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

TLDR

The FAIR data guiding principles, designed to improve findability, accessibility, interoperability, and reuse of digital assets amid growing data volume and complexity, are broadly adopted but their technological implementation remains the responsibility of industries and organizations seeking to maximize data value. The study aims to describe data management and curation methodologies and best practices for FAIRifying clinical exploratory biomarker data from over 250 studies, and to propose prospective planning for FAIR data to optimize management and maximize value. The authors developed and applied data management and curation methodologies and best practices to FAIRify exploratory biomarker data from more than 250 clinical studies, and outlined prospective planning for future FAIR data initiatives. The curation effort produced outputs that deliver business and scientific impact.

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.