The FAIR data point populator: collaborative FAIRification and population of FAIR data points

Area of Science:

• Biomedical Informatics
• Data Science
• Scientific Data Management

Background:

• The FAIR principles (Findable, Accessible, Interoperable, Reusable) are crucial for managing and reusing the increasing volume of biomedical data.
• Metadata is a key component of FAIR data, but current methods for publishing it to FAIR Data Points (FDPs) are either not scalable or require programming expertise.
• Existing FDP interfaces and APIs present limitations for widespread adoption by researchers without technical backgrounds.

Purpose of the Study:

• To introduce a novel tool, the FAIR Data Point Populator, designed to overcome the scalability and accessibility limitations of current metadata publication methods.
• To provide a user-friendly solution for populating FAIR Data Points with metadata, targeting both non-technical and technical users.
• To lower the barrier to entry for FAIR data principles implementation in biomedical research.

Main Methods:

• Development of a tool combining a GitHub workflow with user-friendly Excel templates featuring tooltips, validation, and documentation.
• Excel templates are designed for collaborative use by non-technical users in online spreadsheet software.
• A GitHub workflow processes the Excel data, transforms it into machine-readable metadata, and automatically uploads it to a FAIR Data Point.

Main Results:

• The FAIR Data Point Populator successfully facilitated the bulk creation of metadata entries for two datasets and a patient registry.
• The tool demonstrated accessibility for users without programming backgrounds, enabling collaborative metadata population.
• Metadata generated by the tool was automatically uploaded to a FAIR Data Point, allowing for successful data retrieval via the FAIR Data Point Index.

Conclusions:

• The FAIR Data Point Populator effectively addresses the limitations of existing metadata publication methods by enabling scalable, bulk metadata creation for non-programmers.
• The tool enhances collaboration and significantly lowers the barrier to FAIR data implementation.
• Increased accessibility and ease of use promote broader adoption of FAIR data principles, leading to more FAIR data creation by a wider range of researchers.