The FAIR Principles propose key characteristics that all digital resources (e.g. datasets, repositories, web services) should possess to be Findable, Accessible, Interoperable, and Reusable by people and machines. The Principles act as a guide that researchers should expect from contemporary digital resources, and in turn, the requirements on them when publishing their own scholarly products. As interest in, and support for the Principles has spread, the diversity of interpretations has also broadened, with some resources claiming to already “be FAIR”. This talk will elaborate on what FAIR is, why we need it, what it entails, and how we should evaluate FAIRness. I will describe new social and technological infrastructure to support the creation and evaluation of FAIR resources, and how FAIR fits into institutional, national and international efforts. Finally, I will discuss the merits of the FAIR principles (and what we ask of people) in the context of strengthening data-driven scientific inquiry.

1. 1
Michel Dumontier, Ph.D.
Distinguished Professor of Data Science
Director, Institute of Data Science
@micheldumontier::RDA:2018-01-31

2. An increasing number of
discoveries are made using other
people’s data
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3. 3
A common rejection module (CRM) for acute rejection across multiple organs identifies
novel therapeutics for organ transplantation
Khatri et al. JEM. 210 (11): 2205
DOI: 10.1084/jem.20122709
@micheldumontier::RDA:2018-01-31
1. CRM genes correlated with the extent of graft injury and predicted future injury to a graft
2. Mice treated with drugs against the CRM genes extended graft survival

4. However, significant effort was
needed to find the right datasets,
put them together, and use them
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5. @micheldumontier::RDA:2018-01-315

6. If we are ever to realize the full
potential of content we create
then we must find ways to reduce
the barrier to (automatically) find
and reuse that content
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7. To achieve this objective
we must build a social and
technological infrastructure for
the discovery and assessment of
digital resources
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8. Principles to enhance the value of all digital resources
data, images, software, web services, repositories,…
Developed and endorsed by researchers, publishers,
funding agencies, industry partners.
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9. @micheldumontier::RDA:2018-01-319
http://www.nature.com/articles/sdata201618
Dec 2017

10. Rapid Adoption of Principles
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11. @micheldumontier::RDA:2018-01-3111
4 Principles (F,A,I,R) and 15 sub-principles.

12. FAIR Principles - summarized
Findable
• Globally unique, resolvable, and persistent identifiers
• Machine-readable descriptions to support structured search
Accessible
• Clearly defined access and security protocols
• Metadata is always accessible beyond the lifetime of the digital resource
Interoperable
• Extensible machine interpretable formats for data + metadata
• Vocabularies themselves must be FAIR
• Linked to other resources
Reusable
• Provide licensing, provenance, and use community-standards
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13. FAIR Principles are FAIR:
published as a Trusty Nanopublication
in the nanopub server network
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http://purl.org/fair-ontology#FAIR

14. Improving the FAIRness of digital
resources will increase their quality and
their potential for reuse.
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15. What is FAIRness?
FAIRness reflects the extent to which a digital
resource addresses the FAIR principles as per the
expectations defined by a community.
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16. How it might look at DANS
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17. Measuring FAIRness
• A metric is a standard of measurement.
• It must provide clear definition of what is being
measured, why one wants to measure it.
• It must describe the process by which you
obtain a valid measurement result, so that it
can be reproduced by others. It needs to
specify what a valid result is.
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18. Qualities of a Good Metric
• Clear: anyone can understand the purpose of the metric
• Realistic: compliance should not be unduly complicated
• Discriminating: the measure can distinguish between
those that meet and those that do not meet the
objective
• Measurable: the assessment can be made in an
objective, quantitative, machine-interpretable, scalable
and reproducible manner
• Universal: The metric should be applicable to all digital
resources
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19. • 14 universal metrics covering each of the FAIR sub-principles.
• The metrics demand evidence from the community, some of which may
require specific new actions.
• Digital resource providers must provide a web-accessible document with
machine-readable metadata (FM-F2, FM-F3), detail identifier management
(FM-F1B), metadata longevity (FM-A2), and any additional authorization
procedures (FM-A1.2).
• They must ensure the public registration of their identifier schemes (FM-
F1A), (secure) access protocols (FM-A1.1), knowledge representation
languages (FM-I1), licenses (FM-R1.1), provenance specifications (FM-
R1.2).
• They must provide evidence of ability to find the digital resource in search
results (FM-F4), linking to other resources (FM-I3), FAIRness of linked
resources (FM-I2), and meeting community standards (FM-R1.3)
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20. @micheldumontier::RDA:2018-01-3120

21. @micheldumontier::RDA:2018-01-3121
http://www.w3.org/TR/hcls-dataset/
Evidence:
standard is also
registered in
FAIRsharing
https://fairsharing.org

22. smartAPI
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http://smart-api.info

23. @micheldumontier::RDA:2018-01-3123

24. @micheldumontier::RDA:2018-01-3124

25. Availability of Metrics
• The current metrics are available for public discussion
at the FAIR Metrics GitHub, with suggestions and
comments being made through the GitHub comment
submission system (https://github.com/FAIRMetrics).
• They are represented as i) nanopublications and ii)
latex and iii) PDF documents
• They are free to use for any purpose under the CC0
license.
• Versioned releases will be made to Zenodo as the
metrics evolve, with the first release already available
for download
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26. @micheldumontier::RDA:2018-01-3126

27. @micheldumontier::RDA:2018-01-3127

28. @micheldumontier::RDA:2018-01-3128

29. @micheldumontier::RDA:2018-01-3129

30. Next steps
• Open development of universal & resource-specific metrics
(stay tuned)
• Development of shared infrastructure to support metric-based
FAIR assessments
• Applications to create and publish FAIR assessments
• Development of training, and support for implementation and
adoption.
• Measuring the impact of FAIR for research and innovation
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31. Acknowledgements
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FAIR
FAIR metrics
Myles Axton, Jennifer Boyd, Helena Cousijn, Scott Edmunds, Emma Ganley, Andrew Hufton, Rebecca
Lawrence, Thomas Lemberger, Varsha Khodiyar, Robert Kiley, Michael Markie and Jonathan Tedds for their
prospective on the metrics as journal editors and publishers, and their contribution to FAIRsharing
RDA/Force 11 WG.

32. michel.dumontier@maastrichtuniversity.nl
Website: http://maastrichtuniversity.nl/ids
32 @micheldumontier::RDA:2018-01-31
How are you contributing to the FAIR initiative?