A practical guide to FAIR data management in the age of multi-OMICS and AI
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View abstract on PubMed
Summary
This summary is machine-generated.We present a practical framework for multi-modal biological data management and FAIR sharing. This approach enhances data accessibility and reusability, accelerating biomedical discovery and aligning with global data policies.
Area Of Science
- Biomedical research
- Computational biology
- Data science
Background
- Biological systems are complex, requiring quantitative understanding through systems biology.
- High-dimensional multi-modal data (multi-omics) is increasingly available via new technologies.
- Current analytical tools and data sharing practices limit biological insights and scientific collaboration.
Purpose Of The Study
- To outline a practical framework for multi-modal biological data management.
- To promote FAIR (Findable, Accessible, Interoperable, Reusable) data sharing.
- To accelerate scientific discovery by improving data accessibility and utility.
Main Methods
- Developing a practical data management framework.
- Implementing FAIR data sharing principles.
- Aligning with US and EU funder data sharing policies.
Main Results
- The proposed framework facilitates multi-modal data management.
- The approach promotes FAIR data sharing, enhancing data longevity and utility.
- This strategy supports easier data use and reuse by the scientific community.
Conclusions
- Effective data management and FAIR sharing are crucial for systems biology.
- The outlined framework addresses limitations in current data analysis and accessibility.
- Implementing this approach accelerates biomedical research and scientific progress.
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