FAIR Omics Data Management: Overview, Challenges, and Best Practices

Area of Science:

• Bioinformatics
• Data Science
• Genomics

Background:

• Omics data is large-scale and heterogeneous, posing challenges for management.
• Reproducibility, collaboration, and open science are crucial in omics research.

Purpose of the Study:

• To provide a comprehensive overview of FAIR principles for omics data management.
• To address challenges in handling large-scale, heterogeneous omics datasets.
• To promote reproducibility, collaboration, and open science practices.

Main Methods:

• Discussed strategies including Data Management Plans (DMPs).
• Covered file organization, metadata standards, and ontologies.
• Addressed repository selection, interoperability protocols, and security measures.

Main Results:

• Outlined best practices for achieving Findable, Accessible, Interoperable, and Reusable (FAIR) data.
• Provided recommendations for continuous improvement in data stewardship.
• Emphasized strategies for cross-omics integration.

Conclusions:

• Implementing FAIR principles is essential for effective omics data management.
• Adherence to best practices enhances data reproducibility and accessibility.
• Long-term data stewardship requires ongoing attention to FAIR principles and security.