Realising the European open science cloud : first report and recommendations of the Commission high level expert group on the European open science cloud.
Date: 2016-10-10
Institutions: Directorate-General for Research and Innovation
Subjects: exchange of information, innovation, report, research policy, research results, scientific cooperation, scientific research, single market

Realising 
the European 
Open Science Cloud

First report and recommendations
of the Commission High Level Expert Group
on the European Open Science Cloud

DATA SHARING 

OPEN SERVICES

LINKING DATA

CONNECTING SCIENTISTS

SUSTAINABLE

Research and 
Innovation 

CONNECTING DISCIPLINES

BETTER SCIENCE

EUROPEAN COMMISSION

Directorate-General for Research and Innovation
Directorate A – Policy Development and Coordination
Unit A6 – Data, Open Access and Foresight

Contact: Wainer Lusoli

E-mail:  Wainer.LUSOLI@ec.europa.eu 

RTD-EOSC@ec.europa.eu 
RTD-PUBLICATIONS@ec.europa.eu

European Commission
B-1049 Brussels

EUROPEAN COMMISSION

Realising the European  
Open Science Cloud

First report and recommendations  
of the Commission High Level Expert Group  
on the European Open Science Cloud

Drafted by the Commission High Level Expert Group on the European Open Science Cloud

Members : Paul Ayris, Jean-Yves Berthou, Rachel Bruce (Rapporteur), Stefanie 
Lindstaedt, Anna Monreale, Barend Mons (Chair), Yasuhiro Murayama (Observer, Japan), 
Caj Södergård, Klaus Tochtermann, Ross Wilkinson (Observer, Australia).

The Commission High Level Expert Group on the European Open Science Cloud operates in full autonomy and 
transparency. The views and recommendations in this report are those of the Expert Group members acting 
in their personal capacities and do not necessarily represent the opinions of the European Commission or any 
other body; nor do they commit the Commission to implement them.

2016

Directorate-General for Research and Innovation

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Luxembourg: Publications Office of the European Union, 2016

PDF

ISBN 978-92-79-61762-1

doi:10.2777/940154

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©  European Union, 2016
Reproduction is authorised provided the source is acknowledged.

Table of contents 

FOREWORD BY COMMISSIONER CARLOS MOEDAS ............................................................ 4 

PREFACE BY BAREND MONS, CHAIR OF THE HLEG-EOSC ................................................... 5 

EXECUTIVE SUMMARY .................................................................................................... 6 

CHALLENGES AND GENERAL OBSERVATIONS ................................................................... 6 

KEY  FACTORS  FOR  THE  EFFECTIVE  DEVELOPMENT  OF  THE  EOSC  AS  PART  OF  OPEN 

SCIENCE ...................................................................................................................... 7 

SPECIFIC RECOMMENDATIONS TO THE COMMISSION FOR A PREPARATORY PHASE .............. 7 

Policy recommendations ......................................................................................... 7 

Governance recommendations ................................................................................. 7 

Implementation recommendations ........................................................................... 7 

THE EUROPEAN OPEN SCIENCE CLOUD? SOME NUANCES AND DEFINITIONS ....................... 8 

THE EUROPEAN OPEN SCIENCE CLOUD IN THE CONTEXT OF OPEN SCIENCE ........................ 8 

KEY TRENDS OF OPEN SCIENCE AND THEIR RELEVANCE FOR THE EOSC ............................ 10 

DATA EXPERTISE IS LACKING IN THE EU ....................................................................... 12 

HOW WILL THE EUROPEAN OPEN  SCIENCE  CLOUD  BE  REALISED? POLICY,  GOVERNANCE, 

FIRST PHASE IMPLEMENTATION AND GUIDING PRINCIPLES ............................................. 12 

RECOMMENDATIONS OF THE HIGH LEVEL EXPERT GROUP ............................................... 13 

Policy recommendations ....................................................................................... 13 

Governance recommendations ............................................................................... 14 

Implementation recommendations ......................................................................... 14 

GLOSSARY OF TERMS .................................................................................................. 19 

 
 
 
 
 
 
FOREWORD BY COMMISSIONER CARLOS MOEDAS 

I am delighted to preface the first report of the Commission High Level 
Expert Group European Open Science Cloud. Upon set-up of the group, 
back  in  September  2015,  I  tasked  its  members  to  explore  what  was 
then only an embryonic policy idea: the European Open Science Cloud, 
a  future  infrastructure  to  support  Open  Research  Data  and  Open 
Science in Europe. Since then, the Commission made the idea a vision 
for  the  future  of  open  science,  in  the  context  of  the  Digital  Single 
Market. 

Open  Science  is  indeed  one  of  my  priorities.  It  is  a  move  towards 
better science, to get more value out of our investment in science and 
to  make  research  more  reproducible  and  transparent.  The  scientific 
craft has changed beyond recognition in the last decade. Long gone is the era of the lonely scientist 
in  the  lab.  Recent  advances  such  as  the  discovery  of  the  Higgs  boson  and  gravitational  waves, 
decoding of complex genetic schemas, climate change models, all required thousands of scientists 
to collaborate on ideas and, crucially, on data. And that implies that research data are findable and 
accessible and that they are interoperable and re-usable. In essence, this is what the Open Science 
Cloud  is  about:  an  open  and  trusted  environment  where  research  data  can  be  safely  stored  and 
made openly available.  

It  is  my  clear  belief  that  scientific  data  paid  for  by  taxpayers  for  the  benefit  of  society  should  be 
fully  accessible  today  to  all  European  scientists  and  beyond.  There is  an increasing  risk  that  data 
generated  by  publicly  funded  research  is  not  safeguarded  for  future  or  is  locked  away  in  a  form 
that  cannot  be  reused,  or  that  excellent  scientific  data  infrastructures  are  disconnected  across 
fields and regions. 

I  asked  the  High  Level  Expert  Group  to  provide  advice  on  the  governance  and  the  funding  of  an 
Open Science Cloud and to be bold in their recommendations. Among other recommendations, they 
propose a deep rethinking of the way scientific data is funded and a sea change of the professional 
careers  of  what  they  call  'core  data  scientists'.  Such  recommendations  deserve  detailed 
consideration  by  the  scientific  community  and  other  stakeholders.  At  the  same  time,  we  need  to 
advance in making Open Science a reality. This is why, for example, the Commission has already 
decided  to  make  scientific  data  generated  in  Horizon  2020  open  by  default.  The  members  of  the 
High  Level  Expert  Group  also  advocate  that  the  Commission  should  lead  by  example  and  help 
coordinate  the  many  data-sharing  efforts  and  scientific  data  infrastructures  spanning  scientific 
disciplines and Member States.  

The  European  Commission  'European  Cloud  initiative',  issued  in  April  2016,  set  a  very  ambitious 
vision  for  the  European  Open  Science  Cloud;  it  drew  a  clear  roadmap  and  set  concrete 
commitments for the Commission to make this vision a reality by 2020. The continued work of the 
experts  has  inspired  many  of  these  commitments,  and  I  hope  they  will  help us  further  with  their 
implementation.  I  am  resolved  to  make  the  European  Open  Science  Cloud  a  reality  for  European 
scientists and their global partners. It will put Europe at the forefront of Open Science and improve 
the excellence and impact of our research. 

4 

 
 
 
 
PREFACE BY BAREND MONS, CHAIR OF THE HLEG-EOSC 

This  report  aims  to  lay  out  a  living  guide  for  the  realisation  of  the 
European Open Science Cloud (EOSC). The High Level Expert Group, with 
ten  members  from  European  countries,  Japan  and  Australia,  discussed 
extensively  in  several  meetings,  conferences,  policy  events  and  met  with 
key  stakeholders  (30  November  2015)  and  research  funders  (15  March 
2016).  Based  on  these  consultations,  on  many  'white  papers'  and  on  a 
range  of  presentations  and  feed-back  at  international  meetings,  we  are 
confident  that  our  recommendations  count  on  a  high-level  of  consensus 
amongst  all  stakeholders.  This  was  a  solid  basis  to  embark  on  this 
challenging  journey  with  the  Commission,  the  Member  States  and 

International partners in concert. 

The challenge is clear to us: if we do not act, there might be a looming crisis on the horizon. The 
vast majority of all data in the world (in fact up to 90%) has been generated in the last two years. 
Computers have long surpassed individuals in their ability to perform pattern recognition over large 
data  sets.  Scientific  data  is  in  dire  need  of  openness,  better  handling,  careful  management, 
machine  actionability  and sheer  re-use.  One  of  the  sobering  conclusions  of  our  consultations  was 
that research infrastructure and communication appear to be stuck in the 20th century paradigm of 
data scarcity. We should see this step-change in science as an enormous opportunity and not as a 
threat. The EOSC is a positive 'Cloud on the Horizon' to be realised by 2020. Ultimately, actionable 
knowledge and translation of its benefits to society will be handled by humans in the 'machine era' 
for decades to come, machines are just made to serve us. 

But  let's  not  ignore  the  facts:  the  science  system  is  in  landslide  transition  from  data-sparse  to 
data-saturated.  Meanwhile,  scholarly  communication,  data  management  methodologies,  reward 
systems and training curricula do not adapt quickly enough if at all to this revolution. Researchers, 
funders  and  publishers  (I  always  thought  that  meant  making  things  public)  keep  each  other 
hostage in a deadly embrace by continuing to conduct, publish, fund and judge science in the same 
way as in the past century. 

So far, no-one seems to be able to break this deadlock. Open Access articles are indispensable but 
solve only a fraction of the problem. Neither 'open research data' alone will do. We still try to press 
petabytes  of  results  in  length-restricted  narrative,  effectively  burying  them  behind  firewalls  or  in 
'supplementary  data  behind  decaying  hyperlinks  and  then  trying  to  mine  them  back  again. 
Computers  hate  ambiguous  human  language  and  love  structured,  machine  actionable  data,  while 
machine readable data are a turnoff for the human mind. As computers have become indispensable 
research  assistants,  we  better  make  what  we  publish  understandable  to  them.  We  need  both  in 
concert to form social machines; in order to do pattern recognition in complex, interlinked data as 
well  as  confirmational  studies  on  methodology  and  rhetorics  in  plain  understandable  human 
language. 

We hope that this report will be part of a game-changing effort of all European Member States and 
our international partners towards true Open Science. 

It has been an enormous pleasure to work with the members of the HLEG, with great support from 
Commission staff; I also wish to acknowledge the continuous and open discussions and advice from 
colleagues from the United States. 

5 

 
 
 
 
 
 
 
 
EXECUTIVE SUMMARY 

The European Open Science Cloud (EOSC) aims to accelerate and support the current transition to 
more  effective  Open  Science  and  Open  Innovation  in  the  Digital  Single  Market.  It  should  enable 
trusted  access  to  services,  systems  and  the  re-use  of  shared  scientific  data  across  disciplinary, 
social  and  geographical  borders.  The  term  cloud  is  understood  by  the  High  level  Expert  Group 
(HLEG)  as  a  metaphor  to  help  convey  both  seamlessness  and  the  idea  of  a  commons  based  on 
scientific  data.  This  report  approaches  the  EOSC  as  a  federated  environment  for  scientific  data 
sharing  and  re-use,  based  on  existing  and  emerging  elements  in  the  Member  States,  with  light-
weight  international  guidance  and  governance  and  a  large  degree  of  freedom  regarding  practical 
implementation.  The  EOSC  is  indeed  a  European  infrastructure,  but  it  should  be  globally 
interoperable and accessible. It includes the required human expertise, resources, standards, best 
practices as well as the underpinning technical infrastructures. An important aspect of the EOSC is 
systematic and professional data management and long-term stewardship of scientific data assets 
and services in Europe and globally. However, data stewardship is not a goal in itself and the final 
realm of the EOSC is the frontier of science and innovation in Europe. 

CHALLENGES AND GENERAL OBSERVATIONS 

  The majority of the challenges to reach a functional EOSC are social rather than technical. 

  The  major  technical  challenge  is  the  complexity  of  the  data  and  analytics  procedures  across 

disciplines rather than the size of the data per se. 

  There is an alarming shortage of data experts both globally and in the European Union. 

  This  is  partly  based  on  an  archaic  reward  and  funding  system  for  science  and  innovation, 

sustaining the article culture and preventing effective data publishing and re-use. 

  The lack of core intermediary expertise has created a chasm between e-infrastructure providers 

and scientific domain specialists. 

  Despite  the  success  of  the  European  Strategy  Forum  on  Research  Infrastructures  (ESFRI), 

fragmentation across domains still produces repetitive and isolated solutions. 

  The short and dispersed funding cycles of core research and e-infrastructures are not fit for the 

purpose of regulating and making effective use of global scientific data. 

  Ever  larger  distributed  data  sets  are  increasingly  immobile  (e.g.  for  sheer  size  and  privacy 
reasons) and centralised HPC alone is insufficient to support critically federated and distributed 
meta-analysis and learning. 

  Notwithstanding  the challenges, the components needed to create a first generation  EOSC are 
largely there but they are lost in fragmentation and spread over 28 Member States and across 
different communities. 

  There  is  no  dedicated  and  mandated  effort  or  instrument  to  coordinate  EOSC-type  activities 

across Member States. 

6 

 
 
 
 
KEY FACTORS FOR THE EFFECTIVE DEVELOPMENT  
OF THE EOSC AS PART OF OPEN SCIENCE 

  New  modes  of  scholarly  communication  (with  emphasis  on  machine  actionability)  need  to  be 

implemented. 

  Modern reward and recognition practices need to support data sharing and re-use. 

  Core data experts need to be trained and their career perspective significantly improved. 

 

Innovative,  fit  for  purpose  funding  schemes  are  needed  to  support  sustainable  underpinning 
infrastructures and core resources. 

  A real stimulus of multi-disciplinary collaboration requires specific measures in terms of review, 

funding and infrastructure. 

  The transition from scientific insights towards innovation needs a dedicated support policy. 

  The  EOSC  needs  to  be  developed  as  a  data  infrastructure  commons,  that  is  an  eco-system  of 

infrastructures. 

  Where  possible,  the  EOSC  should  enable  automation  of  data  processing  and  thus  machine 

actionability is key. 

  Lightweight but internationally effective guiding governance should be developed. 

  Key performance indicators should be developed for the EOSC. 

SPECIFIC RECOMMENDATIONS TO THE COMMISSION  
FOR A PREPARATORY PHASE 

Policy recommendations 

  P1: Take immediate, affirmative action on the EOSC in close concert with Member States. 

  P2: Close discussions about the 'perceived need'. 

  P3: Build on existing capacity and expertise where possible. 

  P4:  Frame  the  EOSC  as  the  EU  contribution  to  an  Internet  of  FAIR  Data  and  Services 

underpinned with open protocols. 

Governance recommendations 

  G1: Aim at the lightest possible, internationally effective governance. 

  G2:  Guidance  only  where  guidance  is  due  (this  relates  to  technical  issues,  best  practices  and 

social change). 

  G3: Define Rules of Engagement for service provision in the EOSC. 

  G4: Federate the gems and amplify good practice. 

Implementation recommendations 

 

 

 

 

 

 

 

 

I1: Turn the HLEG report into a high-level guide to scope and guide the EOSC initiative. 

I2: Develop, endorse and implement the Rules of Engagement for the EOSC. 

I2.1: Set initial guiding principles to kick-start the initiative as quickly as possible. 

I3: Fund a concerted effort to develop core data expertise in Europe. 

I4: Develop a concrete plan for the architecture of data interoperability of the EOSC. 

I5: Install an innovative guided funding scheme for the preparatory phase. 

I6: Make adequate data stewardship mandatory for all research proposals. 

I7:  Provide  a  clear  operational  timeline  to  deal  with  the  early  preparatory  phase  of  the  EOSC. 

7 

 
 
 
 
THE EUROPEAN OPEN SCIENCE CLOUD?  
SOME NUANCES AND DEFINITIONS 

Imagine a federated, globally accessible environment where researchers, innovators, companies 
and citizens can publish, find and re-use each other's data and tools for research, innovation and 
educational purposes. Imagine that this all operates under well-defined and trusted conditions, 
supported by a sustainable and just value for money model. This is the environment that must be 
fostered in Europe and beyond to ensure that European research and innovation contributes in full 
to knowledge creation, meet global challenges and fuel economic prosperity in Europe. This we 
believe encapsulates the concept of the European Open Science Cloud (EOSC), and indeed such a 
federated European endeavour might be expressed as the European contribution to an Internet of 
FAIR Data and services. 

The European Open Science Cloud is a supporting environment for Open Science and not an 'open 
Cloud' for science.  

The EOSC aims to accelerate the transition to more effective Open Science and Open Innovation in 
a Digital Single Market by removing the technical, legislative and human barriers to the re-use of 
research data and tools, and by supporting access to services, systems and the flow of data across 
disciplinary, social and geographical borders. The term European Open Science Cloud requires 
some reflection to dispel incorrect associations and clarify boundaries; in fact the term 'cloud' is a 
metaphor to help convey the idea of seamlessness and a commons. 

  European: research and innovation are global. The EOSC cannot be built exclusively in and for 
Europe.  Serious  efforts  are  needed  to  ensure  coordinated  action  with  other  regions.  Europe, 
being inherently federated, is in a strong position to lead this initiative  

  Open: the use of Open in relation to research has been widely discussed over recent years, and 
it is acknowledged that not all data and tools can  be  open. There are exceptions to openness, 
such  as  confidentially  and  privacy.  Open  is  also  often  confused  with  'for  free'.  Free  data  and 
services  do  not  exist1.  These  nuances  need  to  be  respected  and  intelligently  open  is  what  we 
mean,  often  referring  more  to  accessibility  under  proper  and  well  defined  conditions  for  all 
elements of the EOSC2 

  Science: the use of the term science explicitly includes the arts and humanities, and in fact no 
current  or  future  discipline  should  be  excluded  from  the  EOSC.  In  addition  the  Science  Cloud 
infrastructure should support not only innovative scientific research but also societal  innovation 
and  productivity,  which  takes  place  predominantly  in  collaboration  between  research  institutes 
and  the  private  sector.  The  EOSC  should  also  support  broad  societal  participation  in  Open 
Innovation and Open Science 

  Cloud: the term cloud can cause considerable confusion as it has many connotations. It can be 
misinterpreted to indicate that the EOSC is mostly about hard ICT infrastructure and much less 
about  a  commons  of  data,  software,  standards,  expertise  and  policy  related  to  data-driven 
science and innovation. 

THE EUROPEAN OPEN SCIENCE CLOUD IN THE CONTEXT  
OF OPEN SCIENCE 

The  EOSC  is  a  need  emerging  from  science  in  transition3.  The  EOSC  is  indeed  European,  but  it 
should  also  be  interoperable  with  the  Internet  of  FAIR  data  and  services  and  be  an  accessible 
infrastructure  for  modern  research  and  innovation.  It  includes  the  required  human  expertise, 
resources,  standards,  best  practices  and  underpinning  infrastructures.  It  will  have  to  support  the 
Finding,  Access,  Interoperation  and  in  particular  the  Re-use  of  open,  as  well  as  sensitive  and 
properly secured data. It will also have to support the data related elements (software, standards, 
protocols, workflows) that enable re-use and data driven knowledge discovery and innovation. An 
important  aspect  of  the  EOSC  is  therefore  professional  data  management  and  long  term  data 
stewardship. The latter aspect is presently lacking. 

1 Although scientists may perceive scientific data services that are at no cost to them to be free, and oppose 

commercial approaches even if they are demonstrably better then free alternatives. 

2 See for basic principles the UK report Science as an Open Enterprise, https://royalsociety.org/topics-

policy/projects/science-public-enterprise/report/  

3 The following points are all supported by a range of recent policy and position papers by stakeholders. These 

will be placed online alongside this report. 

8 

 
 
 
 
                                                 
Mostly due to current methods capture and data 
malpractice,  approximately  50%  of  all  research 
data  and  experiments 
is  considered  not 
reproducible,  and  the  vast  majority  (likely  over 
80%)  of  data  never  makes  it  to  a  trusted  and 
sustainable  repository.  At  an  investment  of 
Europe  in  data-generating  research  of  €120B 
between  2014-20204, 
capital 
destruction  is  consequently  very  substantial5. 
This does not take into consideration associated 
losses  from  inefficient  data  analysis  and  the 
economic  impact  of  stalling  innovation  and 
societal  non-applicability  of  knowledge.  Good 
data  stewardship  and  a  globally  operational 
Internet  of  FAIR  data  and  services  will 
significantly reduce these losses and fuel science 
and innovation. 

the  annual 

specific 

European 
cross-domain 

Europe  enjoys  a  long  tradition  and  a  relatively 
infrastructure,  served  via 
healthy  research 
Research 
domain 
Infrastructures  and 
ICT  e-
infrastructures, as well as other disciplinary and 
cross  disciplinary  collaborations  and  services. 
States 
Many 
provide 
infrastructures  and 
that  support 
initiatives 
research  and  data  access  and  use.  Although 
these  were  largely  built  in  the  earlier  phases  of 
the  data  revolution,  they  are  nevertheless 
important  foundations  for  the  EOSC  and  should 
be built upon. 

Member 

also 

However,  a  step  change  is  required  to  realise 
the  ambition  of  increased  seamless  access, 
reliable  re-use  of  data  and  in  fact  all  digital 
research  objects  and  collaboration  across 
different  services  and  infrastructures,  where 
data  access  and  re-use  is  open  to  all  actors 
across public and private spheres. This will mean 
a  new  way  of  working  through  deep,  equal 
partnerships  between  the  science  communities 
and  the  ICT  communities  so  that  the  EOSC  can 
optimally  benefit  from  all  expertise.  Science 
itself is in an unprecedented phase of transition, 
driven  by  the  power  of  rich  and  complex  data,  networked  digital  technology  and  its  ability  to 
underpin new approaches to research, knowledge management and innovation. As a consequence, 
practices, social  structures and  infrastructures that have gradually developed over  centuries, now 
need  to  undergo  a  step-transition.  Many  of  these  practices  are  rooted  deeply  in  the  scientific 
community and in the support structures of research and they appear to be quite resilient to this 
required  change.  A  fundamental  shift  nevertheless  is  needed  to  match  the  potential  to  generate 
ever increasing amounts of data and to turn these data into knowledge as renewable, sustainable 
fuel  for  innovation  in  turn  to  meet  global  challenges.  This  transition  was  marked  by  the  EC  as  a 
transition to Open Science. The EOSC is an environment that needs to be realised to underpin and 
enable this transition. 

4 Abbott A, Butler D, Gibney E, Schiermeier Q, Van Noorden R. Boon or burden: what has the EU ever done for 

science? Nature. 2016 Jun 15;534(7607):307-9. doi: 10.1038/534307a. 

5 a: 90% of world's data generated over last two years 

(http://www.sciencedaily.com/releases/2013/05/130522085217.htm)  
b:   US$28B/year (50%) spent on preclinical research is not reproducible: Freedman et al. 

http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002165  

c: Only 12% of NIH funded datasets are demonstrably deposited in recognised repositories: Read et al. 

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0132735  

9 

 
 
 
 
 
 
                                                 
KEY TRENDS OF OPEN SCIENCE AND THEIR RELEVANCE  
FOR THE EOSC 

New  modes  of  scholarly  communication:  scholarly  communication,  which  has  been  dominated  by 
narrative  and  verbal  means  of  delivery  for  centuries,  should  be  moving  rapidly  towards 
communication  and  re-use  formats  that  also  better  suit  our  main  research  assistants:  the  data 
generating machines and data processing machines. 

Modern  rewards  and  recognition:  assessment,  selection,  funding  and  reward  systems  in  research 
have to be urgently adapted and updated. The current systems, mainly based on the data-sparse 
and  narrative  ages,  strongly  bias  the  science  system  towards  narrative  publishing  and  new  -
publishable-  tool  generating  research.  The  current  system  provides  little  if  any  support  and 
incentive  for  data  publishing  and  for  tool  sharing  nor  for  the  development  and  reward  of  data 
related  expertise;  data  stewardship  and  (re-)  analysis  to  support  the  final  aim  of  science: 
knowledge discovery. 

Increasing  reliance  on  data  experts,  especially  in  academia  where  they  are  most  severely 
undervalued, a lack of data related core expertise may well be among the risks for Europe losing a 
leading  position  in  science.  New  forms  of  output  publication  for  data  and  software  are  emerging 
that need to be given credit in research assessment and as part of promotion decisions if we are to 
support the change towards open and data-driven science.  

Cross-disciplinary  collaboration:  cross-disciplinary  collaboration  is  critically  needed,  as  scientists 
increasingly  use  raw  and  curated  data  resources  and  analytics  tools  from  disciplines  other  than 
their  own.  However,  currently,  other  people's  data  is  notoriously  difficult  to  discover  even  within 
one's own discipline. Discovering relevant (other people's) data from other disciplines will be even 
more  difficult.  For  example  health  researchers  now  want  to  use  data  from  social  media  and  the 
'quantified  self'  .  But,  how  would  a  health  researcher  know  about  a  valuable  dataset  in  say,  the 
humanities,  when  terminology,  data  formats  and  meta-data  standards  are  completely  different? 
With  the  current  absence  of  proper  meta-data  standards  and  the  lack  of  data  and  tool  search 
engines  researchers  cannot  be  blamed  for  re-inventing  a  new  wheel.  This  is  further  amplified 
across  disciplines.  Use  of  text  and  data  mining  techniques  are  essential  for  the  EOSC,  research 
analysis  and  support  of  cross-disciplinary  use.  However  too  often  there  are  legal  barriers.  In 
Europe  this  is  currently  the  subject  of  discussion  in  the  Commission  and  it  is  likely  we  will  see  a 
pan-European exception for text and data mining in 2016.  

Fostering  transition  from  science  to  innovation:  Although  severely  sub-optimal,  knowledge 
discovery  nevertheless  has  reached  such  a  pace  that  the  translational  and  innovation  capacity  of 
society  has  difficulty  to  keep  pace.  Especially  in  Europe,  where  the  support  for  one  of  the  most 
innovative elements in society; SMEs, is relatively weak. Multidisciplinary research and innovation 
projects  and  public-private  consortia  are  supported  on  paper  in  more  policy-papers  than  we  can 
possibly  read,  but  in  actual  practice  the  European  financing  and  review  climate  is  severely 
hampering the actual flourishing of these crucial partnerships. 

A complex eco-system of infrastructures: it may seem counter-intuitive but the challenges of ever 
bigger data can no longer be solved only by ever bigger infrastructure. Next to advanced computer 
science,  which  will  bring  innovative  computing  and  storage  and  new  advanced  algorithms  for 
knowledge  extraction  from  data,  we  need  fundamentally  to  rethink  infrastructure  as  we  know  it. 
With  the  growth  of  data  in  more  and  more  disciplines  outpacing  the  increase  of  transfer  speed, 
many comprehensive datasets are simply too big to move efficiently from one location to another. 
Moreover,  data  are  in  many  cases  so  privacy  sensitive  that  legislation  effectively  precludes  their 
moving  outside  the  environment  in  which  they  have  been  collected  .  Therefore,  relatively 
lightweight  workflows  (e.g.  process  virtual  machines)  containing  parallel  and distributed  analytics 
algorithms  increasingly  visit  data  where  they  reside,  with  supporting  reference  data  and 
transporting  only  conclusions  outside  the  safe  data  vault.  This  is  an  early  instantiation  of  an 
Internet  of  data  and  services  where  containers  with  software  applications  are  routed  to  relevant 
data  and  vice  versa.  This  approach  will  unleash  enormous  distributed  analytics  power,  but  there 
are intellectual challenges to address and the hardware containing the data must have tailored and 
them.  Centralised 
appropriate  high 
supercomputing locations that  are  crucial  for solving  high  capacity  HPC scientific challenges alone 
will not adequately support this irreversible trend. Complementary infrastructures are needed. 

throughput  compute  (HTC)  capacity  connected 

to 

Machine  understanding:  the  size  and  complexity  of  many  data  sets  is  such  that  only  powerful 
computers can process them and reveal patterns that may lead to actionable knowledge extraction 
by  and  for  human  users.  Machines  have  become  essential  research  assistants,  both  for  data 
generation,  data  processing  and  analytics.  Data  formatting,  terminology/identifier  mappings  and 
provenance must therefore be optimally organised in order to support machine processing as well 
as the human-mind knowledge extraction. However, the tools supporting these two processes are 

10 

 
 
 
 
 
 
 
fundamentally  different,  pattern  recognition  tools  being  mainly  for  machines  and  tools  for 
confirmational  reading  and  interpretation  being  mainly  for  humans.  Machine  actionability  of 
whatever is published is therefore a crucial consideration in modern narrative and data publishing. 

Research  integrity:  there  is  an  alarming  lack  of  reproducibility  of  current  published  research, 
together  with  scientific  fraud,  this  causes  enormous  damage  to  the  reputation  of  science.  This  is 
partly due to the  lack of deep  and  rigorous knowledge on how to render data and  the associated 
methodology and tools in a format that allows others to reproduce results. An important nuance is 
that  not  all  conclusions  in  the  literature  for  which  the  results  cannot  be  easily  reproduced 
elsewhere are wrong, but reproducibility and early detection of fraud-signals and optimal re-use of 
assets  will  increase  as  a  result  of  good  data  stewardship  and  core  data  processing  and  analysis 
expertise. 

A clash of cultures? 
A  'clash  of  cultures'  emerged  as  a  key  challenge6  from  the  stakeholder  consultation,  as 
historical  developments  may  have  led  to  a  chasm  between  domain  specialists  and  e-
infrastructure specialists. Open Science  drives two very different communities and cultures 
together and we lack connective tissue. In the earlier transition of scientific research from a 
largely individual, elite  and  intellectual  activity  to  a mainstream,  institute-based  activity,  a 
new  profession  emerged:  the  research  analyst  who  soon  became  indispensable  and  highly 
recognised research professionals, in academia as well as industry. They were co-publishing 
and  involved  in  all  aspects  of  the  research  and  throughout  the  experimental  cycle.  When 
data-generating  machines  became  mainstream  and  high-throughput  data  generation 
boomed, the experts who knew how to operate these data generating machines only gained 
in  importance.  However,  computer  and  data  specialists  –  new  key  actors  in  modern 
research  –  who  knew  how  to  operate  data  analytics  machines  were  not  given  the  same 
credit in the scientific research process as wet-lab analysts in the past. 
These  new  data  experts  come  from  scientific  and  engineering  cultures  with  very  different 
reward  systems  and  incentives7,  different  jargons  and  very  different  skill  sets.  These 
cultural  differences  resulted  in  understandable  but  unnecessary  mismatches  and  in  an 
alarming scarcity and loss of crucial data-related skills in research. This, in turn, has created 
a  divide  between  researchers  and  those  that  support  research  with  data  processing  and 
software. As a consequence, these two communities that are both essential to Open Science 
have not closely co-evolved. Often, frontline ICT development for science takes place rather 
independently  from  day  to  day  science  practice.  In  contrast  to  other  lab-equipment  or 
research  infrastructure,  scientists  frequently  misjudge  ICT  infrastructures  as  supporting 
infrastructure,  a  commodity  or  tool  that  can  be  relatively  easily  purpose-built;  they 
underestimate the complexity and the need for professionalism. The added value of working 
together is not obvious and it deserves more recognition. 

In  addition,  financial  support  for  data  generating  scientific  activity  and  support  for  the 
underpinning  research  infrastructure  have  traditionally  been  separated,  both  in  many 
Member  States  and  at  the  EC  level.  This  may  have  aggravated  a  separation  of  worlds,  a 
major  challenge  emerging  from  the  consultation  process,  as  it  has  undermined 
communication and collaboration between the top-tier ICT experts and top-tier experimental 
and other scientists. The few examples where these professionals have been able to bridge 
the  divide  and  have  effectively  collaborated  are  positive  exceptions  to  the  rule.  Most 
researchers are still struggling forward with severely suboptimal solutions, sometimes out of 
ignorance  of  what  is  available,  but  often  because  actual  collaboration  with  computer 
scientists and engineers takes time and is not easy. 

This complex state of affairs results in fundamental misunderstandings between the current 
main  stakeholders  in  ICT  and  e-infrastructure  providers  and  scientific  domain-users.  The 
complexity,  cost  and  intellectual  challenges  in  the  other  domain  are  systematically 
underestimated  and  undervalued  in  both  directions8.  This  hampers  the  user-driven  and 
expert co-development of core scientific data infrastructures, which is required so that Open 
Science can meet its full potential. 
Moreover, there are very few incentives in the reward system for user support and for the 
support  for  data  or  tool  re-use,  such  as  proper  documentation  of  code,  versioning  and 
scalability considerations.  

6  See online additional material at http://ec.europa.eu/research/openscience/index.cfm?pg=open-science-

cloud.  

7 For example the publication for IEEE conferences versus first or last author on papers in high impact journals 

(which is not the same as high impact papers, let alone high impact research)  

8 This is known as the Dunning-Kruger effect, https://nl.wikipedia.org/wiki/Dunning-krugereffect  

11 

 
 
 
 
 
 
                                                 
Scientists  expect  that  Open  Source,  project-funded  software  tools  will  stay  magically 
updated, online; they will continue to produce and consume new data types, even when the 
project  generating  them  is  long-gone.  Furthermore,  once  ICT  infrastructures  and  tools 
become  commodities  many  scientists  do  not  see  the  logic  of  co-authorship  of  the  data 
analysts on scientific publications and only acknowledge them for analysing the data. 
Finally,  the  divide  frequently  precludes  agile  co-development  with  continuous  power-user 
feedback and rigorous testing of prototypes is also precluded as domain-users do not always 
conceive  of  the  possibilities  the  latest  developments  in  ICT  enable.  This  is  contributing  to 
the  lack  of  data  scientists  that  venture  out  from  classical  computer  or  data  science 
departments into other scientific fields. 

DATA EXPERTISE IS LACKING IN THE EU 

As  a  side  effect  of  the  above,  there  is  an  alarming  shortage  of  data  expertise  in  the  EU  and  a 
pressing requirement with regards to the data expertise needed to support the aims of the EOSC is 
apparent.  It  became  clear-and  has  been  reflected  in  nearly  all  stakeholder  contributions  to  the 
HLEG-  that  there  is  a  major  hole  in  the  EOSC  planning  if  we  do  not  repair  the  significant  lack  of 
Core Data Experts. We use the term Core Data Experts here deliberately, emphasising that we are 
dealing  with  a  range  of  skills  that  warrant  the  definition  of  a  new  class  of  colleagues  with  core 
scientific  professional  competencies  and  the  communication  skills  to  fill  the  gap  between  the  two 
cultures. 

Core data experts are neither computer savvy research scientists - although the latter also need to 
be  educated  to  the  point  where  they  hire,  support  and  respect  Core  Data  Experts  -  nor  are  they 
hard-core  data  or  computer  scientists  or  software  engineers.  They  should  be  technical  data 
experts, though proficient enough in the content domain where they work to be routinely consulted 
in the research team  at  the very  beginning  (experimental design,  proposal writing) until the  very 
end of the data discovery cycle. They will work to secure that good data management plans are an 
essential  part  of  good  research  practice  (including  data  re-use  and  stewardship  planning  and 
proper  budgeting)  and  the  proper  capturing  of  new  data  capture  (formats,  metadata  richness, 
standards,  provenance,  publishing,  linking  and  analysis),  they  will  also  support  analysis.  This 
package of skills and expertise is rare and the few people with this skill set are often attracted to 
industry or outside Europe where they are more respected and valued.  

The  number  of  people  with  these  skills  needed  to  effectively  operate  the  EOSC  is,  we  estimate, 
likely  exceeding  half  a  million  within  a  decade.  As  we  further  argue  below,  we  believe  that  the 
implementation of the EOSC  needs to include  instruments to help train, retain and recognise  this 
expertise,  in  order  to  support  the  1.7  million  scientists  and  over  70  million  people  working  in 
innovation9. The success of the EOSC depends upon it.  

HOW WILL THE EUROPEAN OPEN SCIENCE CLOUD BE 
REALISED? POLICY, GOVERNANCE, FIRST PHASE 
IMPLEMENTATION AND GUIDING PRINCIPLES 

If  one  strong  consensus  arose  from  the  consultations,  policy  papers  and  debates  with  the 
stakeholders  it  is  that  the  EOSC  should  not  be  a  new  major,  localised  and  centrally  governed 
initiative. We believe that discussions and broad agreements on minimal standards and early rules 
of  engagement  were  by  default  a  first  step  in  the  realisation  of  the  EOSC  to  be  technically 
conceived as an Internet of FAIR data and Services.  

We notice a clear analogy here with the early days of the Internet. The creation of NSFNET, choice 
of the TCP/IP standard and the authorised development of Domain Names enabled the boom of the 
Internet in the 1990's, where the development of the HTTP and HTML drove its major application 
domain,  the  largely  textual  WWW.  This  combination  of  authorisation,  key  support  by  a  major 
leading agency (NSF) and a dedicated community (W3C) setting and enforcing minimal standards 
allowed virtually everyone to start building standard-compliant tools and services in the ecosystem. 
The  Internet  still  has  no  major  centralised  governance  in  either  technological  implementation  or 
policies  for  access  and  usage;  each  constituent  network  sets  its  own  policies.  Still,  the  early 
shaping of it and the openness of standards effectively prevented a situation where a few privately 
owned  companies  or  public  parties  could  entirely  dominate  and  monopolise  the  developing 

9 http://ec.europa.eu/eurostat/statistics-explained/index.php/R_%26_D_personnel 

12 

 
 
 
 
 
 
                                                 
Internet10.  Internet  standards  were  so  minimal  and  rigorous  that  even  after  25  years  of 
overwhelming growth and development, they are still essentially the same. Only recently was there 
a  move  to  IPV6  to  allow  a  much  broader  range  of  IP  addresses,  which  does  not  fundamentally 
change the structure. When XML and RDF developed in a first attempt to develop schema free and 
self-defining components in the internet, nothing fundamentally changed.  

We  will  need  to  stay  as  closely  to  the  lessons  learned  and  the  choices  made  for  the  hypertext 
applications on the Internet and the early stage semantic web as well as the early developments in 
the Internet of Things. The EOSC will succeed only if it will have low barriers to data and services 
interoperability and light-weight governance and financing structure. 

At the European policy- and organisational level the EOSC should take an approach similar to that 
of  the  successful  ESFRI  roadmap  where  a  preparatory  phase  is  followed  by  an  implementation 
phase.  However,  to  meet  the  step  change  and  ambition  of  the  EOSC  a  more  agile  approach  is 
required and so there are some key differences with the ESFRI approach. For instance we cannot 
afford a preparatory phase of many years, as the need of many disciplines for an early functional 
EOSC is very clear. It emerged  from  all position papers that most elements have been judged as 
being  there  but  hidden  in  fragmentation.  The  consultation  also  highlighted  many  good  practices, 
which the EOSC should work to amplify and build upon. 

We need to commence defragmentation actions immediately, including the setting up of light and 
appropriate  guidance  and  governance  structures  and  prototyping  as  well  as  implementation  for 
new  solutions  that  are  needed  during  the  preparatory  phase.  We  recognise  that  paths  to 
implementation  are  not  yet  crystal  clear  and  we  heard  from  various  stakeholders  that  in  some 
cases  the  tough  economic  climate  will  need  to  be  considered  as  well  as  the  diverse  practices, 
policies and funding for data stewardship exist across Member States and disciplines. A lightweight, 
agile and phased approach will  be critical  for rapid,  tangible progress. The recommendations that 
follow are mainly for this proposed preparatory phase. 

RECOMMENDATIONS OF THE HIGH LEVEL EXPERT GROUP 

Policy recommendations 

  P1:  Take  immediate,  affirmative  action  on  the  EOSC  in  close  concert  with  Member 
States. Our first and overarching recommendation is that in order for Europe to have a modern 
and  thriving  research  and  innovation  environment  it  is  essential  that  Member  States, 
internationally  collaborating  through  the  current  instruments,  take  immediate  and  solidly 
supported  affirmative  action  to  realise  the  first  phase  of  a  federated,  globally  accessible 
environment where researchers, innovators, companies and citizens can publish, find and re-use 
each  other's  data  and  tools  for  research,  innovation  and  educational  purposes  under  well 
defined,  secure    and  trusted  conditions,  supported  by  a  sustainable  and  just  and  value-for-
money model.  

  P2: Close discussions about the  'perceived need'. Although the EOSC ultimately will need 
to  involve  all  ESFRIs,  all  e-infrastructures,  private  industry  and  stakeholders  from  Member 
States and beyond, the preparatory phase needs not be long. It is true that often preparation is 
required  for  landscape  inventories  and  consensus  building,  as  was  the  case  for  most  of  the 
ESFRIs. However in the case of the EOSC there has been a long consultation phase and we have 
no lack of position papers from stakeholders (see online accompanying material to this report), 
and there is no lack of consensus on the extremely urgent need for it.  

  P3: Build on existing capacity and expertise where possible. It is clear from the position 
papers and other inputs that for an overwhelming majority the elements of the EOSC exist and 
are of high quality; the issue is that they are hidden in fragmentation. Of course this does not 
mean that there are no major challenges left, but these are mainly cultural and reasonably well 
defined.  As  well  as  about  infrastructure  and  services  for  open  science,  we  learnt  about  many 
examples  of  good  practice  in  terms  of  collaboration  and  policy  that  can  be  built  upon.  We 
therefore  believe  that  many  of  the  actions  we  propose  for  the  preparatory  phase  of  the  EOSC 
can be significantly progressed or even completed by the end of 2017.  

10 Hart, Strawn, A Brief History of NSF and the Internet, August 2003, 

https://www.nsf.gov/od/lpa/news/03/fsnsf_internet.htm and https://mitpress.mit.edu/books/inventing-
Internet. 

13 

 
 
 
 
                                                 
  P4: Frame the EOSC as the EU contribution to 
an  Internet  of  FAIR  Data  and  Services 
underpinned  with  open  protocols.  The  current 
internet  application  domain  has  avoided  the 
dominance  of  a  very  limited  number  of  private  or 
public  parties.  Its  'hourglass  model'  with  minimal, 
rigorous  standards  and  protocols  and  maximum 
freedom of implementation has major advantages. 
We strongly advise to follow a similar approach to 
implement  the  EOSC  which  is  based  on  minimal 
rigorous standards. It will allow open and common 
implementation  and  so  it  will  prevent  costly  and 
time  consuming  exercises  to  decide  who  has  the 
best  solutions.  Instead,  it  will  allow  participation  from  all  stakeholders,  including  research 
infrastructure  providers,  Member  States,  research  institutes  and  businesses.  All  providers, 
public and private, can start implementing prototype applications for the Internet of FAIR data 
and Services on the day minimal standards and the minimal rules of engagement are released. 

Governance recommendations 

  G1: Aim at the lightest possible, internationally effective governance. Given the urgency 
and  the  number  and  variety  of  stakeholders  and  participants  required  to  realise  the  EOSC,  a 
tightly governed, new infrastructure built 'somewhere' is not the right model for the EOSC to be 
a  success.  Instead  a  more  inclusive,  flexible,  transparent  and  less  centralised  approach  is 
required,  one  that  also  enables  effective  global  collaboration.  The  Commission  needs  to 
establish  a  lightweight,  sustainable  and  collaborative  governance  model  for  the  EOSC  for  all 
players to contribute.   

  G2:  Guidance  only  where  guidance  is  due.  While  we  advocate  lightweight  governance  we 
need  a  degree  of  regulation.  For  instance  the  harmonisation  of  the  current  'standards  jungle' 
needs  to  be  actively  coordinated.  With  no  regulation,  some  major  players,  public  and  private, 
may claim an unjust and counterproductive share in the EOSC. The EOSC will have a myriad of 
small and very large players, as is the case in the current internet, but it should be perceived by 
regulators  and  stockholders  alike  as  a  commons  where  citizens,  researchers  and  innovators 
need  to  use  each  other's  data  and  tools  in  a  trusted  affordable  and  sustainable  environment. 
Europe  should  take  a  lead  in  this  due  guidance  element  of  the  Internet  of  FAIR  Data  and 
Services.  

  G3:  Define  Rules  of  Engagement  for  service  provision  in  the  EOSC.  To  support  wide 
participation, innovation and sustainability the EOSC needs to be open to all players, public and 
private, European and non-European  and  the  development of  the  desired expert infrastructure 
will be guided and governed by a minimal set of rigorously applied and enforced protocols and 
developed  by  parties  that  endorse  so  called  Rules  of  Engagement  (RoE)  that  specify  the 
conditions  under  which  stakeholders  participate.  These  RoE  can  be  used  to  brand  providers  in 
the  EOSC  as  trustworthy  and  compliant  with  the  RoE,  comparable  to  Conformant  Cloud 
Providers in the USA11. It should be clear that  non-EOSC  approved players are free  to explore 
any role in the Open Science ecosystem they wish, even if they do not adhere to the RoE. They 
will just not be able to brand their services as EOSC approved/certified12.    

  G4:  Federate  the  gems  (and  amplify  good  practice).  Based  on  the  consensus  that  most 
foundational  building  blocks  of  the  Internet  of  FAIR  data  and  Services  are  operational 
somewhere, but that they operate in silos per domain, geographical region and funding scheme, 
we  recommend  that  early  and  strong  action  is  taken  to  federate  these  gems.  Optimal 
engagement is required of the e-infrastructure communities, the ESFRI communities and other 
disciplinary groups and institutes. Several of these cross-ESFRI building blocks begin to operate 
in individual Member States. Simultaneously, the wealth of small and large industrial players in 
Europe should be engaged. All partners and stakeholders that adhere to standards and sign off 
on the Rules of Engagement (RoE) should be eligible. 

Implementation recommendations 

  I1:  Turn  the  HLEG  report  into  a  high-level  guide  to  scope  and  guide  the  EOSC 
initiative.  This report can be the basis for  a formal  paper to  be  approved by the Commission 

11 https://grants.nih.gov/grants/guide/notice-files/NOT-LM-16-002.html  
12 Comparable to https://en.wikipedia.org/wiki/CE_marking. CE marking is the manufacturer's declaration that 
the product meets the requirements of the applicable EC directives.  

14 

 
                                                 
and  subsequently  endorsed  by  Member  States.  The  resulting  report  can  serve  as  a  high  level 
guiding  document  for  actual  developments  and  implementations  in  the  Member  States  and 
Horizon  2020,  as  well  as  a  discussion  basis  for  further  international  consensus  building  and 
collaboration.  

  I2:  Develop,  endorse  and  implement  the  Rules  of  Engagement  for  the  EOSC.  The 
Commission,  in  close  collaboration  with  appropriate  stakeholders  in  Member  States,  should 
develop as a matter of first priority, the Rules of Engagement for any player wanting to provide 
a component of the EOSC. RoE should be based on the assumption that all Data in the EOSC (in 
fact  all  Research  Objects)  are  FAIR  (Please  note:  the  FAIR  principles  do  not  enforce  specific 
implementation  choices  beyond  checking  them  on  rendering  the  research  objects  Findable, 
Accessible,  Interoperable and  Reusable).  We  recommend  that  the  HLEG-EOSC  should  urgently 
establish and guide a dedicated group to draft a proposal for the Rules of Engagement in 2016. 
As the EOSC develops, compliance with the RoE could be implemented as a seal of approval for 
web resources and services shared and provided on the EOSC. In call cases, we envisage that 
implementation  will  be  phased  and  that  there  might  be  immediate,  simple  RoE  for  the  short-
term 

then  medium-term  work 

sustainable 

long-term 

towards 

and 

take  up.  

  I2.1: Set initial guiding principles to kick-start the initiative as quickly as possible. We 

offer the following principles to guide the initial, preparatory phase: 

-  The EOSC will build on a community-based, lightweight sustainable governance  

-  The EOSC will support existing excellence wherever possible 

-  The EOSC will support both scientists and innovators and will be user driven  

-  While  collaboration  is  needed,  this  does  not  mean  that  scientists  and  innovators  dictate  to 
developers  what  to  build,  but  that  data  experts  and  engineers  contribute  their  respective 
knowledge 
developments  
a)  scientists  and  innovators  need  to  coordinate  to  speak  with  one  voice  to  provide  the 
developers  with 
needs  
b) scientists and innovators need to commit to agile development with regular feedback and 
user testing to avoid expensive solutions that are not fit for purpose  

consistent  message 

about  what 

expertise 

possible 

about 

clear 

agile 

their 

and 

and 

for 

is 

a 

-  A first cohort of core data experts should be trained immediately to translate the needs for 
data driven science into technical specifications to be discussed with hard-core data scientists 
and engineers. This new class of core data experts will also help translate back to the hard-
core scientists the technical opportunities and limitations 

-  Based  on  an  internet-type  hour-glass  model,  the  EOSC  will  need  community-endorsed, 

internationally governed and enforceable set of protocols.  

-  These  protocols  should  be  absolutely  minimal,  open  and  transparent  so  that  all  scientists, 
innovators,  engineers  and  service  providers  understand  them,  see  their  value  and  can 
adhere to them, even if technology and data formats rapidly develop (as will be the case)  
-  These  protocols should  be  tuned  down  to the  very basics of what data and related services 
need, what they support at the most  basic level and  only where strictly necessary to make 
the EOSC work (comparable to TCP/IP, HTTP and HTML for the Internet)  

-  These protocols should count for all Research Objects13  and they should enable the minimal 

requirements for Research Objects to be widely and effectively (re)-used  

-  The  FAIR  principles14    will  guide  implementations  to  make  research  objects  Findable, 

Accessible, Interoperable, ultimately to make them Re-usable and citable15  

-  Within the scope of FAIR principles, the standards and protocols should again be restricted to 
the absolute minimum, to mitigate the risk that future developments will require adaptations 
of protocols  

-  The complexity of the current data-sharing practices and mechanisms requires gentle rather 
than  restrictive  regulation  of  existing  ontologies,  especially  across  domains,  with  identifier 
mappings as practiced already in various communities  

-  The  EOSC  should  distinguish  domain  specific  standards  and  protocols  (e.g.  Preferred 
Persistent Identifiers in a discipline) and protocols for concepts and data formats that are of 
general utility  

13 See glossary 
14 See footnote 11 
15 https://www.force11.org/group/joint-declaration-data-citation-principles-final 

15 

 
 
                                                 
a) Domain specific protocols should emerge from  community best practices and we endorse 
a  lead  role  for  the  existing  and  future  ESFRIs,  ERICs  and  other  disciplinary  research 
community federations 

b) Generic protocols should be the responsibility of international scientific organisations both 
formal  and  informal  (e.g.  ORCID  for  researcher  PIDs),  ICT  standards  of  e-infrastructure 
communities, legislators and industrial producers of hardware, software and governments 
(e.g. visualisation and analytics procedures, generic standards pertaining to software and 
hardware, single sign-on, authentication, authorisation and protection) 

-  The RoE should guide the participation of compliant developers and service providers and of 
other  stakeholders.  The  authorisation  mechanism  for  providers  may  be  based  on  self-
reporting  practices  already  used  in  the  European  Union  and  on  some  relevant  international 
schemes 

-  Like protocols, RoE will have to be open, transparent, co-developed with and acceptable by 
user and provider communities and strict enough to prevent undesired and unacceptable use 
such  as  abuse  of  data,  scientific  malpractice,  unfair  pricing,  vendor  lock-in,  monopolisation 
and unjust exclusion of users  

-  While RoE should be generic, they should focus on specific categories of stakeholders in the 

EOSC-ecosystem as necessary 

  I3: Fund a concerted effort to develop core data expertise in Europe. We recommend a 
very  substantial  training  initiative  in  Europe  so  as  to  locate,  create,  maintain  and  sustain  the 
required core data expertise. This should be a community-based effort led by the major training 
stakeholders and consortia in the ESFRIs, e-Infrastructures and beyond, such as in national and 
international  training  consortia.  The  aim  of  this  training  and  education  effort  should  be 
ambitious:  

-  By 2022, to train hundreds of  thousands of certified core data experts with a demonstrable 
long-term 

effect  on  ESFRI/e-INFRA  activities  and  collaboration  and  prospects 
sustainability of this critical human resource  

for 

-  Consolidate and further develop assisting material and tools for the construction and review 
of  Data  Management  Plans  (including  budgeting  for  re-use  of  data)  and  Data  Stewardship 
plans (including budgeting for data publication and long-term preservation in FAIR status)   

-  By 2020, to have in each Member State and for each discipline at least one certified institute 

to support implementation of Data Stewardship per discipline 

  I4:  Develop  a  concrete  plan  for  the  architecture  of  data  interoperability  of  the  EOSC. 
The EC and the relevant constituencies in Member States should have a guiding and governance 
role  in  the  appointment  of  implementation  bodies  and  in  the  oversight  and  monitoring  of  RoE 
compliance.  We  recommend  the  following  concrete  actions  for  the  EC  in  concert  with  the 
relevant constituencies in the Member States. 

-  Delegate the setting of standards and protocols for domain specific issues to existing national 
and international constituencies, including ESFRIs, scientific associations/academies; in their 
absence,  physical  or  functional,  stimulate  their  rapid  development  via  a  Roadmap  or  other 
suitable guiding instrument  

-  Actively stimulate and support  multiple  ESFRI-type communities in  the same  broad  domain 
to  collaborate  on  these  issues  and  collectively  set  a  minimal  set  of  norms  for  a  Preferred 
Persistent Identifier (PPID) scheme in their domain as well as mappings to other PID  

-  Stimulate cross domain collaboration at ESFRI level for more generic semantic types such as 
people,  organisations  and  geographical  locations.  In  modern  science  and  innovation, 
research by other researchers are key; we also recommend that the EOSC governance take 
a  stand  on  the  federation  of  social  networking  scientific  applications  and  try  to  engage 
dedicated  person-oriented  applications  such  as  Google  Scholar,  ResearchGate  and 
Academia.edu via the RoE  

-  Specify  the  role  of  the  Research  Data  Alliance  (RDA)16.  While  many  RDA  working  groups 
address protocols and other  instruments for  scientific collaboration based on data, stronger 
coordination  and  collaboration  with  ESFRIs,  e-Infrastructures  and  national  infrastructures  is 
needed 

16 Obviously, each organisation before getting a formal or informal mandate will need to sign off on the RoE 

meaning that we need these as a very first deliverable. 

16 

 
 
 
 
 
                                                 
-  Alongside  minimal  and  rigorous  protocols,  safeguard  maximum  freedom  in  the  design  of 

protocol-compliant templates, tools, datasets and services  

  I5:  Install  an 

innovative  guided  funding  scheme  for  the  preparatory  phase 
To  stimulate  the  required  change  and  innovation  and  deliver  an  EOSC  by  2020,  all  measures 
proposed should not follow traditional and rigid  funding schemes of the past for scientific data 
management - e.g. as a small, unaccounted part of a time-limited and space-bound grant in a 
discrete  national  /  EC  funding  schemes  -  but  have  the  character  of  a  game-changer  scheme, 
modelled on the highly effective DARPA challenges in the US17. In addition, well budgeted data 
stewardship  plans  should  be  made  mandatory  and  we  expect  that  on  average  about  5%  of 
research expenditure should be spent on properly managing and stewarding data (see I5).  The 
game-changer scheme should have precise aims that are linked to core EOSC areas, rather than 
being broad and bottom-up topical calls, in the scope of accelerating the development of a fully 
functional  EOSC.  Scoping,  evaluation,  selection  and  awards  of  the  scheme  should  be  done 
through mechanisms specifically designed to rapidly prototype, test and reach the goal. The first 
result will be neither a regular research project nor a long-term infrastructure, rather a proof of 
concept  and  implementation  vehicle  to  kick-start  the  initiative,  to  provide  a  point  of  reference 
with a clear offer and sustainability model once the scheme is in place. Within the single game-
changer scheme, multiple work packages can be funded to test different approaches.  

The  European  Structural  and  Cohesion  Fund  might  be  used  for  this  scheme  alongside  Horizon 
2020  funding,  thereby  stimulating  regions  to  develop  broadly  applicable  components  of  the 
EOSC. We strongly recommend that the Commission set up structured discussions on the game-
changer scheme  with Member  states to start,  extend  and sustain rapid prototyping make data 
and tools compatible with FAIR principles.  

We suggest to make rapid, agile prototyping and reference implementations a critical element of 
the preparatory phase of the EOSC so that already in 2017 exemplar working environments can 
be implemented in key disciplines in guiding Member States, which can be then be extended in 
other settings, communities and countries.  

Priority actions of the EOSC game-changer scheme:  

-  Solve  socio-technical  bottlenecks  to  reach  full  operational  status  of  the  EOSC.  These  could 
include  for  instance:  connectivity  issues,  security  and  trust  issues,  performance  issues, 
standards or format issues and socio-technical hurdles, such as lack of incentives and reward 
for  data  publication  and  sharing.  Proposed  solutions  may  be  technical,  cultural  or  a 
combination of both 

-  Develop  and  sustain  core  data  assets  for  the  EOSC  and  make  them  available  to  the 
include  workflows,  analytics 

community  under  well-defined  conditions.  These  may 
programmes and notably existing datasets with FAIR status (including metadata creation) 

-  Create and implement a plan for the sustainable provision and funding of core resources of 

the EOSC in terms of connectivity, scientific data storage and computing 

-  Support  the  development  of  one  or  more  publicly  available  data  search  engine(s)  that  find 

FAIR metadata across trusted EOSC repositories 

-  Develop  technologies  and  approaches  to  meaningfully  measure  re-use  and  scientific  impact 
of  Research  Objects  after  their  initial  publication  (e.g.  metrics  that  matter  and  get 
recognised) 

-  Develop  schemes  to  improve  funding  and  rewards  for  open  data  sharing  at  research 

performing organisations and funders 

-  Start dedicated efforts to prepare data and research objects for inclusion in the EOSC 

-  Combine single sign-on issues with the connection of social and professional people oriented 
web applications resulting in a federated identity and credentials for all people in the EOSC 

-  A repository of research vocabularies and a software application to support wider access, re-

use and development of vocabularies thereby enhancing interoperability 

-  Urgently develop adequate data stewardship capacity in European Member States 

-  Engage  stakeholder  communities  in  a  guided  and  dedicated  effort  to  develop  and  offer  on-
line,  scalable  and  re-usable  training  modules.  Not  only  to  train  experts  but  also  to  drive 
convergent evolution of standards and practices used 

17 http://www.darpa.mil/work-with-us/public/prizes  

17 

 
 
 
 
 
 
                                                 
  I6:  Make  adequate  data  stewardship  mandatory  for  all  research  proposals.  We 
recommend  that  use  of  present  and  future  instruments  in  research  programming,  including 
Horizon  2020,  should  only  support  projects  that  properly  address  Data  Stewardship  issues  for 
open data. Projects that develop isolated and solipsistic data infrastructures, that do not specify 
FAIR  conditions  for  data,  without  a  sustainability  plan  and  without  a  clear  plan  on  how  the 
action and its supporting data and human infrastructure, will not contribute to the development 
of  the  EOSC  vision  of  a  commons  as  laid  out  in  this  plan,  and  consequently  should  not  be 
eligible  for  funding.  On  the  contrary,  projects  with  achievable  requirements,  that  are  data-
multidisciplinary,  that  properly  address  post-project  sustainability  or  otherwise  advance  the 
common  aims  of  the  EOSC  should  be  streamlined  for  funding  in  the  EOSC  game-changer 
scheme. 

  I7: Provide a clear operational timeline to deal with the early preparatory phase of the 

EOSC. 

In order to kick-start the EOSC, the timeline should envisage addressing a number of concrete 
tasks:  

-  Define the RoE of the EOSC and practically test them   

-  Create and foster cross-domain working collaboration including ESFRIs and e-Infrastructures 

and address cross disciplinary join-up  

-  Define  training  needs  for  the  necessary  data  expertise  and  draw  models  for  the  necessary 

training infrastructure  

-  Develop a governance plan for the EOSC, as lightweight and inclusive as possible  

-  Establish minimal technical standards for the EOSC and plan for their long-term maintenance 

and compliance  

-  Establish  guidance  and  oversight  mechanisms  for  the  EOSC  fame  changer  scheme  as  a 

development outside of regular programming calls  

-  Liaise  with  Member  States  to  establish  plans  for  certified  institutes  for  data  expertise  and 

stewardship and to help building capacity for certification.  

18 

 
 
 
 
 
GLOSSARY OF TERMS  

1.  Data: When the term data is used in this report we refer to digital research objects in a broad 
sense,  including  regular  research  data  and  also  meta-data,  the  associated  services  and 
workflows,  analytics  algorithms  and  all  other  data-related  instruments  that  modern  scientific 
research uses.  

2.  Data  Stewardship:  The  entire  process  that  deals  responsibly  with  one's  own  and  other 

peoples data throughout and after the scientific discovery process. 

3.  Commons:  we  consider  the  EOSC  as  the  European  contribution  to  a  global  scientific 
commons.  When  we  use  this  term  we  refer  to  the  concept  of  a  common  public  good  that 
allows data publication,  stewardship  and  re-use.  We  emphasise  that public  as  well  as  private 
service providers can participate in this commons. 

4.  Data Experts:  this  term refers  to  a  distinct  and  largely  novel  class of  research  professional. 
They  are  not  traditional  core  computer  or  data  scientist,  but  embedded  data  specialists  that 
are  able  to  support  domain  specific  researcher  throughout  the  entire  knowledge  discovery 
cycle. They typically do not end up with high impact factors in traditional systems but should 
become  indispensable  core  partners  in  any  modern  data  driven  research  team  with  a  solid 
perspective. 

5. 

Innovation:  we  explicitly  refer  to  the  transition  from  scientific  insights  to  novel  societal 
assets and not to the creative knowledge generation process of scientific activity. 

6.  Machine  Actionable:  when  this  term  is  used,  we  explicitly  refer  to  machine  readable  and 
executable data or meta-data. The FAIR principles emphasise that need as well. It is pertinent 
in data driven science that machines can operate on data as relatively independent agents. 

7.  ESFRI: this term formally refers to the European Strategy Forum on Research Infrastructures. 
In this report the term is loosely used to refer to large, domain oriented international research 
infrastructures, regardless of whether they are funded under the European or National ESFRI 
Roadmaps. 

8.  e-Infrastructures:  this  term  is  used  to  refer  in  a  broader  sense  to  all  ICT-related 
infrastructures  supporting  ESFRIS  or  research  consortia  or  individual  research  groups, 
regardless  of  whether  they  are  funded  under  the  CONNECT  scheme,  nationally  or  locally.  

9.  Rules  of  Engagement  (RoE):  this  term  is  used  to  refer  to  the  rules  of  engagement  and 

conduct that need to be developed in the preparatory phase of the EO. 

19 

 
 
 
 
 
 
 
 
 
 
 
 
 
HOW TO OBTAIN EU PUBLICATIONS

Free publications:
•  one copy:  

via EU Bookshop (http://bookshop.europa.eu);

•  more than one copy or posters/maps:  

from the European Union’s representations (http://ec.europa.eu/represent_en.htm);  
from the delegations in non-EU countries (http://eeas.europa.eu/delegations/index_en.htm);  
by contacting the Europe Direct service (http://europa.eu/europedirect/index_en.htm) or 
calling 00 800 6 7 8 9 10 11 (freephone number from anywhere in the EU) (*). 

(*) The information given is free, as are most calls (though some operators, phone boxes or hotels may charge you).

Priced publications:
•  via EU Bookshop (http://bookshop.europa.eu).

The European Open Science Cloud (EOSC) aims to accelerate 
and support the current transition to more effective Open 
Science and Open Innovation in the Digital Single Market. 
It should enable trusted access to services, systems and 
the re-use of shared scientific data across disciplinary, 
social and geographical borders. This report approaches 
the EOSC as a federated environment for scientific data 
sharing and re-use, based on existing and emerging elements 
in the Member States, with light-weight international 
guidance and governance, and a large degree of freedom 
regarding practical implementation.

Studies and reports

DATA SHARING 

OPEN SERVICES

LINKING DATA

CONNECTING SCIENTISTS

SUSTAINABLE

CONNECTING DISCIPLINES

BETTER SCIENCE