Presentation at the 8th OpenAIRE workshop - 4 April 2017

1. From vision to action
From open to FAIR data
OpenAIRE workshop - Legal issues in Open Research Data
April 4, Barcelona
Jean-Claude Burgelman
Daniel Spichtinger
DG RTD European Commission

2. 10 years to become open
FP7
OA Green or
Gold+Green
Pilot
H2020
OA Green or
Gold+Green
obligation
& ORD Pilot
H2020
OA Green or
Gold+Green
obligation
& ORD by
default

3. "To increase the circulation and exploitation of knowledge, open access to scientific
publications should be ensured. Furthermore, open access to research data resulting from
publicly funded research under Horizon 2020 should be promoted, taking into account
constraints pertaining to privacy, national security and intellectual property rights
 Open access to scientific publications resulting from publicly
funded research under Horizon 2020 shall be ensured [...].
 Open access to research data resulting from publicly funded
research under Horizon 2020 shall be promoted. [...]."
Now: Regulation establishing H2020

4. ORD pilot extension: implementation
Extension of limited Open Research Data (ORD) Pilot to all areas of Horizon 2020
whilst retaining its key characteristics:
• Robust opt outs options for IPR, confidentiality/privacy and security reason as well
as if OA runs against the main objective of the project
• Targeted primarily towards data underlying publications (other data as specified in
DMP)
• a Data Management Plan (DMP) is obligatory for projects that do not opt-out
• Costs for open access to research data fully eligible
• Whether projects opt-out or not does not affect the evaluation
General approach: as open as possible, as closed as needed

5. ORD Pilot: opt-out reasons among proposals
Calls in core-areas:
 65% stay in
 Opt out 35%
Other areas:
 Voluntary
opt in 14%

6. FAIR data management in
Horizon 2020

7. Clarifying terminology…
In the past our policy mainly addressed
the 'accessibility' part of FAIR.
• Started off with 'open access to research
data'
• Moved towards open (research) data
with the ORD pilot (which also covered
further aspects)
• We are now seeing openness as one
component of FAIR data and aim to
address all of the FAIR aspects in
Horizon 2020

8. A FAIR DMP has to adress that data are
o 'Findable', i.e. discoverable with metadata, identifiable and locatable by
means of a standard identification mechanism;
o 'Accessible', i.e. always available and obtainable;
o 'Interoperable', i.e. both syntactically parseable and semantically
understandable, allowing data exchange and reuse between
researchers, institutions, organisations or countries; and
o 'Reusable', i.e. sufficiently described and shared with the least restrictive
licences, allowing the widest reuse possible and the least cumbersome
integration with other data sources.

9. FAIR Data Management DMP
o Template DMP (Annex to Guidelines on FAIR Data Management)
 Provided as a service, its use is currently optional
o Standard DMP template is light and flexible
 Set of questions + summary table
o One DMP per project not per dataset
 but mention if there are specific issues for a particular dataset)
o DMP as a living document
 Updated as part of periodic evaluation and/or at least at the end of the
project for final reporting

10. Guidelines on FAIR Data Management
• Available here on
the Participant
Portal!

11. Initial DMP experiences
o Additional guidance on data management is needed for all groups of actors in
research projects (researchers, peer reviewers and funder administrators ('project
officers') including roles supporting researchers with data management tasks
(data librarians or IT professionals working in data centres).
o Aspects such as data preservation, IPR or standards are too often not well
developed in the DMPs that have been submitted so far
o Nevertheless research projects with excellent RDM performance are not rare.
Some high quality DMPs from H2020 projects have already been published
online, see
o http://www.dcc.ac.uk/resources/data-management-plans/guidance-examples
Source: REA 2016 assessment of
H2020 Societal Challenge 6
projects.

12. FAIR data = the DNA of
the EOSC

13. "Europe's final transition must be one from
fragmented data sets to an integrated European
Open Science Cloud. By 2020, we want all
European researchers to be able to deposit,
access and analyse European scientific data
through a European Open Science Cloud."
The Commissioner's vision on EOSC
Speech by Commissioner Carlos Moedas in Amsterdam, NL:
“Open science: share and succeed”, 4 April 2016
= EOSC is about (FAIR) research data

14. Part of DSM strategy (19 April 2016), strong political
support.
o 'Game-changing policy', a 'vision'.
o Commissioners Moedas & Oettinger
o Supported by Pres. Juncker, VP Ansip, Ch. Merkel, LUX
Presidency, NL Presidency, 2 sets of COMPET Council
Conclusions, EP Report on DSM Act, EESC, …
Communication 2016/178 : European Cloud Initiative

15. Governance
Develop roadmap for governance and financing
Create a global level playing field for research data sharing
Widen user-base to public services, Industry and EU-13
(Open data) Infrastructure
Action Plan for scientific data Interoperability (e.g. FAIR)
Connect key EU RI (e.g. ESFRIs)
Consolidate / federate data-infrastructures
Content (open data)
Make Open research data default in H2020
Foster scientific data sharing in MS
Hardware
Infrastructure (CNECT)
High-Performance
Computing
Big-data
storage
High-speed
connectivity
Policy actions foreseen in the COM 2016
FAIR
FAIR

16. What is next?

17. o EOSC Summit will include inputs from OSPP, HLEG EOSC, FAIR
expert group, and EOSC Pilot– 12 June 2017
o 'Declaration of intent' and 'Coalition of the willing' will include parts on
FAIR data – end of summer 2017
o Interim governance board of EOSC will need to have FAIR people on
board
o Action Plan for FAIR data Interoperability - Summer 2018
The next practical steps

18. Our common challenge?

19. Get more of these
• 1.3 Billion EUR per year
• Benefits identified by the European
Bioinformatics Institute to users and
their funders just by making scientific
information freely available to the
global life science community
• This is equivalent to more than 20
times the direct operational cost of
the Institute
Source: Charles Beagrie Ltd. For EMBL-EBI

20. Define data dynamically to fit open science:
- data
- methods
- algorithms
- SW
in one word: all that is needed (FAIR) to make
science reproducible

21. We should not give in on scientific and independent QUALITY

22. So
lets move FAIR from concept to operational reality
that serves OPEN SCIENCE
(and lets do that asap)