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Conference Paper/Proceeding/Abstract 152 views 11 downloads

"Can I Implement Your Algorithm?": A Model for Reproducible Research Software / Tom, Crick

2nd Workshop on Sustainable Software for Science: Practice and Experiences

Swansea University Author: Tom, Crick

Abstract

The reproduction and replication of novel results has become a major issue for a number of scientific disciplines. In computer science and related computational disciplines such as systems biology, the issues closely revolve around the ability to implement novel algorithms and approaches. Taking an...

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Published in: 2nd Workshop on Sustainable Software for Science: Practice and Experiences
Published: New Orleans, USA 2014
Online Access: https://arxiv.org/abs/1407.5981
URI: https://cronfa.swan.ac.uk/Record/cronfa43757
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spelling 2018-10-15T14:49:20.4097597 v2 43757 2018-09-11 "Can I Implement Your Algorithm?": A Model for Reproducible Research Software 200c66ef0fc55391f736f6e926fb4b99 0000-0001-5196-9389 Tom Crick Tom Crick true false 2018-09-11 EDUC The reproduction and replication of novel results has become a major issue for a number of scientific disciplines. In computer science and related computational disciplines such as systems biology, the issues closely revolve around the ability to implement novel algorithms and approaches. Taking an approach from the literature and applying it to a new codebase frequently requires local knowledge missing from the published manuscripts and project websites. Alongside this issue, benchmarking, and the development of fair --- and widely available --- benchmark sets present another barrier.In this paper, we outline several suggestions to address these issues, driven by specific examples from a range of scientific domains. Finally, based on these suggestions, we propose a new open platform for scientific software development which effectively isolates specific dependencies from the individual researcher and their workstation and allows faster, more powerful sharing of the results of scientific software engineering. Conference Paper/Proceeding/Abstract 2nd Workshop on Sustainable Software for Science: Practice and Experiences New Orleans, USA 16 11 2014 2014-11-16 https://arxiv.org/abs/1407.5981 2nd Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE2) COLLEGE NANME School of Education COLLEGE CODE EDUC Swansea University 2018-10-15T14:49:20.4097597 2018-09-11T07:45:25.5660184 College of Arts and Humanities College of Arts and Humanities Tom Crick 0000-0001-5196-9389 1 Benjamin A. Hall 2 Samin Ishtiaq 3 0043757-11092018074653.pdf 1407.5981v2.pdf 2018-09-11T07:46:53.4900000 Output 215055 application/pdf Version of Record true 2018-09-11T00:00:00.0000000 true eng
title "Can I Implement Your Algorithm?": A Model for Reproducible Research Software
spellingShingle "Can I Implement Your Algorithm?": A Model for Reproducible Research Software
Tom, Crick
title_short "Can I Implement Your Algorithm?": A Model for Reproducible Research Software
title_full "Can I Implement Your Algorithm?": A Model for Reproducible Research Software
title_fullStr "Can I Implement Your Algorithm?": A Model for Reproducible Research Software
title_full_unstemmed "Can I Implement Your Algorithm?": A Model for Reproducible Research Software
title_sort "Can I Implement Your Algorithm?": A Model for Reproducible Research Software
author_id_str_mv 200c66ef0fc55391f736f6e926fb4b99
author_id_fullname_str_mv 200c66ef0fc55391f736f6e926fb4b99_***_Tom, Crick
author Tom, Crick
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url https://arxiv.org/abs/1407.5981
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description The reproduction and replication of novel results has become a major issue for a number of scientific disciplines. In computer science and related computational disciplines such as systems biology, the issues closely revolve around the ability to implement novel algorithms and approaches. Taking an approach from the literature and applying it to a new codebase frequently requires local knowledge missing from the published manuscripts and project websites. Alongside this issue, benchmarking, and the development of fair --- and widely available --- benchmark sets present another barrier.In this paper, we outline several suggestions to address these issues, driven by specific examples from a range of scientific domains. Finally, based on these suggestions, we propose a new open platform for scientific software development which effectively isolates specific dependencies from the individual researcher and their workstation and allows faster, more powerful sharing of the results of scientific software engineering.
published_date 2014-11-16T04:06:53Z
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