FAIR

FAIR assesses the FAIRness of life sciences datasets by evaluating datasets against FAIR maturity indicators to support data sharing, interoperability, and reuse.

Key Features:

• Reproducible computational workflow: Implements a reproducible workflow to evaluate the FAIRness of life sciences datasets following recommendations from the maturity indicator authoring group and related literature.
• FAIR Balloon Plot: Produces a FAIR Balloon Plot visualization to summarize and compare dataset FAIRness metrics.
• Repository and registry integration via APIs: Retrieves metadata and repository information using APIs from ArrayExpress, Gene Expression Omnibus, re3data.org, and Google Dataset Search.
• Assessment against maturity indicators: Evaluates datasets using criteria defined by the FAIR maturity indicators, including metadata standards and repository attributes.
• Identification of improvement areas: Highlights specific dataset and metadata attributes that require enhancement to better satisfy FAIR principles.

Scientific Applications:

• Dataset selection and evaluation: Assists researchers in identifying datasets suitable for their scientific questions by assessing FAIRness.
• Comparative FAIRness analysis: Enables comparison of FAIRness across datasets to identify relative strengths and weaknesses.
• Guidance for metadata and repository improvement: Informs curation and metadata standardization efforts by pinpointing areas for enhancement.

Methodology:

Metadata and repository information are retrieved via APIs from ArrayExpress, Gene Expression Omnibus, re3data.org, and Google Dataset Search; datasets are evaluated against FAIR maturity indicator criteria (including metadata standards and repository attributes), results are summarized with the FAIR Balloon Plot, and areas for improvement are identified.