- Update attendees about WG subgroup activities
- Provide an opportunity for the community to contribute to the work of the CURE-FAIR WG
- Engage with the RDA community and aligned IGs/WGs
Collaborative meeteing notes: https://docs.google.com/document/d/1wTsK0O7XDsJak64B9jLr3ij0Vqx6HgZjnXoA2YNCuKo/edit?usp=sharing
Collaborative meeting notes (repeat session): https://docs.google.com/document/d/1mCPQgD7HzoJOEpQ6h1VEw2yzOHC7yYwQJEtxhS6Inrc/edit?usp=sharing
- Brief introduction of WG objectives and case statement for new members (10 minutes)
- Updates on ongoing work from subgroups (40 minutes)
- Discussion and next steps (30 minutes)
- Wrap up (10 minutes)
We invite researchers across the disciplines, data professionals, data archivists, archive and repository managers, technologists, software developers, academic officers, publishers, and participants from related WG/IG, including but not limited to, FAIR4RS, FAIR data maturity model, Reproducible Health Data Services WG, RDA/WDS Publishing Data Workflows WG, RDA/FORCE11 Software Source Code Identification WG, Data Fabric IG, Preservation Tools, Techniques, and Policies IG, Professionalising Data Stewardship IG, Libraries for Research Data IG.
Even as FAIR data practices become normative in the research community, FAIR data alone is not sufficient to guarantee the computational reproducibility of published research findings. Computational reproducibility (the ability to recreate computational results from the data and code used by the original researcher) is essential to preserve a complete scientific record, to verify scientific claims, to do science and build upon the findings, and to teach. Curating for reproducibility (CURE) includes activities that ensure that statistical and analytic claims about given data can be reproduced with that data. To curate for computational reproducibility requires code review and result verification. The goal of the CURE-FAIR WG is to establish guidelines and identify best practices for curating for reproducible and FAIR data and code. The ultimate objective is to improve FAIR-ness and long-term usability of “reproducible file bundles” across domains. This working group will be a focal point within RDA for guidelines and standards for curating for reproducible and FAIR data and code and engaging the RDA community on the issue. CURE-FAIR will work with aligned RDA IGs and WGs. As stated in the group’s Case Statement, final output will be presented in January 2022.
The CURE-FAIR WG has been recognized and endorsed by RDA Council in July 2020. After a successful BoF on “Curating for FAIR and reproducible data and code” at the 14th RDA Plenary in Helsinki, the CURE-FAIR WG held two joint sessions with the Reproducible Health Data Services WG at the 15th RDA Virtual Plenary. At these meetings, we explained the goals of the group, described previous work, presented a draft case statement, and received input from attendees. Leading up to VP16, the group determined the sub-groups and their charge, begin collecting use cases, stories, and interviews with researchers trying to reproduce computational workflows to learn about any pain points, especially across domains. The WG will describe progress at VP17.
RDA WG home page: https://www.rd-alliance.org/groups/cure-fair-wg
Additional information: https://www.rd-alliance.org/group/cure-fair-wg/wiki/cure-fair-wg-information
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