Abstract:
Problem-solving based on available research data, especially in the context of open science and research infrastructures, should ensure the possibility of their multiple reuse. Data quality metrics are important characteristics that affect not only the accuracy of research results
but also the assessment of data suitability, the feasibility of solving specific research problems, the choice of methods for working with data, object matching, data compatibility, and other aspects of reuse. This requires an assessment of various data quality dimensions at different
levels of aggregation, from entire datasets to individual values. This study presents an approach to integrated data quality management based on data specifications, as well as data and metadata quality requirements. Various data quality assessment dimensions, including
accuracy, completeness, and provenance, are discussed. The developed approach is applied to problem-solving using multiple data sources in stellar astronomy.
Keywords:data quality, data reuse, formal specifications, non-functional requirements.
Presented by the member of Editorial Board:A. A. Galyaev
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