Implementation of The FAIR Data Principles for Exploratory Biomarker Data from Clinical Trials
Data Intelligence, 2021
Alexander Arefolov, Laura Adam, Shoshana Brown, Yelena Budovskaya, Cong Chen, Diya Das, Chen Farhy, Rebecca Ferguson, Hongmei Huang, Kimberly Kanigel, Christina Lu, Oksana Polesskaya, Tracy Staton, Rajeev Tajhya, Maryann Whitley, Jee-Yeon Wong, Xiangpei Zeng, Mark McCreary. (2021). Implementation of The FAIR Data Principles for Exploratory Biomarker Data from Clinical Trials. Data Intelligence 1-25. https://doi.org/10.1126/sciadv.abc5801
Abstract
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.