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One of the major challenges of data-driven research is to facilitate knowledge discovery by assisting humans and machines in their discovery of, access to, integration and analysis of data and their associated research objects, e.g., algorithms, software, and workflows. To address this, an initial effort to define a "DATA FAIRPORT"  began in 2014 at the Lorentz workshop and transitioned into developing a set of FAIR data Guiding Principles in 2016. The details of the FAIR data principles  strongly contribute to addressing this challenge with regard to research data, and the principles, at a high level, are intended to apply to all research objects; both those used in research and that form the outputs of research. Here we focus on the adaptation and adoption of the FAIR principles for the case of research software.
Software has become essential for research. To improve the findability, accessibility, interoperability, and reuse of research software  , it is desirable to develop and apply a set of FAIR Guiding Principles for software. Many of the high-level FAIR data principles can be directly applied to research software by treating software and data as similar digital research objects. However, specific characteristics of software — such as its executability, composite nature, and continuous evolution and versioning — make it necessary to revise and extend the original data principles.
Application of the FAIR principles to software will continue to advance the aims of the open science movement. The FAIR For Research Software Working Group (FAIR4RS WG) will be jointly convened as an RDA Working Group, FORCE11 Working Group, and Research Software Alliance (ReSA) Taskforce, in recognition of the importance of this work for the advancement of the research sector. FAIR4RS WG will enable coordination of a range of existing community-led discussions on how to define and effectively apply FAIR principles to research software, to achieve adoption of these principles.
The working group will deliver:
- A document developed with community support defining FAIR principles for research software
- A document providing guidelines on how to apply the FAIR principles for research software (based on existing frameworks)
- A document summarising the definition of the FAIR principles for research software, implementation guidelines and adoption examples.
To start the group's activities, there are four subgroups working on:
- A fresh look at FAIR for Research Software is examining the FAIR principles in the context of research software from scratch, not based on pre-existing work. Lead: Daniel S. Katz
- FAIR work in other contexts is examining efforts to apply FAIR principles to different forms including workflows, notebooks and training material, to provide insights for the definition and implementation of FAIR principles for research software. Lead: Mateusz Kuzak
- Definition of research software is reviewing existing definitions of research software and will specify the scope for the WG outputs. Lead: Morane Gruenpeter
- Review of new research related to FAIR Software is reviewing new research around FAIR software that has come out since the release of the Towards FAIR principles for research software paper in August 2019. Lead: Neil Chue Hong
1 See also DTL, 2014; and Kok, 2014.
2 See also Wilkinson et al., 2016.
3 For further information refer to Clément-Fontaine et al., 2019.