FAIR for Research Software WG Case Statement

Update on the FAIR4RS WG Case Statement - 31 July 2020

The FAIR4RS Steering Committee would like to thank the community and the RDA TAB for their valuable feedback to the Case Statement. Here we offer a resubmission addressing the minor changes suggested.

Summary of changes:

• Minor additions in audience, RDA WG/IG engagments, and references.
• A review of section 4 Work Plan, reorganising the milestones to align work with existing Working Groups and community feedback.
• A review of engagement activities in section 5 Adoption Plan, making it clear and explicit that the general community will be made aware of the developments, invited to contribute and consulted regularly for feedback.
• Editing section 6, adding aproximate current member numbers. A clarification that community members can join or leave the steering committee, with ongoing continuous disclosure of membership changes.
• Update of contributors.

We commit to continue working with the members of the community to achieve the deliverables on time.

With kind regards,

Paula Andrea Martinez,  on behalf of the FAIR4RS Steering Committee.

Updated Case Statement:
https://www.rd-alliance.org/sites/default/files/case_statement/2020_RDA_FAIR4RS_WG_CaseStatement_REVISION_0.pdf
Original Case Statement: 
https://www.rd-alliance.org/sites/default/files/case_statement/2020_RDA_FAIR4RS_WorkingGroup_CaseStatement.pdf

Original text of this page

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"¹ 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 principles² 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 software³ , 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.

Please see https://www.rd-alliance.org/sites/default/files/case_statement/2020_RDA_FAIR4RS_WorkingGroup_CaseStatement.pdf for the full Case Statement.

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.