Grapevine and Wine Metabolomics-Based Guidelines for FAIR Data and Metadata Management

Area of Science:

• Grapevine and wine science
• Metabolomics
• Data management

Background:

• Big and omics data necessitate robust organization and management for high-quality datasets.
• FAIR (Findable, Accessible, Interoperable, Reusable) data principles are increasingly required by journals for data sharing.
• Effective data management is crucial for reliable publications and unlocking data reuse potential.

Purpose of the Study:

• To provide step-by-step guidelines for FAIR data and metadata management tailored to grapevine and wine science.
• To assist researchers in organizing and describing experimental data effectively.
• To promote data sharing and reuse within the grapevine and wine research community.

Main Methods:

• Development of specific recommendations for data and metadata organization.
• Inclusion of details on experimental design, phenotyping, sample collection, and preparation.
• Guidance on chemotype analysis, data analysis, metabolite annotation, and ontologies.

Main Results:

• A comprehensive set of guidelines for FAIR data and metadata management in grapevine and wine research.
• Recommendations cover the entire data lifecycle from experimental design to metabolite annotation.
• The guidelines address key aspects like experimental design, phenotyping, sample handling, and data analysis.

Conclusions:

• The developed guidelines will aid the grapevine and wine metabolomics community in managing their data effectively.
• Adherence to these guidelines will enhance data quality, reproducibility, and the potential for data reuse.
• Implementing these FAIR data principles will facilitate the generation of new knowledge from grapevine and wine research.