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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
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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
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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
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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
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Metrics Dimensions should be independant
Evaluation and ranking should be practical Automatic versus manual evaluation Direct versus indirect evaluation (proxies)
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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)
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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
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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)
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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)
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Completeness & Quality of Content Evidence: metadata & data
Metadata & data correctness Content description matches data content Validity of used methods (needs domain expertise)
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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)
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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)
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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
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Service
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