Overview of FAIRsharing RDA and Force11 WG and work to describe data resources

1. Core Data Resources
The role of FAIRsharing, its community and adopters
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Deprecated as subsumed or superseded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 data resource

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

20. 602 databases implement 1 or more standards
Work in progress
Data resources and standards

21. • 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

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

23. “….we investigate, first, which data repositories are
recommended by various stakeholders (publishers, funders,
and community organizations) and second, which repositories
are certified by a number of organisations…. Although the
criteria used by organisations recommending and certifying
repositories are similar, the lists of repositories that are
recommended by the various agencies are very different.
Out of all of the recommended repositories, less than 6%
obtained certification….”
Force11 DCIP and FAIRsharing – ongoing discussion
http://doi.org/10.5334/dsj-2017-042

24. In scope:
• A shared list of recommended deposition
repositories to save editors/publishers time and
provide authors with a consistent guidance
• focus on repositories accepting submission
Out of scope:
• Become or compete with
• a certification for repositories, like the
recently launched CoreTrustSeal (CTS:
Data Seal of Approval & World Data
System initiatives)
• evaluation by a community authority in a
given area, e.g. by ELIXIR
Force11 DCIP and FAIRsharing – ongoing discussion

25. Objectives:
1. Ensure that FAIRsharing will provide a
means by which repository certifications
(e.g. CTS) and/or community-driven
evaluations (e.g. ELIXIR) can be used to
display, filter and search for repository
discovery
1. Review existing recommendations by
publishers/journals’ to identify common set
of criteria (currently used) for selecting
repositories
Force11 DCIP and FAIRsharing – ongoing discussion

26. Force11 DCIP and FAIRsharing – ongoing discussion
Criteria Description
Status The status of a repository in its development life cycle; only repositories that are
production-level (‘ready’) should be selected. FAIRsharing uses three indicators (ready,
deprecated, in development).
Record maintainer Someone from the repository who has claimed and maintained its description. Records
that have maintainers hold more reliable information. Maintainers can link their name to
their ORCID profile.
Access condition Terms of access to the data; if freely available or subject to a request and approval
process.
Reuse condition Licence or terms of use for reusing existing data.
Deposition
condition
For repositories accepting submissions, where there is information on deposition
restriction(s) (e.g. by location, country, organization, etc.). This also helps to distinguish
primary from secondary databases. i.e. secondary databases do not accept direct
submissions.
Identifier schema The type of global, unique identifier schema assigned to the deposited data.
Data preservation
policy
Policy details that ensure ongoing access to, deposition and preservation of the data.
Data versioning The ability (and trackability) to make edits to a dataset after deposition.
Funder The organisation(s) that fund the repository; awareness of ongoing funding stream is
valuable. FAIRsharing works to link names to FundRef.
Standards The standards for data citation and data/metadata annotation that the repository (e.g. its
curation team and/or submission tool) implements.
User support It should be clear who they contact (e.g. a helpdesk) for support during or after
submission.

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
Metrics of FAIRness and FAIRsharing
in review at
FAIRmetrics.org
https://github.com/FAIRMetrics

28. 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