FAIR and Quality Assured Data - The Use Case of Trueness.
Stud Health Technol Inform
; 289: 25-28, 2022 Jan 14.
Article
en En
| MEDLINE
| ID: mdl-35062083
ABSTRACT
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.
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Banco de datos:
MEDLINE
Asunto principal:
Exactitud de los Datos
/
Metadatos
Idioma:
En
Año:
2022
Tipo del documento:
Article