Current state of curation practices that support computational reproducibility

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04 Aug 2020
Group(s) submitting the application: 
Meeting objectives: 

 

1.    Update attendees about WG activities and present the case statement, timeline, and work plan
2.    Provide an opportunity for attendees to contribute to the work of the CURE-FAIR WG
3.    Engage with the RDA community and aligned IGs/WGs

Meeting agenda: 

 

Collaborative Notes Link: https://docs.google.com/document/d/1MlAjHLkjF4-Kb3RjpmLvDcJ_o186VlV-LzA8...

 

1.    Brief introduction of WG and case statement
2.    Update on ongoing work
3.    Kick off sub-groups
4.    Activity to invite contributions on CURE-FAIR practices, gaps, and opportunities for collaboration and integration

 

Target Audience: 

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.

Group chair serving as contact person: 
Brief introduction describing the activities and scope of the group: 

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 standards 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.

 

Short Group Status: 

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 will determine 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 goal is to have a holistic view of the issue from multiple stakeholder points of view, including researchers, data professionals, and technical infrastructure experts across research domains and geographical boundaries.

Type of Meeting: 
Working meeting
Avoid conflict with the following group (1): 
Avoid conflict with the following group (2): 
Avoid conflict with the following group (3): 
Meeting presenters: 
Anthony Juehne, Limor Peer, Thu-Mai Christian, Florio Arguillas