10 Things for Curating Reproducible and FAIR Research
By Limor Peer
Recommendation title: 10 Things for Curating Reproducible and FAIR Research
Lead Authors: Florio Arguillas, Thu-Mai Christian, Mandy Gooch, Tom Honeyman, Limor Peer (CURE-FAIR WG co-chairs)
Contributors: Erin Clary, Christopher Erdmann, Ana Van Gulick, Daniel S. Katz, Katherine E. Koziar, Wanda Marsolek, Peter McQuilton, Qian Zhang, and members of the CURE-FAIR WG
The "10 Things for Curating Reproducible and FAIR Research" offer a framework for implementing effective curation workflows for achieving greater FAIR-ness and long-term usability of research data and code. Adoption of the guidelines for curating reproducible and FAIR research will improve the prospects for a reproducible scholarly record.
The "10 CURE-FAIR Things" provide guidance for better practices for those entrusted with stewardship of the scholarly record, including repository managers, curators, and preservation and archival experts; the scholarly community, including researchers who generate and use data and code; policy-setting institutions, including research organizations, publishers, and funders; as well as others involved in the production, dissemination, and preservation of research.
The document focuses primarily on research compendia produced by quantitative data-driven social science. It serves as a starting point for the development of curatorial guidelines to extend beyond the specific concerns of the social sciences community and other domains and disciplines that use similar methods, and to the particular curatorial concerns and requirements of an archives or publisher.
Citation: Arguillas, F., Christian, T.-M., Gooch, M., Honeyman, T., & Peer, L. (2022). 10 Things for Curating Reproducible and FAIR Research (Version 1.1). Research Data Alliance. https://doi.org/10.15497/RDA00074
This document, "10 Things for Curating Reproducible and FAIR Research," describes the key issues of curating reproducible and FAIR research (CURE-FAIR). It lists standards-based guidelines for ten practices, focusing primarily on research compendia produced by quantitative data-driven social science.
The "10 CURE-FAIR Things" are intended primarily for data curators and information professionals who are charged with publication and archival of FAIR and computationally reproducible research. Often the first reusers of the research compendium, they have the opportunity to verify that a computation can be executed and that it can reproduce prespecified results. Secondarily, the "10 CURE-FAIR Things" will be of interest to researchers, publishers, editors, reviewers, and others who have a stake in creating, using, sharing, publishing, or preserving reproducible research.
The "10 CURE-FAIR Things" are:
- Completeness: The research compendium contains all of the objects needed to reproduce a predefined outcome.
- Organization: It is easy to understand and keep track of the various objects in the research compendium and their relationship over time.
- Economy: Fewer extraneous objects in the compendium mean fewer things that can break and require less maintenance over time.
- Transparency: The research compendium provides full disclosure of the research process that produced the scientific claim.
- Documentation: Information describing compendium objects is provided in enough detail to enable independent understanding and use of the compendium.
- Access: It is clear who can use what, how, and under what conditions, with open access preferred.
- Provenance: The origin of the components of the research compendium and how each has changed over time is evident.
- Metadata: Information about the research compendium and its components is embedded in a standardized, machine-readable code.
- Automation: As much as possible, the computational workflow is script- or workflow-based so that the workflow can be re-executed using minimal actions.
- Review: A series of managed activities needed to ensure continued access to and functionality of the research compendium and its components for as long as necessary.
Maintenance: This output will be hosted on Github and maintained by the Odum Institute
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