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Building a
Terminology for FAIR Data Competency
…..
Susanna-Assunta Sansone,
University of Oxford
On behalf of the terms4FAIRskills members:
https://terms4fairskills.github.io
EOSC Symposium, Budapest, 15-29 November 2019
Who are we?
Which organization we represent?
The terms4FAIRskills initiative
Scope is to building a terminology for the competencies, skills and
knowledge necessary to make data FAIR and to keep it FAIR
Main uses of the terminology include:
● Discovery: facilitate the annotation, search and evaluation of FAIR-
enabling materials (e.g. training) and resources
● Design: assist the creation and assessment of stewardship curricula
● Training: help trainers who teach FAIR data skills, researchers who
wish to identify skill gaps in their teams
● Formalisation: enable the definition of job descriptions and CVs with
recognised, structured competencies
Community-driven terminology
● Focus on usability and applications
○ Reused FAIR4S (output D7.5 of EOSCpilot) as starting point of the concepts
○ Collected use cases: what this is for is this for and for whom
○ Defined some competency questions: questions this terminology will help user to answer
○ Discussed structure: the richness and type of terminology (e.g. taxonomy or ontology) will
be informed by the points above
● Phase one: create the working structure and list of relevant terms
○ Defined every term using a standalone, context-independent description and add
provenance and editor details
○ Addes synonyms and terms from other terminologies
○ Defined levels of hierarchy and necessary relationships
○ Created an OWL file and continue work via WebProtege to ease the collaborative editing
○ Currently over 255 terms, details: https://github.com/terms4fairskills/FAIRterminology
● Unfunded, high-collaborative work
○ Started in Jan 2019 at GO-FAIR event, in the GO-TRAIN Implementation Network
○ 1st workshop, May 2019, CODATA, Paris; 2nd workshop, Oct 2019, DANS, The Hague
○ Currently discussing about next steps and funding opportunities (work so far is unfunded)
View of the high level terms
View of the high level terms
Initial community use cases
● ELIXIR
○ Annotation of training materials in TeSS
○ Creation of learning paths for FAIR skills training
○ EOSC-Life FAIRassist tool
● FAIRsFAIR competence centre (WP6)
○ Annotation of training materials
● SSHOC inventory of training materials
○ Annotation of training materials
● FAIRsharing
○ Annotation of resources to easily interlink FAIRsharing to
training/educational portals
terms4FAIRskills and EOSC
● Our work fills a niche and complements other activities
○ Not a catalog but a terminology that could work across communities
○ Puts the FAIR4S concepts ‘in action’ for use cases important to the
training community
○ Complements other metadata annotation schemas, incl schema.org,
bioschemas (which have more generic tags for training material), and
IEEE LOM
● The value proposition
○ Route towards ‘interoperability' of FAIR-enabling learning resources
○ Enables a federated approach to finding these learning resources
across different community catalogues/collections they are curated in
○ Levels the playing field if RIs and other institutions can benefit by
(re)using those materials
○ Would value participation and advice on funding!
Acknowledgements

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Terms4FAIRskills inititive for EOSC

  • 1. Building a Terminology for FAIR Data Competency ….. Susanna-Assunta Sansone, University of Oxford On behalf of the terms4FAIRskills members: https://terms4fairskills.github.io EOSC Symposium, Budapest, 15-29 November 2019
  • 4. The terms4FAIRskills initiative Scope is to building a terminology for the competencies, skills and knowledge necessary to make data FAIR and to keep it FAIR Main uses of the terminology include: ● Discovery: facilitate the annotation, search and evaluation of FAIR- enabling materials (e.g. training) and resources ● Design: assist the creation and assessment of stewardship curricula ● Training: help trainers who teach FAIR data skills, researchers who wish to identify skill gaps in their teams ● Formalisation: enable the definition of job descriptions and CVs with recognised, structured competencies
  • 5. Community-driven terminology ● Focus on usability and applications ○ Reused FAIR4S (output D7.5 of EOSCpilot) as starting point of the concepts ○ Collected use cases: what this is for is this for and for whom ○ Defined some competency questions: questions this terminology will help user to answer ○ Discussed structure: the richness and type of terminology (e.g. taxonomy or ontology) will be informed by the points above ● Phase one: create the working structure and list of relevant terms ○ Defined every term using a standalone, context-independent description and add provenance and editor details ○ Addes synonyms and terms from other terminologies ○ Defined levels of hierarchy and necessary relationships ○ Created an OWL file and continue work via WebProtege to ease the collaborative editing ○ Currently over 255 terms, details: https://github.com/terms4fairskills/FAIRterminology ● Unfunded, high-collaborative work ○ Started in Jan 2019 at GO-FAIR event, in the GO-TRAIN Implementation Network ○ 1st workshop, May 2019, CODATA, Paris; 2nd workshop, Oct 2019, DANS, The Hague ○ Currently discussing about next steps and funding opportunities (work so far is unfunded)
  • 6. View of the high level terms
  • 7. View of the high level terms
  • 8. Initial community use cases ● ELIXIR ○ Annotation of training materials in TeSS ○ Creation of learning paths for FAIR skills training ○ EOSC-Life FAIRassist tool ● FAIRsFAIR competence centre (WP6) ○ Annotation of training materials ● SSHOC inventory of training materials ○ Annotation of training materials ● FAIRsharing ○ Annotation of resources to easily interlink FAIRsharing to training/educational portals
  • 9. terms4FAIRskills and EOSC ● Our work fills a niche and complements other activities ○ Not a catalog but a terminology that could work across communities ○ Puts the FAIR4S concepts ‘in action’ for use cases important to the training community ○ Complements other metadata annotation schemas, incl schema.org, bioschemas (which have more generic tags for training material), and IEEE LOM ● The value proposition ○ Route towards ‘interoperability' of FAIR-enabling learning resources ○ Enables a federated approach to finding these learning resources across different community catalogues/collections they are curated in ○ Levels the playing field if RIs and other institutions can benefit by (re)using those materials ○ Would value participation and advice on funding!