Computational Reproducibility: A Practical Framework for Data Curators

• Main Authors: Sandra L. Sawchuk, Shahira Khair
• Format: Article
• Language: English
• Published: University of Massachusetts Medical School, Lamar Soutter Library 2021-08-01
• Series: Journal of eScience Librarianship
• Subjects: computational reproducibility; data curation; libraries; data reuse
• Online Access: https://escholarship.umassmed.edu/jeslib/vol10/iss3/7

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.