Implementation of the FAIR Data Principles for Exploratory Biomarker Data from Clinical Trials

分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-18 合作期刊: 《数据智能（英文）》

Arefolov, Alexander Adam, Laura Brown, Shoshana Budovskaya, Yelena Chen, Cong Das, Diya Farhy, Chen Ferguson, Rebecca Huang, Hongmei Kanigel, Kimberly Lu, Christina Polesskaya, Oksana Staton, Tracy Tajhya, Rajeev Whitley, Maryann Wong, Jee-Yeon Zeng, Xiangpei McCreary, Mark

摘要： The FAIR data guiding principles have been recently developed and widely adopted to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets in the face of an exponential increase of data volume and complexity. The FAIR data principles have been formulated on a general level and the technological implementation of these principles remains up to the industries and organizations working on maximizing the value of their data. Here, we describe the data management and curation methodologies and best practices developed for FAIRification of clinical exploratory biomarker data collected from over 250 clinical studies. We discuss the data curation effort involved, the resulting output, and the business and scientific impact of our work. Finally, we propose prospective planning for FAIR data to optimize data management efforts and maximize data value.

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