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Arnold Kuzniar edited this page Jun 18, 2018 · 62 revisions

Useful links

TODO

  1. Get to know your data(sets) and use case.
  • Which data are relevant or subject of this use case? (e.g. see LOFAR DBView)
  • How do you access the (meta)data using which protocols?
  • Which data can (not) be referred to by persistent IDs?
  • In what formats are the data distributed?
  • How are the data structured or organized? (e.g. catalog->dataset->distribution for use of the FAIR Data Point metadata service)
  • Is this structure machine-readable? Suggestion: Install the (Python) FDP and try to fill in the config file.
  1. Make the (meta)data available in a machine-readable and semantically interoperable format(s).
  • Which (domain-specific) vocabularies and/or ontologies are relevant for this use case? (e.g. DCMI, DCAT, VoID, IVOA Ontology)
  • Which terms (or URIs/IRIs) can be used to describe (identify) the data and associated metadata?
  • "Semantify" the (meta)data i.e. transform into RDF and make these available through services.

Complementary Ideas

  1. Improve the Search UI
  1. Apply IVOA standards and tools

Ontology-Schema Mapping

See Ontology page.

References

Wilkinson et al. (2018) A design framework and exemplar metrics for FAIRness. bioRxiv, doi: 10.1101/225490

Wilkinson et al. (2016) The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3. doi: 10.1038/sdata.2016.18

Wilkinson et al. (2017) Interoperability and FAIRness through a Novel Combination of Web Technologies. PeerJ Computer Science, 3. doi: 10.7717/peerj-cs.110

Mons et al. (2018) Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud. Information Services and Use, 37, 49-56. doi: 10.3233/ISU-170824

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