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Article Type

Full-Length Paper

Publication Date

2021-08-11

DOI

10.7191/jeslib.2021.1206

Abstract

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.’

Keywords

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

Rights and Permissions

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

Creative Commons License

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

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