This document discusses the FAIR data principles and increasing adoption of FAIR. It begins by explaining the 15 FAIR principles for findable, accessible, interoperable and reusable data. It then discusses how adoption is increasing through funder requirements, the role of FAIR within EOSC, and related projects. However, it notes that most data is still not managed or shared according to FAIR principles due to barriers like time and effort required as well as lack of incentives and rewards. The document argues that both cultural and technical aspects must be addressed to fully implement FAIR.

1. www.geant.org
1 |
Click to edit Master title style
• Click to edit Master text styles
• Second level
• Third level
• Fourth level
• Fifth level
01/07/2021 1
The Future of FAIR
www.geant.org
Sarah Jones
EOSC Engagement Manager
sarah.jones@geant.org
Twitter: @sarahroams
N8 library managers workshop
Wednesday 30th June 2021

2. What is FAIR?
Image Israel Palacio https://unsplash.com/photos/P6FgiDNe6W4

3. www.geant.org
A set of principles to ensure that data are
shared in a way that enables & enhances
reuse, by humans and machines
What is FAIR?
Image CC-BY-SA by SangyaPundir

4. www.geant.org
What FAIR means: 15 principles
Findable
F1. (meta)data are assigned a globally unique and
eternally persistent identifier.
F2. data are described with rich metadata.
F3. (meta)data are registered or indexed in a searchable
resource.
F4. metadata specify the data identifier.
Interoperable
I1. (meta)data use a formal, accessible, shared, and
broadly applicable language for knowledge
representation.
I2. (meta)data use vocabularies that follow FAIR
principles.
I3. (meta)data include qualified references to other
(meta)data.
Accessible
A1 (meta)data are retrievable by their identifier using a
standardized communications protocol.
A1.1 the protocol is open, free, and universally
implementable.
A1.2 the protocol allows for an authentication and
authorization procedure, where necessary.
A2 metadata are accessible, even when the data are no
longer available.
Reusable
R1. meta(data) have a plurality of accurate and relevant
attributes.
R1.1. (meta)data are released with a clear and
accessible data usage license.
R1.2. (meta)data are associated with their provenance.
R1.3. (meta)data meet domain-relevant community
standards.
Slide CC-BY by Erik Schultes, Leiden UMC
doi: 10.1038/sdata.2016.18

5. The FAIR data principles explained
• Clarifications from the Dutch
Techcentre for Life Sciences
• Each principle is a link to further
clarification, examples and
context
https://www.dtls.nl/fair-data/fair-
principles-explained
R1. Meta(data) are richly described with a plurality of accurate and relevant attributes
• By giving data many ‘labels’, it will be much easier to find and reuse the data
• Provide not just metadata that allows discovery, but also metadata that richly describes
the context under which that data was generated
• “plurality” indicates that metadata should be as generous as possible, even to the point of
providing information that may seem irrelevant

6. FAIR is nothing new
• Various research communities have been sharing their
data in a ‘FAIR’ way long before the term emerged
• Meaningful and memorable articulation of concepts
• Natural desire to want to be ‘fair’
• FAIR is gaining significant international traction

7. Open data and FAIR data
FAIR
data
Open
data
FAIR and Open are
not synonymous.
Data can be both,
one or neither.
Degrees of Open and FAIR
And both are on a scale

8. www.geant.org
How do Open, FAIR & RDM intersect?
Open
FAIR data
Managed data
Internal
Self-interest
External
Community
benefit

9. Open, FAIR and RDM
• Paper explores overlaps between
concepts of Open, FAIR and RDM.
• Proposes using Open and FAIR as
ways to engage researchers in
managing data well, as this is a
prerequisite for both.
• Recommends making data FAIR
and Open wherever possible
Higman, R., Bangert, D. and Jones, S., 2019. Three camps, one destination: the
intersections of research data management, FAIR and Open. Insights, 32(1), p.18.
DOI: http://doi.org/10.1629/uksg.468

10. Increasing uptake
Image CC-BY by Ian Dooley https://unsplash.com/photos/DuBNA1QMpPA

11. Increasing adoption by funders

12. www.geant.org
FAIR is a central part of EOSC
• FAIR is the glue which
enables us to federate
data and services
• Principles have been
used as the basis for the
EOSC Interoperability
Framework
12

13. Turning FAIR into Reality: Report and Action Plan - https://doi.org/10.2777/1524
Report and Action Plan: Take a holistic approach to lay out
what needs to be done to make FAIR a reality, in general
and for EOSC
Addresses the following key areas:
1. Concepts for FAIR
2. Creating a FAIR culture
3. Creating a technical ecosystem for FAIR
4. Skills and capacity building
5. Incentives and metrics
6. Investment and sustainability
Recommendations and Actions: 27 clear recommendations,
structured by these topics, are supported by precise actions
for stakeholders.
Report is out!
FAIR Expert Group
FAIR Data Expert Group (2016-2018)

14. www.geant.org
FAIR WG of EOSC Exec Board (2018-2020)
• Six recommendations for
implementation of FAIR
practice
• EOSC Interoperability
Framework
• Persistent identifier
policy for EOSC
• FAIR metrics for EOSC
• Recommendations on
certifying services to
enable FAIR
https://www.eoscsecretariat.eu/working-
groups/fair-working-group

15. www.geant.org
EOSC Association Task Forces… (2021 on)
• FAIR metrics & data quality
• Semantic interoperability
• Interoperability of data & services
• Research careers, recognition & credit
• Data stewardship curricula and career paths
• And more….
https://www.eosc.eu/news/call-members-eosc-
association-task-forces
15 |
Call for members
closes on Friday
30 July 2021 at
18.00 CEST

16. Many FAIR-related projects
All funded by the European Commission
clusters
National initiatives
• EOSC-Nordic
• EOSC-Pillar
• EOSC-synergy
• ExPaNDS
• NI4OS-Europe

17. It’s all going swimmingly!
17 |
Image CC-BY by Brian Matangelo https://unsplash.com/photos/gRof2_Ftu7A

18. www.geant.org
Most data isn’t managed, let alone Open or FAIR…
18 |
Why?...

19. www.geant.org
For his most recent paper:
1. Double checking the main dataset and
reformatting to submit to Dryad: 5 hours
2. Creating complementary file and preparing
metadata: 3 hours
3. Submission of these two files and the metadata
to Dryad: 45 minutes
4. Preparing a map of the locations: 1 hour
5. Submission of map to Figshare: 15 minutes
6. Cleaning up and documenting the code,
uploading it to GitHub: 25 hours
7. Cost of archiving in Dryad: US$90
8. Page Charges: $600
It takes a lot of time and effort
19 |
By Emilio Bruna
http://brunalab.org/blog/2014/09/04/the-
opportunity-cost-of-my-openscience-was-35-
hours-690

20. www.geant.org
Recognition and rewards are not there yet
20 |
It’s hard to overcome
your personal
investment.. It’s like
giving away your baby
Quote from a researcher at
Glasgow University as part
of the Incremental project

21. Communities are at different stages of maturity
• Some like astronomy and physics are well-organised
• Others still need to develop standards
• Tension between domain approaches and
interoperability cross-domain
https://doi.org/10.5281/
zenodo.1246815
https://doi.org/
10.2777/986252

22. How do we implement FAIR?
Image Israel Palacio https://unsplash.com/photos/P6FgiDNe6W4

23. • Findable
- Persistent ID
- Metadata online
• Accessible
- Data online
- Restrictions where needed
• Interoperable
- Use standards, controlled vocabs
- Common (open) formats
• Reusable
- Rich documentation
- Clear usage licence
FAIR data checklist
https://doi.org/10.5281/zenodo.1065991

24. Adopt a model for FAIR Digital Objects
• Digital objects can include data, software, and other
research resources
• Universal use of PIDs
• Use of common formats
• Data accompanied by code
• Rich metadata
• Clear licensing

25. www.geant.org
Address culture AND technology
Incentives
Metrics
Skills
Investment
Cultural and
social aspects
that drive the
ecosystem and
enact change
Cloud
of
registries
Two sides of one whole

26. Support interoperability
● Support research communities to develop and maintain their
interoperability frameworks for FAIR sharing
● Engage in international collaboration fora to do this
● Exchange of good practices, define case studies and success stories
● Common standards to support disciplinary frameworks and promote
interoperability and reuse across disciplines

27. www.geant.org
Principle Researcher role Service role
F1. Assign a PID Choose a relevant service Assign PIDs
F2. Rich metadata Create appropriate metadata Link data and metadata
F3. Indexed, searchable resource Choose a relevant service Ensure metadata search
F4. Metadata specify PID Choose a relevant service Link metadata and PID
A1. Standard protocol for retrieval Choose a relevant service Use standard protocols
A1.1 Open, free protocol Choose a relevant service Use open, free protocols
A1.2 Authenticated access if needed Choose a relevant service Provide authenticated access
A2. Metadata remain accessible Choose a relevant service Provide tombstone records
I1. Use of formal language (standards) Adopt standards Support appropriate standards
I2. Metadata vocabularies are FAIR Advocate for FAIR metadata Support FAIR metadata
I3. Qualified references (linked data) Cross-reference resources Cross-reference resources
R1. Rich metadata (plurality of attributes) Enrich metadata/documentation Advocate for good metadata
R1.1 Clear data usage licence Choose appropriate licence Require licences
R1.2 Metadata covers provenance Say where data came from Require provenance
R1.3 Community standards Adopt community standards Support community standards
FAIR is a joint responsibility…
Equal, if not more, responsibility on data services

28. 1. Adopt relevant standards as you create data
Researcher role
2. Create rich metadata and documentation which
• conforms to community standards
• explains provenance
• assigns a clear usage licence
• cross-links data, metadata, code and other resources
3. Choose appropriate data services which
• assign Persistent Identifiers
• enhance discoverability via indexes / catalogues
• use standard protocols for (authenticated) access
4. Advocate for / contribute to community
standards

29. www.geant.org
Institutional role
1. Raise awareness of community standards
2. Help researchers select appropriate data services
3. If running a repository:
• assign Persistent Identifiers
• ensure metadata specifies the PID
• expose metadata via indexes / catalogues / harvesting…
• use standard protocols for (authenticated) access
• cross-reference resources
• keep metadata accessible, even when data aren’t
4. Set requirements / advocate for good practice

30. Inherent link: data and services
In order for data to be FAIR,
you need services that enable FAIR

31. And a community responsibility….
31 |
As the global community adopts &
becomes dependent on FAIR, the
principles themselves need to be
community-owned and governed

32. www.geant.org
Click to edit Master title style
• Click to edit Master text styles
• Second level
• Third level
• Fourth level
• Fifth level
01/07/2021 32
Thank you
www.geant.org
Any questions?
© GÉANT Association on behalf of the GN4 Phase 2 project (GN4-2).
The research leading to these results has received funding from
the European Union’s Horizon 2020 research and innovation
programme under Grant Agreement No. 731122 (GN4-2). 32 |