Wish List

From Data on the Web Best Practices
Jump to: navigation, search

The list bellow suggests what the DWBP WG see as possible topics to be developed, that were not in the scope or could not be covered by this WG.

  • Multimedia data on the Web
  • DCAT update see in particular:
    • For DQV:
      • Elaborate on Parameters for metrics (Issue-223)
      • Investigate alignment between DQV and quality features of hcls dataset profile (Issue-221)
      • Alignment with other quality vocabularies - and try to have these vocabularies use DQV patterns instead of keeping different wheels (Radulovic et al., Fürber et al, sister-ontologies of daQ).
      • Guidance on how DQV can work with quality statistics vocabulary shall be provided with future versions of the DQV documentation.
      • Discuss adding attributes for the severity of a quality problem, as per discussion with Amrapali Zaveri https://lists.w3.org/Archives/Public/public-dwbp-comments/2016Aug/0028.html
      • Discuss adding attributes for the 'provenance' of a quality measurement in a part of a dataset, as per discussion with Amrapali Zaveri https://lists.w3.org/Archives/Public/public-dwbp-comments/2016Aug/0028.html
      • Multilingual Translation for DQV
      • Should we rename QualityCertificate? the current name is a little misleading, it seems it is a quality certificate rather then an annotation pointing to a quality certificate .
      • Check how the existing implementations implement DQV, and update the page if new implementations.
      • Investigate whether we want to have more integrity conditions (in SHACL?) to enhance interoperability between DQV implementations.
      • Check the 3-4 editor's notes in the DQV document that call for further action and investigation, e.g. update of the references to Web Annotation (once it is a Proposed Recommendation)
    • Best practices for creating vocabularies,
  • BPs for JSON-LD API
  • A way to use web annotations and/or web mentions with DQV and DUV to facilitate finding out about other people's use/opinions of a given dataset. In particular, it would be helpful to be able to discover reuses and references with minimal effort on the part of the person reusing or referencing.