Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions

Scientific code is different from production software. Scientific code, by producing results that are then analyzed and interpreted, participates in the elaboration of scientific conclusions. This imposes specific constraints on the code that are often overlooked in practice. We articulate, with a s...
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Autor*in:	Fabien C. Y. Benureau [verfasserIn] Nicolas P. Rougier [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	replicability reproducibility of results reproducible science reproducible research computational science software development

Übergeordnetes Werk:	In: Frontiers in Neuroinformatics - Frontiers Media S.A., 2008, 11(2018)
Übergeordnetes Werk:	volume:11 ; year:2018

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fninf.2017.00069

Katalog-ID:	DOAJ001556878

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spelling	10.3389/fninf.2017.00069 doi (DE-627)DOAJ001556878 (DE-599)DOAJf44e7980f90f4aa49b8c2ba4eaf7c0c0 DE-627 ger DE-627 rakwb eng RC321-571 Fabien C. Y. Benureau verfasserin aut Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Scientific code is different from production software. Scientific code, by producing results that are then analyzed and interpreted, participates in the elaboration of scientific conclusions. This imposes specific constraints on the code that are often overlooked in practice. We articulate, with a small example, five characteristics that a scientific code in computational science should possess: re-runnable, repeatable, reproducible, reusable, and replicable. The code should be executable (re-runnable) and produce the same result more than once (repeatable); it should allow an investigator to reobtain the published results (reproducible) while being easy to use, understand and modify (reusable), and it should act as an available reference for any ambiguity in the algorithmic descriptions of the article (replicable). replicability reproducibility of results reproducible science reproducible research computational science software development Neurosciences. Biological psychiatry. Neuropsychiatry Fabien C. Y. Benureau verfasserin aut Fabien C. Y. Benureau verfasserin aut Nicolas P. Rougier verfasserin aut Nicolas P. Rougier verfasserin aut Nicolas P. Rougier verfasserin aut In Frontiers in Neuroinformatics Frontiers Media S.A., 2008 11(2018) (DE-627)57982652X (DE-600)2452979-5 16625196 nnns volume:11 year:2018 https://doi.org/10.3389/fninf.2017.00069 kostenfrei https://doaj.org/article/f44e7980f90f4aa49b8c2ba4eaf7c0c0 kostenfrei http://journal.frontiersin.org/article/10.3389/fninf.2017.00069/full kostenfrei https://doaj.org/toc/1662-5196 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2018
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allfieldsGer	10.3389/fninf.2017.00069 doi (DE-627)DOAJ001556878 (DE-599)DOAJf44e7980f90f4aa49b8c2ba4eaf7c0c0 DE-627 ger DE-627 rakwb eng RC321-571 Fabien C. Y. Benureau verfasserin aut Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Scientific code is different from production software. Scientific code, by producing results that are then analyzed and interpreted, participates in the elaboration of scientific conclusions. This imposes specific constraints on the code that are often overlooked in practice. We articulate, with a small example, five characteristics that a scientific code in computational science should possess: re-runnable, repeatable, reproducible, reusable, and replicable. The code should be executable (re-runnable) and produce the same result more than once (repeatable); it should allow an investigator to reobtain the published results (reproducible) while being easy to use, understand and modify (reusable), and it should act as an available reference for any ambiguity in the algorithmic descriptions of the article (replicable). replicability reproducibility of results reproducible science reproducible research computational science software development Neurosciences. Biological psychiatry. Neuropsychiatry Fabien C. Y. Benureau verfasserin aut Fabien C. Y. Benureau verfasserin aut Nicolas P. Rougier verfasserin aut Nicolas P. Rougier verfasserin aut Nicolas P. Rougier verfasserin aut In Frontiers in Neuroinformatics Frontiers Media S.A., 2008 11(2018) (DE-627)57982652X (DE-600)2452979-5 16625196 nnns volume:11 year:2018 https://doi.org/10.3389/fninf.2017.00069 kostenfrei https://doaj.org/article/f44e7980f90f4aa49b8c2ba4eaf7c0c0 kostenfrei http://journal.frontiersin.org/article/10.3389/fninf.2017.00069/full kostenfrei https://doaj.org/toc/1662-5196 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2018
allfieldsSound	10.3389/fninf.2017.00069 doi (DE-627)DOAJ001556878 (DE-599)DOAJf44e7980f90f4aa49b8c2ba4eaf7c0c0 DE-627 ger DE-627 rakwb eng RC321-571 Fabien C. Y. Benureau verfasserin aut Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Scientific code is different from production software. Scientific code, by producing results that are then analyzed and interpreted, participates in the elaboration of scientific conclusions. This imposes specific constraints on the code that are often overlooked in practice. We articulate, with a small example, five characteristics that a scientific code in computational science should possess: re-runnable, repeatable, reproducible, reusable, and replicable. The code should be executable (re-runnable) and produce the same result more than once (repeatable); it should allow an investigator to reobtain the published results (reproducible) while being easy to use, understand and modify (reusable), and it should act as an available reference for any ambiguity in the algorithmic descriptions of the article (replicable). replicability reproducibility of results reproducible science reproducible research computational science software development Neurosciences. Biological psychiatry. Neuropsychiatry Fabien C. Y. Benureau verfasserin aut Fabien C. Y. Benureau verfasserin aut Nicolas P. Rougier verfasserin aut Nicolas P. Rougier verfasserin aut Nicolas P. Rougier verfasserin aut In Frontiers in Neuroinformatics Frontiers Media S.A., 2008 11(2018) (DE-627)57982652X (DE-600)2452979-5 16625196 nnns volume:11 year:2018 https://doi.org/10.3389/fninf.2017.00069 kostenfrei https://doaj.org/article/f44e7980f90f4aa49b8c2ba4eaf7c0c0 kostenfrei http://journal.frontiersin.org/article/10.3389/fninf.2017.00069/full kostenfrei https://doaj.org/toc/1662-5196 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2018
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author	Fabien C. Y. Benureau
spellingShingle	Fabien C. Y. Benureau misc RC321-571 misc replicability misc reproducibility of results misc reproducible science misc reproducible research misc computational science misc software development misc Neurosciences. Biological psychiatry. Neuropsychiatry Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions
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topic_title	RC321-571 Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions replicability reproducibility of results reproducible science reproducible research computational science software development
topic	misc RC321-571 misc replicability misc reproducibility of results misc reproducible science misc reproducible research misc computational science misc software development misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc replicability misc reproducibility of results misc reproducible science misc reproducible research misc computational science misc software development misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions
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title_sort	re-run, repeat, reproduce, reuse, replicate: transforming code into scientific contributions
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title_auth	Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions
abstract	Scientific code is different from production software. Scientific code, by producing results that are then analyzed and interpreted, participates in the elaboration of scientific conclusions. This imposes specific constraints on the code that are often overlooked in practice. We articulate, with a small example, five characteristics that a scientific code in computational science should possess: re-runnable, repeatable, reproducible, reusable, and replicable. The code should be executable (re-runnable) and produce the same result more than once (repeatable); it should allow an investigator to reobtain the published results (reproducible) while being easy to use, understand and modify (reusable), and it should act as an available reference for any ambiguity in the algorithmic descriptions of the article (replicable).
abstractGer	Scientific code is different from production software. Scientific code, by producing results that are then analyzed and interpreted, participates in the elaboration of scientific conclusions. This imposes specific constraints on the code that are often overlooked in practice. We articulate, with a small example, five characteristics that a scientific code in computational science should possess: re-runnable, repeatable, reproducible, reusable, and replicable. The code should be executable (re-runnable) and produce the same result more than once (repeatable); it should allow an investigator to reobtain the published results (reproducible) while being easy to use, understand and modify (reusable), and it should act as an available reference for any ambiguity in the algorithmic descriptions of the article (replicable).
abstract_unstemmed	Scientific code is different from production software. Scientific code, by producing results that are then analyzed and interpreted, participates in the elaboration of scientific conclusions. This imposes specific constraints on the code that are often overlooked in practice. We articulate, with a small example, five characteristics that a scientific code in computational science should possess: re-runnable, repeatable, reproducible, reusable, and replicable. The code should be executable (re-runnable) and produce the same result more than once (repeatable); it should allow an investigator to reobtain the published results (reproducible) while being easy to use, understand and modify (reusable), and it should act as an available reference for any ambiguity in the algorithmic descriptions of the article (replicable).
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title_short	Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions
url	https://doi.org/10.3389/fninf.2017.00069 https://doaj.org/article/f44e7980f90f4aa49b8c2ba4eaf7c0c0 http://journal.frontiersin.org/article/10.3389/fninf.2017.00069/full https://doaj.org/toc/1662-5196
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up_date	2024-07-03T21:08:24.995Z
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Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions

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