FAIR and Quality Assured Data - The Use Case of Trueness

Autor: Jürgen, Stausberg, Sonja, Harkener, Ekkehart, Jenetzky, Patrick, Jersch, David, Martin, Rüdiger, Rupp, Martin, Schönthaler
Rok vydání: 2022
Předmět:
Zdroj: Studies in health technology and informatics. 289
ISSN: 1879-8365
Popis: The FAIR Guiding Principles do not address the quality of data and metadata. Therefore, data collections could be FAIR but useless. In a funding initiative of registries for health services research, trueness of data received special attention. Completeness in the definition of recall was selected to represent this dimension in a cross-registry benchmarking. The first analyses of completeness revealed a diversity of its implementation. No registry was able to present results exactly as requested in a guideline on data quality. Two registries switched to a source data verification as alternative, the three others downsized to the dimension integrity. The experiences underline that the achievement of appropriate data quality is a matter of costs and resources, whereas the current Guiding Principles quote for a transparent culture regarding data and metadata. We propose the extension to FAIR-Q, data collections should not only be findable, accessible, interoperable, and reusable, but also quality assured.
Databáze: OpenAIRE