A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences

Area of Science:

• Life Sciences
• Bioinformatics
• Data Science

Background:

• Scientific progress relies heavily on data sharing and reuse.
• Challenges in data reuse stem from inadequate infrastructures, standards, and policies.
• The FAIR principles (findable, accessible, interoperable, reusable) offer guidelines for enhancing data reuse.

Purpose of the Study:

• To propose a reproducible computational workflow for assessing data FAIRness in life sciences.
• To develop maturity indicators for determining dataset FAIRness.
• To visualize and compare dataset FAIRness using a FAIR balloon plot.

Main Methods:

• Developed a computational workflow based on maturity indicator guidelines and literature.
• Integrated concepts for assessing FAIRness.
• Utilized APIs to retrieve data from repositories like ArrayExpress, Gene Expression Omnibus, and eNanoMapper.
• Included data from registries and Google Dataset Search.

Main Results:

• The proposed workflow was evaluated on six datasets across three use cases.
• The datasets met most FAIRness criteria defined by maturity indicators.
• Identified specific areas where dataset FAIRness can be improved.

Conclusions:

• A reproducible workflow effectively assesses data FAIRness in life sciences.
• Standardized metadata schemas and repository attributes can enhance dataset FAIRness.
• The FAIR balloon plot provides a useful visualization for comparing dataset FAIRness.