hired  specifically for  coordinating this project. As a project 
coordinator,  he  was  responsible  for  the  communication  with  the  European 
Commission, and was the head of the steering committee, which acted as the main 
decision-making  and  arbitration  body.  The  steering  committee  consisted  of  five 
partners:  two  representing  an  SME  (ISM-3D  and  Alesamonti),  one  representing  a 
large  industrial  company  (FIDIA),  one  representing  a  national  laboratory  (INRIM) 
and one representing academia (SUPSI). Besides the steering committee, a general 
assembly was established;  the assembly was the ultimate  decision-making body of 
the  consortium  and  was  composed  of  one  representative  from  each  consortium 
partner. 

According to the interviews, the clear structure had a range of positive effects. One 
was that each partner was designated with a clear role to fill in the project making it 

276 

 
 
easier  to  manage  the  project.  Another  advantage  was  that  it  enhanced  the 
transparency  of  the  decisions  made  during  the  project.  The  integration  of  the 
partners  also  allowed  for  fruitful  discussions  generating  synergies  between  the 
different types of partners. 

Within the aspect of project management, one key challenge was to make sure that 
the partners would solve their task sufficiently. As each partner had a range of tasks 
in each WP, it was one of the coordinator’s main tasks to be specific about each task 
and explain how it relates to the overall objective. However, it was not considered a 
problem  because  of  the  clear  and  transparent  division  of  responsibilities  and  tasks 
devoted  to  each  partner.  In  addition,  the  construction  of  the  consortium,  together 
with the general assembly and the tight link with the coordinating team secured that 
information  and  decisions  made  in  the  steering  committee  was  disseminated  to  all 
the partners. 

Another challenge was that the SOMMACT project included many interests including 
large  business  firms,  public  research  organisations,  higher  education  organisations 
and  a  majority  group  of  SMEs.  These  different  institutions  all  had  differing 
expectations  to  the  benefits  they  could  gain  from  the  consortium.  It  was  clear  for 
the  SMEs  that  they  had  the  possibility  of  selling  new  products  produced  by  the 
project, the academics had  opportunity  to do good research and the association  of 
enterprise  could  promote  the  project  activities  to  their  members.  According  to  the 
interviews,  however,  these  conflicts  of  interest  were  not  an  obstacle.  One  reason 
being that the project emphasised synergies, and from the coordinator’s side it was 
a priority to simply ask the  partners,  what they  expected to gain from the project, 
and then specify for them, what they could possibly gain from the project. Already 
in  the  formation  of  the  partnership,  balancing  expectations  was  a  key  component 
and priority. 

In the interviews, the project is generally regarded as an ambitious project that was 
technically innovative. All in all  the project  succeeded  in reaching new product and 
process  innovation,  but  it  was  a  constant  challenge  to  find  a  way  between  the 
ambition  of  achieving  technological  innovation  while  at  the  same  time  set  up  a 
realistic set of corrections to be developed and implemented. 

Contextual factors 

An  important  contextual  factor  of  the  project  was  the  market  situation.  Facing  an 
increasing level of competition from the Chinese market that is more cost-effective 
than  the  European  market,  the  objective  of  the  project  was  to  contribute  to  the 
transformation of the  European manufacturing  industries from  a resource  intensive 
to a knowledge intensive condition. This meant that the project was very ambitious 
in terms of innovative solutions and made the project time-consuming and resource 
intensive. 

Another  contextual  factor  was  that  the  partners  knew  each  other  beforehand,  and 
because of their mutual understanding and respect built up on previous projects, the 
partnership was described as smooth and well-functioning. 

Impact at network level 

Partners  from  all  over  Europe  and  from  different  types  of  institutions  worked 
together in the SOMMACT project. The survey results show that the SME Alesamonti 
strengthened  collaboration  with  researchers  both  locally  and  on  an  EU  level  to  a 
high degree. 

277 

 
 
The project succeeded in bridging different sectors and competences as a means to 
achieve  the  objectives  of  the  project.  According  to  the  interviews  carried  out,  the 
participation  of  academia  and  industry  strengthened  the  project  because  of  the 
success  in  creating  synergies  between  the  two  types  of  sectors.  As  described,  the 
initial  formation  phase  was  highly  important  for  creating  the  synergies  as  this  laid 
the  foundation  of  the  project  by  composing  a  project  team  with  partners  having 
complementary skills for reaching the objectives of the project. Thus from a project 
management  perspective,  the  network  had  the  right  size,  because  the  project  had 
the  right  set  of  partners.  However,  it  was  suggested  that  the  number  of  partners 
could have been smaller at the end of the project, as they were not all taking part in 
the dissemination of the project.  

To establish links outside the project, a dissemination plan was drawn up. The main 
target group  was  industry,  because  it  was  a  key  aim  of  the  project  to  support  the 
transformation  of  the  machine  tool  industry  to  strengthen  Europe’s  ability  to 
compete  in  terms  of  high  benefits  for  the  customers.  The  research  partners  were 
another  target  group.  For  this  group,  the  ambition  was  to  involve  them  in  the 
dissemination of the research results in the training programmes. A key aspect was 
to  establish  synergies  with  other  relevant  projects  and  initiatives.  For  promoting 
this,  the  initiative  Intelligent  Manufacturing  System  (IMS)  got  involved.  It  is  an 
industry-led,  international  research  and  development  initiative  established  to 
develop  the  next  generation  of  manufacturing  and  processing  technologies. 
Companies and research institutions from the 27 member countries of the European 
Union, Japan, Korea, Switzerland, and the United States participate in this.  

Outputs, outcomes, and impact 

According to the survey results, the SOMMACT project has resulted in the production 
of the following types of scientific & technological knowledge: 

  Other academic publications(e.g. books, proceedings) 

 

Presentation of results at conferences or workshops 

  Development of a new models or approaches 

  Submission of patent applications 

The survey results also  show  a high  level  of  satisfaction with the project regarding 
direct  outputs  and  long-term  outcomes.  The  dissemination  of  the  results  has 
enhanced  the  knowledge  transfer  from  science  to  market,  and  there  is  a 
considerable  potential  for  societal  impact.  According  to  the  final  report,61  an 
industrial  application  of  the  outcomes  of  SOMMACT  will  have  considerable  socio-
economic  and  environmental  impact.  The  socio-economic  impact  is  generated  by 
adding a higher value for the end users. The knowledge-based content will increase, 
and  the  service  related  content  of  their  revenues  will  increase.  In  this  way,  the 
socio-economic  impact  is  manifested  in  increased  productivity  for  companies  that 
increases  their  competitive  advantages  and  thus  protects  jobs.  In  regards  to 
environmental impact, it is estimated that the SOMMACT units will have a significant 
impact  by  reducing  manufacturing  time  with  20–30%  for  small  and  single-batch 
production and by reducing workpiece displacement phases with 70%. The improved 
product  quality  stemming  from  the  outcomes  of  SOMMACT  will  reduce  waste  and 
energy consumption significantly. 

61 SOMMACT: Final publishable summary report 

278 

 
 
                                                
Reflection on the success factors 

According  to  the  survey  results,  the  benefits  that  Alesamonti  gained  from  the 
participation  in  the  FP7  project  greatly  exceeded  the  costs.  According  to  the 
interviews carried out, the project was successful due to the following factors: 

 

The  emphasis  placed  on  complementarity  as  a  driver  for  the  partner  selection 
was important for creating a well-balanced project, where all the partners had a 
defined role and could see a benefit for themselves.  

  A key for reaching success in terms of creating synergies between industry and 
academia  was  that  there  was  a  clear  path  for  what  the  knowledge  should  do. 
From  the  coordinator’s  point  of  view,  it  was  important  that  the  innovation  was 
not for the benefit for one or two partners, but for all partners in the project.  

 

The company Api Varese ensured the wide-range dissemination of the project’s 
results.  By  engaging  in  a  strategic  alliance  with  IMS,  the  project  managed  to 
create synergies with other projects and initiatives. 

In one interview, it was suggested that the project was too time consuming because 
of the ambition of delivering a sufficiently far going innovation instead of looking for 
a more realistic approach. At the same time, however, a reason for receiving public 
funding is that the project addresses important societal and scientific challenges. 

279 

 
 
 
 
Case Study XI: SSRR, Smart Structured Rotating Reactors 

Summary of the case 

The  FP7-project  ‘Smart  Structured  Rotating  Reactors’  (SSRR)  aimed  at  extending 
the  current  research  projects  on  rotating  chemical  reactors.  Generally,  the  SSRR 
project has contributed to the academic field by applying the concept of rotation in a 
chemical reactor. In this way, the project has improved energy efficiency, compared 
to methods that are more traditional. 

The  project  started  out  as  a  basic  research  project  at  Eindhoven  University  of 
Technology  (TU/e)  with  the  aim  of  extending  the  current  research  projects  on 
rotating chemical reactors. The industry – DSM and Alfa Laval joined the network as 
partners.  Both  companies  contributed  financially  to  the  research.  Alfa  Laval’s  role 
was  to  assist  in  looking  at  the  chemical  engineering  design,  the  equipment  and, 
originally,  as  the  commercial  partner,  and  DSM  was  interested in  the  technological 
aspects  and  the  application  of  the  research  to  their  business.  For  bringing  the 
technology  to  the  market,  the  spin-off  company  FlowId  B.V.  (www.flowid.nl)  got 
involved  in  the  commercialisation  and  application  of  the  technology.  Today  FlowId 
employs 10 people. 

The key findings of the case study are: 

 

The FP7 is evaluated positively as a programme. Within the context of FP7 there 
is  a  high  degree  of  freedom  to  design  the  project  compared  to  national  or 
regional funding schemes. 

  Aligning academic interest with industry’s interest has been a key challenge. The 
industry’s main focus was on the results and the application of the technology, 
whereas the university was more interested in the fundamental science. Finding 
a  balance  between  the  two  was  the  main  concern  for  the  coordinator.  For  this 
reason, an innovation consultant was hired to bridge the gap between academia 
and industry and to utilise the strengths of each partner in the network.  

  Originally, Alfa Laval was chosen to play the commercial role but due to a policy 
and focus change in their company, another approach needed to be developed: 
commercialisation via FlowId. 

  Due  to  a  strong  tradition  of  research–industry  collaboration,  the  network  is 
based on the concept of open innovation, where each partner has had a defined 
role to play in the project. 

 

 

For  bringing  the  technology  to  the  market,  the  challenge  is  to  move  from  the 
technical feasibility demonstrated in the project to show the economic feasibility 
of the technology.  

It  is  suggested  that  the  commercialisation  of  the  technology  could  have  been 
promoted  by  involving  the  spin-off  company  FlowId  in  an  earlier  phase  of  the 
project. 

Introduction 

The  FP7-project  ‘Smart  Structured  Rotating  Reactors’  (SSRR)  is  an  example  of  a 
project  that  has  formed  and  reinforced  ties  between  academia  and  industry  in  the 
area  of chemical reactor research. The case  was selected for highlighting a project 
with  ‘mitigated  success  in  networking  and  mitigated  success  in  outputs  and/or 
outcomes’ that  has  received  funding  either  through  the  IDEAS  or  the  PEOPLE  sub-

280 

 
 
programme.62  As  this  case  study  shows,  the  project  has  largely  expanded  and 
strengthened ties between academia and industry, but the case study also identifies 
a  conflict  of  interest  in  the  research–industry  collaboration.  The  outputs  and 
outcomes need to be assessed in the context of the funding scheme ‘ERC Advanced 
Grant’, which aims at supporting ‘Investigator-driven “frontier” research, within the 
framework  of  activities  commonly  understood  as  “basic  research”’.63  Thus,  this 
project could be studied as an initial basic research project that in the long run may 
lead  to  commercialisation.  The  interviews  and  the  survey  data  clearly  indicate, 
however,  that  this  project  not  only  aimed  at  generating  contributions  to 
fundamental  science,  but  was  equally  concerned  with  preparing  the  ground  for 
applications outside the academic sector. 

The case study builds on survey data64 conducted in earlier phases in the project, on 
project  relevant  material  and  on  four  interviews  with  project  participants  including 
an interview with the project coordinator, who represents a university. The partners 
were  selected  after  consultation  with  the  project  coordinator—one  represents  a 
university,  and  the  two  others  represent  the  industry.  Responses  have  been 
anonymised. 

The  primary  objective  of  this  case  study  is  to  outline  how  knowledge  is  created  in 
the networks and through cooperation, but also how the transition from invention to 
innovation occurs within and outside the networks.  

Description of the case 

The  SSRR  project  ran  for  a 
five-year  period 
from  1 
April  2009  to  31  March 
2014  with  a  total  cost  of 
€1,999,833.  The  project 
aimed  at  producing  new 
scientific  and  technological 
contributing 
results  and 
new  knowledge 
the 
academic discipline. 

to 

Background data for SSRR 

Project period 

01.04.2009 – 31.03.2014 

Total cost 

€1,999,833  

EU contribution 

€1,999,833  

Coordinated in 

The Netherlands 

Sub-programme 

ERC-AG-PE8  -  ERC  Advanced 
Grant  -  Products  and  process 
engineering 

The  idea  behind  the  FP7 
project  took  shape  because 
the  research  team,  led  by 
Jacob  Cornelis  Schouten  at 
Eindhoven  University  of 
Technology,  had  already 
been  working  on  structured 
reactors  and  were  front-
runners 
field. 
Through  receiving  new  funds  for  a  project,  the  aim  then  was  to  extend  the 

ERC-AG - ERC Advanced Grant 

‘Engineering’, ‘Chemistry’ 

Funding Scheme 

Scientific fields 

the 

in 

62  The  case  selection  is  based  on  data  from  a  comprehensive  network  analysis  and  survey 
conducted  by  Science-Metrix  and  Fraunhofer  ISI  (see  appendix  I  for  a  methodological 
description). 

63 The Cordis website: http://cordis.europa.eu/programme/rcn/845_en.html 

64 The survey data is based on the answers from one survey respondent 

281 

 
 
                                                
exploratory  research  projects  that  were  already  taking  place  on  rotating  chemical 
reactors.  Eindhoven  University  of  Technology  had  previously  undertaken  several 
research projects funded by national or regional funding programmes, but according 
to  the  interviews  and  survey  data,  FP7  made  it  feasible  to  achieve  the  project’s 
objectives  of  reaching  critical  mass  by  accumulating  sufficient  financial  resources, 
equipment  and  researchers.  The  FP7  funding  also  helped  in  bringing  the  project 
forward  because  there  was  an  openness  for  them  in  selecting  and  choosing  which 
partners to collaborate  with.  In this way, there was  more freedom in deciding how 
to proceed than in other funding schemes. 

The  two  reactor  concepts  that  the  project  aimed  at  developing,  were  (1)  the 
‘multiple spinning disks reactor’  and  (2) the  ‘rotating foam reactor’. These  reactors 
are used for the production of high-value chemical products. The main advantage of 
these  reactors  is  that  more  compact  reactors  can  be  developed  with  a  higher 
productivity than conventional reactors. This is possible since centrifugal forces are 
being used to obtain higher mass transfer rates and are also more controllable. Just 
as  important  is  the  higher  selectivity  that  this  allows  for,  which  results  in  smaller 
waste  streams.  The  higher  selectivity  also  results  in  cost  saving  in  the  energy-
consuming purification steps of the final product. Catalyst re-usage will be easier as 
well.  More  fundamental  knowledge  will  become  available  on  flow  and  physical 
transport  processes  in  rotating  reactors,  which  will  further  extend  the  application 
area of these reactors. One key aim of the project is to develop a rotating chemical 
plant (a complete small-scale chemical plant on a rotating axis), in which multi-step 
reactions are  conducted in a compact  production unit.  Making use of the higher g-
forces, it will also be possible to integrate the reaction with product separations. The 
new  technology  will  allow  for  a  greener  production  technology,  which  is  more 
efficient and consumes less energy. 

282 

 
 
 
 
Formation of the partnership 

Eindhoven University of Technology  in the Netherlands  has coordinated  the project 
and is officially the only partner of the FP7 project, as illustrated in figure 29 below. 

Figure 29  Map of the participating partners 

Source: 

Cordis, website: http://cordis.europa.eu/home_en.html 

In  the  first  phase  of  the  project,  Eindhoven  University  of  Technology  asked  the 
industry if they would be interested in joining the project. DSM and Alfa Laval joined 
the  network  and  acted  as  the  two  main  industrial  partners.  Both  companies 
contributed  financially  to  the  research.  Alfa  Laval’s  role  was  to  assist  in  looking  at 
the reactor engineering and design, and at the equipment, and was foreseen as the 
technology  commercialising  partner.  DSM  was  interested  in  the  technological 
aspects  and  the  application  of  the  research  to  their  business.  Two  PhD  students 
were  connected  to  each  of  the  companies  for  establishing  links  between  the 
university and the companies and to promote mutual learning. 

Besides  the  Eindhoven  University  of  Technology,  the  German  research  institution 
Helmholtz-Zentrum Dresden Rossendorf was engaged in the project with the role of 
conducting  experimental  work.  No  other  universities  were  involved,  but  during  the 
project,  the  core  research  team  had  regular  contact  with  other  European 
researchers  that  had  an  interest  in  the  results.  For  commercial  purposes  FlowId 
became involved as a spin-off company. 

Taking  the  whole  project  into  consideration,  Eindhoven  University  of  Technology 
collaborated with teams from the following universities and companies: 

  Helmholtz-Zentrum Dresden Rossendorf (DE) 

283 

 
 
 
  DSM, Heerlen (NL) 

  Alfa Laval, Groningen (NL) 

 

FlowId, Eindhoven (NL) 

From the coordinator’s side, it was a priority to keep the network small and bring in 
the  right  partners  by  balancing  research  and  industry.  In  the  interviews,  it  was 
emphasised  that  selecting  fewer  partners  made  the  coordination  of  the  project  an 
easier task than in a large network consisting of a plethora of interests and agendas. 
In a small network the level of transparency is generally higher, especially when the 
partners  are  located  in  a  close  proximity  to  each  other,  and  knowledge  is  more 
accessible for all the partners and easily circulated around in the network. 

Process and Implementation 

The  SSRR  project  can  be  divided  into  three  main  phases:  1)  The  basic  research 
phase  with  the  aim  of  furthering  the  exploratory  research  projects  on  rotating 
chemical  reactors,  2)  The  industrial  phase,  where  the  collaboration  with  the 
industrial partners DSM and Alfa Laval were intensified and Akzo Nobel joined in, 3) 
The  commercial  phase  with  the  aim  of  bringing  the  technology  to  the  market 
through the spin-off company FlowId. 

SSRR  started  out  as  a  basic  research  project  aimed  at  extending  the  exploratory 
research projects on rotating chemical reactors. Halfway through the SSRR project, 
a  new  project  in  using  spinning  disc  technology  in  chlorine-alkaline  processing 
(SPINCHAL)  was  developed.  This  meant  that  four  new  PhD  positions  were  created 
and a collaboration with AkzoNobel was initiated. A range of new projects based on 
the  SSRR  model  started  –  most  importantly  the  spin-off  company  FlowId,  which 
aimed  at  commercialising  the  technology.  In  the  project,  two  PhD  students  were 
connected  to  DSM  and  Alfa  Laval,  and  one  PhD  student  worked  more  on  the 
fundamental science aspect and had less contact with industry, whereas the second 
PhD  student  worked  on  the  applied  side  and  developed  reactors  that  interested 
industry. 

During  the  whole  project,  the  research  activities  were  placed  at  the  Eindhoven 
University of Technology, but to involve the other partners, progress meetings were 
held on a regular basis to give the industrial partners a chance to comment on the 
results achieved and give directions for further research. One of the main challenges 
in  the  network  was  to  align  the  academic  interest  with  the  industry’s  interest.  The 
industry’s  main  focus  was  on  the  results  and  the  application  of  the  technology, 
whereas  the  university  was  more  interested  in  the  fundamental  science.  Finding  a 
balance between the two has been the main concern from the coordinator’s side. For 
this reason, an innovation consultant was hired to bridge the gap between academia 
and industry and to utilise the strengths of each  partner in the network. According 
to the interviews carried out, there was a general consensus that the  network had 
been  based  on  the  concept  of  open  innovation,  where  each  partner  had  a  role  to 
fill—the research partner and the end-users  (industry) that  know  how to  apply  the 
technology.  The  industrial  partners  generally  hold  the  view  that  the  Eindhoven 
University  of  Technology  has  an  open  approach  for  applying  research  to  the 
industry.  Generally,  for  promoting  academic-industrial  collaboration  the  Eindhoven 
University of Technology established TU/e Innovation Lab.  

Another  key  challenge  from  the  coordinator’s  perspective  was  to  reach  the 
ambitious scientific goal of the project—to develop a new reactor and a new area of 
research.  This  involved  plenty  of  tasks  related  to  the  specific  research,  but  also 
required good coordination skills, e.g. finding the right people outside the university 
to  assist  in  the  project.  There  was  not  a  specific  plan  set  up  for  coordinating  the 

284 

 
 
project, and the coordinator used his prior experience from other research projects 
to organise the project. He was the primary research supervisor and promoter of the 
PhD  projects,  where  he  supervised  the  PhD  projects  and  the  actual  research.  The 
method  for  accomplishing  this  was  to  have  regular  meetings,  which  opened  up 
discussions about the process and provided a space for generating new ideas. Other 
department  members  at  the  university  carried  out  the  daily  guidance  of  the  PhD 
students. Generally, the interviews suggest  that  the project has been successful in 
terms of the constant circulation of knowledge and in keeping momentum. 

For commercial purposes, FlowId joined as a spin-off company. FlowId got involved 
in  a  later  phase  of  the  project,  when  the  coordinator  obtained  an  ERC  Proof  of 
Concept grant.  FlowId  is actively testing and  experimenting  with  the new  reactors, 
focusing  on  bringing  out  the  technology  and  meet  the  clients’  needs.  FlowId  has 
received  funds  for  designing  the  reactors  for  commercial  purposes.  The  two  PhD 
students that worked with the industry for DSM and Alfa Laval are now collaborating 
with FlowId. In this way, they can use their knowledge gained in the project actively 
in the development of the technology. 

Within the network, two types of industrial engagements can be detected. One type 
is the spin-off company FlowId, which is an example of the classic mean of research 
commercialisation.  As  described  FlowId  came  in  later  in  the  project  solely  for 
commercial  purposes  and  took  a  license  on  the  spinning  disc  technology  that  was 
filed  for  a  patent  by  Eindhoven  University  of  Technology.  In  the  case  of  FlowId 
Eindhoven University of Technology has financial ties with the company and owns 30 
% of the shares  (FlowId  is a  spin-off company  of  the university).  In addition, they 
are  located  in  buildings  next  to  each  other,  within  100  m  to  each  other.  The  tight 
collaboration between the university  and  the  industry  motivates the research team 
from  Eindhoven  University  of  Technology  to  engage  in  the  development  of  the 
technology.  The  other  type  are  the  two  large  multi-national  companies  Alfa  Laval 
and DSM that have been  the primary  industrial  partners from  the beginning of the 
project  and  have  been  included  in  the  research  process.  Originally,  Alfa  Laval  was 
chosen to play the commercial role but due to a change in policy and focus of Alfa 
Laval, another approach needed to be developed, i.e. commercialisation via FlowId. 
One  of  the  interviewees  suggests  that  Alfa  Laval  could  have  introduced  the 
technology  to  the  market  in  a  faster  pace  than  FlowId,  because  Alfa  Laval  as  a 
company has many years of commercial experience, many resources, a specialised 
technological knowledge, and strong ties with the end-users.  

Contextual factors 

The main contextual factor is the tight connection between Eindhoven University of 
Technology  and  the  industry.  There  is  a  long  local  tradition  of  strong  collaboration 
between  Eindhoven  University  of  Technology  and  the  industry.  According  to  the 
interviews  carried  out,  the  Innovation  Lab  at  Eindhoven  University  of  Technology 
has been a significant factor for connecting research and industry. One major aim of 
the  Innovation  Lab  is  to  ‘bring  knowledge  to  the  market’,  and  this  can  be  done  in 
form  of  starting  spin-off  companies  through  direct  contact  with  companies.  The 
Innovation Lab has their own business developers who play a key role in the process 
from idea protection, establishing the companies to setting up contacts with external 
companies. 

Impact at network level 

The  network  of  the  SSRR  project  has  mainly  been  Dutch,  and  the  FP7  project  has 
allowed partners to establish links with industrial actors and researchers locally. The 
survey results show that the Eindhoven University  of  Technology has strengthened 

285 

 
 
collaboration with researchers in the local environment to a very high degree and to 
a  high  degree  within  the  EU.  When  it  comes  to  other  regions  of  the  country  and 
outside the EU, the project has not had any significant impact on collaboration. 

According to the interviews carried out, the impact at the network level has mainly 
been  informal.  New  contacts  have  been  made  with  other  universities  and  research 
institutions,  but  the  contact  is  described  as  informal.  However,  the  Eindhoven 
University  of  Technology  may  consider  collaborating  with  these  other  universities 
when  applying  for  new  project  funding.  As  described,  Eindhoven  University  of 
Technology  collaborated  with  the  German  research  institution  Helmholtz-Zentrum 
Dresden  Rossendorf,  and  to  some  degree  the  project  strengthened  collaborations 
with other researchers in the EU. For example, there was a research group in Italy 
with which one of the PhD students in the project shared his simulations, and in this 
way contributed  to strengthening their  academic  relationship. This relationship  was 
formed, when the Italian group found out about the project through publications and 
personal contacts.  

According to the interviews, to do a project within the context of FP7 was a unique 
opportunity for the project coordinator and their team to communicate their results 
to a broad field of actors, to demonstrate good work, build up their reputation, and 
become  more  appealing  to  other  EU  institutions.  However  one  of  the  participants 
expresses the view that there was a problem with communication of the ERC to the 
outside  world.  Even  though  the  project  achieved  many  successes,  these  research 
achievements  could  be  communicated  clearer  through  the  ERC  communication 
channels. Thus, there is a task for the ERC to focus even more on providing better 
communication and clearer messages about the research and its impact. 

Outputs, outcomes, and impact 

Generally,  the  SSRR  project  has  contributed  to  the  academic  field  by  applying  the 
concept  of  rotation  in  a  chemical  reactor.  In  this  way,  the  project  has  improved 
energy efficiency  compared to  methods that are more traditional. In the project, it 
was demonstrated how the reactors work and a basis has been built to develop new 
types  of  reactors.  The  research  team  at  Eindhoven  University  of  Technology  is  the 
only group in the world that is doing this type of work.  

According to the survey results, the SSRR project has resulted in the production of 
the following types of scientific & technological knowledge: 

 

Peer-reviewed articles 

  Other academic publications (e.g. books, proceedings) 

 

Presentation of results at conferences or workshops 

  Submission of patent applications 

  Spin-off projects 

  Awarded recognition for contribution to fields 

The  survey  data  also  shows  a  high  level  of  satisfaction  with  the  project  regarding 
direct  outputs  and  long-term  outcomes.  Based  on  the  interviews  and  the  survey 
results it is clear that the FP7 project has enhanced knowledge transfer from science 
to  the  market  to  a  very  high  degree—among  other  things  through  technology 
licensing  to  the  spin-off  company  FlowId.  However  the  project  has  not  yet 
contributed to knowledge transfer from science to society. There is still a long way 
to go, before the project will have an actual societal impact. For reaching impact on 

286 

 
 
society,  the  technology  has  to  develop 
commercialisation.  

from 

its  current  pilot  stage  to 

The SSRR project has introduced process innovations with FlowId as the commercial 
driver.  One  of  the  milestones  for  FlowId  was  to  develop  a  business  plan.  Today 
FlowId employs 10 people, and their main task is to encourage industry to adopt the 
new technology. The key challenge in the commercialisation process is to move from 
the technical feasibility demonstrated in the project to show the economic feasibility 
of  the  technology.  In  this  regard,  the  project  is  on  the  right  track,  in  2013  the 
research devices were transformed into products, and in 2014, a new test platform 
is being setup in The Netherlands. An investment of almost €2 million is foreseen in 
integrating the technology in one of the largest bio-based pilot test  facilities in the 
Netherlands (Chemelot InSciTe). 

Reflection on the success factors 

According  to  the  survey  results,  the  benefits  that  Eindhoven  University  of 
Technology  gained  from  the  participation  in  the  FP7  project  greatly  exceeded  the 
costs.  From  the  coordinator’s  point  of  view  the  project  was  very  successful  due  to 
the following factors:  

 

 

There was a good relationship with the EU representatives  

There was a high degree of freedom to apply the project in the way the principal 
investigator wanted.  

  Because  the  collaboration  with  industry  partners  was  already  in  place,  the 

network could be activated immediately. 

From  the  industrial  partners  it  is  pointed  out  that  it  would  strengthen  the  outputs 
and outcomes of the project, if the spin-off company got involved in an earlier phase 
of  the  project.  Alfa  Laval  was  originally  foreseen  as  the  commercial  partner,  and 
because  of  their  change  of  focus  and  policy,  FlowId  got  involved  later  on.  It  was 
time  consuming  for  FlowId  implement  the  technology,  which  could  have  been 
avoided  if they  were in  the  project  from  the beginning.  Generally  the  collaboration 
between the university and spin-off company is evaluated positively, as the project 
has actually succeed in bridging research with commercialisation.  

287 

 
 
 
 
Case Study XII: TRAIN 2009, Training through research application Italian initiative 

Summary of the case 

Training through Research  Application Italian  Initiative (TRAIN 2009) is a five-year 
international mobility scheme established by the Italian National Institute for Health 
(ISS)  and  Alliance  Against  Cancer  (ACC)  to  retain  the  country’s  most  talented 
scientists  and  to  provide  an  attractive  environment  for  experienced  scientists  from 
abroad to come to Italy.  

TRAIN 2009 represents a case with mitigated success in outputs and outcomes and 
the  programme  has  experienced  challenges  in  attracting  the  expected  number  of 
qualified  applicants  to  the  programme.  The  challenges  are  primarily  due  to 
difficulties  in  disseminating  the  existence  of  the  programme,  the  lack  of  culture  in 
Italy for taking part in such programmes, the limited appeal of Italy as a destination 
for  cancer  researchers, the limited possibilities to  secure additional funding in Italy 
after  the  two  year  fellowship  as  well  as  the  original  one-year  duration  of  the 
fellowships. The change in the duration of the fellowships from one to two years has 
helped make the programme more attractive and increased the number of qualified 
applicants. 

From  a  technical  standpoint  the  creation  of  a  database  of  potential  reviewers  of 
applications has been a success and led to a scientific publication. It is beyond the 
scope  of  this  project  to  evaluate  the  outcome  of  the  research  conducted  by  the 
fellows but indications are that the fellows have established important new networks 
and lines of research. 

Introduction 

TRAIN 2009 is an example of a project, funded in part by the FP7 programme, with 
mitigated  success  in  outputs  and  outcomes.  The  project  is  funded  under  the  MC-
COFUND  –  Co-funding  of  Regional,  National  and  International  Programmes  and  is 
also  an  example  of  a  FP7  project  with  a  national  focus  given  that  all  four  partners 
behind  the  programme  are  Italian.  At  the  same  time,  non-Italian  post-doctorate 
scientists can also be awarded fellowships in Italy through TRAIN 2009.  

This  case  study  is  based  on  four  interviews.  An  interview  was  conducted  with  the 
project  coordinator  as  well  as  two  partners  in  the  consortium  and  a  participant  in 
the  programme  (a  recipient  of  a  fellowship).  Responses  from  the  participant 
interviews have been anonymised in this case study. Data was also drawn from the 
network  analysis  and  survey  conducted  in  earlier  phases  of  the  evaluation  and 
integrated into this case study.  

288 

 
 
 
 
Description of the case 

 The  TRAIN  2009  programme 
began on February 2, 2011, and is 
expected  to  run  until  January  31, 
received 
2016.  TRAIN  2009 
European 
funding 
Commission 
the  FP7 
programme  under  the  scheme  of 
MC-COFUND  –  Co-funding  of 
Regional, 
and 
International Programmes.  

the 
through 

National 

from 

from 

The  programme  received  €1,000, 
620 from the FP7 programme and 
another  €1,500,930 
the 
Italian  Ministry  of  Health.  The 
programme was initiated to retain 
Italy’s  most  talented  scientists 
and 
experienced 
scientists  from  abroad.  Through 
the  support  and 
funding  of 
COFUND,  TRAIN  2009  is  able  to 
increase the number of fellowships 
for  researchers  focused  on  cancer 
research.  

attract 

to 

Background data for TRAIN 2009 

Project period 

01.02.2011 – 31.01.2016 

Total cost 

€2,501,550  

EU contribution 

€1,000,620  

Coordinated in 

Italy 

Sub-programme  FP7-PEOPLE-COFUND-2008 
- Marie Curie Co-funding of 
Regional, National and 
International Programmes 
(COFUND) 

Funding Scheme  MC-COFUND - Co-funding 
of Regional, National and 
International Programmes 
(COFUND) 

Scientific fields 

Cancer research 

The  Italian  Ministry  of  Health  and  the  Italian  National  Institute  for  Health  (ISS) 
established the association Alliance Against Cancer (ACC) in 2002. Their aim was to 
better  coordinate  cancer  research  and  care  and  to  stimulate  innovation  and 
competitiveness.  ISS  and  ACC’s  objective  is  to  enhance  Italian  participation  in 
building  the  European  Research  Area  in  regards  to  cancer  research.  As  cancer 
research  is  a  highly  complex  field,  it  is  ISS  and  ACC’s  intention  to  provide 
continuous education to Italy’s top cancer scientists and researchers. Italy has found 
it  difficult  to  retain  the  country’s  most  talented  scientists  and  in  providing  an 
attractive  environment  for  experienced  scientists  from  abroad  to  come  to  Italy. 
Because of the challenges faced by the country and to promote translational cancer 
research  in  Italy,  ISS  and  ACC  established  a  five-year  international  mobility 
schemed  called  Training  through  Research  Application  Italian  Initiative  (TRAIN 
2009). 

The  programme  is  targeted  at  Italian  and  non-Italian  post-doctorate  scientists  or 
scientists with at least four years of full time research experience who would like to 
improve and further their training. There are three schemes within the TRAIN 2009 
programme. The first scheme targets Italian scientists who have completed at least 
3 years of research abroad in a third country, and who are interested in returning to 
Italy.  The  second  scheme,  or  the  mobility  scheme,  is  open  for  experienced  non-
Italian researchers who would like to spend  two years in Italy. The third scheme is 
addressed  at  Italian  scientists  who  have  a  desire  to  further  their  expertise  by 
spending  two  years  abroad.  Although  TRAIN  2009  is  an  Italian  programme,  their 
mobility scheme is open to international scientists and researchers as well. Besides 
supporting and furthering the  expertise  of  scientists and researchers in the field of 
cancer,  the  programme  also  aims  at promoting international  research  mobility  and 
cross-border  research  collaboration.  Candidates  apply  for  fellowships  through  the 
programme  and  applications  are  evaluated  by  an  international  corps  of  leading 
cancer scientists. 

289 

 
 
Formation of the partnership 

This project has four partners involved in managing the programme: 

 

ISS. Istituto Superiore di Sanità, the Italian National Institute for Health 

  ACC. Alleanza Contro il Cancro, Alliance Against Cancer 

  Dist. Dipartimento di Informatica, Sistemistica e Telematica, Nanobiotechnology 

and Medical Informatics Laboratory, University of Genova 

 

IST. Istituto Nazionale Per La Ricerca Sul Cancro, a hospital in Genova 

Figure 30  Map of the participating partners 

Source: 

Cordis, website: http://cordis.europa.eu/home_en.html 

As mentioned earlier, the Italian Ministry of Health and the Italian National Institute 
for Health (ISS) established Alliance Against Cancer (ACC) in 2002. The 4 partners 
in  the  programme  had  worked  together  previously  on  a  large  Italian  oncology 
project.  The oncology  project had  a component devoted  to  internationalisation and 
the idea to create TRAIN 2009 was born out of the work in this project. 

ISS is the project manager and leader of the consortium. Dist is mainly involved in 
setting  up  and  maintaining  the  project  website,  managing  the  application  process, 
setting up a system on the website for the registration of experts for peer reviewers 
and dissemination of the calls through the website.  

Process and implementation 

When  TRAIN  2009  was  formed,  ISS  and  ACC  were  able  to  support  30  outgoing, 
incoming  and  reintegration  one-year  fellowships.  With  the  additional  funding  and 

290 

 
 
 
support  from  the  FP7  programme,  however,  TRAIN  2009  was  able  to  increase  the 
number  of  one-year  fellowships  to  51.  As  the  programme  was  rolled  out,  the 
partners  realised  that  one  year  fellowships  were  too  short  and  changed  the 
programme to offer two year fellowships. The change was also made because it was 
difficult to get qualified applicants and reach the target of 51 fellowships. All fellows 
who had already received a one year fellowship were offered a two year fellowship 
instead. 

TRAIN 2009 has launched four calls for fellowships. The first three calls are finalised 
and TRAIN 2009 has awarded 12 fellowships: four from the first call, one from the 
second call and seven from the third call. Originally the programme was expected to 
have awarded 17 fellowships after the third call. The fourth call is still not finalised. 
At  the  end  of  the  five-year  programme,  TRAIN  2009  is  expected  to  have  provided 
25 fellowships. 

The  partners  interviewed  for  the  case  study  highlight  a  number  of  reasons  why 
TRAIN 2009 has received less qualified applications than expected. The first reason 
is  the  tradition  and  visibility  of  the  programme.  There  is  not  a  strong  tradition  for 
Italians  to  take  part  in  this  type  of  programme  and  it  is  unusual  that  ISS  offers 
fellowships.  Therefore,  relevant  researchers  normally  look  to  other  agencies  and 
programmes  to  find  funding  opportunities  and  mobility  programmes.  Italy’s  appeal 
has  also  impacted  how  many  applications  the  programme  has  received.  For 
international  researchers,  Italy  is  not  considered  as  one  of  the  most  attractive 
countries  to  go  to.  Many  researchers  would  prefer  doing  a  fellowship  in  countries 
such  as  the  United  Kingdom  or  Germany,  as  it  is easier  for  researchers  to go  to  a 
country where a larger share of the population speak English than in Italy.  

Furthermore,  when  they  were  first  established,  the  fellowships  were  too  short.  As 
mentioned  earlier,  the  programme  was  initially  designed  to  offer  one  year 
fellowships, which was not enough time to complete a translational cancer research 
project and one of the reasons why so few researchers applied in the first two calls. 
The  fellowships  were  then  changed  to two  years  at the  end  of the  second  call  and 
the  programme  received more  applications  for  the  third  term.  Another  reason  that 
the programme has not been as successful in recruiting applicants was that it could 
not guarantee that the fellows could stay after the two year fellowship expired. The 
Italian  economy  is  still  suffering  from  the  financial  crisis  and  large  cuts  on  funding 
for  research  has  been  introduced.  Lastly,  there  is  a  large  tax  burden  on  research 
fellows.  As  the  fellows  get  paid  in  Italy  and  need  to  pay  tax  there,  the  fellows  are 
not  paid  very  well  if  they  go  abroad  as  a  TRAIN  2009  fellow.  TRAIN  2009  has  not 
been  as  successful  as  they  had  hoped  in  recruiting  applicants  and  granting 
fellowships  over  the  course  of  the  programme  thus  far.  This  finding  is  in  line  with 
the survey conducted in earlier phases of the evaluation showing that the project is 
rated as having mitigated success in outputs and outcomes. 

Contextual factors 

As  mentioned  earlier,  the  programme  was  running  at  a  time  when  the  Italian 
economy had  been  going through  a  severe crisis and large cuts to  public spending 
had been introduced. The cuts also hit spending on cancer research, which has been 
reduced by half. 

This  has  on  the  one  hand  made  the  programme  more  relevant  because  it  is 
becoming increasingly difficult to find alternative sources of funding of research but 
it  has  also  been  a  challenge  for  the  programme  and  for  the  fellows  because  it  has 
made Italy less attractive as a destination for cancer researchers and made it more 

291 

 
 
difficult  for  the  incoming  researchers  to  find  funding  in  Italy  when  the  two-year 
fellowships conclude. 

Impact at network level 

The programme has potentially created networks at three levels: 

  Between the partners in the programme 

  Between the fellows and the receiving institutions 

  Between the fellows 

The  partners  in  running  the  TRAIN  2009  programme  have  worked  together  before 
the initiation of the programme  and  the  idea to  create TRAIN 2009 was developed 
under  a  large  Italian  oncology  project  they  took  part  in.  Thus,  no  new  networks 
between  the  partners  were  established  but  existing  networks  were  reinforced.  The 
project  partners  used  a  mix  of  face-to-face  meetings  and  teleconferences  to 
coordinate  the  programme,  identify  peer  reviewers,  evaluate  the  applicants  and 
select  the  fellows  for  the  programmes.  Since  the  partners  are  based  in  Rome  and 
Genoa  teleconference  meetings  had  to  supplement  face-to-face  meetings.  During 
the  project  the  partners  also  meet  for  the  oncology  project.  During  the  interviews 
the  partners  have  highlighted  that  they  know  each  other  well  and  work  well 
together.  

It  is  not  yet  certain  whether  the  partners  will  work  together  again  in  the  future. 
ACC,  ISS  and  Dist  have  applied  for  funding  within  the  framework  of  Horizon  2020 
for a project on regenerative medicine but the application was evaluated just below 
the threshold for getting funded. The consortium will adjust the proposal and submit 
it again. 

The programme has also contributed to creating new networks between the fellows 
and he researchers in the  receiving labs. The fellow interviewed for the case study 
has been well received and integrated into the ISS laboratory they are in and they 
are  working  on  an  academic  article,  which  will  be  co-authored  by  some  of  the 
researchers  from  ISS.  The  fellow  interviewed  also  anticipates  that  they  will  keep 
working with the laboratory even if they return to their home country after the two 
year  fellowship  ends.  The  fellow  has  come  to  ISS  to  explore  if  ecology  theory  can 
apply to cancer research and therefore has a different scientific background than the 
researchers 
therefore 
multidisciplinary  and  the  interview  participant  is  learning  new  techniques  from 
researchers in the lab. 

laboratory.  The 

receiving 

research 

ISS 

the 

in 

is 

There have not been any networking activities for the receivers of the fellowships to 
meet each other and exchange experience.  

Outputs, outcomes, and impact 

As  mentioned  it  has  been  a  challenge  to  find  25  qualified  fellows  to  receive  the 
fellowships and a number of challenges have been encountered. Still, 12 researchers 
have been awarded fellowships and are working on scientific publications in labs in 
Italy  and  around  Europe.  The  last  round  of  up  to  13  fellowships  will  be  awarded 
shortly. 

One of the main achievements of the project has been the definition and creation of 
a database of reviewers containing metadata on reviewers which was used to set up 
a  semi-automatic  system  for  selection  of  reviewers.  The  database  was  created  by 

292 

 
 
Dist and has provided the foundation for the scientific publication ‘A semi-automatic 
web  based  tool  for  the  selection  of  research  projects  reviewers’  published  in  the 
book  ‘eHealth  –  For  Continuity  of  Care’  and  co-authored  by  researchers  from  ISS 
and ACC. 

As  mentioned  ACC,  ISS  and  Dist  are  currently  also  revising  an  application  for 
funding under the Horizon 2020 framework. 

The  project  stands  out  from  the  other  case  studies  because  the  main  output, 
outcome  and  impact  are  not  produced  directly  by  the  partners  but  by  the 
researchers  receiving  the  fellowships.  It  is  beyond  the  scope  of  this  project  to 
uncover all effects and networks generated by the fellows. 

Reflection on the success factors 

As mentioned this case represents an example of a project with mitigated success in 
outputs and outcomes. The one year fellowships was initially a significant barrier for 
the  success  of  the  project  but  the  extension  of  the  fellowships  to  two  years  has 
made  the  programme  more  attractive  and  helped  raise  the  number  of  qualified 
applicants for the third and fourth calls. The quality of the applicants is important for 
the overall success of the programme and the scientific output from the programme. 

From a technical stand point, the project has been successful and the database set 
up  has  helped  in  successfully  assigning  reviewers  semi-automatically  to  review 
applications.  This  has  also  led  to  a  joint  scientific  publication  among  project 
partners.  

The  partners  had  worked  together  before  TRAIN  2009  and  cooperation  within  the 
programme  has  been  smooth.  The  project  has  reinforced  existing  networks  and 
three  of the  partners  have  applied  for  funding  together  outside of  the  TRAIN  2009 
project. 

293 

 
 
 
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This publication is the Methodological Annex accompanying the Final Report 
of  the  Study  on  Network  Analysis  of  the  7th  Framework  Programme 
Participation.  Herein  are  explained  the  methods  used  in  examining  the 
effectiveness of the Seventh Framework Programme’s network approach in 
achieving EU research policy objectives and fostering Europe’s international 
competitiveness  in  Science  &  Technology.  In  investigating  the  potentials 
and  limits  of  this  approach,  the  study  investigated  the  effects  of 
multidisciplinary, 
international 
collaborations  on  achieving  the  positive  outcomes  sought  in  FP7.  This  was 
one of several preparatory studies for the overall ex-post evaluation of FP7, 
which  is  to  be  carried  out  over  two  years  following  completion  of  the 
Programme. 

interdisciplinary, 

intersectoral 

and 

Studies and Reports 

doi:10.2777/739048