Provide Proactive Reproducible Analysis Transparency with Every Publication
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909290075258880 |
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| author | Meijer, Paul Howard, Nicole Liang, Jessica Kelsey, Autumn Subramanian, Sathya Johnson, Ed Mariz, Paul Harvey, James Ambrose, Madeline Tereshchenko, Vitalii Beaubien, Aldan Inala, Neelima Aggoune, Yousef Pister, Stark Vetto, Anne Kinsey, Melissa Bumol, Tom Goldrath, Ananda Li, Xiaojun Torgerson, Troy Skene, Peter Okada, Lauren La France, Christian Thomson, Zach Graybuck, Lucas |
| author_facet | Meijer, Paul Howard, Nicole Liang, Jessica Kelsey, Autumn Subramanian, Sathya Johnson, Ed Mariz, Paul Harvey, James Ambrose, Madeline Tereshchenko, Vitalii Beaubien, Aldan Inala, Neelima Aggoune, Yousef Pister, Stark Vetto, Anne Kinsey, Melissa Bumol, Tom Goldrath, Ananda Li, Xiaojun Torgerson, Troy Skene, Peter Okada, Lauren La France, Christian Thomson, Zach Graybuck, Lucas |
| contents | The high incidence of irreproducible research has led to urgent appeals for transparency and equitable practices in open science. For the scientific disciplines that rely on computationally intensive analyses of large data sets, a granular understanding of the analysis methodology is an essential component of reproducibility. This paper discusses the guiding principles of a computational reproducibility framework that enables a scientist to proactively generate a complete reproducible trace as analysis unfolds, and share data, methods and executable tools as part of a scientific publication, allowing other researchers to verify results and easily re-execute the steps of the scientific investigation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09103 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Provide Proactive Reproducible Analysis Transparency with Every Publication Meijer, Paul Howard, Nicole Liang, Jessica Kelsey, Autumn Subramanian, Sathya Johnson, Ed Mariz, Paul Harvey, James Ambrose, Madeline Tereshchenko, Vitalii Beaubien, Aldan Inala, Neelima Aggoune, Yousef Pister, Stark Vetto, Anne Kinsey, Melissa Bumol, Tom Goldrath, Ananda Li, Xiaojun Torgerson, Troy Skene, Peter Okada, Lauren La France, Christian Thomson, Zach Graybuck, Lucas Computational Engineering, Finance, and Science The high incidence of irreproducible research has led to urgent appeals for transparency and equitable practices in open science. For the scientific disciplines that rely on computationally intensive analyses of large data sets, a granular understanding of the analysis methodology is an essential component of reproducibility. This paper discusses the guiding principles of a computational reproducibility framework that enables a scientist to proactively generate a complete reproducible trace as analysis unfolds, and share data, methods and executable tools as part of a scientific publication, allowing other researchers to verify results and easily re-execute the steps of the scientific investigation. |
| title | Provide Proactive Reproducible Analysis Transparency with Every Publication |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2408.09103 |