Welcome to the showroom with demo dataset quality descriptions, demonstrating the usage of DQV-AP-NO!
DQV-AP-NO ⧉ (in Norwegian only) is the Norwegian application profile of W3C’s Data Quality Vocabulary ⧉, for describing dataset quality.
Feedback: Please provide your feedback as Github Issues ⧉, if any.
Disclaimer: The demo resources made available from the various showrooms are meant for demo purposes only.
Purpose
This showroom is meant to
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demonstrate how to use DQV-AP-NO to describe data quality;
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demonstrate some cross-references between data quality descriptions and other resources demonstrated in some of the other showrooms;
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provide reusable, machine-readable data quality descriptions, for testing and demonstration purposes.
Disclaimer: The machine-readable demo data quality descriptions are meant for demo purposes only. They do not represent any real world phenomena at all. On the other hand, they are supposed to syntactically conform to DCAT-AP-NO.
Demo resources in this showroom
In this showroom we have a demo dataset with a set of quality measurements and quality annotations:
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It has two quality annotations:
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one referring direct to <https://data.norge.no/vocabulary/dqvno#isAuthoritative>, which is a predefined instance of
dqv:QualityCertificateindicting that the dataset is authoritative. -
another referring to <demoQA1>, which is an instance of
dqv:UserQualityFeedbackcontaining a user feedback on the quality of the dataset. It also refers to a predefined quality dimension, <https://data.norge.no/vocabulary/quality-dimension#completeness> ("completeness"), which is one of the categories in the controlled vocabulary Quality (sub)dimension ⧉.
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It has a number of quality measurements, <demoQM1>, …, <demoQM17>:
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Each quality measurement refers to a predefined instance of
dqv:Metricindicating the quality metric that particular measurement is a measurement of. For example, <demoQM1> is a measurement of the predefined quality metric <https://data.norge.no/vocabulary/quality-metric#qm-completeness-1001> (wether or not there are missing objects in the dataset); <demoQM2> is a measurement of the predefined quality metric <https://data.norge.no/vocabulary/quality-metric#qm-completeness-1002> (number of missing object); <demoQM3> is a measurement of the predefined quality metric <https://data.norge.no/vocabulary/quality-metric#qm-completeness-1003> (rate of missing object) etc.
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Some cross-references with other demo resources
Note: Not all possible cross-references are demonstrated in this showroom. The lists are thus not exhaustive.
Data quality descriptions and classifications, for example:
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In order to have common understanding of the quality (sub)dimensions and quality metrics, there are a set of predefined quality (sub)dimensions published in the controlled vocabulary Quality (sub)dimension ⧉, and a set of predefined quality metrics published in the controlled vocabulary Quality metric ⧉.
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Quality descriptions may therefore refer to the predefined categories of quality (sub)dimensions and quality metrics, as explained above.
Data quality descriptions and concepts, for example:
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The predefined quality (sub)dimensions and the predefined quality metrics that quality descriptions refer to, are concepts.
Data quality descriptions and datasets, for example:
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Data quality descriptions are about the quality of datasets and referred to from descriptions of datasets.