From vision to actionFrom open to FAIR dataOpenAIRE workshop - Legal issues in Open

Research DataApril 4, Barcelona

Jean-Claude BurgelmanDaniel Spichtinger

DG RTD European Commission

10 years to become open

FP7OA Green orGold+Green Pilot

H2020OA Green orGold+Green obligation& ORD Pilot

H2020OA Green orGold+Green obligation& ORD by default

From 2017

2008

2014

"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

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

ORD Pilot: opt-out reasons among proposals

Calls in core-areas: 65% stay in

Opt out 35%Other areas:

Voluntary opt in 14%

FAIR data management in Horizon 2020

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

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.

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

Guidelines on FAIR Data Management

• Available here on the

Participant Portal!Read it!!!

Only 12 Pages!!!

Initial DMP experiences • 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).

• Aspects such as data preservation, IPR or standards are too often not well developed in the DMPs that have been submitted so far

• Nevertheless research projects with excellent RDM performance are not rare. Some high quality DMPs from H2020 projects have already been published online, see

• http://www.dcc.ac.uk/resources/data-management-plans/guidance-examples

Source: REA 2016 assessment of H2020 Societal Challenge 6 projects.

FAIR data = the DNA of the EOSC

"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

Part of DSM strategy (19 April 2016), strong political support.

o 'Game-changing policy', a 'vision'.o Commissioners Moedas & Oettingero 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

GovernanceDevelop roadmap for governance and financing

Create a global level playing field for research data sharingWiden user-base to public services, Industry and EU-13

(Open data) InfrastructureAction 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

HardwareInfrastructure

(CNECT)

High-PerformanceComputing

Big-data storage

High-speed connectivity

Policy actions foreseen in the COM 2016

FAIRFAIR

What is next?

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

Our common challenge?

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

Define data dynamically to fit open science:

- data- methods- algorithms- SW

in one word: all that is needed

(FAIR) to make science reproducible

We should not give in on scientific and independent QUALITY

So lets move FAIR from concept to operational

reality that serves OPEN SCIENCE

(and lets do that asap)