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Published on: November 22, 2019
Good Data Governance Practice and a Grading Initiative for Life Sciences Data
Han Liu1,2, Jing Li2, Sheng-Fa Zhang3
1Human Phenome Institute, Fudan University, Shanghai 201203, China.
Abstract:
Life sciences have entered the era of big data, uncovering the complexity of human biological systems and advancing precision medicine and scientific wellness. In alignment with the Findable, Accessible, Interoperable, and Reusable (FAIR) principles, integrating population-level biological data resources from the academic and industrial sectors can substantially increase the efficiency of data utilization and foster innovative breakthroughs. Here, we introduce a novel framework for Good Data Governance Practice (GDGP) coupled with a grading initiative for the life sciences, focusing on traceability and openness. The GDGP framework systematically defines governance constraints, influencing factors, and functional capabilities to streamline data governance and management efficiency. This achievement lays the groundwork for compliant cross-institutional and cross-border data sharing and collaborative processing, poised to pave the way for standardized, ethical, and scalable data-driven research in precision medicine and beyond.
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