Are we FAIR yet?

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

An increasing number of
discoveries are made using other
people’s data

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
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

However, significant effort was
needed to find the right datasets,
put them together, and use them

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

To achieve this objective
we must build a social and
technological infrastructure for
the discovery and assessment of
digital resources

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.

http://www.nature.com/articles/sdata201618
Dec 2017

Rapid Adoption of Principles

4 Principles (F,A,I,R) and 15 sub-principles.

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

FAIR Principles are FAIR:
published as a Trusty Nanopublication
in the nanopub server network
http://purl.org/fair-ontology#FAIR

Improving the FAIRness of digital
resources will increase their quality and
their potential for reuse.

What is FAIRness?
FAIRness reflects the extent to which a digital
resource addresses the FAIR principles as per the
expectations defined by a community.

How it might look at DANS

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.

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

• 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)

http://www.w3.org/TR/hcls-dataset/
Evidence:
standard is also
registered in
FAIRsharing
https://fairsharing.org

smartAPI
http://smart-api.info

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

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

Acknowledgements
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.

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

Are we FAIR yet?

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