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Making experimental data tables in the life sciences more FAIR: a pragmatic approach
Daniel Jacob1,2, Romain David3,4, Sophie Aubin5
1INRAE, Université de Bordeaux, UMR BFP, 71 av E Bourlaux, 33140 Villenave d'Ornon, France.
Gigascience
|December 15, 2020
Summary
Researchers can make their data FAIR (Findable, Accessible, Interoperable, Reusable) more easily with a new data management approach. This model simplifies achieving FAIR data compliance using experimental data tables and offers tools for better data practices.
Area of Science:
- Data Science
- Research Methodology
Background:
- Achieving FAIR Data principles (Findable, Accessible, Interoperable, Reusable) presents challenges for researchers.
- Uncertainty exists regarding the priority and methods for meeting FAIR criteria.
Purpose of the Study:
- To propose a model for research data management that facilitates FAIR data compliance.
- To provide researchers with tools to improve data management practices and simplify FAIR data dissemination.
Main Methods:
- Utilizing experimental data tables linked to Design of Experiments.
- Developing a structured approach for data management and dissemination.
Main Results:
- Demonstrated an approach to meet key FAIR criteria without excessive effort.
- Illustrated the application with experimental data examples.
Conclusions:
- The proposed approach serves as a model for effective research data management.
- This strategy simplifies the FAIR compliance process and enhances data practices for researchers.
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