Overview of FAIRsharing RDA and Force11 WG and work with the FAIR metrics WG

1. FAIRsharing and the FAIRmetrics
Research Data Alliance (RDA) – Plenary 11th, Berlin, 19-23 March, 2018
Slides at: https://www.slideshare.net/SusannaSansone
Associate Professor, Associate Director
Susanna-Assunta Sansone, PhD
ORCiD: 0000-0001-5306-5690
@SusannaASansone
Consultant, Honorary Academic Editor

3. rd-alliance.org
known as the BioSharing WG
known as the FAIRsharing WG

4. Access this document from the RDA BioSharing WG or the Force11 FAIRsharing WG pages, or click here

5. 1. The FAIRsharing registry of curated and interlinked records on
o standards (for identifying, reporting, and citing data and metadata),
o databases (repositories and knowledge-bases) and
o data policies (from journals, publishers, funders and other
organizations)
A resource - with a team of curators and developers, and an international advisory board -
embedded in many international infrastructure projects and programmes, e,g, ELIXIR, GO-
FAIR, NIH FAIR Data Commons, EOSC, and recognized by a number of publishers,
journals, standardization initiatives and stakeholders in all RDM-related sectors.

7. Databases/data
repositories
Metadata standards
Formats Terminologies Guidelines
Ready for use, implementation, or recommendation
In development
Status uncertain
Deprecated as subsumed or superseded
All records are manually curated
in-house and verified and claimed by
the community behind each resource
Assign ‘indicators’ to describe their status
Data policies by
funders, journals and
other organizations

8. 1. The FAIRsharing registry of curated and interlinked records on
o standards (for identifying, reporting, and citing data and metadata),
o databases (repositories and knowledge-bases) and
o data policies (from journals, publishers, funders and other
organizations)
2. The related FAIRsharing recommendations
o to guide users and the producers of standards and databases to select
and describe these resources, or to recommend them in data policies

11. Examples of use and adoption

15. Example of a record describing
a metadata standard

20. Analysing the FAIRsharing
registry content
(work in progress)

21. Classification – work in progress
Classifying data
resources and
standards by
disciplines in not
trivial
Work in progress

22. 602 databases implement 1 or more standards
Work in progress
Data resources and standards

23. • Top recommended databases are all repositories, as expected
• Outliers are knowledgebases such as model organism data resources, such as
FlyBase
Work in progress
Data resources recommended by publishers/journals

24. Other indicators for data resources:
community evaluations,
stakeholders criteria,
certifications,
and metrics of FAIRness
(work in progress)

25. Defining FAIRness
FAIRness reflects the extent to which a digital resource addresses
the FAIR principles as per the expectations defined by a community.

26. in review at
Proposed metrics and framework
The FAIRmetrics.org
Working Group:
a core set of semi-quantitative
metrics (measurable indicators)
for the evaluation of FAIRness
FAIRmetrics.org
https://github.com/FAIRMetrics
Quality 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 resources that
meet the criteria and those that do not
• 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

27. • We are working with the FAIR metrics WG to:
• Serve as registry to describe digital assets (databases/repositories, standards,
policies), enhance discoverability (schema.org), citability (DOIs)
• Be a look up service for identifier schemas and standards
• Engage with journals and publishers on their needs and use of the metrics
Varsha K, Iain H, Andrew H = Springer Nature
Emma G = PloS
Theo B = BMJ
Jennifer B = OUP
Scott E = Giga
Amye K = BMC
Rebecca L, Mikael M = F1000
Robert K, DavidC = WT Open Research
Thomas L = EMBO
Helena C = Elsevier
Jonathan T = Ubiquity
Myles A = Nat Gen
Role of FAIRsharing
in review at
FAIRmetrics.org
https://github.com/FAIRMetrics

28. • 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)
• 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)
in review at
FAIRmetrics.org
https://github.com/FAIRMetrics

29. Philippe
Rocca-Serra, PhD
Senior Research Lecturer
Alejandra
Gonzalez-Beltran, PhD
Research Lecturer
Milo
Thurston, DPhD
Research Software Engineer
Massimiliano
Izzo, PhD
Research Software Engineer
Peter
McQuilton, PhD
Knowledge Engineer
Allyson
Lister, PhD
Knowledge Engineer
David
Johnson, PhD
Research Software Engineer
Melanie
Adekale, PhD
Biocurator Contractor
Delphine
Dauga, PhD
Biocurator Contractor
Susanna-Assunta Sansone, PhD
Associate Professor, Associate Director
Research Software Engineer
Research Software Engineer
contact@fairsharing.org
@FAIRsharing_org
fairsharing.org/communities