The Journey to a FAIR CORE DATA SET for Diabetes Research in Germany.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Esther Thea Inau, Angela Dedié, Ivona Anastasova, Renate Schick, Yaroslav Zdravomyslov, Brigitte Fröhlich, Andreas L Birkenfeld, Martin Hrabě de Angelis, Michael Roden, Atinkut Alamirrew Zeleke, Martin Preusse, Dagmar Waltemath
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Abstract

The German Center for Diabetes Research (DZD) established a core data set (CDS) of clinical parameters relevant for diabetes research in 2021. The CDS is central to the design of current and future DZD studies. Here, we describe the process and outcomes of FAIRifying the initial version of the CDS. We first did a baseline evaluation of the FAIRness using the FAIR Data Maturity Model. The FAIRification process and the results of this assessment led us to convert the CDS into the recommended format for spreadsheets, annotating the parameters with standardized medical codes, licensing the data set, enriching the data set with metadata, and indexing the metadata. The FAIRified version of the CDS is more suitable for data sharing in diabetes research across DZD sites and beyond. It contributes to the reusability of health research studies.

德国糖尿病研究 FAIR 核心数据集之旅。
德国糖尿病研究中心(DZD)于 2021 年建立了与糖尿病研究相关的临床参数核心数据集(CDS)。该核心数据集是当前和未来 DZD 研究设计的核心。在此,我们将介绍对 CDS 初始版本进行 FAIR 化的过程和结果。我们首先使用 FAIR 数据成熟度模型对 FAIR 度进行了基线评估。FAIR 化过程和评估结果促使我们将 CDS 转换为电子表格的推荐格式,用标准化医疗代码注释参数,许可数据集,用元数据丰富数据集,并为元数据编制索引。FAIR 化版本的 CDS 更适用于 DZD 站点内外的糖尿病研究数据共享。它有助于提高健康研究的可重用性。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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