This working group has completed its outcomes. Thanks for your contributions!!! The RDA Software Source Code Interest Group is the maintenance home for the principles. Concerns or queries about the principles can be raised at RDA plenary events organised by the SSC IG, where there may be opportunities for adopters to report back on progress.
- Barker, M., Chue Hong, N.P., Katz, D.S. et al. Introducing the FAIR Principles for research software. Sci Data 9, 622 (2022). https://doi.org/10.1038/s41597-022-01710-x
- May 24th, 2022. The RDA Council have endorsed the FAIR4RS Principles as an official output! Citation and download: Chue Hong, N. P., Katz, D. S., Barker, M., Lamprecht, A-L, Martinez, C., Psomopoulos, F. E., Harrow, J., Castro, L. J., Gruenpeter, M., Martinez, P. A., Honeyman, T., et al. (2022). FAIR Principles for Research Software version 1.0. (FAIR4RS Principles v1.0). Research Data Alliance. DOI: https://doi.org/10.15497/RDA00068
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FORCE11 on GitHub https://github.com/force11/FAIR4RS
Michelle Barker, Paula Andrea Martinez, Leyla Garcia, Daniel S. Katz, Neil Chue Hong
(from June 2021) Morane Gruenpeter, Fotis Psomopoulos, Jennifer Harrow, Carlos Martinez
Former members Mateusz Kuzak from March 2020 - October 2020
One of the grand 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. 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, could (and were intended to) apply to all research objects. This includes research objects used as part of the research, as well as research objects that form the outputs of research. However, the adoption of the FAIR principles outside data poses challenges not fully addressed by these principles as each research object presents particularities that require some tuning. We focus here on the adoption and adaptation 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 an equivalent 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 Task Force, 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.
As of June 2020, we have started 4 subgroups to build towards the first working group milestone:
- A fresh look at FAIR for Research Software
- FAIR work in other contexts
- Research software definition
- Review of new research related to FAIR Software
See our outputs: