1. Helena Cousijn, Claire Austin, Jonathan Petters & Michael Diepenbroek
WDS/RDA Publishing Data IG WDS/RDA Certification of Digital Repositories IG Assessment of Data Fitness for Use
Introduction
Helena Cousijn, Claire Austin, Jonathan Petters & Michael Diepenbroek

2. Initiatives F A I R 5 ★ Open Data (Tim Berners Lee) FAIR principles
A design framework & exemplar metrics for FAIRness
GEO label facets
ESIP Information Quality Cluster
Enabling FAIR Data Across the Earth & Space Sciences
Certification of data centers/repositories
F A I R
2 User Reviews
1 Archivist Assessment
24 Downloads

3. Criteria I Inherent properties Non-inherent properties
objectively verifiable or even measurable
e.g. validity of used methodologies, completeness of metadata
Non-inherent properties
~subjective descriptions assigned to data
e.g. social tagging, downloads as indicator to data quality

4. Criteria II properties directly related to data objects
E.g. PIDs, citation, precision of data values
properties related to data findability & accessibility
E.g. quality of services for data discovery and interoperability
properties characterizing data management processes
E.g. curational workflows, tools, human resources!
Not transparent to users

5. Metrics Dimensions should be independant
Evaluation and ranking should be practical
Automatic versus manual evaluation
Direct versus indirect evaluation (proxies)

6. Agenda
Data Fitness for Use as part of the CoreTrustSeal: Mustapha Mokrane (ICSU WDS) – Chair of the CoreTrustSeal Board
Assessing FAIRness within the Enabling FAIR Data project: Shelley Stall - Director of the AGU Data Program
A design framework and exemplar metrics for FAIRness: Peter Doorn – Director of Data Archiving and Networked Services (DANS)
Proposed criteria Data Fitness for Use WG: Michael Diepenbroek (PANGAEA) – Co-Chair of the Data Fitness for Use WG
Discussion on governance (30 minutes)

7. Dimensions & evidence required
Completeness & Quality of Content
Evidence: Metadata & data
Findability
Evidence: Services exposed, metadata & data
Accessibility
Evidence: Services exposed, metadata, documentation of system & services
Interoperability
Evidence: Services exposed, metadata
Curation
Evidence: Services exposed, metadata & data, documentation of system & services

8. Completeness & Quality of Content Evidence: metadata
Metadata completeness
Citation (authorship, year, comprehensive title, PID)
Content description (listing of measurement & obvervation types incl. used methods)
Coverage (spatial, temporal)
Provenance
authorship (PIs, institutions, labs)
data collection/generation (sampling events, processing steps, experimental setup)
references to related work (literature)
Terms of usage: licenses, other conditions, protection (ethical issues)
Persistent identifier (for the data set, others for literature, authors, projects, terms etc.)
Metadata adequate to science domain (domain expertise needed)

9. Completeness & Quality of Content Evidence: metadata & data
Data completeness
difficult to evaluate. Minimum: content description should match data content (for data matrices comparison of column headers with content description)

10. Completeness & Quality of Content Evidence: metadata & data
Metadata & data correctness
Content description matches data content
Validity of used methods (needs domain expertise)

11. Completeness & Quality of Content Evidence: metadata & data
Machine readibility of data & metadata
data & metadata consistently structured (consistent, standard formatting)
data & metadata harmonized (consistent use of metadata elements (possibly needs complementary information from other requirements, e.g. usage of RDBMS & standard terminologies (ontologies), type of curation)

12. Completeness & Quality of Content Evidence: metadata & data
Machine readibility of data & metadata
data & metadata consistently structured (consistent, standard formatting)
data & metadata harmonized (consistent use of metadata elements (possibly needs complementary information from other requirements, e.g. usage of RDBMS & standard terminologies (ontologies), type of curation)

13. All other dimensions
Do supplied services match results from certification?
Findability: Sufficient discovery metadata - usually metadata are enriched with related terms, e.g. using terminologies/ontologies. Such terms might not be visible in the metadata or data
Curation: Curation level claimed by the repository matches the completeness, correctness, structuring, and harmonization of metadata & data

14. Service