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

TL;DR

This paper proposes CUF-Links, a simple, integrated mechanism to enhance reproducibility in research by creating continuous FAIR data linkages throughout the research process.

Contribution

It introduces CUF-Links, a novel approach for continuous, ubiquitous FAIRness linkages that improve documentation of provenance for reproducible research.

Findings

CUF-Links effectively create continuous FAIR data linkages.

The approach integrates easily with current scientific workflows.

Practical examples demonstrate improved reproducibility.

Abstract

Despite much creative work on methods and tools, reproducibility -- the ability to repeat the computational steps used to obtain a research result -- remains elusive. One reason for these difficulties is that extant tools for capturing research processes do not align well with the rich working practices of scientists. We advocate here for simple mechanisms that can be integrated easily with current work practices to capture basic information about every data product consumed or produced in a project. We argue that by thus extending the scope of findable, accessible, interoperable, and reusable (FAIR) data in both time and space to enable the creation of a continuous chain of continuous and ubiquitous FAIRness linkages (CUF-Links) from inputs to outputs, such mechanisms can provide a strong foundation for documenting the provenance linkages that are essential to reproducible research. We give examples of mechanisms that can achieve these goals, and review how they have been applied in practice.