University Duisburg-Essen, Faculty of Medicine, Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), Essen, Germany.
Faculty of Health/School of Medicine, Witten/Herdecke University, Witten, Germany.
Stud Health Technol Inform. 2022 Jan 14;289:25-28. doi: 10.3233/SHTI210850.
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.
FAIR 指导原则并未涉及数据和元数据的质量。因此，数据集合可能是 FAIR 的，但却是无用的。在一项针对卫生服务研究登记处的资助倡议中，数据的真实性受到了特别关注。在跨登记处基准测试中，召回定义的完整性被选中来代表这一方面。对完整性的首次分析揭示了其实施的多样性。没有一个登记处能够完全按照数据质量指南中的要求提交结果。两个登记处转而选择源数据验证作为替代方法，另外三个登记处将其缩小到完整性维度。这些经验表明，实现适当的数据质量是一个成本和资源的问题，而当前的指导原则则引用了一个关于数据和元数据的透明文化。我们建议将 FAIR-Q 扩展到数据集合不仅应该是可发现的、可访问的、可互操作的和可重复使用的，而且还应该是质量保证的。
