Abstract
The FAIR Guiding Principles, published in 2016, aim to improve the findability, accessibility, interoperability and reusability of digital research objects for both humans and machines. Until now the FAIR principles have been mostly applied to resear
Highlights
The FAIR Guiding Principles [1] were published and promoted to improve the reuse of scholarly data by making it more findable, accessible, interoperable and reusable by humans and machines
Depending on whether research software is considered as an individual product or as part of an ecosystem, the associated metadata might differ [28,56,57], with workflows having specific mechanisms to capture it through their specifications, e.g., using Common Workflow Language (CWL) [58,59] and/or Workflow Description Language (WDL) [60], among others
It is desirable to apply the FAIR Guiding Principles, which have so far mostly been interpreted as principles for scientific data management and stewardship, to research software
Summary
The FAIR Guiding Principles [1] were published and promoted to improve the reuse of scholarly data by making it more findable, accessible, interoperable and reusable by humans and machines. Implementing FAIR helps researchers demonstrate the impact of their work by enabling the reuse and citation of the data they produce, and can promote collaboration among them. Applying the FAIR principles in a useful way to research software will provide similar benefits of enabling transparency, reproducibility and reusability of research, making it easier for industry, science, education and society to have effective access to software-based knowledge. We discuss what makes software different from data with regard to the application of the FAIR principles (Section 2), and argue why quality considerations about research software go beyond FAIR (Section 3). The conclusions provide a summary and directives for future work on FAIR for research software
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