- Update attendees about WG activities and wrap up
- Present WG main output “10 Things for Curating Reproducible and FAIR Research”
- Opportunities for output adoption
- Engage with the RDA community and aligned IGs/WGs on cross-cutting issues related to reproducible research
Collaborative meeting notes: https://docs.google.com/document/d/1XQTNi_EUJ8h9vv1y8sgLn18HCt_f1enzazjR...
- Brief overview of the CURE-FAIR WG: Objectives, case statement, supporting outputs (CURE-FAIR Definitions, Practices, and Challenges) (20 minutes)
- Flagship WG output (RDA Recommendation): 10 Things for Curating Reproducible and FAIR Research (20 minutes)
- Adopter stories (10 minutes)
- Q&A: Adopting 10 CURE-FAIR Things (10 minutes)
- What’s next for reproducibility at RDA? (30 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.
The goal of the CURE-FAIR WG is to establish guidelines and identify best practices for curating for reproducible and FAIR data and code. As stated in the group’s Case Statement, final output will be presented in 2022.
The ultimate objective of the WG is to improve FAIR-ness and long-term usability of “reproducible file bundles” across domains. 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. This WG is 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.
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 provided an update on progress at VP17 and, following a community review period, the WG’s first interim output, a report on the Challenges of Curating for Reproducible and FAIR Research Output, has been posted. Subgroup work has been presented in VP18. The last six months of the WG was devoted to the final output, “10 Things for Curating Reproducible and FAIR Research,” which the WG plans to share as a RDA recommendation outlining guidelines for CURE-FAIR best practices in publishing and archiving computationally reproducible studies.