FAIR for research software
Dr Michelle Barker
Director, Research Software Alliance (ReSA)
Twitter: @michelle1Barker
Slides: www.tinyurl.com/cw21fair4rs
Once upon a time lived Snow-ware, who wanted to grow up to be the FAIRest software of them all …
Mission: To bring research software communities together to collaborate on the advancement of research software.
Challenges to recognition of software
2021 OECD broadened the 2006 Recommendation on Access to Research Data to include "bespoke algorithms, workflows, models and software (incl. code) that are essential for their interpretation". (Paic, 2021, Making data for science as open as possible to address global challenges)
Proportion of software cited is low (Howison & Bullard, 2015, Software in the scientific literature: Problems with seeing, finding, and using software mentioned in the biology literature)
For <50% of papers can obtain code and build it with some effort (Collberg & Proebsting, 2015, Repeatability in computer systems research
The concept of FAIR
Work on FAIR software 2017-
6
“Five recommendations for FAIR software” at NL-RSE 2019
“FAIR principles for Software” at 2019 Workshop on Sustainable Software Sustainability (WOSSS19)
“FAIR Software” Birds of a Feather meeting at deRSE 2019
Top 10 FAIR Data & Software Global Sprint, including “10 easy things to make your software FAIR” 2019
“Sharing Your Software – What is FAIR?” at the 2018 American Geophysical Union (AGU) Fall Meeting
“FAIRness assessment for software” at the 2018 DBCLS/NBDC BioHackathon
“Making Software FAIR” at the DTL Communities@Work 2018 Conference
“Applying FAIR Principles to Software” at the 2017 Workshop on Sustainable Software Sustainability (WOSSS17)
Towards FAIR principles for research software 2019 DOI: 10.3233/DS-190026
FAIRsFAIR T2.4: FAIR assessment for research software
FAIR Computational Workflows 2020 DOI: 10.1162/dint_a_00033
Lorentz Workshop 9-13 March 2020 (Automated Workflow Composition in the Life Sciences)
FAIR for Research Software (FAIR4RS)
Defining FAIR principles for research software
Thanks to our supporters:
FAIR data principles (Wilkinson et al. 2016) via GO FAIR | FAIR software principles (Katz et al. 2021), changes in bold | Changes |
F. Findable The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process. | F. Findable The first step in (re)using software is to find it. Metadata and software should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of software, so this is an essential component of the FAIRification process. | "Data" replaced by "software" |
F1. (Meta)data are assigned a globally unique and persistent identifier | F1. Software is assigned a globally unique and persistent identifier | "Data" replaced by "software" |
F2. Data are described with rich metadata (defined by R1 below) | F2. Software is described with rich metadata (defined first by R1 below, and then by the original FAIR principles for metadata) | "Data" replaced by "software"; no need to redefine principles for metadata |
F3. Metadata clearly and explicitly include the identifier of the data they describe | F3. Metadata clearly and explicitly include the identifier of the software they describe | "Data" replaced by "software" |
F4. (Meta)data are registered or indexed in a searchable resource | F4. Software is registered or indexed in a searchable resource | "Data" replaced by "software" |
Wilkinson et al., 2016. The FAIR Guiding Principles for scientific data management and stewardship. https://doi.org/10.1038/sdata.2016.18
Katz et al., 2021. A Fresh Look at FAIR for Research Software. https://arxiv.org/abs/2101.10883
How do we balance between principles that are very general and specific, actionable instructions?
Is a digital research object only “fully FAIR” if the objects it builds on are also FAIR?
Join the FAIR4RS Working Group
European Commission (2018) Turning FAIR into Reality
The FAIR4RS Roadmap outlines how to make FAIR research software a reality.
Indicators metrics maturity models certification
curriculums career profiles reward structures policy change
certification of FAIR services interoperability frameworks metadata
FAIR4RS Metrics Working Group formed Feb 2021
Thanks to Wellcome Trust for their support.
The FAIR4RS Roadmap outlines
how to make FAIR research software a reality.
What would success
look like?
Is FAIR enough?
https://github.com/fair-software/howfairis-github-action
Research Software Engineers acknowledged in publications: 42-53% (Philipe, 2018, What do we know about RSEs? Results from our international surveys)
Based on a tweetstorm by @BrianNosek
Infrastructure What software should be preserved and/or maintained?
How much research software is already open source?
People What skills will a new RSE need in 5 years need?
Why do people become RSEs?
Policy What are suitable merit evaluation schemes / metrics for RSEs?
How can support for RSE groups be improved?
How can you help?