An iterative and interdisciplinary categorisation process towards FAIRer digital resources for sensitive...

Area of Science:

• Life Sciences
• Bioinformatics
• Data Management

Background:

• Sensitive data in life science infrastructures presents significant interoperability challenges for cross-domain categorization and discovery.
• Existing methods struggle to effectively manage and locate digital resources pertinent to sensitive data.

Purpose of the Study:

• To develop a toolbox demonstrator to facilitate the discovery of digital objects related to sensitive data.
• To support the FAIRification process for sensitive data within life science research infrastructures.

Main Methods:

• A harmonized categorization system was developed and applied across 6 life science research infrastructures.
• Three versions of the toolbox were iteratively built and tested through pilot studies.
• A software tool was created for searching digital objects linked to sensitive data using a 7-category filtering system.

Main Results:

• A categorization system with 7 main categories was established: sensitive data type, resource type, research field, data type, data sharing life cycle stage, geographical scope, and specific topics.
• The toolbox demonstrator was populated with 109 tagged resources from pilot studies.
• The developed software tool enables filtering of digital objects based on the defined categorization system.

Conclusions:

• The developed toolbox demonstrator offers a viable solution for improving the discovery of digital resources associated with sensitive data.
• Future work includes usability evaluation, resource expansion, broader community adoption, and ensuring long-term sustainability and maintenance.