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Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge
Reproducibility is fundamental to science, and an important component of reproducibility is computational reproducibility: the ability of a researcher to recreate the results of a published study using the original author’s raw data and code. Although most people agree that computational reproducibi...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10260256/ https://www.ncbi.nlm.nih.gov/pubmed/37309413 http://dx.doi.org/10.1177/2378023119849803 |
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author | Liu, David M. Salganik, Matthew J. |
author_facet | Liu, David M. Salganik, Matthew J. |
author_sort | Liu, David M. |
collection | PubMed |
description | Reproducibility is fundamental to science, and an important component of reproducibility is computational reproducibility: the ability of a researcher to recreate the results of a published study using the original author’s raw data and code. Although most people agree that computational reproducibility is important, it is still difficult to achieve in practice. In this article, the authors describe their approach to enabling computational reproducibility for the 12 articles in this special issue of Socius about the Fragile Families Challenge. The approach draws on two tools commonly used by professional software engineers but not widely used by academic researchers: software containers (e.g., Docker) and cloud computing (e.g., Amazon Web Services). These tools made it possible to standardize the computing environment around each submission, which will ease computational reproducibility both today and in the future. Drawing on their successes and struggles, the authors conclude with recommendations to researchers and journals. |
format | Online Article Text |
id | pubmed-10260256 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
record_format | MEDLINE/PubMed |
spelling | pubmed-102602562023-06-12 Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge Liu, David M. Salganik, Matthew J. Socius Article Reproducibility is fundamental to science, and an important component of reproducibility is computational reproducibility: the ability of a researcher to recreate the results of a published study using the original author’s raw data and code. Although most people agree that computational reproducibility is important, it is still difficult to achieve in practice. In this article, the authors describe their approach to enabling computational reproducibility for the 12 articles in this special issue of Socius about the Fragile Families Challenge. The approach draws on two tools commonly used by professional software engineers but not widely used by academic researchers: software containers (e.g., Docker) and cloud computing (e.g., Amazon Web Services). These tools made it possible to standardize the computing environment around each submission, which will ease computational reproducibility both today and in the future. Drawing on their successes and struggles, the authors conclude with recommendations to researchers and journals. 2019 2019-09-10 /pmc/articles/PMC10260256/ /pubmed/37309413 http://dx.doi.org/10.1177/2378023119849803 Text en Article reuse guidelines: sagepub.com/journals-permissions (https://us.sagepub.com/en-us/journals-permissions) https://creativecommons.org/licenses/by-nc/4.0/Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Article Liu, David M. Salganik, Matthew J. Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge |
title | Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge |
title_full | Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge |
title_fullStr | Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge |
title_full_unstemmed | Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge |
title_short | Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge |
title_sort | successes and struggles with computational reproducibility: lessons from the fragile families challenge |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10260256/ https://www.ncbi.nlm.nih.gov/pubmed/37309413 http://dx.doi.org/10.1177/2378023119849803 |
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