Cost-benefit analysis for FAIR research data : policy recommendations.
Date: 2019-01-16
Institutions: Directorate-General for Research and Innovation
Subjects: access to information, artificial intelligence, cost-benefit analysis, data recording, data transmission, financing, open source software, organisation of research, research body, research policy, research results, right to reuse information, scientific cooperation

Policy Recommendations 

Cost-Benefit analysis for FAIR research data 

Written by PwC EU Services 
May 2018 

 
 
 
 
 
 
 
 
 
 
 
 
Cost-Benefit analysis for FAIR research data - Policy recommendations 
European Commission 
Directorate-General for Research and Innovation 
Directorate A — Policy Development and Coordination 
Unit A.2 — Open Data Policy and Science Cloud 
Contact   Athanasios Karalopoulos 
E-mail   Athanasios.Karalopoulos@ec.europa.eu  
              RTD-PUBLICATIONS@ec.europa.eu 
European Commission 
B-1049 Brussels 

Manuscript completed in May 2018. 

The information and views set out in this report are those of the author(s) and do not necessarily reflect the official opinion of 
the Commission. The Commission does not guarantee the accuracy of the data included in this study. Neither the Commission 
nor any person acting on the Commission’s behalf may be held responsible for the use which may be made of the information 
contained therein. 

More information on the European Union is available on the internet (http://europa.eu). 

Luxembourg: Publications Office of the European Union, 2018  

PDF  

ISBN 978-92-79-98885-1 

doi: 10.2777/706548 

KI-02-19-022-EN-N 

© European Union, 2018. 
Reuse is authorised provided the source is acknowledged. The reuse policy of European Commission documents is regulated by 
Decision 2011/833/EU (OJ L 330, 14.12.2011, p. 39). 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EUROPEAN COMMISSION 

Policy Recommendations 

Cost-Benefit analysis for FAIR research data 

Written by PwC EU Services 

Directorate-General for Research and Innovation 
Open Science 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

EXECUTIVE SUMMARY .................................................................................... 3 

INTRODUCTION .............................................................................................. 7 

Policy context ............................................................................................................... 7 
Scope and objective of this report ................................................................................ 9 
Structure of the report ................................................................................................. 9 

RECOMMENDATIONS  FOR  COVERING  THE  INITIAL  COSTS  FOR  FAIR  RESEARCH 
DATA IMPLEMENTATIONS IN EUROPE ............................................................ 10 

Work out the business case for FAIR at the national level............................................ 10 
Measure progress and impact of FAIR implementation ................................................ 15 
Share  and  reuse  knowledge  and  solutions  within  and  across  countries  and  disciplines, 
and mutualise resources .............................................................................................. 16 

RECOMMENDATIONS  FOR  THE  SUSTAINABILITY  OF  FAIR  RESEARCH  DATA 
IMPLEMENTATIONS IN EUROPE ...................................................................... 19 

Explore mixed business models for FAIR research data infrastructures ....................... 19 
Secure  public 
implementation ............................................................................................................ 27 
Develop a community and an ecosystem around FAIR data ......................................... 28 

implementing  and  sustaining  FAIR  research  data 

funding 

for 

CONCLUSION .................................................................................................. 30 

Table of figures 
Figure 1 - The four foundational characteristics of FAIR .............................................. 7 

Figure 2 High level categories of recommendations .................................................... 9 

Figure 3 Overview recommendations on initial investment .........................................11 

Figure 4 Cost reduction via Emerging Technologies ...................................................12 

Figure 5 Maturity levels of business models combined with FAIR data prioritisation .......19 

Figure 6 Mixed business models for FAIR research data .............................................20 

Figure 7 Overview recommendations on Business continuity ......................................25 

Figure 8: Business Canvas ModelTM FAIR research data infrastructure .........................26 

Figure 9 The FAIR research data ecosystem .............................................................28 

2 

 
 
 
 
EXECUTIVE SUMMARY 

To drive the implementation of the FAIR principles in Europe, the European Commission 
together with a number of pioneering European research stakeholders is taking measures 
to raise the awareness about costs and benefits of FAIR data, and is encouraging funding 
bodies  to  set  guidelines  or  support  the  development  of  an  infrastructure  for  publishing 
FAIR data. In a study which preceded this report, the cost of not having FAIR data for the 
EU-28  has  been  estimated  at  EUR  10,2  bn  per  year,  and  this  is  bound  to  grow  unless 
action is taken.  

Despite  this,  many  research  performing  organisations  and  infrastructures  are  still 
reluctant  to  apply  the  FAIR  principles  and  share  their  datasets  due  to  real  or  perceived 
costs,  including  time  investment  and  money.  To  answer  such  concerns,  this  report 
formulates 36 policy recommendations on cost-effective funding and business models to 
make  the  model  of  FAIR  data  sustainable.  It  provides  evidence  to  decision  makers  on 
setting up short and long-term actions pertinent to the practical implementation of FAIR 
principles. 

The recommendations presented in this report are organised in two groups:  

•  The  first  covers  recommendations  for  covering  the  initial  costs  for  FAIR  research 

data implementations in Europe, while  

•  The  second  covers  recommendations  for  covering  the  sustainability  of  FAIR 

research data implementations in Europe.  

Recommendations  for  covering  the  initial  costs  for  FAIR  research  data 
implementations in Europe: 

(a)  Work out the business case for FAIR at the national level 

Rec. 1.  Apply  the  cost-of-not-having  FAIR  methodology  in  every  EU  member 

state 

Rec. 2.  Apply the cost benefit mechanism for the strategic research centres in EU 
member states, such as data-intensive research labs (e.g. genomics), and 
data infrastructures (e.g. ELIXIR, CLARIN) 

Rec. 3.  Think  beyond  of  organisations  and  disciplines.  Cross-disciplinary  FAIR 
data  use  cases  have  the  potential  to  create  positive  externalities,  spill-
over effects and innovation 
Integrate the  outcomes of the national FAIR  cost benefit assessments at 
the  European  level  to  identify  and  quantify  positive  spillovers  and 
externalities 

Rec. 4. 

(b)  Prioritise investments in the national FAIR implementation roadmap 

Rec. 5.  Build  a  solid  FAIR  baseline  across  Europe  by  prioritising  high-impact  and 
high-feasibility  activities  to  maximise  ROI.  Start  with activities  related  to 
Findability  and  Accessibility,  such  as  common  data  management  policies 
and  practices,  metadata  standards,  persistent  identifiers  and  common 
research data infrastructures 

Rec. 6.  ROI  will  come  only  if  the  current  working  behaviours  around  data 
management and  sharing change.  Invest early enough in culture change 
and skills development 

Rec. 7.  Establish a working group under EOSC which will be mandated to decide 
on  FAIR  investment  priorities,  evaluate  current  progress  and  prepare 
future development roadmaps 

 
 
Rec. 8.  Move towards shared national and European cross-discipline Cloud-based 
data  infrastructures  which  can  significantly  reduce  the  data  storage  and 
compute  costs,  and  can  drive  time  and  cost  efficiencies  in  data  access, 
sharing and collaboration 

Rec. 9.  Use  emerging  technology,  such  as  artificial  intelligence  and  robotic 
process  automation 
industrialising  repetitive, 
standardised and time-consuming activities, such as data transformation, 
data classification or assignment of identifiers, to reduce operational costs 
linked to FAIR implementation 

for  automating  and 

Rec. 10.  Working  in  iterations  and  increasing  the  maturity  of  FAIR  research  data 
implementations  in  well-defind  cycles  helps  to  align  investments  with 
progress towards achieving the policy objectives 

Rec. 11.  Engage  in  the  iterations  at  least  one  discipline  or  country  which  has  not 
yet started their FAIR implementation or is lagging significantly behind to 
achieve  buy-in,  encourage  them  and  share  with  them  lessons  learnt, 
actionable advice and reusable outcomes of others 

Rec. 12.  Opt  for  demand-driven  provisioning  of  FAIR  data,  within  and  across 
research  disciplines,  to  optimise  investment  expenditure  and  maximise 
ROI.  Sustainable growth in the maturity of the FAIR implementations as 
well as in the number of FAIR data available will lead to network effects 

Rec. 13.  Provide  financial  incentives,  such  as  grants  and  funding,  for  making 

legacy data FAIR on a demand-driven basis 

(c)  Measure progress and impact of FAIR implementation 

Rec. 14.  Endorse  and  provide  financial  support  to  a  working  group  under  EOSC 
which  coordinates  and  monitors  FAIR  implementation  at  the  European 
level  to  ensure  the  alignment  between  investments  and  spending 
compared to the level of achievement of the FAIR policy objectives 

Rec. 15.  Create a European mechanism for measuring progress, for example based 

on earned value management  

Rec. 16.  Create a European FAIR implementation maturity model which will define 
the activities required to achieve a specific level, the associated costs and 
the expected benefits.  

Rec. 17.  Provide the  expertise and financial assistance  for helping countries apply 
the  maturity  level  and  making  the  transition  from  one  level  to  the  next 
one.  

Rec. 18.  Define  templates  for  service-level  agreements  with  which  trusted  FAIR 
research  data  infrastructures  will  need  to  comply,  for  establishing  a 
European baseline for service quality 

(d)  Share and reuse knowledge and solutions within and across countries and disciplines 

Rec. 19.  Benefit from significant efficiency savings in the total cost of ownership of 
FAIR data implementation by reusing solutions, technical assets, practices 
and  experiences  between  FAIR  and  other  data-related  policy 
implementation  initiatives,  such  as  those  of  the  revised  PSI  directive, 
GDPR, INSPIRE and CEF Telecom  

Rec. 20.  Mutualise  FAIR 

investments  across 
countries  and  disciplines  by  co-investing  in  common  frameworks, 
solutions, technical assets and shared services 

implementation  resources  and 

Rec. 21.  Explore  business  models  for  FAIR  research  data  infrastructures  and 
services based on shared service provision, e.g. following the example of 
the CEF building blocks  

Rec. 22.  Provide  financial  support  for  developing  and  customising  FAIR-compliant 
open  source  software  in  collaboration  with  the  European  open  source 

4 

 
 
community,  and  multiplicate  savings  by  sharing  across  the  EU  research 
community 

Recommendations for the sustainability of FAIR research data implementations 
in Europe 

(e)  Explore mixed business models for FAIR research data infrastructures 

Rec. 23.  For  the  sustainability  of  FAIR  research  data  implementation,  research 
data infrastructures and research performing organisations must shift the 
focus towards data monetisation and value-added data services.  

Rec. 24.  Several alternatives exist for FAIR data and data services pricing models, 
from  profit  maximisation  and  cost  recovery  through  to  charging  only  for 
marginal costs (hence coming closer to the open data paradigm). Several 
parameters  have  to  be  considered  for  the  selection  of  the  right  one, 
including the way data creation was funded, applicable IP or patents, data 
management costs, and value added.  

Rec. 25.  FAIR research data infrastructures must be encouraged and supported via 
fiscal  incentives,  such  as  seed  funding,  tax  breaks  or  deductions,  and 
policy  interventions,  including  legislation,  to  explore  mixed  business 
models,  which  combine  a  healthy  balance  between  public  funding  and 
other revenue streams.  

Rec. 26.  Fiscal incentives, e.g. tax breaks or deductions, will encourage industry to 
form  partnerships,  collaborate,  sponsor,  fund  or  buy  data/services  from 
FAIR research data infrastructures, to broaden the market for FAIR data. 

(f)  Secure  public  funding  for  implementing  and  sustaining  FAIR  research  data 

implementation 

Rec. 27.  Funding FAIR data implementation has to remain available not only at the 
European level, e.g. as part of Horizon 2020 and Horizon Europe, but also 
as part of the national research and innovation programmes. FAIR applies 
to all publicly funded research in Europe 

Rec. 28.  FAIR-related  costs,  e.g.  for  data  stewardship  and  management,  or  data 
infrastructure  operational  costs  must  be  made  eligible  for  specific  cases 
and  only  if  repored  at  a  granular  level  respecting  transparent  cost 
accounting practices 

Rec. 29.  Culture  change  related  costs,  including  training  and  awareness  raising 
activities,  must  be  made  eligible  based  on  transparent  cost  accounting 
practices 

Rec. 30.  FAIR-by-default  policies  and  mandatory  FAIR  compliance  must  be 

included in the award criteria of research grants.  

Rec. 31.  Incentivise  research  data 

infrastructures  and  research  performing 
organisations  to  reinvest  savings  made  as  a  result  of  FAIR  in  the 
sustainability of FAIR implementations 

(g)  Develop a community and an ecosystem around FAIR data 

Rec. 32.  Provide 

financial  support 

for  communication,  knowledge  sharing, 
community building and marketing projects and activities for example via 
continuing  with  coordination  and  support  actions  through  European  and 
national research programmes. Full costs to be made eligible for all types 
of participants 

Rec. 33.  Provide  financial  support  for  organising  mutual  learning  exercises,  in  the 
context  of  which  member  states,  third  countries,  FAIR  data  practitioners 
and  experts  work  together  on  a  topic  of  common  interest,  such  as  FAIR 
data  management  on  the  open  science  cloud,  FAIR  data  management  in 
AI  applications,  or    the  costs  and  revenues  for  preparing  a  data 

5 

 
 
infrastructure  for  joining  the  open  science  cloud  using  the  cost  benefit 
mechanism developed by the current study  

Rec. 34.  Take measures and make the means available for encouraging innovation 
through  cross-disciplinary  projects  and  applications.  Such  means  may 
include promoting the use of FAIR research data in projects funded under 
the  current  focus  areas  of  existing  European  and  national  research 
programmes, or defining new innovation stream with a focus on research, 
life  sciences  and/or  SMEs  under  future  European  and  national  research 
programmes, such as Horizon Europe 

Rec. 35.  Consider  the  establishment  of  a  public-private  partnership  focusing  on 

creating societal and economic value from FAIR data 

Rec. 36.  Place  universities  at  the  heart  of  the  European  FAIR  data  community  of 
practice.  They  are  the  most  important  type  of  research  performing 
organisations and are also the ones preparing the workforce of tomorrow 
who  will  be  able  to  support  the  implementation  of  FAIR  principles  in 
Europe.  To  this  end,  support  and  incentives  should  be  provided  to  them 
for  reviewing  their  curricula  and  current  data  management  practices  in 
the light of FAIR. 

6 

 
 
INTRODUCTION 

Policy context 

federated  model1, 

To drive the implementation of the FAIR 
principles  in  Europe  and  realise  the 
European  Open  Science  Cloud  (EOSC) 
the 
following  a 
European  Commission  together  with  a 
number of pioneering European research 
stakeholders  is  taking  measures  to  raise 
the  awareness  about  costs  and  benefits 
of FAIR data, and is encouraging funding 
bodies  to  set  guidelines  or  support  the 
development  of  an  infrastructure  for 
publishing  FAIR  data.  To  this  end,  the 
EOSC  declaration2  at  the  end  of  2017 
acknowledged  the  importance  of  the 
FAIR  principles  and  how  it  should  be 
incorporated  in  building  the  EOSC  data 

format,  what 

community4, about how to share data, in 
which 
information  or 
metadata should be provided etc. Others 
cultures  and 
touch  upon  existing 
behaviours  in  conducting  research,  from 
research  funders  forbidding  researchers 
to  share  their  data  to  researchers  not 
even  considering  that  the  data  they 
produce  can  be  valuable  for  others,  the 
lack of attention given to the preparation 
of  a  data  management  plan,  missing 
metadata5,  various  competing  standards 
for research data and metadata, and the 
lack  of  persistent  identifiers  for  data, 
datasets and metadata6. 

Moreover,  many  research  performing 
organisations and infrastructures are still 
reluctant  to  apply  the  FAIR  principles 
and share their datasets because of real 
including  time 
or  perceived  costs, 

infrastructure. 

investment and money.  

The fact that the FAIR principles3 are not 

As  the  costs  and  benefits  of  FAIR 

Figure 1 - The four foundational characteristics of FAIR 

common practice yet is due to numerous 
reasons.  Some  are  concerned  about  the 
the  research 
lack  of  awareness 

in 

1 
https://ec.europa.eu/research/openscience/pdf/eos
c_strategic_implementation_roadmap_short.pdf 
#view=fit&pagemode=none 
2 
https://ec.europa.eu/research/openscience/pdf/eos
c_declaration.pdf  
3 
https://www.go-fair.org/fair-principles/; 
Principles 
described 
https://www.force11.org/group/fairgroup/fairprinci
ples; and (Wilkison, Dumontier, & Mons, 2016). 

FAIR  Principles  described  by  GO-FAIR, 
FAIR 
11, 

Force 

by 

research  data  and  infrastructures  are 
being  heavily  debated  among 
the 
stakeholders, the European Commission, 
under 
contract,  has 
commissioned  two  studies;  one  on  the 
cost  of  not  having  FAIR  research  data, 
and  a  Cost-Benefit  Analysis  of  FAIR 
studies  have 
research  data.  Both 

specific 

this 

4 Interview with Barend Mons, 2018-01-17 
5  (Zahedi,  Haustein,  &  Bowman,  2014),  and 
(Parsons, Grimshaw, & Williamson, 2013) 
6  (Johnson,  Parsons,  Chiarelli,  &  Kaye,  JISC 
Research  Data  Assessment  Support  -  Findings  of 
framework  (DAF) 
the  2016  data  assessment 
surveys,  2016),  Stehouwer  &  Wittenburg,  2014 
and Tenopir C. , et al., 2011 

 
 
                                                
 
                                                
revealed  a  number  of  areas  of 
investment to be made in order to move 
towards  sustainable  provision  of  FAIR 
data. 

of 

the 

implementation 

The 
FAIR 
principles  can  bring  direct  and  indirect 
benefits  to  research  stakeholders,  from 
research  funders  and  infrastructures  to 
research  perfoming  organisations  and 
researchers,  and  can  have  a  positive 
impact  on  the  quality  and  the  return  on 
investment  (ROI)  of  research 
itself. 
Studies  investigating  the  impact  of  the 
implementation  of  the  FAIR  principles 
converge  on 
following  positive 
impacts and externalities:  

the 

proposed 

•  Reducing  duplication  in  research, 
in  terms  of  time,  effort  and 
funding.  A  study7  by  Garner,  H., 
McIver  L.,  and  Waitzkin,  M. 
(2013) 
factual 
approach  to  measure  the  amount 
of  funds  allocated  to  duplicate 
grants.  When 
this 
methodology  to  the  EU  economy, 
we  estimated  the  approximate 
cost  of  research duplication to 60 
million euro per year; 

applying 

a 

•  Better 

and 

management 

and 
stewardship  of  digital  resource 
helps  researchers  adhere  to  the 
expectations  and  requirements  of 
their  funding  agencies8.  Through 
our  study,  we  saw  that  costs 
related  to  the  current  way  of 
research  are  not 
conducting 
the 
justify 
sustainable, 
investments 
for 
example in data stewardship skills 
development  and  in  day-to-day 
data  management  activities.  The 
important  measurable 
most 
benefit 
time 
efficiency  gains  for  each  person 
dealing  with  existing  research 
data.  This  benefit 
is  directly 
the  number  of 
correlated 

required, 

concerns 

the 

to 

7 https://www.healthra.org/download-
resource/?resource-url=/wp-
content/uploads/2013/11/Garner_Nature_1_2013.
pdf 
8 (Wilkison, Dumontier, & Mons, 2016) 

infrastructures, 

people  reusing  data.  In  the  case 
of  data 
this 
benefit  will  be  considered  as  a 
spillover  effect,  not  directly 
perceived  by  them  but  significant 
for  all  organisations  reusing  their 
datasets; 

•  Scaling  up 

research 

findings 
based on integrated and analysed 
from  multiple 
existing  data 
disciplines  and  regions9.  As  more 
and  more  data  will  become 
machine-readable  in  standardised 
formats,  not  only 
time 
integrate  different 
required  to 
datasets 
decrease 
will 
significantly  but  new  studies  at  a 
larger  scale  will  be  possible, 
clearly  realising  positive  network 
effects; 

the 

•  Enabling  research  to  focus  more 
on adding value activities such as 
interpreting  the  data  rather  than 
on  searching,  collecting  or  re-
creating existing data10; and  

•  Enhancing 

findability, 

the 
to 
discovery 

science 
support 
infrastructure 
knowledge 
and 
innovation.  While  the  impact  of 
accessibility, 
the 
interoperability  and  reusability  of 
data  on 
innovation  has  been 
acknowledged  by  several  studies 
from 
macroeconomic 
perspective11,  it  has  so  far  only 
been  measured  based  on  case 
studies12131415. 

a 

9 (Bonino da Silva Santos, et al., 2016) 
10 

http://visit.crowdflower.com/rs/416-ZBE-

142/images/CrowdFlower_DataScienceReport_201
6.pdf 
11 
https://ufm.dk/en/publications/2018/filer/prelimina
ry-analysis-introduction-of-fair-data-in-
denmark_oxford-research-og-hbs.pdf  
12  https://beagrie.com/static/resource/EBI-impact-
report.pdf  
13 
http://www.vises.org.au/documents/2013_Beagrie
&Houghton_Value& 
Impact_Brit_Atmospheric_Data_Centre.pdf  
14 https://beagrie.com/krds.php  

8 

 
 
                                                
                                                
Scope and objective of this report 

As  discussed,  two  key  outcomes  have 
preceeded this report:  

•  A study on the cost of not having 
FAIR  research  data  for  the  EU 
science  and  innovation  system 
and  as  a  result  to  the  EU  data 
economy; and 

with 

•  A  mechanism  to  estimate  the 
costs  and  benefits  of  becoming 
compliant 
FAIR 
research 
principles, 
performing 
and 
data  infrastructures,  which  has 
been applied in a number of real-
life cases1617.  

organisations 

the 

for 

The objective of this report, which builds 
further  on  the  findings  of  the  two 
aforementioned ones, is to provide policy 
recommendations  on  the  next  steps 
concerning  making  research  data  FAIR, 
to  propose  cost-effective  funding  and 
business  models  to  make  the  model  of 
FAIR  data  sustainable  and  provide 
evidence  to  decision  makers  on  setting 
up short and long-term actions pertinent 
to  the  practical  implementation  of  FAIR 
principles. 

This  report  is  intended  primarily  for 
research 
funders,  addressing  policy 
makers  both  at  the  European  and  the 
national levels. 

this  end, 

To 
set  of  policy 
this 
recommendations, focusing on costs and 
financial  aspects  of  the  implementation 
of  the  FAIR  principles  provides  answers 
to the following questions:  

•  What  are  the  main  areas  where 
are 
financial 
implementing  the 
required 
FAIR  principles  within  an  EU 
member state?  

interventions 

for 

•  What are the type of investments 
needed  for  the  FAIR  principles  to 
be implemented by research data 
research 
infrastructures 
performing organisations?  

and 

be 

•  How  can  FAIR  implementation 
to 
activities 
maximise  ROI  and  establish 
quickly  a  FAIR  baseline  on  which 
one can build further? 

prioritised 

•  How to avoid one-off investments 
which  focus  on  single  disciplines 
or  projects  and  excessive  upfront 
investment  in  the  hope  of  broad-
based adoption? 

•  What  are  possible  business  and 
revenue  models  for  sustaining 
FAIR 
and 
infrastructures?  

research 

data 

Structure of the report 

Figure 2 High level categories of recommendations 

The  policy  recommendations  introduced 
in  this  report  are  organised  in  two  main 
categories:  

1.  Recommendations 

for  covering 
the  initial  costs  for  FAIR  research 
data  implementations  in  Europe, 
presented in Chapter 2, and;  

2.  Recommendations 

sustainability 

for  covering 
FAIR 
the 
research  data  implementations  in 
Europe, presented in Chapter 3.  

of 

Chapter 4 concludes the report. 

15 
http://repository.jisc.ac.uk/5509/1/ADSReport_fina
l.pdf  
16 Cost-Benefit analysis mechanism 
17 Cost-Benefit analysis guidebook 

9 

 
 
                                                                    
 
RECOMMENDATIONS FOR 
COVERING THE INITIAL COSTS 
FOR FAIR RESEARCH DATA 
IMPLEMENTATIONS IN EUROPE 

Work out the business case for FAIR 
at the national level  

the 

study 

current 

While 
took  a 
macroscopic view based on existing data 
for  estimating  the  costs  and  benefits  of 
becoming  FAIR-compliant,  the  costs  and 
benefits  should  be  assessed  as  part  of 
each national, specific context in Europe. 
Few member states have already started 
with  FAIR 
implementation,  whereas 
others  are  still  exploring  ‘what’s  in  it  for 
them’.  The  answer  to  this  question  is 
neither  easy  nor  straightforward.  FAIR 
implementation  requires  a  number  of 
initial  investments  as  well  as  associated 
recurring  costs.  At  the  same  time, 
benefits and efficiencies identified in one 
country  cannot  be  directly  projected  or 
replicated in another, because of cultural 
differences, non-harmonised policies and 
technological  choices.  As  for  every  EU 
policy,  the  implementation  of  the  FAIR 
principles  will  not  be  achievable  without 
the 
active 
the 
involvement  of 
the  member  states. 
Therefore, each member state has to be 

commitment 

and 

the  one  hand 

invited  to  perform  detailed  analysis  to 
estimate  on 
the 
investments  for  implementing  the  FAIR 
principles  in  each  country,  and  on  the 
other  hand  to  identify  and  quantify  the 
benefits. The outcomes of this study can 
be used as a basis for this.  

Combining  the  data  collected  at  the 
national  level,  an  aggregated  European 
view  will  be  built.  While  the  national 
perspectives  will  provide  estimates  to 
the specific context of the countries, the 
European  assessment  will  cover  the 
spillover  effects  and  externalities  of  the 
FAIR  principles.  This  is  important  since 
the FAIR principles are supposed to bring 
substantial  spillover  benefits  such  as  on 
the  time  gained  by  external  researchers 
for  manipulating  data,  on  the  quality  of 
the 
the  new, 
researches  or  on 
innovative, researches undertaken. 

During  the  preparation  of  national  FAIR 
cases,  or 
implementation  business 
organisation-specific  ones, 
emphasis 
should  be  placed  on  cross-disciplinary 
use  cases.  Sharing  FAIR  data  across 
disciplines  is  expected  to  create  positive 
externalities, 
spill-over  effects  and 
ultimately  to  drive  innovation  at  the 
touching  points  between  disciplines.

Our recommendations 

Rec. 1 

Rec. 2 

Rec. 3 

Rec. 4 

Apply  the  cost-of-not-having  FAIR  methodology  in  every  EU  member 
state. 

Apply the cost benefit mechanism for the strategic research centres in EU 
member states, such as data-intensive research labs (e.g. genomics), and 
data infrastructures (e.g. ELIXIR, CLARIN). 

Think beyond of organisations and disciplines. Cross-disciplinary FAIR data 
use  cases  have  the  potential  to  create  positive  externalities,  spill-over 
effects and innovation. 

Integrate  the  outcomes  of  the  national  FAIR  cost  benefit  assessments  at 
the  European  level  to  identify  and  quantify  positive  spillovers  and 
externalities. 

10 

 
 
 
 
Figure 3 Overview recommendations on initial investment 

Prioritise  investments  in  the  national 
FAIR  implementation  roadmapResources 
available  for  implementing  FAIR  are 
limited.  On  the  one  hand,  organisations 
and  countries  cannot  afford  one-off 
investments  which 
focus  on  single 
disciplines  or  projects  and  excessive 
upfront investment in the hope of broad-
based  adoption.  On  the  other  hand,  our 
study  has  shown  that  the  cost  of  not 
having  FAIR  will  only  increase  if  no 
action is taken. As  such, prioritisation of 
activities 
across 
different dimensions is imperative. 

investments 

and 

Major  investments  have  already  been 
made  in  FAIR  data  infrastructures  both 
with national scope in individual member 
level.  The 
states  and  at 
investments already made and those still 
to  be  made  for  implementing  the  FAIR 
and 
concern 
principles 
developments in different areas: 

activities 

the  EU 

•  developing  and  agreeing  on 
data and metadata standards, 
•  developing  solutions  for  data 

management, 

•  persistent 
storage, 

identifiers 

and 

• 

raising  awareness,  changing 
culture and increasing skills.  

Some  activities,  such  as  describing 
standardised 
datasets  with  quality 
metadata  and  assigning  persistent 
identifiers  may  yield  a  seemingly  high 
initial  costs  because  they  require  an 
understanding  of  new  metadata  models, 
creating  mappings  and  transformations. 
However,  these  initial  costs  are  in  the 
medium  term  evened  out  and  paid  back 
by savings and positive externalities due 
to  more  easily  findable  and  accessible 
data (priority to F and A, I to follow) and 
consequently  more  reusable  data  (R). 
Hence,  these  activities  form  an  integral 
part of what we call the FAIR baseline. 
The FAIR baseline underpins the right to 
have access to FAIR data. 

It  is  clear  that  research  infrastructures 
and  technology  or  FAIR  data  availabity 

11 

 
 
 
alone will not deliver the expected value 
and  benefits  without  strong  culture  and 
capability development. Current research 
sharing 
data  management 
behaviours  and  practices  need 
to 
change.  Hence,  emphasis  must  be  put 
there.  

and 

data 

Once  findability  and  access  are  put  in 
place, implementing data interoperability 
is  the  next  step.  Data  interoperability  is 
a  very  broad  area.  For  some  research 
disciplines, 
interoperability 
standards  and  services  have  already 
been  developed  such  as  in  the  cases  of 
health  and  geospatial  information.  In 
those 
interoperability-related 
FAIR  implementation  costs  will  be  lower 
because  of  efficiencies  achieved  via 
reuse  and  prior  work.  In  other  cases, 
however, 
interoperability 
agreements need to be defined. In those 
cases, 
interoperability  costs  will  be 
significantly higher.  

common 

cases, 

FAIR 

input 

In practice, the investment priorities will 
have  to  be  decided  after  consultation 
with  the  main  stakeholders  of  the  FAIR 
ecosystem.  Their  needs  with  regards  to 
FAIR  implementation  must  be  collected 
and  assessed  based  on  feasibility  and 
return  on  investment.  The  national  or 
organisation-specific 
business 
cases,  discussed  previously,  can  also 
provide 
the  prioritisation 
exercise.  It  is,  in  our  view  one  of  the 
EOSC  governance 
to 
monitor 
exercises 
prioritisation 
nationally,  and  coordinate  those  within 
the 
and  across  disciplines  and  at 
European  level.  For  this,  we  recommend 
the  establishment  of  a  working  group 
under  EOSC  mandated  to  to  decide  on 
FAIR 
investement  priorities,  evaluate 
current  progress  and  prepare  future 
development roadmaps.  

responsibilities 

to 

cross-discipline 

Cloud-based  models  should  also  be 
explored  for  implementing  national  and 
data 
European 
infrastructures for sharing and federating 
services.  Private,  public  or  hybrid  cloud 
models  are  all  possible  and  can 
the  high  data 
significantly 

reduce 

storage  and  computing  costs,  and  the 
total cost of ownership. The cost of cloud 
computing  has  been  steadily  decreasing 
the  last  years.  At  the  same  time  they 
can  drive  time  and  cost  efficiencies  in 
data  access,  sharing  and  collaboration. 
Data preservation costs and costs due to 
(partial)  data  loss  are  also  expected  to 
be  driven  down  as  result  of  shared 
cross-discipline 
research 
data infrastructures. 

cloud-based 

Figure 4 Cost reduction via Emerging Technologies 

and 
for 

as 
robotic 
automating 

We  also  advise  the  use  of  emerging 
artificial 
such 
technologies, 
process 
intelligence 
automation, 
and 
industrialising  repetitive,  standardised 
and  time-consuming  activities,  such  as 
data  transformation,  data  classification 
or  assignment  of  identifiers.  This  will 
to  significant  operational  cost 
lead 
savings for research organisations.  

FAIR. 

The need for prioritisation applies also to 
the selection of datasets to be published 
as 
performing 
Research 
organisations  own  hundreds  of  datasets 
which  could  fit  the  selection  criteria  for 
being  published  as  FAIR.  Some  of  this 
data  dates  years  ago,  and  is  stored  in 
legacy  formats  and  proprietary  systems. 
Is it worth going for a big bang or a one-
for 
size-fits  all  approach  that  opts 
publishing 
data 
simultaneously?  And  even  more,  do  all 
datasets have the same reuse and value 
potential?  

(legacy) 

all 

Experience from implementation of other 
data-related  policies, 
the 
Revised  PSI  directive,  say  that  even  in 
open  data  by  default  scenarios,  data 
supply  should  be  supply-driven  and 

such  as 

12 

 
 
targeted.  A  market  segmentation  study 
and  a  market  analysis  can  help  identify 
high-value  datasets  and  prioritise  their 
provisioning  as  FAIR  data.  A  line  should 
be  drawn  between  data  published  as  of 
today,  where  FAIR  by  default  must  be 
the  guiding  principles,  and  legacy  data 
which  must  be  published  as  FAIR  on  a 
demand-driven  basis.  We  believe  that 
the  sooner  in  the  research  lifecycle  data 
is  made  FAIR  compliant,  the  more  cost-
efficient it is.  

underlying 

A  possible  way  of  prioritising  FAIR 
publication of legacy data is to start with 
data 
access 
publications.  Such  a  market  study  will 
also  help  identify  and  select  machine-
readable  formats  and  data  provision 
paradigms,  e.g.  data  as  a  service,  to  be 
first.
implemented 

open 

that 

means 

Following such a “think big, start  smart” 
approach 
FAIR 
implementation  activities  have  to  be 
be 
in 
organised 
To 
iteration 
representative, 
can 
engage  at 
two  disciplines  or 
research  organisations  and  should  cover 
a  minimum  set  of  characteristics  from 
each  of  the  FAIR  principles  to  gradually 
start developing the FAIR baseline.  

iterations. 
each 

least 

is  also  an 
Iterative  development 
opportunity 
to  move  disciplines  or 
countries  from  the  long-tail  of  silence  to 
the  adopters  space.  Hence,  we  propose 
to  engage  in  the  iterations  at  least  one 
discipline  or  country  which  has  not  yet 
started  their  FAIR  implementation  or  is 
significantly 
lagging  behind.  Working 
with  countries  or  disciplines  that  are 
more 
FAIR 
their 
trajectory  will  help 
implementation 
late  comers, 
achieve  buy-in  of  the 
encourage 
from 
lessons  learnt,  actionable  advice  and 
reusable  outcomes  of  others.  This  is 
expected 
initial 
implementation  costs  for  late  comers.

them  and  benefit 

advanced 

reduce 

the 

to 

in 

Our recommendations 

Rec. 5 

Rec. 6 

Rec. 7 

Rec. 8 

Build  a  solid  FAIR  baseline  across  Europe  by  prioritising  high-impact  and 
high-feasibility  activities  to  maximise  ROI.  Start  with  activities  related  to 
Findability  and  Accessibility,  such  as  common  data  management  policies 
and  practices,  metadata  standards,  persistent  identifiers  and  common 
research data infrastructures. 

ROI  will  come  only  if  the  current  working  behaviours  around  data 
management  and  sharing  change.  Invest  early  enough  in  culture  change 
and skills development. 

Establish a working group under EOSC which will be mandated to decide on 
FAIR  investement  priorities,  evaluate  current  progress  and  prepare  future 
development roadmaps. 

Move  towards  shared  national  and  European  cross-discipline  cloud-based 
data  infrastructures  which  can  significantly  reduce  the  data  storage  and 
compute  costs,  and  can  drive  time  and  cost  efficiencies  in  data  access, 
sharing and collaboration. 

Rec. 9 

Use emerging technology, such as artificial intelligence and robotic process 
automation  for  automating  and  industrialising  repetitive,  standardised  and 
time-consuming  activities,  such  as  data  transformation,  data  classification 

13 

 
 
 
or  assignment  of  identifiers,  to  reduce  operational  costs  linked  to  FAIR 
implementation 

Rec. 10  Working  in  iterations  and  increasing  the  maturity  of  FAIR  research  data 
implementations in well-defind cycles helps align investments with progress 
towards achieving the policy objectives. 

Rec. 11  Engage in the iterations at least one discipline or country which has not yet 
started  their  FAIR  implementation  or  is  lagging  significantly  behind  to 
achieve  buy,  encourage  them  and  share  with  them  lessons  learnt, 
actionable advice and reusable outcomes of others 

Rec. 12  Opt  for  demand-driven  provisioning  of  FAIR  data,  within  and  across 
research disciplines, to optimise investment expenditure and maximise ROI.  
Sustainable growth in the maturity of the FAIR implementations as well as 
in the number of FAIR data available will lead to network effects. 

Rec. 13  Provide financial incentives, such as grants and funding, for making legacy 

data FAIR on a demand-driven basis. 

14 

 
 
 
 
Measure  progress  and 
FAIR implementation 

impact  of 

to 

The  execution  of  national  and  European 
FAIR  implementation  roadmaps  should 
be  managed  as  programmes.  This  calls 
for  the  establishment  of  programme 
management  office  capabilities  and  the 
development of monitoring and progress 
assess 
evaluation  mechanisms 
investments  and  spending  compared  to 
the  level  of  achievement  of  the  FAIR 
policy  objectives.  In  the  previous  group 
of  recommendations,  we  have  already 
referred to the need for a working group, 
part  of  EOSC, 
  which  has  to  be 
established  and  mandated  to  prioritise 
investement, 
activities, 
monitor  progress,  bring  together  the 
stakeholders in a vibrant community and 
assess  the  provision  of  trusted  and 
quality  services  by  FAIR  research  data 
infrastructures.  

coordinate 

is 

to 

illustrate 

required.  Such 

comparability,  a 

To  ensure 
single 
monitoring  and  progress  evaluation 
a 
mechanism 
mechanism  should  be  complemented  by 
a  maturity  model 
the 
implementation  progress.  The  maturity 
model  will  define  the  activities  required 
to achieve a specific level, the associated 
costs and the expected benefits. Member 
research  data 
states  as  well  as 
infrastructures  and  research  performing 
organisation  will  need  to  be  assisted 
through  expertise  and  financial  means, 
such  as  grants,  with  progressing  from 
one maturity level to next. 

At  the  same  time,  to  have  a  European 
standard  service-level  baseline  for  FAIR 
data implementations, the working group 
should create templates for  service-level 
trusted  FAIR 
agreements  by  which 
research  data 
infrastructures  should 
abide.

Our recommendations 

Rec. 14  Endorse  and  provide  financial  support  to  a  working  group  under  EOSC 
which coordinates and monitors FAIR implementation at the European level 
to  ensure  the  alignment  between  investments  and  spending  compared  to 
the level of achievement of the FAIR policy objectives. 

Rec. 15  Create  a  European  mechanism  for  measuring  progress,  for  example  based 

on earned value management. 

Rec. 16  Create  a  European  FAIR  implementation  maturity  model  which  will  define 
the  activities  required  to  achieve  a  specific  level,  the  associated  costs  and 
the expected benefits. 

Rec. 17  Provide  the  expertise  and  financial  assistance  for  helping  countries  apply 
the maturity level and making the transition from one level to the next one. 

Rec. 18  Define  templates  for  service-level  agreements  with  which  trusted  FAIR 
research  data  infrastructures  will  need  to  comply,  for  establishing  a 
European baseline for service quality. 

 
 
 
 
 
 
 
Share  and  reuse  knowledge  and 
across 
solutions  within 
countries 
and 
mutualise resources  

and  disciplines, 

and 

Several  countries  have  already  made 
significant  investements  in  FAIR  data 
implementations.  In parallel, the revised 
PSI  directive,  GDPR, 
INSPIRE,  CEF 
Telecom  and  other  data-related  policies 
have  all  required  organisations  to  make 
investments 
data 
related 
management  and  data  infrastructures, 
e.g. open data catalogues, (secure) data 
persistent 
infrastructures, 
exchange 
identifier 
semantic 
and 
services 
standards. Active communities have also 
been  established,  which  are  creating 
knowledge, 
learnt  and  good 
practices  based  on  their  implementation 
challenges and experiences.  

lessons 

to 

in 

the 

Significant  savings  and  efficiencies  can 
be  achieved 
total  cost  of 
ownership of FAIR data impementation if 
assets, 
and 
solutions, 
knowledge  are  shared  and 
reused 
between  the  FAIR  data  community  and 
data-intensive  policy  areas,  for  example 
between  PSI,  INSPIRE  or  CEF  Telecom 
and FAIR.  

services 

assets, 

between 

practices 

Likewise, cost savings and efficiencies in 
time  and  effort  can  be  achieved  by 
sharing  and  reusing  solutions,  services, 
and 
technical 
experiences 
FAIR 
implementation  communities  active  in 
different  countries  and/or  disciplines, 
especially  between  the  pioneers  and  the 
long  tail  of  research.  For  example, 
federating 
sharing 
commodity services such as storage and 
compute can lead to economies of scale.  

services 

and 

European 
Framework18;  

Interoperability 

– 

the 

that 

open 

•  Developing  reusable  open  source 
software 
the  CEF 
following 
building  blocks  paradigm  and 
business  model.  It  should  not  be 
FAIR 
forgotten 
implementation 
is  by  default 
required  to  reuse  –  wherever 
possible 
source 
software19.  Creating  synergies 
FAIR 
between 
national 
the 
implementation 
roadmaps  and 
the  agenda  and  priorities  of  the 
European open source community 
can  lead  in  the  long  term  to 
signficant  savings  through  reuse. 
Hence,  policy  makers  need  to 
foresee 
for 
example  via  the  provision  of 
or 
grants 
customising  FAIR-compliant  open 
source  software  solutions  in  the 
different countries or disciplines. 

financial  support, 

implementing 

for 

• 

cloud-based 
Implementing 
platforms and solutions which can 
be  used  across  countries  and 
disciplines  for  example  in  the 
form  of  shared  services.  Such 
services  can  also  be  a  source  of 
income  for  FAIR  research  data 
infrastructures;  

•  Facilitating 

cross-country 

and 
cross-discipline 
knowledge 
sharing  and  collaboration  by 
making  use  of  digital  platforms 
and  community  building  events

This  requires  investments  and  financial 
support, in the form of grants or funding, 
for: 

•  Defining  a  common  interoperable 
and 
reference 
reference  components  for  FAIR 
research  data 
infrastructures, 
based  on  the  principles  of  the 

architecture 

18 https://ec.europa.eu/isa2/eif  
19 https://www.crcpress.com/Data-Stewardship-
for-Open-Science-Implementing-FAIR-
Principles/Mons/p/book/9780815348184  

 
 
                                                
We  recommend  referring  to  the  Sharing  and  Reuse  Framework  for  further  guidelines  in 
this area 21. 

Our recommendations 

Rec. 
19 

Rec. 
20 

Rec. 
21 

Rec. 
22 

Benefit  from  signficant  savings  in  effiencies  in  the  total  cost  of  ownership  of 
FAIR  data  implementation  by  reusing  solutions,  technical  assets,  practices  and 
experiences  between  FAIR  and  other  data-related  policy  implementation 
initiatives,  such  as  those  of  the  revised  PSI  directive,  GDPR,  INSPIRE  and  CEF 
Telecom. 

Mutualise FAIR implementation resources and investments across countries and 
disciplines  by  co-investing  in  common  frameworks,  solutions,  technical  assets 
and shared services. 

Explore  business  models  for  FAIR  research  data  infrastructures  and  services 
based  on  shared  service  provision,  e.g.  following  the  example  of  the  CEF 
building blocks20. 

Provide  financial  support  for  developing  and  customising  FAIR-compliant  open 
source software in collaboration with the European open source community, and 
multiplicate savings by sharing across the EU research community 

20 https://ec.europa.eu/cefdigital/wiki/display/CEFDIGITAL/CEF+Digital+Home  
21 https://joinup.ec.europa.eu/collection/sharing-and-reuse-it-solutions/sharing-and-reuse-framework-it-
solutions-pdf  

 
 
 
 
 
                                                
 
 
 
RECOMMENDATIONS FOR THE 
SUSTAINABILITY OF FAIR 
RESEARCH DATA 
IMPLEMENTATIONS IN EUROPE 

Explore mixed business models for 
FAIR research data infrastructures  

the 

FAIR 

research 

instrumental 

data 
Successful 
implementation  relies  heavily  on  reliable 
research  data  infrastructures.  Their  role 
is 
long-term 
in 
stewardship  and  preservation  of  FAIR 
research  data.  We  know  however  that 
good  data  stewardship,  data  provision 
and  preservation  is  costly  and  requires 
serious investments in infrastructure and 
operations.  At  the  same  time,  these 
critical pillars of the FAIR  ecosystem are 
in  an  environment  where 
operating 
research  budget  is  limited  and  other 
sources  of  income/revenue  are  hard  to 
find.  To  this  end,  the  development  of 
sustainable  business  models  for  FAIR 
research data infrastructures should be a 
top priority for all countries.  

Different  business  models 
for  FAIR 
research  data  infrastructures  can  be 
imagined  depending  on  the  type  of  the 
infrastructure. Looking at the future FAIR 
landscape,  we 
data 
infrastructures,  open  for  new  business 
models,  which  are  in  turn  federating 
infrastructures  and  scientific 
existing 

see, 

new 

operating  since  their  foundation  already 
following some business model, but may 
be  open  to  reinventing  themselves  and 
reviewing their business model.  

data 

in  the 

infrastructure. 

The  ultimate  objective  of  a  business 
model is to ensure the sustainability of a 
FAIR 
Hence, 
business models need to explore existing 
as  well  as  new  revenue  sources.  We 
long-term,  the 
believe  that, 
objective must be to diversify away from 
public  funding  as  the  sole  source  of 
income.  We  understand  that  for  an 
existing  data  infrastructure,  even  for 
those committed to review their business 
model,  change  is  gradual.  We  are  not 
talking  about  a  one-shot  transformation 
from  a  public  funding  business  model  to 
one  based  primarily  on  private  initiative 
and  revenues 
from  selling  data  or 
services. In this process, a research data 
infrastructure  can  start  with  business 
models that allow to recover opportunity 
costs  and  cover  operational  costs,  and 
with  the  current  financial,  commercial 
and 
towards 
legal  assistance  move 
profit-making business models. 

gradual 

business 

This 
model 
transformation  path  for  FAIR  data  is 
summarised in Figure 5. We are plotting 
the  business  models 
two-
dimensional  space,  looking  on  the  one 
hand,  at  the  volume  of  FAIR  data 
available,  and  on  the  other  hand  at  the 

in  a 

Figure 5 Maturity levels of business models combined with FAIR data prioritisation 

clouds.  The 

latter  ones  have  been 

increase in innovation potential. 

19 

 
 
of 

opportunity 

1.  FAIR  legacy  research  data:  We  are 
expecting  that  existing  or  new 
prioritise 
players 
initially  will 
and 
recovery 
operational  costs  focusing  at  the 
same time on making (on-demand) 
legacy  science  data  FAIR.  Specific 
funding  and  intervention  at  the 
European  level  will  be  required  for 
this,  especially  in  case  of  large-
scale  applications,  for  example  the 
legacy 
scenario  of  making  all 
Copernicus  data  FAIR.  The  aim  of 
this  first  step  is  to  create  the  FAIR 
baseline and a critical mass of FAIR 
data available. Data reuse will most 
likely  still  be  limited  within  the 
scope a particular discipline.  

2.  The  second  level  is  adding  the 
publication of new research data as 
FAIR,  even  by  default  (unless 
specific  privacy,  safety,  security, 
legal or  competition barriers  exist). 
All  other  parameters  remain  the 
same.  

3.  At 

this  point,  well-established 
research 
and 
organisations 
infrastructures  are  looking  at  new 
cross-discipline  business  models, 
with  high 
innovation  potential, 
which  can  generate  revenues  and 
profit (see later in this section). We 
are  still  however  at  a  level  where 
all players are science and research 
organisations.  

the 

with 

market 

4.  Industry  players  are  now  also 
and 
entering 
collaborating 
research 
organisations  common  projects  or 
develop 
their  own  value-added 
services  and  applications  reusing 
FAIR  research  data.  Profit-making 
opportunities  arise  in  this  scenario 
not  only  for  industry  but  also  for 
research  data  infrastructures  and 
research performing organisations 

Inspired  from  a  2017  OECD  report  on 
business models for sustainable research 
data  repositories22  and  a  previous  study 
of PwC for the  European Commission  on 
linked  open 
business  models 

for 

government  data23,  we  are  citing  below 
possible  business  model  archetypes, 
which  are  in  our  view  the  most  feasible. 
The  proposed  business  model  can  also 
be  implemented  in  combination  with 
each other.  

We  decided  not  to  refer  to  business 
models which are predominantly focused 
on  funding  via  public  research  grants, 
even  if  those  may  be  longer-term  and 
multi-year  in  nature.  We  believe  in  the 

Figure 6 Mixed business models for FAIR research data 

importance of continuing public funding  

that  FAIR 

(see  next  section),  as  FAIR  data  is  by 
default  a  common  good.  We  are  though 
convinced 
research  data 
infrastructures  should  be  encouraged 
and supported via fiscal incentives, such 
tax  breaks  or 
as 
deductions,  and  policy 
interventions, 
including  legislation,  to  explore  mixed 
business  models,  which  combine  a 
healthy  balance  between  public  funding 
and other revenue streams.  

funding, 

seed 

also 
business  models  will 
Mixed 
contribute to the distribution of the FAIR 
benefits  more  evenly  between  research 
performing  organisations  and  data 
the  extreme 
infrastructures.  Taking 
performing 
scenario, 
research 
the  benefits 
organisations  enjoy  all 
data 
because 
reaching 
management, 

of  more 

efficient 

wider 

22 https://www.oecd-
ilibrary.org/docserver/302b12bb-
en.pdf?expires=1527066558&id=id&accname 
=guest&checksum=727DFBA8383566F0EE7E4F26
DF6B1C7C  

https://joinup.ec.europa.eu/document/study-

23 
business-models-linked-open-government-data-
bm4logd  

20 

 
 
                                                
                                                
and 

outreach, 

dissemination 
new 
research  and  innovation,  whereas  data 
infrastructures  have  to  carry  the  vast 
can 
majority  of 
discourage  data 
from 
taking  up  their  instrumental  role  in  the 
FAIR ecosystem.  

costs.  This 
infrastructures 

the 

data 

FAIR 

We  are 
looking  primarily  at  cross-
institutional, cross-industry and/or cross-
research 
country 
infrastructures  and  federations,  in  order 
to avoid fragmentation, the development 
of  institutional  silos,  and  the  duplication 
of  initial  investments  and  operational 
costs.  

Such 

a 
normally 

We  believe  in  business  models  for  FAIR 
data  infrastructures  that  move  from 
charging  for  data  to  charging  for  data 
services. 
data-driven 
entails 
transformation 
monetising  data  assets,  integrating  data 
from  different  sources,  and  developing 
analytics to gain powerful insights, which 
will  underpin  the  development  of  new 
products  and  services.  In  this  spirit, 
some  examples  of  business  model 
archetypes follow:  

paid 

such 

FAIR 
as 

1.  Provide 
data 
services 
data 
data 
wrangling, 
transformation  based  on  FAIR 
data  standards,  provision  of 
identifiers,  data 
persistent 
documentation  or  data  export 
in  specific  FAIR  data  formats 
and  at  (near)  real-time.  Such 
services  can  be  provided  on  top 
of  the  traditional  data  storage 
services.  While  more  and  more 
research performing organisations 
will 
include  data  stewardship 
activities  in  their  budgets,  there 
will  always  be  organisations  who 
prefer  to  pay  for  creating  FAIR 
data.  

As  a  consequence,  FAIR  data 
in  a 
infrastructures  will  be 
position 
to  provide  additional 
cleansing  and  wrangling  services 
traditional 
on 

top  of 

their 

services. The research performing 
organisations  would  pay  for  the 
services used in a flexible manner 
such as when preparing a dataset 
for  publication  as  FAIR.  At  the 
same  time,  research  performing 
to 
organisations  who  prefer 
manage  their  data  themselves 
could  still  be 
for 
example if they contribute quality 
data  by  being  able  to  publish 
their data with no charge. 

rewarded, 

2.  Provide  paid  data  analytics 
services 
targeting  different 
customer  segments.  With  the 
right  capabilities,  FAIR  research 
might 
infrastructures 
data 
propose analytical services on the 
FAIR  data  they  are  hosting.  A 
FAIR  research  data  infrastructure 
can  be  seen  as  a  cloud-based 
data  lake  which  allows  data  to 
easily  be  accessed, 
integrated 
and  analysed.  Depending  on  the 
demand,  services  throughout  the 
continuum  may  be 
analytics 
considered, 
descriptive 
from 
analytics  and  data  reporting  to 
advanced  prescriptive  analytics 
and  Artificial  Intelligence.  Such 
research 
services  could  help 
performing 
and 
industry  to  identify  new  areas  of 
research, 
cross-
links 
discipline,  discover  new 
between  data  and  reveal  hidden 
or unknown patterns. 

organisations 

especially 

3.  Charge  big  industry  players 
for  access  to  data  or  obtain 
private  investments.  We  know 
from  experience  that  knowledge-
intensive 
industries  with  high 
R&D  budgets,  such  as  pharma, 
chemicals,  oil  and  gas  and 
professional  services  are  willing 
to  pay  for  services  that  provide 
quality  reusable  data.  Companies 
such  as  Thomson  Reuters  and 
Wolters  Kluwer  are  implementing 
in  fact  this  business  model  for 
other types  of data, which in raw 
formats is even available as open 

21 

 
 
from 

fiscal 

incentives 

data.  Private  companies  could 
receive 
for 
reusing  FAIR  data  and  creating 
it.  One 
additional  value 
example  could  be  to  be  partially 
or  completely  exempt  from  the 
tax on the revenue created as an 
outcome  of  the  reuse  of  FAIR 
thread, 
data.  On  a  different 
sponshorships  and 
investment 
from industry may also be subject 
to  tax  breaks  or  deductions. 
Revenue 
profit-making 
opportunities  exist  here  for  both 
research  data  infrastructures  and 
research organisations. 

and 

fees 

However,  we  would  not  advice  to 
charge  data  access 
to 
research 
performing 
organisations,  spin-offs,  start-
ups, scale-us or SMEs as this can 
be 
and  may 
negatively impact data reuse. 

disincentive 

4.  Provide 

a 

have 

FAIR 

data 
certification. FAIR research data 
infrastrastructures 
the 
know-how  and  the  means  to 
provide  validation  services  which 
can check FAIR conformance, and 
can  consequently  “certify”  the 
FAIRness  of  datasets  or  research 
performing 
organisations/projects.  The  cost 
charged  would  have 
to  be 
marginal,  otherwise  the  intention 
to  pay  among 
research 
performing  organisations  will  be 
very  low.  FAIR  certification  costs 
can  be  made  eligible  in  research 
grants.  

the 

have 

research 

5.  Act  as  a  training  provider. 
data 
FAIR 
infrastrastructures 
the 
know-how  and  the  means  to 
provide  training  services  on  FAIR 
and 
data 
stewardship 
other 
management 
other 
stakeholders, 
research  data  infrasturctures  and 
research 
performing 
organisations,  in  order  to  help 
them  with  capability  buidling  and 

to 
including 

upskilling of research staff. This is 
partly  the  business  model  of 
GoFAIR.  

6.  Provide  legal  assistance.  FAIR 
research  data  infrastrastructures 
the 
the  know-how  and 
have 
means to provide legal assistance 
to  research  projects  on  the  best 
licensing model to be selected for 
publishing the data produced in a 
specific  project  as  FAIR  data. 
Very  often  project  outcomes  are 
non-concluding 
in 
locked 
conversations 
legal 
technology 
departments 
research 
transfer 
and 
performing 
companies 
in 
research projects. In this context, 
paid  legal  assistance  provided  by 
data 
a 
infrastructure,  can  be  there  to 
serve  and  safeguard  the  interest 
FAIR 
the  priorities  of 
and 
implementation.  

of 
organisations 
collaborating 

research 

between 

offices 

FAIR 

and 

For facilitating the implementation of any 
of the cited business models archetypes, 
we  are  putting  forward  a  framework 
which  is  based  on  the  Business  Model 
CanvasTM 24, a de-facto industry standard 
technique 
business 
models.  Hence,  a  FAIR  research  data 
infrastructure  would  have  to  respond  to 
the following questions:  

developing 

for 

1.  What  is the value proposition, 
namely  the  collection  of  value-
added  services  which  a  FAIR 
infrastructures 
research 
services  can  provide 
its 
customers, and in fact what is the 
value 
each 
proposition 
customer segment.  

data 

for 

to 

2.  What  are  the  key  resources 
that are necessary to create value 
for  the  customer.  Such  resources 
may 
datasets 
available  via  the  infrastructure, 
policies, 
data  management 

include 

FAIR 

24 
model-canvas  

https://strategyzer.com/canvas/business-

22 

 
 
                                                
and 

persistent  identifier  management 
the  underlying  data 
policies, 
storage 
provision 
persistent 
infrastructure, 
identifier  services,  and  last  but 
not  least  the  human  resources 
required  skills  and 
with 
competences  for  performing  the 
activities (non-exhaustive list).  

the 

a 

3.  Who  are  the  key  partners, 
namely  those  organisations  who 
either  on  voluntarily  basis  or 
under  some  formal  cooperative 
in 
agreement  will  collaborate 
order  to  create  value  for  the 
customer.  This  can  include  other 
data 
research 
FAIR 
infrastructures 
in  other 
(e.g. 
countries),  sofware  or  platform 
providers, 
funders, 
research 
standards  organisations,  learning 
organisations 
research 
community associations. 

and 

4.  Which  are  the  key  activities 
that are necessary to create value 
for  the  customer.  Such  activities 
may include data stewardship and 
service 
management, 
management, communication and 
promotion,  knowledge  sharing, 
training  and  customer  support 
(non-exhaustive list).  

the 

representation 

5.  What  is  the  cost  structure, 
in 
namely 
money of all the means employed 
in  the  business  model.  The  cost-
benefit  mechanism  for  FAIR  data 
developed  in  this  study  can  be 
used  as  a  basis  for  quantifying 
and calculating such costs. 

to 

6.  Which are the target customer 
segments.  Data  infrastructures 
will  need 
identify  market 
segments  and  assess  their  value 
potential  (who  is  willing/going  to 
pay  for  what).  This  will  enable 
them to identify the main revenue 
streams.  We  encourage  FAIR 
research  data  infrastructures  to 
look  beyond  research  performing 
organisations  and  connect  with 
industry  as  potential  buyer  of 

experience 

FAIR  data  and/or  services.  We 
know 
that 
from 
industries, 
knowledge-intensive 
such  as  pharma,  chemicals,  oil 
and gas and professional services 
are  willing  to  pay  for  services 
providing quality reusable data.  

of 

an 

for 

7.  Which  are  revenue  streams 
through  which  the  FAIR  research 
data  infrastructure  will  make  its 
income.  FAIR  data  has  to  be 
provided 
free  by  default, 
therefore,  we  can  see  different 
pricing  policies,  which  can  be 
applied  either  across  all  datasets 
available via a FAIR research data 
infrastructure  or  even  at  the 
granularity 
individual 
dataset. The selection of the right 
pricing 
on 
different  aspects,  including  the 
way  data  creation  was  funded, 
e.g. through public and/or private 
funds,  existing  use  terms  and 
conditions,  IP  or  pattents,  data 
management 
services 
available  which  provide  extra 
value  on  top  of  the  data  etc. 
These  pricing  policies,  which  may 
apply  to  data  and/or  to  services 
provided  by  the  FAIR  research 
data infrastructure, include:  

depends 

costs, 

policy 

o  Profit  maximisation, 
price 

a 

i.e. 
setting 
to 
maximise  the  profit  either 
in  the  short  run  or  in  the 
run  and  creating 
long 
data/or 
via 
revenue 
service  usage  fees.  On-
platfom  advertising  may 
provide 
additional 
revenues.  FAIR  research 
data  infrastructures  that 
want 
their 
to  decrease 
public 
dependency 
to 
likely 
funding 
explore such policies. 

are 

on 

o  Cost  recovery,  i.e.  setting 
a  price  equal  to  average 
long  run  operational  costs 
earlier 
(see 
and 
under 

activities) 

examples 

23 

 
 
creating  revenue  via  data 
fees. 
or  service  usage 
FAIR 
data 
research 
infrastructures that rely on 
public funding are likely to 
explore  such  models.  On-
platform  advertising  may 
also  be 
for 
covering operational costs. 

explored 

In 

data 

o  Marginal  (or  zero)  costs, 
i.e. setting a price equal to 
the  short  run  marginal 
cost  of  providing  FAIR 
this 
research  data. 
case, we are talking about 
FAIR  and  open  research 
data  and  we  are  refering 
research  data 
to  FAIR 
infrastructures 
which 
resemble  (or  may  be) 
catalogues. 
open 
Such  FAIR  research  data 
infrastructures  will  most 
likely  rely  solely  on  public 
or 
donations 
funding, 
advertising.  Here, 
the 
regulator  will  need  to  find 
the  right  balance  between 
regulations and incentives: 
the  regulation  will  help  to 
reach  a  critical  mass  of 
FAIR  data  available  while 
the  incentives  will  support 
value creation. 

already, 

indicated 

these 
As 
pricing policies, in particular profit 
maximisation  and  cost  recovery, 
may still combine different pricing 

models,  from  free  of  charge  for 
bulk  data  to  premium  for  quality 
data  available  at  real-time.  The 
pricing  options  may  also  be 
different  from  on-demand  fees  to 
monthtly  or  annual  subscription 
fees.  

servicing 
this  case, 

8.  Which  are  channels  that  the 
FAIR  research  data  infrastructure 
its 
for 
use 
will 
customers.  In 
the 
primary  channel  would  be  via 
Web  API  which  allows  for  data 
discovery,  access  and  retrieval. 
However,  depending  on 
the 
selected  business  model,  one 
that  a 
could  also 
customer 
be 
provided,  allowing  customers  to 
manage  services,  subscriptions, 
relationships  with  each  other 
(networking 
community 
and 
building)  or  published  datasets 
(for FAIR data providers).  

imagine 

portal 

could 

9.  What 

to 

of 

type 

customer 
relationships  a  FAIR  research 
data 
infrastructure  establishes 
with its customers, spanning from 
automated 
self-service 
and 
personalised 
services 
for 
premium 
assistance 
customers 
the 
to 
through 
animation  of  user  communities 
that 
exchange 
promote 
knowledge 
innovations  through  co-creation 
the  platform. 
of  services  on 

collaborate, 
and 

24 

 
 
 
 
Figure 7 Overview recommendations on Business continuity 

Our recommendations 

Rec. 23 

Rec. 24 

Rec. 25 

For the sustainability of FAIR research data implementation, research data 
infrastructures and research performing organisations must shift the focus 
towards data monetisation and value-added data services. 

Several alternatives exist for FAIR data and data services pricing models, 
from  profit  maximisation  and  cost  recovery  through  to  charging  only  for 
marginal costs (hence coming closer to the open data paradigm). Several 
parameters  have  to  be  considered  for  the  selection  of  the  right  one, 
including the way data creation was funded, applicable IP or pattents, data 
management costs, and value added. 

FAIR research data infrastructures must be encouraged and supported via 
fiscal  incentives  and  policy  interventions  to  explore  mixed  business 
models,  which  combine  a  healthy  balance  between  public  funding  and 
other  revenue  streams.Mixed  business  models  will  also  contribute  to  the 
distribution of the FAIR benefits more evenly between research performing 
organisations and data infrastructures. 

Rec. 26 

Fiscal incentives, e.g. tax breaks or deductions, will encourage industry to 
form  partnerships,  collaborate,  sponsor,  fund  or  buy  data/services  from 
FAIR research data infrastructures, to broaden the market for FAIR data. 

25 

 
 
 
 
Figure 8: Business Canvas ModelTM FAIR research data infrastructure 

Channels 

Web API 
Customer portals 

Key partners 

Research data 
infrastructure, 
Platform providers 
Research funders,… 

Key resources 

FAIR datasets, 
Management policies, 
Infrastructure, … 

Value proposition 

Key activities 

Data stewardship & 
management 
Service management 
Communication, … 

New services that FAIR 
research data 
infrastructure services 
can provide 

Cost structure 

Based on the results of the 
Cost-Benefit analysis 

Revenue streams 

Pricing policies that take 
into account profit 
maximisation, cost recovery 
& marginal costs. These can 
be uniform for all datasets 
or specific per individual 
dataset 

Customer segments 

Research performing organisations 
Industries such as pharma, chemicals, oil & gas 

Customer relationships 

Self-service & automated services 
Personalised assistance for premium customers 
User communities 
Knowledge exchange platforms 

 
 
 
 
 
 
 
 
 
public 

Secure 
for 
implementing  and  sustaining  FAIR 
research data implementation 

funding 

As  discussed  earlier,  in  our  view,  the 
sustainability  of  FAIR  research  data 
implementations  should  in  the  future 
move  towards  mixed  business  models, 
combining  public 
funding  with  other 
revenue  streams.  That  said,  we  do  not 
anticipate  in  the  short  or  medium  term 
cases  where 
research  data 
FAIR 
implementations will not be funded, even 
as  a  small  fraction  of  their  total  budget, 
via public funding.  

This  means,  on  the  one  hand,  that 
research  funding  for  FAIR  data  should 
continue  being  available  not  only  at  the 
European  level,  for  example  as  part  of 
Horizon  2020  and  Horizon  Europe,  but 
also as part of national publically funded 
research  and  innovation  programmes. 
On  the  other  hand,  it  implies  that  FAIR-
related  costs,  such  as  data  stewardship 
and  management,  or  data  infrastructure 
operational  costs  need  to  be  made 
funding.
eligible 

public 

for 

Costs  must  be  reported  unbundled  and 
clearly  indicate  the  cost  category,  e.g. 
data  management,  storage  or  training, 
to  allow  for  transparent  cost  accounting 
and  avoid  misuse  of  public  money.  For 
example,  market-dominant 
internet 
players, who could also act as privately-
funded  research  data 
infrastructures, 
could  claim  back  storage  costs,  thus 
indirectly 
competitive 
services.  Such  phenomena  must  not  be 
allowed,  and  hence  storage  costs  must 
be  non-eligible 
type  of 
for 
organisations.  

subsidising 

this 

policies  may 

be 
FAIR-by-default 
explored,  and  FAIR  compliance  can  be 
included in the award criteria of research 
grants, 
the 
data 
requirement 
management  plan.  As  a  by-product, 
research  funders  will  also  have  the 
to  monitor  more  easily 
opportunity 
duplicate grants. 

building 
for 

further 
having 

on 
a 

Finally, research data infrastructures and 
research  performing  organisations  could 
be encouraged or incentivised to reinvest 
savings  made  due  to  FAIR,  e.g.  from 
reducing  data  management,  storage  or 
the 
research  duplication 
sustainability  of  FAIR  implementations. 
For  example,  a  formula  can  be  devised 
where the government is matching twice 
every euro reinvested, i.e. if a university 
reivests  1000  euro  it  will  receive  an 
additional  2000  euro  from  the  research 
funder.

costs, 

in 

Our recommendations 

Rec. 27 

Funding  FAIR  data implemenation  has  to  continue  being  available  not  only 
at the European level, e.g. as part of Horizon 2020 and Horizon Europe, but 
also  as  part  of  the  national  research  and  innovation  programmes.  FAIR 
applies to all publicly funded research in Europe. 

Rec. 28 

FAIR-related  costs,  e.g.  for  data  stewardship  and  management,  or  data 
infrastructure operational costs must be made eligible for specific cases and 
only  if  repored  at  a  granular  level  respecting  transparent  cost  accounting 
practices. 

Rec. 29  Culture  change  related  costs,  including  training  and  awareness  raising 
activities,  must  be  made  eligible  in  research  grants  based  on  transparent 

 
 
 
cost accounting practices. 

Rec. 30 

FAIR-by-default  policies  and  mandatory  FAIR  compliance  must  be  included 
in the award criteria of research grants. 

Develop  a  community  and  an 
ecosystem around FAIR data  

At  various  points 
this 
document,  we  have  emphasised  the 

throughout 

Figure 9 The FAIR research data ecosystem 

importance  of building a community and 
ecosystem  around  FAIR  data.  Such  a 
community  should  be  broad  and  open 
to  engage  and 
involve  all 
enough 
relevant 
including 
stakeholders, 
technology  providers,  research-intensive 
industries, 
performing 
research 
infrastructures, 
data 
organisations, 
research  funders,  standardisation  bodies 
and public institutions.  

The  support  of  an  FAIR  data  community 
of practice in Europe will greatly help the 
implementation  of  the  FAIR  principles 
and its sustainability. Such a community 
plays an orchestrating role in: 

• 

strengthening the link between all 
the stakeholders;  
•  organising  mutual 

learning 
exercises, in the context of which 
member  states,  third  countries, 

FAIR 

cloud, 

FAIR  data  practitioners  and 
experts  work  together  on  a  topic 
of common interest, such as FAIR 
data  management  on  the  open 
science 
data 
management  in  AI  applications, 
or    the  costs  and  revenues  for 
preparing a data infrastructure for 
joining  the  open  science  cloud 
using the cost benefit mechanism 
developed by the current study; 
•  raising  awareness  for  the  FAIR 
principles,  implementations  and 
progress;  
•  exchanging 

knowledge 

and 

sharing solutions and assets;  
•  developing  skills  and  capabilities 
related  to  FAIR  data  stewardship 
management; 

•  managing 

equilibrium 
data. 

supply-demand 
the 
for  FAIR  research 

from 

At  the  same  time,  we  believe  that  the 
links  between  FAIR  research  data  and 
open or legacy data from other domains, 
such  as  geo-spatial  data,  public  data, 
within  the  European  data  economy  have 
to  be  strengthened  as  well.  The  value 
potential 
cross-disciplinary 
applications  and  the  network  effects  can 
be  massive.  We  therefore  believe  that 
policy 
interventions,  measures  and 
means should be put in place in order to 
encourage  such  cross-disciplinary  data 
innovation  projects  and  applications. 
Such  means  may  include  promoting  the 
use  of  FAIR  research  data  in  projects 
funded  under  the  current  focus  areas  of 
European 
research 
programmes,  such  as  Horizon  2020,  or 
think  of  a  new  innovation  stream  with  a 
focus  on  research,  life  sciences  and/or 
SMEs  to  be  included  under  under  future 
research 
European 
programmes, such as Horizon Europe.  

national 

national 

and 

and 

Animating  an  active  community  of 
practice is neither trivial nor cheap.  It is 

28 

 
 
 
however  of 
importance. 
fundamental 
Therefore, the financial means should be 
made available for the governance of the 

Our recommendations 

community, 
the  community  building 
activities  and  the  community  platforms 
physical.
both 

digital 

and 

Rec. 32 

Rec. 33 

Rec. 34 

financial  support 

Provide 
for  communication,  knowledge  sharing, 
community building and marketing projects and activities for example via 
continuing  with  coordination  and  support  actions  through  European  and 
national research programmes. Full costs to be made eligible for all types 
of participants. 

Provide  financial  support  for  organising  mutual  learning  exercises,  in  the 
context  of  which  member  states,  third  countries,  FAIR  data  practitioners 
and  experts  work  together  on  a  topic  of  common  interest,  such  as  FAIR 
data  management  on  the  open  science  cloud,  FAIR  data  management  in 
AI  applications,  or    the  costs  and  revenues  for  preparing  a  data 
infrastructure  for  joining  the  open  science  cloud  using  the  cost  benefit 
mechanism developed by the current study. 

Take measures and make the means available for encouraging innovation 
through  cross-disciplinary  projects  and  applications.  Such  means  may 
include promoting the use of FAIR research data in projects funded under 
the  current  focus  areas  of  existing  European  and  national  research 
programmes, or defining new innovation stream with a focus on research, 
life  sciences  and/or  SMEs  under  future  European  and  national  research 
programmes, such as Horizon Europe. 

Rec. 35 

Consider  the  establishment  of  a  public-private  partnership  focusing  on 
creating societal and economic value from FAIR data. 

Rec. 36 

Place  universities  at  the  heart  of  the  European  FAIR  data  community  of 
practice.  They  are  the  most  important  type  of  research  performing 
organisations and are the ones also preparing the workforce of tomorrow 
who  will  be  able  to  support  the  implementation  of  FAIR  principles  in 
Europe.  To  this  end,  support  and  incentives  should  be  provided  to  them 
for reviewing their curricula and current data management practices in the 
light of FAIR. 

29 

 
 
 
 
 
 
CONCLUSION 

Despite  the  significant  annual  cost  of  not  having  FAIR  research  data,  which  has  been 
estimated  by  our  previous  study  at  EUR  10,2  bn,  many  research  performing 
organisations and infrastructures are still reluctant to apply the FAIR principles and share 
their datasets because of real or perceived costs, mostly related to time investment and 
money. At the same time, countries and organisations who have already invested in the 
implementation  of  the  FAIR  principles  are  now  raising  questions  regarding  the 
sustainability of those investments. 

This report formulated 36 policy recommendations on cost-effective funding and business 
models  to  make  FAIR  data  sustainable.  The  recommendations  will  help  the  Commission 
and funders in the Member States and in third countries provide pragmatic solutions for 
the optimising the costs and the ensuring the sustainable financing of the EOSC. 

These  recommendations  put  forward  a  step-wise  approach  towards  sustainable  FAIR 
research  data  implementations  in  Europe,  which  allows  policy  makers  and  research 
funders to set up short and long-term actions. The first key milestone in this approach is 
the establishment of a FAIR baseline in Europe. The FAIR baseline gives priority to data 
findability and accessibility, and recognises interoperability as the catalyst for improving 
those, and for increasing further FAIR research data use and reuse. 

The development of the FAIR baseline requires in our view to answer first the “what’s in 
it for me question” posed by research funders, research data infrastructures and research 
performing  organisations.  Identifying  and  quantifying  the  costs  and  the  benefits,  and 
finding  ways  of  funding  the  initial  costs  for  achieving  the  FAIR  baseline  is  key  for 
unlocking investments. Hence, we proposed ways for working out the business cases for 
FAIR  data  at  the  national  level,  and  use  this  as  the  basis  for  prioritising  investments 
following a “think big, start smart” approach. In this process, cross-disciplinary use cases 
have to be given priority, as this is where most of the benefits will be realised and where 
innovation will be driven. 

We  also  identified  a  strong  need  for  governace  and  monitoring  of  progress,  looking  at 
benefits vis-à-vis investments, which should be established as part of EOSC. The role of 
this governance structure would also expand to community building and the nurturing of 
an  ecosystem  around  FAIR  data.  This  ecosystem  will  facilitate  sharing  of  solutions, 
software,  services,  knowlewde  and  good  practices  and  would  bring  the  different 
stakeholders  closer,  hence  creating  network  effects,  positive  externalities  and  fueling 
innovation. The importance of this governance  structure emphasises the need to secure 
the necessary funding and support. 

Once  the  FAIR  baseline  has  been  achieved,  the  sustainability  of  FAIR  research  data 
investments and implementations becomes a key priority for funders and policy makers. 
Public  funding  will  remain  one  of  the  sources  of  income,  as  FAIR  data  is  a  public  good. 
However,  FAIR  data  in  combination  with  advances  in  big  data  analytics,  AI  and  Cloud 
computing  open  new  horizons  to  research  data  infrastructures,  who  can  now  start 
thinking  about  data  monetisation  and  value-added  data  services.  This  way  mixed 
business  models  like  the  ones  discussed  earlier  will  start  emerging,  and  alternative 
pricing  models  pricing  models,  from  profit  maximisation  and  cost  recovery  through  to 
charging  only  for  marginal  costs  (hence  coming  closer  to  the  open  data  paradigm),  will 
have  to  be  explored  and  evaluated.  Intervention  from  the  Commission  and  national 
funders, in the form of fiscal incentives, such as seed funding, tax deductions, and policy 
interventions, including legislation, can encourage and accelerate the shift towards mixed 
business models. 

30 

 
 
Getting in touch with the EU 

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non-commercial purposes. 

 
 
 
 
 
 
 
 
 
 
 
 
 
FAIR research data encompasses the way to create, store and 
publish research data in a way that they are findable, accessible, 
interoperable and reusable. In order to be FAIR, research data 
published should meet certain criteria described by the FAIR 
principles. Despite this, many research performing organisations 
and infrastructures are still reluctant to apply the FAIR principles 
and share their datasets due to real or perceived costs, including 
time investment and money. To answer such concerns, this report 
formulates 36 policy recommendations on cost-effective funding 
and business models to make the model of FAIR data sustainable. 
It provides evidence to decision makers on setting up short and 
long-term actions pertinent to the practical implementation of FAIR 
principles. 

Studies and reports