Computational Reproducibility: A Practical Framework for Data Curators
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, DCN
How to Cite:
Sawchuk, S. L. & Khair, S., (2021) “Computational Reproducibility: A Practical Framework for Data Curators”, Journal of eScience Librarianship 10(3): 7. doi: https://doi.org/10.7191/jeslib.2021.1206
Rights: © 2021 Sawchuk & Khair. This is an open access article licensed under the terms of the Creative Commons Attribution License.
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