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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.
Summary
Life sciences are generating big data for precision medicine. A new Good Data Governance Practice (GDGP) framework promotes data sharing and ethical research by focusing on traceability and openness.
Area of Science:
- Life sciences
- Bioinformatics
- Data science
Background:
- The life sciences are experiencing a data explosion, necessitating efficient data management for advancing precision medicine and scientific wellness.
- Integrating diverse, population-level biological data resources is crucial for maximizing data utility and driving innovation, adhering to Findable, Accessible, Interoperable, and Reusable (FAIR) principles.
Purpose of the Study:
- To introduce a novel framework for Good Data Governance Practice (GDGP) tailored for the life sciences.
- To establish a grading initiative focused on data traceability and openness within biological data resources.
Main Methods:
- Development of a systematic GDGP framework outlining governance constraints, influencing factors, and functional capabilities.
- Implementation of a grading system to assess and promote data traceability and openness.
Main Results:
- The GDGP framework provides a structured approach to streamline data governance and management efficiency.
- The initiative facilitates compliant cross-institutional and cross-border data sharing and collaborative processing.
Conclusions:
- The proposed GDGP framework and grading initiative are essential for standardized, ethical, and scalable data-driven research.
- This work paves the way for enhanced data utilization in precision medicine and other life science applications.
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