• Output Type: Working Group Supporting Output
  • Review Status: Endorsed
  • Review Deadline: 2021-05-21
  • Author(s): Limor Peer
  • Abstract


    Group co-chairs: Limor PeerFlorio ArguillasThu-Mai ChristianTom Honeyman

    Supporting Output title: Challenges of Curating for Reproducible and FAIR Research Output

    Authors: Limor Peer, Florio Arguillas, Tom Honeyman, Nadica Miljković, Karsten Peters-von Gehlen and CURE-FAIR subgroup 3

    Impact: This report identified the current set of key challenges associated with generating, sharing, and using reproducible computation-based scientific results. The report will inform the WG’s forthcoming standards-based guidelines for curating for FAIR and reproducible research outputs.

    DOI: 10.15497/RDA00063

    Citation: Peer, L., Arguillas, F., Honeyman, T., Miljković, N., Peters-von-Gehlen, K., & CURE-FAIR WG Subgroup 3. (2021). Challenges of Curating for Reproducible and FAIR Research Output. Research Data Alliance. DOI: 10.15497/RDA00063


    Computational reproducibility is the ability to repeat the analysis and arrive at the same result (National Academies of Sciences, Engineering, and Medicine, 2019). Computational reproducibility contributes to the preservation of a complete scientific record, verification of scientific claims, building upon the findings, and teaching. In this framework, the object of the curation is a “reproducible file bundle,” which optimally includes FAIR data and code. This output reports on the challenges of preparing and reusing materials required for computational reproducibility.

    Context: The goal of the CURE-FAIR WG is to establish standards-based guidelines for curating for reproducible and FAIR data and code (Wilkinson et al., 2016). The final deliverable of the WG is a document outlining CURE-FAIR standards-based guidelines for best practices in publishing and archiving computationally reproducible studies. To support the deliverable, the WG created four subgroups, each tasked with studying and summarizing a particular aspect of this work: CURE-FAIR Definitions, Practices, Challenges, and Alliances. 

    Objective: The goal of this output is to provide a review of the literature and collected use cases, stories, and interviews with various stakeholders (researchers, publishers, funders, data professionals, IT, repositories) who are trying to reproduce computation-based scientific results and processes. We believe that this report is an accurate and comprehensive survey of the current state of CURE-FAIR. We plan to complement this output with a report examining curation practices and their alignment with FAIR principles as currently implemented by various organizations. Our ultimate objective is to improve FAIR-ness and long-term usability of “reproducible file bundles” across domains.

    Request for comment: We invite the RDA community to review and comment on the CURE-FAIR WG output as part of the open process for endorsement and recognition by RDA. Comments are welcome and should be made no later than May 21st 2021.

    Version history:

    Please note that Versions 1.0 and 1.1 of the Output underwent review by the WG. Version 2.0 is based on these comments and underwent RDA community review. Version 2.1 is the final version and was created after the community review.



  • Group Technology focus: Data (Output) Management Planning
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