Openness and Computational Reproducibility in Plant Pathology: Where We Stand and a Way Forward
Open research practices have been highlighted extensively during the last 10 years in many fields of scientific study as essential standards needed to promote transparency and reproducibility of scientific results. Scientific claims can only be evaluated based on how protocols, materials, equipment,...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/95227 |
| _version_ | 1848765988927963136 |
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| author | Sparks, Adam Del Ponte, E.M. Alves, K.S. Foster, Z.S.L. Grünwald, N.J. |
| author_facet | Sparks, Adam Del Ponte, E.M. Alves, K.S. Foster, Z.S.L. Grünwald, N.J. |
| author_sort | Sparks, Adam |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Open research practices have been highlighted extensively during the last 10 years in many fields of scientific study as essential standards needed to promote transparency and reproducibility of scientific results. Scientific claims can only be evaluated based on how protocols, materials, equipment, and methods were described; data were collected and prepared; and analyses were conducted. Openly sharing protocols, data, and computational code is central to current scholarly dissemination and communication, but in many fields, including plant pathology, adoption of these practices has been slow.We randomly selected 450 articles published from 2012 to 2021 across 21 journals representative of the plant pathology discipline and assigned them scores reflecting their openness and computational reproducibility. We found that most of the articles did not follow protocols for open science and failed to share data or code in a reproducible way. We propose that use of open-source tools facilitates computationally reproducible work and analyses, benefitting not just readers but the authors as well. Finally, we provide ideas and suggest tools to promote open, reproducible computational research practices among plant pathologists. |
| first_indexed | 2025-11-14T11:44:00Z |
| format | Journal Article |
| id | curtin-20.500.11937-95227 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | eng |
| last_indexed | 2025-11-14T11:44:00Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-952272024-07-04T03:37:11Z Openness and Computational Reproducibility in Plant Pathology: Where We Stand and a Way Forward Sparks, Adam Del Ponte, E.M. Alves, K.S. Foster, Z.S.L. Grünwald, N.J. computational biology data science techniques Reproducibility of Results Plant Diseases Reproducibility of Results Plant Diseases Open research practices have been highlighted extensively during the last 10 years in many fields of scientific study as essential standards needed to promote transparency and reproducibility of scientific results. Scientific claims can only be evaluated based on how protocols, materials, equipment, and methods were described; data were collected and prepared; and analyses were conducted. Openly sharing protocols, data, and computational code is central to current scholarly dissemination and communication, but in many fields, including plant pathology, adoption of these practices has been slow.We randomly selected 450 articles published from 2012 to 2021 across 21 journals representative of the plant pathology discipline and assigned them scores reflecting their openness and computational reproducibility. We found that most of the articles did not follow protocols for open science and failed to share data or code in a reproducible way. We propose that use of open-source tools facilitates computationally reproducible work and analyses, benefitting not just readers but the authors as well. Finally, we provide ideas and suggest tools to promote open, reproducible computational research practices among plant pathologists. 2023 Journal Article http://hdl.handle.net/20.500.11937/95227 10.1094/PHYTO-10-21-0430-PER eng http://creativecommons.org/licenses/by/4.0/ fulltext |
| spellingShingle | computational biology data science techniques Reproducibility of Results Plant Diseases Reproducibility of Results Plant Diseases Sparks, Adam Del Ponte, E.M. Alves, K.S. Foster, Z.S.L. Grünwald, N.J. Openness and Computational Reproducibility in Plant Pathology: Where We Stand and a Way Forward |
| title | Openness and Computational Reproducibility in Plant Pathology: Where We Stand and a Way Forward |
| title_full | Openness and Computational Reproducibility in Plant Pathology: Where We Stand and a Way Forward |
| title_fullStr | Openness and Computational Reproducibility in Plant Pathology: Where We Stand and a Way Forward |
| title_full_unstemmed | Openness and Computational Reproducibility in Plant Pathology: Where We Stand and a Way Forward |
| title_short | Openness and Computational Reproducibility in Plant Pathology: Where We Stand and a Way Forward |
| title_sort | openness and computational reproducibility in plant pathology: where we stand and a way forward |
| topic | computational biology data science techniques Reproducibility of Results Plant Diseases Reproducibility of Results Plant Diseases |
| url | http://hdl.handle.net/20.500.11937/95227 |