Presentation about FAIRsFAIR deliverable 2.1. at EOSC Symposium on 27 Oct 2019 in Budapest

1. Report on FAIR requirements
for persistence and interoperability 2019
Jessica PvE WP2

2. FAIRsFAIR in a nutshell
Call: H2020-INFRAEOSC-5c
Budget: 10 million euro
Length: 36 months
Starting date: March 1 2019
22 partners from 8 MS
6 core partners

3. Work Packages
Practice and
policy
Training and
education
Certification

4. What we were looking at
4
• Practical implementations of semantic
interoperability across infrastructures (esp. ESFRIs)
• Recommendations and relevant work in RDA
• What are seen as the most critical factors for
success in FAIR and semantic interoperability
• What are the most serious omissions in currently
available tools and specifications
• Focus on semantic interoperability and persistent
identifiers

5. Challenges
5
• Very large and diverse scope
• Rapid development
• Incoherent terminology
• Scope creep of FAIR principles and technologies
Gustavo.candela [CC BY-SA 4.0]

6. First of three reports (D2.1) Landscaping
6
• Desk research
• Survey data
• Interviews with
focus
infrastructures

7. The elements of
FAIRness
7
• FAIR technologies and
methods
• Semantic
interoperability
• Semantic artefacts
• PIDs and PID services
• FAIR in the context of the
data life cycle
• Repositories
• Evolving datasets and
data citation
The OBO Foundry

8. The current status at
a glance
8
• International efforts to
promote FAIR principles
• EOSC
• FORCE11
• GO FAIR
• FAIRsharing
• DataCite and
Re3Data.org
• Freya and Open
Citations
• Research Data Alliance
• The landscape of
infrastructures
• Energy
• Environment
• Health&Food
• Physical Sciences &
Engineering
• Social &Cultural
innovation
• Data & computing

9. Semantic artefacts adoption
9
1. Coverage in field (external):
· They should be widely approved and adopted by the scientific
community (indicator: use within community, mandates)
2. Coverage of content (internal):
· They must cover a sufficient amount of the terminology needed
(indicators: coverage, completeness and coherence).
· They must have a structure that corresponds to the ontology of
the field (indicators: certification, quality, community approval)
3. Governance (technical and legal):
· They must be usable and fit the purpose (compatibility, format,
granularity, workflow etc)
· They must be actively maintained by a trusted, authoritative
party (curation, versioning, persistence)
· They must be open and documented

10. Conclusions
10
FAIRness on a more generic level is not ready
and clearly defined.
The landscape is diverse in all aspects.
Differences inside domains are often bigger than
differences between domains.
Semantic artefacts are a key element in building
interoperability and good quality (meta)data.

11. Conclusions
11
The landscape is diverse in all aspects.
The development should be research rather than
technology driven.
Community adoption and trust are the decisive
factors.

12. Conclusions
12
Crosswalks, mappings and semantic application
profiles should be published and registered in
machine readable formats.
Reuse of semantic artefacts should be
promoted by publishing application profiles.
Curated registries like the EOSC Hub,
FAIRsharing and re3data.org are important
resources.

13. Conclusions
13
Data citation and machine actionable solutions
should be developed in parallel.
The most popular, potentially most useful, and
most complex approaches on improving
FAIRness of data are based on technologies
using Linked Data.
Solutions should be user friendly, context
sensitive and transparent to the users.

14. Next steps
14
A lot of relevant work will be done within the next year
in EOSC project including our own WP. This will be
included in the updated report
Please, give feedback on the deliverable!
D2.1 Report on FAIR requirements for persistence
and interoperability 2019