CUF-Links: Continuous and Ubiquitous FAIRness Linkages for reproducible research

Area of Science:

• Computational science
• Data science
• Research methodology

Background:

• Reproducibility in computational research is a persistent challenge.
• Existing tools often fail to capture complex interdependencies in research projects.
• Difficulty in tracking data lineage hinders the verification of research results.

Purpose of the Study:

• To present simple methods for preserving interstitial connections in research processes.
• To enhance the capture of provenance linkages essential for reproducible research.
• To extend the scope of findable, accessible, interoperable, and reusable (FAIR) data.

Main Methods:

• Integrating simple data capture mechanisms into existing work practices.
• Recording information on all data products consumed or produced within a project.
• Establishing Continuous and Ubiquitous FAIRness linkages (CUF-links) from inputs to outputs.

Main Results:

• Demonstrated methods for capturing basic information about data flow.
• Facilitated the creation of a continuous chain of provenance linkages.
• Showcased practical applications of the proposed methods.

Conclusions:

• Simple, integrated methods can significantly improve research reproducibility.
• CUF-links enhance data provenance and support FAIR data principles.
• The proposed approach aids in overcoming challenges in complex computational research.