This repository contains the data and code for our article:
Sparks, A.H., Del Ponte, E.M., Alves, K. S., Foster, Z., Grünwald, N. J. (YYYY). Reproducibility in plant pathology: where do we stand and a way forward. Name of journal/book https://doi.org/xxx/xxx
Our pre-print is online on the agriRxiv preprint server:
Sparks, A.H., Del Ponte, E.M., Alves, K. S., Foster, Z., Grünwald, N. J. (2021). Reproducibility in plant pathology: where do we stand and a way forward. agriRxiv, Accessed 13 Oct 2021. Online at https://doi.org/10.31220/agriRxiv.2021.00082
The paper is a systematic and quantitative review of articles published in 20 plant pathology journals that spans five years of publications. It provides a basis for identifying what has been done so far in the discipline of plant pathology’s published research to ensure computational reproducibility. The results show that as a discipline, plant pathologists are not widely sharing data or code openly, making the works largely unreproducible. Based on these results and our own experiences, we offer suggestions as to how we can further improve reproducibility in the discipline of plant pathology, but which are not unique to the discipline, that would allow reviewers to make better suggestions, readers to learn more from the work and earns author more citations for their work.
Please cite this compendium as:
Sparks, A.H., Del Ponte, E.M., Alves, K. S., Foster, Z., Grünwald, N. J. (2021). Compendium of R code and data for ‘Status and Best Practices for Reproducible Research In Plant Pathology’. Accessed 13 Oct 2021. Online at https://doi.org/10.5281/zenodo.1250665
This repository is organized as an R package. There are custom R
functions, table_of_journals() and workflow_dia() that are used in
this repository, along with a bibliography file of the articles that
were examined and the notes that are located in inst/extdata
directory. We have used the R package structure to help manage
dependencies, to take advantage of continuous integration for automated
code testing and for file organisation.
You can download the compendium as a zip from from this URL: https://github.com/openplantpathology/Reproducibility_in_Plant_Pathology/archive/main.zip
Or you can install this compendium as an R package, Reproducibility.in.Plant.Pathology, from GitHub with:
if (!require("remotes"))
install.packages("remotes")
remotes::install_github("openplantpathology/Reproducibility_in_Plant_Pathology"
)Once the download is complete, open the
Reproducibility_in_Plant_Pathology.Rproj in RStudio to begin working
with the package and compendium files.
Get the latest instance from Dockerhub, launch it and go to
localhost:8787 in your browser. Login with rstudio, password is
rstudio.
docker pull adamhsparks/reproducibility_in_plant_pathology
docker run -d -p 8787:8787 adamhsparks/reproducibility_in_plant_pathologyThe file structure follows a normal R package with one exception. The top-level “/analysis” directory contains the directories and files necessary to re-knit the MS Word document of the paper from an Rmd file, “/analysis/paper/paper.Rmd”.
A script, knit_paper.R, is located in analysis/paper/knit_paper.R
that will knit the Word document in a
Docker session.
Code: MIT year: 2021, copyright holder: Adam H. Sparks
Data: CC-0 attribution requested in reuse
Adam H. Sparks Senior Research Scientist Farming Systems Innovation Primary Industries Development Department of Primary Industries and Regional Development Level 6.35, 1 Nash St., Perth WA 6000
Please note that the Reproducibility.in.Plant.Pathology project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.