standards > model/format > DOI:10.25504/FAIRsharing.tnu8V7


ready Data Quality Vocabulary

Abbreviation: DQV


General Information
Aiming to facilitate the publication of such data quality information on the Web, especially in the growing area of data catalogues, the W3C Data Web Best Practices Working (DWBP) group has developed the Data Quality Vocabulary (DQV). DQV is a (meta)data model implemented as an RDF vocabulary with properties and classes suitable for expressing the quality of datasets and their distributions. DQV has been conceived as a high-level, interoperable framework that must accommodate various views over data quality. DQV does not seek to determine what "quality" means. Quality lies in the eye of the beholder; and there is no objective, ideal definition of it. Some datasets will be judged as low-quality resources by some data consumers, while they will perfectly fit others' needs. There are heuristics designed to fit specific assessment situations that rely on quality indicators, such as pieces of data content, pieces of data meta-information and human ratings, to give indications about the suitability of data for some intended use. DQV re-uses the notions of quality dimensions, categories and metrics to represent various approaches to data quality assessments. It also stresses the importance of allowing different actors to assess the quality of datasets and publish their annotations, certificates, or mere opinions about a dataset.

Homepage https://www.w3.org/TR/vocab-dqv/

Developed in Worldwide

Created in 2016

Knowledge Domains  BETA

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How to cite this record FAIRsharing.org: DQV; Data Quality Vocabulary; DOI: https://doi.org/10.25504/FAIRsharing.tnu8V7; Last edited: Jan. 9, 2019, 3:06 p.m.; Last accessed: Jan 22 2019 6:44 a.m.


This record is maintained by riccardoalbertoni

Record added: Dec. 19, 2018, 3:46 p.m.
Record updated: Dec. 20, 2018, 2:58 p.m. by The FAIRsharing Team.

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