DRI Director Natalie Harrower, a member of the European Commission's Expert Group on FAIR (Findable, Accessible, Interoperable and Re-usable) data, delivered a lunchtime briefing on the recently published 'Turning FAIR into Reality' report on Tuesday 26 February in the Royal Irish Academy, Dublin. In 2016 the FAIR Data Principles were developed to support the position that effective research data management is ‘not a goal in itself but rather is the key conduit leading to knowledge discovery and innovation’. The new publication is both a report and an action plan for turning FAIR into reality. It offers a survey and analysis of what is needed to implement FAIR and it provides a set of concrete recommendations and actions for stakeholders in Europe and beyond. The briefing provided an overview of the contents of the report, which include the principles of FAIR, as well as the elements required to implement FAIR data.

1. Turning FAIR into Reality
Briefing on the final report from the
European Commission FAIR Data Expert Group
Royal Irish Academy, 26 Feb 2019
Natalie Harrower
Director, Digital Repository of Ireland
Member, EC FAIR data expert group
@natalieharrower

2. Findable
Accessible
Interoperable
Reusable
Open Science/Research/Scholarship
Publications | Research Process | Data
• Openness & transparency
• Increased exposure, usage, impact
• Efficiency
• ROI
• Research Integrity
• Reproducibility
• Acceleration
Better science &
Better public value

3. European Commission Expert Group on FAIR Data:
Background & Objectives
Research funding mandates: Rules of engagement for Horizon 2020
• Open Access: beneficiaries must ensure open access to all peer-reviewed
scientific publications relating to its results (Recently: Plan-S)
• Data: by 2020 open access to research data is the default. “as open as
possible, as closed as necessary’
• Includes “taking measures to make it possible for third parties to access,
mine, exploit, reproduce and disseminate data via a research data
repository”
Role of Expert Group
• To develop recommendations on what needs to be done to turn the
FAIR data principles into reality (EC, member states, international
level).
• Develop the FAIR Data Action Plan with a list of proposed concrete actions

4. Part of wider European
research & policy landscape
EC Open Science Policy Platform
Expert Groups

5. FAIR Data EG: Timeline
May - June
June - October
November
13 June 2018
Interim report launched at EOSC summit
Stakeholder workshop held at the summit
13 June – 5 Aug 2018
Open Consultation period (over 500 comments received)
5 Aug – Nov 2018
Revisions
23 Nov 2018
Official publication at EOSC launch, Vienna presidency event

6. Structure of the Report and Action Plan
1. Concepts: why FAIR?
2. Creating a culture of FAIR data
3. Creating a technical ecosystem for FAIR
4. Skills and capacity building
5. Measuring change
6. Funding and sustaining FAIR data
FAIR action plan

7. The following slides are adapted from the FAIR data expert group presentation at SciDataCon2018 in
Botswana: “Turning FAIR into Reality: Final outcomes from the European Commission FAIR Data Expert Group”
27 Recommendations; 15 priority recommendations

8. Key Points: To make FAIR a reality …
• Report takes a research-ecosystem approach, not a data-centric approach
• Need to address research culture, practices and technologies – not just
focus on the data and its attributes
• Research communities must define how the FAIR principles and related
concepts apply in their context. (Disciplines know their data and practices)
• Need to consider all digital outputs (data, code, metadata etc)
• Objective is to make data and other digital research outputs FAIR for
humans and machines.
• Requires: concept of FAIR digital objects, FAIR ecosystem, interoperability
frameworks for disciplines and across disciplines, FAIR services including
trusted digital repositories, skills, metrics and sustainable funding.

9. Concepts Implied by the Principles
Making FAIR a reality depends on additional concepts that are
implied by the principles, including:
• The timeliness of sharing
• Data appraisal and selection
• Long-term preservation and stewardship
• Assessability – to assess quality, accuracy, reliability
• Legal interoperability – licenses, automated
CONCEPTS

10. FAIR Digital Objects
12
● Implementing FAIR requires a model for FAIR digital objects
● Digital objects can include data, software, and other research
resources
● Universal use of
appropriate PIDs
● Use of common (ideally
open) formats; data
accompanied by code
● Rich metadata and
clear licensing enables
broadest reuse
CONCEPTS

11. The FAIR Ecosystem
• Digital objects rely on an ecosystem of components to realise FAIR
• Essential are:
Policies
Data Management Plans
Identifiers
Standards
Repositories
• Registries to catalogue each component of the ecosystem, and
automated workflows between them.
• Begin by enhancing existing registries and infrastructures
CONCEPTS

12. • FAIR and Open should not be conflated. Data can be FAIR or
Open, both or neither
• Greatest potential reuse comes when data are both
• Even internal or restricted data will benefit from being FAIR,
and there are legitimate reasons for restriction which varyby
discipline
CONCEPTS

13. FAIR and Open
● ‘As Open as possible, as closed as necessary'
● By default, data created by publicly funded research
projects, initiatives and infrastructures should be to made
available as soon as possible.
● Policies could allow for (short) embargo periods to facilitate
the right of first use for creators
● Guidance should be provided to researchers to help find
optimal balance between sharing and privacy
CONCEPTS

14. FAIR data: cultural change
● Some communities share and use FAIR data, some are making
progress, some are reluctant
● FAIR data availability does change the way science is done
● Disciplines know their data and have work to do to provide them FAIRly
● Interdisciplinary work should be enabled in particular to tackle the 'Grand
Challenges'
● Incentives and rewards are fundamental to enable the change
CULTURE

15. Data Management via DMPs
A core element of research projects
• DMPs should cover all research outputs
• DMPs should be living documents
• DMPs should be tailored to disciplinary needs, research communities to
provide input and agree
• Harmonisation of DMP requirements across funders and organisations
• Requirement: support and incentives to research communities
DMP acting as a hub of information on FAIR digitalobjects,
connecting to the wider elements of theecosystem
CULTURE

16. Recognition, Incentives and Rewards
Recognise provision of FAIR data, infrastructure and services in
assessment of research contributions and career progression
• Recognition of the diversity of research contributions and include them
in CVs, researchers’ applications and activity reports, assessments
• Credit should be given to all roles supporting FAIR data and definition of
interoperability frameworks, whether for existing or new
• Evidence of past practice in support to FAIR should be included in
assessments of research contribution
• Contribution to development and operation of certified and trusted
infrastructures that support FAIR data should be recognized, rewarded
and incentivised
CULTURE

17. Building a FAIR data ecosystem
• Infrastructure should build on what is already ‘in the system’, support
best practice, facilitate transition to FAIR practices, be FAIR beyond
data e.g. software, services
• Semantic technologies are essential for interoperability
• Automated processing should supported so machines can interact with
one another throughout the system. Machine readability should be built
into the system (e.g. DMPs)
• Data services should be encouraged and supported to obtain
certification. Existing community-endorsed methods to assess data
services (e.g. CoreTrustSeal for data repositories) should be used an
built on.
TECHNICAL ECOSYSTEM

18. Key drivers needed for change
Metric
s
SkillsInvestment
Cultural and social
aspects that drive
the ecosystemand
enact change
Incentives

19. Skills
• Two cohorts of professionals to support FAIR data:
- data scientists embedded in research projects
- data stewards who will ensure the curation of FAIR data
• Coordinate, systematise and accelerate the pedagogy
• Support formal and informal learning
• Ensure researchers have
foundational data skills Create /
Analyse
Preserve
/ Share
SKILLS

20. Metrics
• A set of metrics for FAIR Digital Objects should be
developed and implemented, starting from the basic
common core of descriptive metadata, PIDs and access.
• Certification schemes are needed to assess all
components of the ecosystem as FAIR services. Existing
frameworks like CoreTrustSeal for repository certification
should be used and adapted rather than initiating new
schemes.
METRICS

21. How metrics relate to incentives
• Use metrics to measure practice but beware misuse
• Generate genuine incentives – career progression for data
sharing & curation, recognise all outputs of research,
include in recruitment and project evaluation processes…
• Implement ‘next-generation’ metrics
• Automate reporting as far as possible
METRICS

22. Investment
• Provide strategic and coordinated funding to maintain the
components of the FAIR ecosystem
• Ensure funding is sustainable – no unfunded mandates
• Economies of scale
FUNDING/SUSTAINABILITY

23. Research communities: practitioners from all research fields, clustered around disciplinary
interests, data types or cross-cutting grand challenges.
Data service providers: domain repositories, research infrastructures and e-infrastructures,
institutional, community and commercial tools and services.
Data stewards: support staff from research communities and research libraries, and those
managing data repositories.
Standards bodies: formal organisations and consortia coordinating data standards and
governing procedures relevant to FAIR
Coordination fora: global and national bodies such as the Research Data Alliance, CODATA,
WDS Communities of Excellence, GO FAIR.
Policymakers: governments, international entities like OECD, research funders, institutions,
publishers and others defining data policy.
Research funders: the European Commission, national research funders, charitable
organisations and foundations, and other funders of research activity.
Institutions: universities and research performing organisations.
Publishers: not-for-profit and commercial, Open Access and paywall publishers of research
papers and data.
Stakeholders with responsibilities

24. The FAIR Action Plan: Next Steps
39
• Report offers an overarching plan
• Needs to be detailed by research
communities and Member states
• FAIR fits under wider remit of
EOSC
• Expert Group has made
recommendations to the EOSC
Executive Board (first meeting 19th
Jan 2019)

25. Proposal to EOSC Executive Board
The FAIR data expert group proposed the establishment of seven
working groups as a starting point to deal with different
components of the ecosystem
1. Policy alignment
2. Fundamental EOSC services
3. Interoperability and integration
4. EOSC-relevant skills
5. Incentives and metrics
6. Business models and sustainability
7. Governance and Rules of Participation

26. FAIR data and Ireland
Snapshot

27. Ireland’s National Open Research Forum (NORF)
Draft Principles on the Management of FAIR Research Data
• Confirms FAIR principles
• Recognises skills required for all in the research process
• Whole-cycle approach: plan from DMP to LTP
• Interoperability across domains, systems
• Robust citation mechanism for data; linked to publications
• Uses of Persistent Identifiers (PIDs), standard metadata
• ‘Assessability’ of data
• ‘Open as possible, closed as necessary’ – TDRs, EOSC
• Open licensing, allow for text mining
• Funders to include DMP requirements & monitoring mechanisms in
grant T&C

28. Ireland: Looking ahead to what is needed
• Continued alignment of National Policy, Funder Policy,
Institutional Policy, International Policy
• Development of a National Roadmap for FAIR implementation
• Direct engagement of researchers esp. at disciplinary level
• Support for training, next generation metrics, data preparation,
archiving and preservation
• Support for infrastructures and human resources to support them
• More collaboration, economies of scale in infrastructures and
training, sharing of strategies and approaches

29. Thank you
Contact
www.dri.ie
n.harrower@ria.ie
@natalieharrower