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Reproducibility in Research: Systems, Infrastructure, Culture
Journal of Open Research Software, Volume: 5
Swansea University Author: Tom Crick
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DOI (Published version): 10.5334/jors.73
Abstract
The reproduction and replication of research results has become a major issue for a number of scientific disciplines. In computer science and related computational disciplines such as systems biology, the challenges closely revolve around the ability to implement (and exploit) novel algorithms and m...
Published in: | Journal of Open Research Software |
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ISSN: | 2049-9647 |
Published: |
Ubiquity Press
2017
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URI: | https://cronfa.swan.ac.uk/Record/cronfa43573 |
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2022-12-18T17:32:26.9117553 v2 43573 2018-08-27 Reproducibility in Research: Systems, Infrastructure, Culture 200c66ef0fc55391f736f6e926fb4b99 0000-0001-5196-9389 Tom Crick Tom Crick true false 2018-08-27 SOSS The reproduction and replication of research results has become a major issue for a number of scientific disciplines. In computer science and related computational disciplines such as systems biology, the challenges closely revolve around the ability to implement (and exploit) novel algorithms and models. Taking a new approach from the literature and applying it to a new codebase frequently requires local knowledge missing from the published manuscripts and transient project websites. Alongside this issue, benchmarking, and the lack of open, transparent and fair benchmark sets present another barrier to the verification and validation of claimed results.In this paper, we outline several recommendations to address these issues, driven by specific examples from a range of scientific domains. Based on these recommendations, we propose a high-level prototype open automated platform for scientific software development which effectively abstracts specific dependencies from the individual researcher and their workstation, allowing easy sharing and reproduction of results. This new e-infrastructure for reproducible computational science offers the potential to incentivise a culture change and drive the adoption of new techniques to improve the quality and efficiency – and thus reproducibility – of scientific exploration. Journal Article Journal of Open Research Software 5 Ubiquity Press 2049-9647 reproducible research, cyberinfrastructure, scientific workflows, computational science, open science, data sharing, code sharing, best practices 9 11 2017 2017-11-09 10.5334/jors.73 https://openresearchsoftware.metajnl.com/articles/10.5334/jors.73/ COLLEGE NANME Social Sciences School COLLEGE CODE SOSS Swansea University 2022-12-18T17:32:26.9117553 2018-08-27T13:43:20.8007775 Faculty of Humanities and Social Sciences School of Social Sciences - Education and Childhood Studies Tom Crick 0000-0001-5196-9389 1 Benjamin A. Hall 2 Samin Ishtiaq 3 0043573-27082018134408.pdf 73-2363-1-PB.pdf 2018-08-27T13:44:08.8030000 Output 1337009 application/pdf Version of Record true 2018-08-27T00:00:00.0000000 This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0). true eng |
title |
Reproducibility in Research: Systems, Infrastructure, Culture |
spellingShingle |
Reproducibility in Research: Systems, Infrastructure, Culture Tom Crick |
title_short |
Reproducibility in Research: Systems, Infrastructure, Culture |
title_full |
Reproducibility in Research: Systems, Infrastructure, Culture |
title_fullStr |
Reproducibility in Research: Systems, Infrastructure, Culture |
title_full_unstemmed |
Reproducibility in Research: Systems, Infrastructure, Culture |
title_sort |
Reproducibility in Research: Systems, Infrastructure, Culture |
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200c66ef0fc55391f736f6e926fb4b99 |
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200c66ef0fc55391f736f6e926fb4b99_***_Tom Crick |
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Tom Crick |
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Tom Crick Benjamin A. Hall Samin Ishtiaq |
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Journal of Open Research Software |
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10.5334/jors.73 |
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Ubiquity Press |
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https://openresearchsoftware.metajnl.com/articles/10.5334/jors.73/ |
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description |
The reproduction and replication of research results has become a major issue for a number of scientific disciplines. In computer science and related computational disciplines such as systems biology, the challenges closely revolve around the ability to implement (and exploit) novel algorithms and models. Taking a new approach from the literature and applying it to a new codebase frequently requires local knowledge missing from the published manuscripts and transient project websites. Alongside this issue, benchmarking, and the lack of open, transparent and fair benchmark sets present another barrier to the verification and validation of claimed results.In this paper, we outline several recommendations to address these issues, driven by specific examples from a range of scientific domains. Based on these recommendations, we propose a high-level prototype open automated platform for scientific software development which effectively abstracts specific dependencies from the individual researcher and their workstation, allowing easy sharing and reproduction of results. This new e-infrastructure for reproducible computational science offers the potential to incentivise a culture change and drive the adoption of new techniques to improve the quality and efficiency – and thus reproducibility – of scientific exploration. |
published_date |
2017-11-09T07:33:23Z |
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11.04748 |