Best practices for genetic and genomic data archiving.

Area of Science:

• Genomics
• Bioinformatics
• Data Science

Background:

• Genetic and genomic data are collected for diverse scientific and applied uses.
• Current data archiving practices are non-standardized, hindering dataset reuse beyond the generating authors.
• Lack of contextual metadata further complicates the accessibility and utility of archived genetic information.

Purpose of the Study:

• To identify challenges in existing genetic and genomic data archives.
• To propose best practices for effective genetic and genomic data archiving.
• To highlight institutional and publisher roles in enhancing data archiving.

Main Methods:

• Review of current genetic and genomic data archiving practices.
• Analysis of challenges leading to limited data reuse.
• Discussion of FAIR data principles (findable, accessible, interoperable, reusable) in the context of macrogenetics.

Main Results:

• Genetic and genomic dataset reuse is uncommon due to poor archiving and metadata.
• The field of macrogenetics demonstrates the potential of accessible, FAIR-compliant data.
• Lack of formal data management training in ecology and evolution contributes to the issue.

Conclusions:

• Implementing standardized archiving practices and comprehensive metadata is essential for maximizing the value of genetic and genomic data.
• Research institutions and publishers must take proactive steps to improve data archiving standards.
• Adoption of FAIR principles will unlock new research avenues through enhanced data accessibility and reusability.