A keynote given on the FAIR Data Principles at the FAIRplus Innovation and SME Forum, Hinxton Genome Campus, Cambridge, UK, 29 January 2020. The history of the principles, issues about the principles and speculations about the future
The University of Manchester, UK
ELIXIR-UK Head of Node & Interoperability Platform
FAIRDOM Association e.V.
FAIRplus Innovation and SME Forum,
29 January 2020, Hinxton, UK
FAIR History and
2. Scientific Data 3, 160018 (2016) doi:10.1038/sdata.2016.18
2014 2015 2016
45 European, 8 USA, 1 South American
5 Companies, 3 Public Orgs
FAIR DO Framework
• Minimal metadata and
Principles need to be developed
for other objects, esp. living
• RDA FAIR Software IG
• FAIR Workflows in EOSC Life
EC’s Turning FAIR into Reality (2018)
14. Jacobsen et all FAIR Principles:
J Data Intelligence (2020)
“FAIR is non-trivial, and domain specific at anything other than the most
Mark Wilkinson 2019
Mons et al Cloudy, increasingly FAIR;
Revisiting the FAIR Data guiding
principles for the European Open
Science Cloud. Information Services
& Use. 37. 1-8. 10.3233/ISU-170824
Principles, not Precise Practice
“the proposed implementation of these principles,
with the goal of an Internet of FAIR Data and
Services, is beginning to raise concern and
“interpretation of the derived guiding principles for
implementation is far from straightforward”
15. The Principles are…
FAIR Mythology Summarised
• An aspiration, a journey.
• A spectrum.
• Domain respectful / specific
• Implementable with todays
protocols and standards.
• A small part of indicators.
• A framework for prompting
• Work in progress.
The Principles are not…
• A standard.
• One size fits all.
• One domain
• Inventing new protocols.
• Technology specific
• Anything to do with quality.
• Synonymous with open.
• An architecture
• Tablets of stone.
Mons et al Cloudy, increasingly FAIR; Revisiting the FAIR Data guiding principles for the European Open Science Cloud. Information Services & Use. 37. 1-8. 10.3233/ISU-170824, Dunning et al Are the
FAIR Data Principles fair? IDCC17, Jacobsen et al FAIR Principles: Interpretations and Implementation Considerations Data Intelligence 2(2020), 10–29. doi: 10.1162/dint_r_000
17. The why and what of FAIR has things to say about FAIR
today and the future.
18. Why FAIR?
Knowledge Turning, Information Flow
Josh Sommer, Chordoma Foundation, 2011
Flow of information across
collaborating yet competing
groups with churning
Flow across all social groups
the individual, the lab, the project, the
organisation, the community
Flow across all tech infra
platforms, repositories, registries
Reduce Knowledge Loss
19. Knowledge Exchange
Producers and consumers.
Retention and flow
Who is judged FAIR?
The repository owners?
The content providers to the
Why GUIDs are important!
Researchers, Company Scientists,
Neylon, Knowledge Exchange Report: http://www.knowledge-
20. Neylon, Knowledge Exchange Report: http://www.knowledge-
Good data management
Rich metadata, open formats
Prepare to share
Submit to a repository …
Future proofed formats
Clear licensing …
Researchers, Company Scientists,
21. Neylon, Knowledge Exchange Report: http://www.knowledge-
Interop drivers speculative
Beneficiaries connected (?)
Interop drivers Competency Questions
Researchers, Company Scientists,
22. Open Science Automation Reproducible
Influences on FAIR
23. The Science of Team Science
Collaboration made up of individual
effort, still individually rewarded.
Even within big projects and
“I” in FAIR means “I” want to find,
access and reuse your/their data.
25. Data citation
Publisher and Funder Policies
Registry and repository explosion
Data Management Planning at the three
The same old concerns
Sloooooooow cultural normalisation
Over a decade, and today…
Data Sharing that is “Open by
Default, Closed as Necessary”
26. republic of science*
regulation of science
G8 Open Data Charter, 2013
Extrinsic drivers on
• Institutions, “regular
researchers” absent, middle
Regulation vs republic
• Capitalising on investments
• Compliance auditing
• Competitive advantages
• Accelerating science
Lightweight mark-up of a
few common terms
A little semantics
• 5 minimal
• 8 recommended
• What’s the license?
• What’s the identifier?OWL ontologies -> Schema.org
RDF -> JSON(-LD) mark-up
SPARQL -> GraphQL
Semantic Web -> Knowledge Graphs
35. FAIR is not about a
Scientific value or
Data Set Prioritisation, CMM ….
36. Thanks to Wei Gu for the Analogy!
Like PacMan not the Holy Grail
A spectrum of indicators with
different levels of maturity and
importance to different players ->
A mixed FAIR data portfolio at
different maturity depths
Requires communities to define
and develop just in time /
38. FAIR is not one size fits all
39. The FAIR intentions of
To improve the exchange
of information and raise
43. 1. What does FAIR really mean?
2. Isn’t this just for Data Repository
3. How do we do FAIR into our lab?
What can we use?
4. Does everything have to be FAIR
when most data I’m not going to
5. Should I bother with legacy data?
6. How do we resource it?
7. If I make the effort how will I benefit?
Sounds Hard …
44. FAIR from the First
Moving FAIR upstream
The leaky data pipeline
Support for metadata collection
through research workflows
Standardised Production vs
Handy data but not for this
Data in Paper
45. Challenges facing FAIR mortals
• Granularity levels
• Overthinking, analysis paralysis
• Disconnect of providers from
• Examples to copy
• Assembling a FAIR mixed skills
• Process + People
46. Practice by Mortals, not Purists
Get Expert Help Skill your Team
Publish your Data
with a licence
Use a data catalogue
Register your repositories
Set FAIR governance
Make a FAIR-aware patient
Develop a Data
that fits into your
Corpas M et al (2018) A FAIR guide for data providers to maximise sharing of human genomic data, PLOS Comp Bio
Boeckhout M et al (2018) The FAIR guiding principles for data stewardship: fair enough?, E J of Human Genetics
53. Eight FAIR Future Virtues
1. Lighten up on Principle Anxiety.
2. Community defined “FAIR enoughs” -> “GO-FAIR Profiles”.
3. Valuing FAIR in the organisations researchers actually work
in OR disintermediation.
4. The rise of the FAIR profession.
5. FAIR methodologies that scales, with toolkits, templates &
6. FAIR Digital Object Framework using todays conventions.
7. Selective FAIR data islands, with bridges.
8. Upstream FAIR via libertarian paternalism.
This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under
grant agreement No. 802750. This Joint Undertaking receives support from the European Union’s
Horizon 2020 research and innovation and EFPIA companies.
Oya Deniz Beyan