status: Recognised & Endorsed

Chair (s): Limor Peer, FLORIO JR ARGUILLAS, Thu-Mai Christian, Tom Honeyman, Mandy Gooch

Group Email: [group_email]

Secretariat Liaison: [field_secretariat_liaison]

Curating for FAIR and Reproducible Research

This workgroup has concluded and is now a "historical group". See below for details on lodging issues with the maintainers of the recommendation.


The recommendation (output) of the working group is the 10 Things for Curating Reproducible and FAIR Research (DOI: 10.15497/RDA00074)

  • Standards-based guidelines for CURE-FAIR best practices in publishing and archiving computationally reproducible studies.
    • Focus on social science research that relies on quantitative data to produce results.
  • Intended audience:
    • Data curators and information professionals who are charged with verifying that a computation can be executed and can reproduce prespecified results.
    • Researchers, publishers, editors, reviewers, and others who have a stake in creating, using, sharing, publishing, or preserving reproducible research.
  • An updated version of the output is available at
  • The maintenance home for this output is the Odum Institute and issues with the content can be lodged at the GitHub repository for the site.


Other supporting outputs for the working group include:

  • CURE-FAIR Challenges: Describe the challenges of preparing and reusing materials required for computational reproducibility; collect information from various stakeholders about their challenges. DOI: 10.15497/RDA00063
  • CURE-FAIR Annotated Bibliography: Provide a broader understanding of what it means to curate research artifacts (e.g., data, code, software) for the purposes of supporting research reproducibility.
  • CURE-FAIR Practitioners: Identify organizations/groups that have fully implemented CURE-FAIR workflows and learn about the various ways researchers and research-supporting organizations have implemented data curation tools, services, and/or workflows that support computational reproducibility; develop a standard form to collect profile information from CURE-FAIR implementers.

The goal of the working group was to establish standards-based guidelines for curating for reproducible and FAIR data and code (Wilkinson et al., 2016). Informed by an examination of current curation practices and their alignment with FAIR principles, these guidelines offer a framework for implementing effective curation workflows for publishing FAIR data and code that support scientific reproducibility. The ultimate objective is to improve FAIR-ness and long-term usability of “reproducible file bundles” across domains.


When we think of specific research outputs, we might think of data, software, codebooks, etc. These individual outputs may have inherent value. For example, a set of observations that is very costly to produce, or that cannot be repeated, or a script that can be used by others for computation. Traditional curation has considered these outputs as its core objects. But in the context of empirical research, these outputs interact with each other, often to produce specific findings or results. Nowadays, the process by which results are generated is captured in computation. Our approach to curation takes into account this process and focuses on computational reproducibility.


Computational reproducibility is the ability to repeat the analysis and arrive at the same results (National Academies of Sciences, Engineering, and Medicine, 2019; Stodden, 2015). It requires using the data and code used in the original analysis, and additional information about study methods and computational environment. The reason to pursue computational reproducibility is to preserve a complete scientific record, to verify scientific claims, to do science and build upon the findings, and to teach (Elman, Kapieszewski, & Lupia, 2018; Resnik & Shamoo, 2017; Stodden, Bailey, & Borwein, 2013).


In this framework, the object of the curation is a “reproducible file bundle” and its component parts, including the files and their elements (e.g., variables), with the goal of enabling continued access and independent reuse of the bundle for the long term.The CURE-FAIR WG is focused on the curation practices that support computational reproducibility and FAIR principles.


By curation we refer to the activities designed for “maintaining, preserving and adding value to digital research data throughout its lifecycle” (Digital Curation Center, n.d.).


See more in our case statement.



10 Things for Curating Reproducible and FAIR Research

by Limor Peer

"10 Things for Curating Reproducible and FAIR Research," describes the key issues of curating reproducible and FAIR research (CURE-FAIR).

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Challenges of Curating for Reproducible and FAIR Research Output

by Limor Peer

Computational reproducibility is the ability to repeat the analysis and arrive at the same result (National Academies of Sciences, Engineering, and Medicine, 2019).

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