Computational Reproducibility: A Practical Framework for Data Curators
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 curat...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
UMass Chan Medical School, Lamar Soutter Library
2021-08-01
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| Series: | Journal of eScience Librarianship |
| Subjects: | |
| Online Access: | https://escholarship.umassmed.edu/jeslib/vol10/iss3/7 |
| _version_ | 1797969669686034432 |
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| author | Sandra L. Sawchuk Shahira Khair |
| author_facet | Sandra L. Sawchuk Shahira Khair |
| author_sort | Sandra L. Sawchuk |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T03:05:51Z |
| format | Article |
| id | doaj.art-26f1f7c354c2432f88bd31ea2ec14c49 |
| institution | Directory Open Access Journal |
| issn | 2161-3974 |
| language | English |
| last_indexed | 2024-04-11T03:05:51Z |
| publishDate | 2021-08-01 |
| publisher | UMass Chan Medical School, Lamar Soutter Library |
| record_format | Article |
| series | Journal of eScience Librarianship |
| spelling | doaj.art-26f1f7c354c2432f88bd31ea2ec14c492023-01-02T13:02:31ZengUMass Chan Medical School, Lamar Soutter LibraryJournal of eScience Librarianship2161-39742021-08-01103120610.7191/jeslib.2021.1206Computational Reproducibility: A Practical Framework for Data CuratorsSandra L. SawchukShahira KhairIntroduction: 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.https://escholarship.umassmed.edu/jeslib/vol10/iss3/7computational reproducibilitydata curationlibrariesdata reuse |
| spellingShingle | Sandra L. Sawchuk Shahira Khair Computational Reproducibility: A Practical Framework for Data Curators Journal of eScience Librarianship computational reproducibility data curation libraries data reuse |
| title | Computational Reproducibility: A Practical Framework for Data Curators |
| title_full | Computational Reproducibility: A Practical Framework for Data Curators |
| title_fullStr | Computational Reproducibility: A Practical Framework for Data Curators |
| title_full_unstemmed | Computational Reproducibility: A Practical Framework for Data Curators |
| title_short | Computational Reproducibility: A Practical Framework for Data Curators |
| title_sort | computational reproducibility a practical framework for data curators |
| topic | computational reproducibility data curation libraries data reuse |
| url | https://escholarship.umassmed.edu/jeslib/vol10/iss3/7 |
| work_keys_str_mv | AT sandralsawchuk computationalreproducibilityapracticalframeworkfordatacurators AT shahirakhair computationalreproducibilityapracticalframeworkfordatacurators |