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Introduction: This paper presents concrete and actionable steps to guide researchers, data curators, and data managers in improving their understanding and practice of computational reproducibility.

Objectives: Focusing on incremental progress rather than prescriptive rules, researchers and curators can build their knowledge and skills as the need arises. This paper presents a framework of incremental curation for reproducibility to support open science objectives.

Methods: A computational reproducibility framework developed for the Canadian Data Curation Forum serves as the model for this approach. This framework combines learning about reproducibility with recommended steps to improving reproducibility.

Conclusion: Computational reproducibility leads to more transparent and accurate research. The authors warn that fear of a crisis and focus on perfection should not prevent curation that may be ‘good enough.’


computational reproducibility, data curation, libraries, data reuse

Corresponding Author

Sandra Sawchuk, Mount Saint Vincent University Library & Archives, 15 Lumpkin Road, Halifax, Nova Scotia, Canada B3M 2J6; sandra.sawchuk@msvu.ca

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© 2021 Sawchuk & Khair. This is an open access article licensed under the terms of the Creative Commons Attribution License.

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This work is licensed under a Creative Commons Attribution 4.0 License.