Openness and Computational Reproducibility in Plant Pathology: Where We Stand and a Way Forward

Article

Sparks, Adam H., Del Ponte, Emerson M., Alves, Kaique S., Foster, Zachary S. L. and Grunwald, Niklaus J.. 2023. "Openness and Computational Reproducibility in Plant Pathology: Where We Stand and a Way Forward." Phytopathology: International Journal of the American Phytopathological Society. 113 (7), pp. 1159-1170. https://doi.org/10.1094/PHYTO-10-21-0430-PER
Article Title

Openness and Computational Reproducibility in Plant Pathology: Where We Stand and a Way Forward

ERA Journal ID2639
Article CategoryArticle
AuthorsSparks, Adam H., Del Ponte, Emerson M., Alves, Kaique S., Foster, Zachary S. L. and Grunwald, Niklaus J.
Journal TitlePhytopathology: International Journal of the American Phytopathological Society
Journal Citation113 (7), pp. 1159-1170
Number of Pages12
Year2023
PublisherAmerican Phytopathological Society
Place of PublicationUnited States
ISSN0031-949X
1943-7684
Digital Object Identifier (DOI)https://doi.org/10.1094/PHYTO-10-21-0430-PER
Web Address (URL)https://apsjournals.apsnet.org/doi/full/10.1094/PHYTO-10-21-0430-PER
AbstractOpen 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.
Keywordscomputational biology; data science; techniques
ANZSRC Field of Research 2020310805. Plant pathology
Byline AffiliationsCentre for Crop Health
Department of Primary Industries and Regional Development, Western Australia
Federal University of Vicosa, Brazil
U.S. Department of Agriculture-Agricultural Research Service, United States
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