A measure of the usefulnessuseableness of data. An ideal dataset is accurate, complete, timely in publication, consistent in its naming of items and its handling of e.g. missing data, and directly machine-readable (see data cleaning), conforms to standards of nomenclature in the field, and is published with sufficient metadata that users can easily understand, for example, who it is published by and the meaning of the variables in the dataset. Source: ODH

From a user perspective, data quality can be summarised as fitness for use/ purpose. This  not only applies to the technical accessibility of a dataset, but also to its legal accessibility (can the dataset be used from a legal perspective), the financial accessibility (can the user afford to pay the price), and intellectual access (does the user understand/ is intellectually capable of using the dataset). ISO defined quality as “the totality of characteristics of an entity that bears its ability to satisfy stated and implied needs” (ISO 8402, 1994; see also Strong D.M., Lee Y.W., Wang R.Y. Data quality in context. Commun. ACM. 1997;40:103–110. doi: 10.1145/253769.253804).

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