DOI：10.31220/agriRxiv.2021.00082

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

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  Abstract: Openness and computational reproducibility in plant pathology: where do we stand and a way forward. Abstract Open research practices have been highlighted extensively during the last ten 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 analyzes were conducted. Openly sharing protocols, data and computational code is central for 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 were not following protocols for open science and were failing to share data or code in a reproducible way. We also propose that use of open-source tools facilitates computationally reproducible work and analyzes benefitting not just readers, but the authors as well. Finally, we also provide ideas and tools to promote open, reproducible computational research practices among plant pathologists. Open preprint This article is a preprint and has not been peer reviewed

  Key words: plant pathology; data; protocols; reproducible; computational reproducibility; practice

  提交时间：2021-01-01

  版权声明：作者本人独立拥有该论文的版权，预印本系统仅拥有论文的永久保存权利。任何人未经允许不得重复使用。
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• 序号	提交日期	编号	操作
  1	2021-01-01	10.31220/agriRxiv.2021.00082V1	下载

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