Presentation given at Macquarie University in support of the ARDC 'institutional role in the data commons' project on "Implementing FAIR: Standards in Research Data Management" https://ardc.edu.au/news/data-and-services-discovery-activities-successful-applicants/

1. What it means to be FAIR
Sarah Jones
Digital Curation Centre
sarah.jones@glasgow.ac.uk
Twitter: @sjDCC
FAIR session, Macquarie University, 7th August 2019

2. What is Digital Curation Centre?
a centre of expertise in digital information curation with a focus
on building capacity, capability and skills for research data
management and open science
www.dcc.ac.uk
Training | Events | Tools | Advocacy | Consultancy | Guidance | Publications | Projects

3. Who am I?
• Archivist with humanities background
• Coordinator of DMPonline service
• Heavily involved in Research Data Alliance
• Co-Chair on Data Science Schools
• Rapporteur of FAIR Expert Group
• Independent member of EOSC Executive
Board
• From a seaside town – hence why I love
beach and sunshine here :o)
FAIR session, Macquarie University, 7th August 2019

4. All the fun of the FAIR
Image Israel Palacio https://unsplash.com/photos/P6FgiDNe6W4

5. What is FAIR?
A set of principles that describe the attributes
data need to have to enable and enhance reuse,
by humans and machines
FAIR session, Macquarie University, 7th August 2019
Image CC-BY-SA by SangyaPundir

6. 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
FAIR session, Macquarie University, 7th August 2019

7. 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.
FAIR session, Macquarie University, 7th August 2019

8. FAIR data checklist
• Findable
- Persistent Identifier
- Metadata online
• Accessible
- Data online
- Restrictions where needed
• Interoperable
- Use standards, controlled vocabs
- Common (open) formats
• Reusable
- Rich documentation
- Clear usage licence
https://doi.org/10.5281/zenodo.1065991FAIR session, Macquarie University, 7th August 2019

9. 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
FAIR session, Macquarie University, 7th August 2019

10. Open, FAIR and RDM – setting FAIR in context
Image Richard Balog https://unsplash.com/photos/P6FgiDNe6W4

11. Ultimately funders expect:
• timely release of data
- once patents are filed or on (acceptance for) publication
• open data sharing
- As open as possible as closed as necessary
• preservation of data
- typically 5-10+ years if of long-term value
• evidence of following policy
- a Data Management Plan or institutional policy and services
See the SPARC Europe funder policy overview:
https://sparceurope.org/latest-update-to-european-open-data-
and-open-science-policies-released

12. Shifting language: policy examples
FAIR session, Macquarie University, 7th August 2019
c.2000 – 2008
• Data management
• Data sharing
• Preservation
• Good research
• conduct codes
c.2010 on
• Open Science
• Open Data
c.2016 on
• FAIR data
• Reproducibility
• Ethical
?
* Anecdotal, not scientific. Personal observation on how I feel global data policy rhetoric and terminology has changed

13. Advice
Terminology changes but ideas persist.
Focus on core concepts:
• managing data well
• ensuring ethical conduct
• good quality, reusable data
• open sharing where possible
FAIR session, Macquarie University, 7th August 2019 Image by Headway
https://unsplash.com/photos/5QgIuuBxKwM

14. Forerunners to FAIR
OECD Principles and Guidelines for Access to
Research Data from Public Funding (2007)
A. Openness
B. Flexibility
C. Transparency
D. Legal conformity
E. Protection of IP
F. Formal responsibility
G. Professionalism
H. Interoperability
I. Quality
J. Security
K. Efficiency
L. Accountability
M. Sustainability
Science as an Open Enterprise (2012)
notion of ‘intelligent openness’ where data are
accessible, intelligible, assessable and useable
“Open scientific research data should be easily
discoverable, accessible, assessable,
intelligible, useable, and wherever possible
interoperable to specific quality standards.”
G8 Science Ministers Statement (2013)
FAIR session, Macquarie University, 7th August 2019

15. How do Open, FAIR & RDM intersect?
Open
FAIR data
Managed data
Internal
Self-interest
External
Community benefit
FAIR session, Macquarie University, 7th August 2019

16. FAIR and Open
• The greatest potential
reuse comes when data
are both FAIR and Open
• Align and harmonise FAIR
and Open data policy
FAIR session, Macquarie University, 7th August 2019
Concepts of FAIR and Open should not be conflated.
Data can be FAIR or Open, both or neither

17. Open, FAIR and RDM
FAIR session, Macquarie University, 7th August 2019
• 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

18. Turning FAIR into Reality
Image Kid Circus https://unsplash.com/photos/7vSlK_9gHWA

19. FAIR Data Expert Group
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
Turning FAIR into Reality: Report and Action Plan
https://doi.org/10.2777/1524

20. Address culture and technology
FAIR session, Macquarie University, 7th August 2019
Incentives
Metrics
Skills
Investment
Cultural and
social aspects
that drive the
ecosystem and
enact change
Cloudofregistries
Two sides of one whole

21. FAIR 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
FAIR session, Macquarie University, 7th August 2019

22. FAIR EG recommendations
FAIR session, Macquarie University, 7th August 2019
• Research communities
• Data service providers
• Standards bodies
• Coordination fora
• Policymakers
• Research funders
• Institutions
• Publishers
Recommendations
aimed at multiple
stakeholders:

23. FAIR metrics: data and services
FAIR session, Macquarie University, 7th August 2019
DATA REPOSITORY
F4. (meta)data are registered or
indexed in a searchable resource
+ TECHNOLOGIES
+ PROCEDURES
+ EXPERTISE
+ PEOPLE
(META)DATA
F1. (meta)data are assigned a
globally unique and persistent
identifier
F2. data are described with
rich metadata
F3. metadata clearly and
explicitly include the identifier
of the data it describes
Assessing FAIRness of data
Critical role of environment &
services in making data FAIR

24. FAIR 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.
• Build on existing work in this space – RDA Working Group
• https://www.rd-alliance.org/groups/fair-data-maturity-model-wg
• Certification schemes are needed to assess all components of
the ecosystem as services that enable FAIR
FAIR session, Macquarie University, 7th August 2019

25. Services that enable FAIR
Many aspects of FAIR apply to services (findability, accessibility,
use of standards…) but you also want to check:
• Appropriate policy is in place
• Robustness of business processes
• Expertise of current staff
• Value proposition / business model
• Succession plans
• Trustworthiness
FAIR session, Macquarie University, 7th August 2019

26. From metrics 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
FAIR session, Macquarie University, 7th August 2019

27. Many, many H2020 FAIR projects
clusters
National initiatives
• EOSC-Nordic
• EOSC-Pillar
• EOSC-synergy
• ExPaNDS
• NI4OS-Europe
FAIR session, Macquarie University, 7th August 2019

28. The European Open Science Cloud
Image Kyle Hinkson https://unsplash.com/photos/xyXcGADvAwE

29. An open festival for science
• Virtual space where science producers and science
consumers come together
• Federation of existing infrastructure and services
• An open-ended range of content and services
• Quality mark « Data made in Europe »
A platform for European research
FAIR session, Macquarie University, 7th August 2019

30. EOSC Governance 2019-2020
EOSC governance structure
FAIR session, Macquarie University, 7th August 2019
FAIR session, Macquarie University, 7th August 2019

31. Executive Board
FAIR session, Macquarie University, 7th August 2019
• Karel Luyben & Cathrin
Stover as Co-Chairs
• 8 representatives of
stakeholder groups
• 3 independent experts
https://www.eoscsecretariat.eu/
eb-profiles
FAIR session, Macquarie University, 7th August 2019

32. EOSC Exec Board Working Groups
GB/EB comms
and engagement
sub-group
Skills WG
Going Global WG
Others under
consideration
FAIR session, Macquarie University, 7th August 2019

33. What is each WG is doing?
• Map EOSC-relevant national infrastructures
• Analyse Member State readiness to provide financial resource (with Sustainability)
• Propose mechanisms to facilitate convergence and alignment
Landscape
• Recommend a minimal set of Rules of Participation that define the rights,
obligations and accountability governing all EOSC transactions
• Embrace the principles of openness, transparency and inclusiveness
Rules of P.
• Define, agree and develop an interoperability layer to federate systems i.e.
standards, open APIs and protocols
• Offer a catalogue of EOSC datasets and services
Architecture
• FAIR practices  EOSC interoperability framework (with Arch. & RoP)
• Persistent Identifier (PID) policy for EOSC (with Architecture)
• Frameworks to assess FAIR data and certify services that enable FAIR
FAIR
• Provide a set of strategic and financing orientations for EOSC post 2020
• In-depth analysis of business models and their different implications
• Options for a governance framework to steer & oversee EOSC operations
Sustainability
FAIR session, Macquarie University, 7th August 2019
https://www.eoscsecretariat.eu/eosc-working-groups
FAIR session, Macquarie University, 7th August 2019

34. All the fun of the FAIR
Keep up to date with progress on the EOSCsecretariat blog:
https://www.eoscsecretariat.eu/news-events-opinion/opinion
FAIR session, Macquarie University, 7th August 2019

35. Where next at Macquarie?
Image David Iskander https://unsplash.com/photos/iWTamkU5kiI

36. Hook DMPs into existing processes
• Good idea to use Infonethica
• Do lots of user testing and be willing to iterate
• Keep the DMP short – reuse info where possible
• Focusing on HDR students can be a good way to
seed good practice up – some UK unis get
supervisors to review / approve DMPs

37. Use existing fora to get advice
• RDA Active DMPs Interest Group
• https://rd-alliance.org/groups/active-data-management-plans.html
• ARDC DMP Community of Practice
• https://ardc.edu.au/resources/communities-of-practice
• Jiscmail list with global RDM community. 1751
subscribers, running since 2008, email archive…
• https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=RESEARCH-DATAMAN
FAIR session, Macquarie University, 7th August 2019

38. Focus on FAIR basics
For researchers
• Document data
• Use standards
• Deposit in a repository
• Assign a licence
• Get a PID
For services
• Get researchers thinking
early – DMP to plan
• Advise on standards
• Offer / point to repositories
• Assign and use PIDs
• Foster a culture of sharing
• Recognise and reward FAIR
FAIR session, Macquarie University, 7th August 2019

39. Reuse existing training materials
• MANTRA – https://mantra.edina.ac.uk
• RDMS MOOC – https://www.coursera.org/learn/data-
management
• Zenodo RDM training collection -
https://zenodo.org/communities/dcc-rdm-training-
materials
• FOSTER online toolkit –
https://www.fosteropenscience.eu/toolkit
• FOSTER trainer’s handbook -
https://www.fosteropenscience.eu/node/2150
FAIR session, Macquarie University, 7th August 2019

40. KEEP
CALM
Lots of others may
have done lots of
FAIR things, but this
is an opportunity.
Learn from their
mistakes and copy
good practice.
Don’t fret about
being behind…
Image Joe DeSousa https://unsplash.com/photos/0MGhdhObDXA

41. Thanks! Any questions?
FAIR session, Macquarie University, 7th August 2019