DCQC is an R package that ships a single Shiny application to help data editors carry out structured quality control of the data and code archived alongside a manuscript.
The app operationalises the SORTEE guidelines for data and code quality control (Pick et al. 2026) as an interactive, six-stage checklist. Rather than working from a PDF of the guidelines and a blank document, an editor works through the relevant stages in the browser, records a Yes/No verdict and a free-text comment against each item, and exports a formatted report to return to the journal or the authors.
The intent is to make data and code checks consistent between editors, transparent to authors, and quick enough to actually do — the three things that most often prevent quality control from happening at all.
The checklist contains 15 items grouped into six stages. Stages are selected at launch, so an editor reviewing only archived data is not shown items that do not apply.
| Stage | Focus | Items |
|---|---|---|
| 1 | Data must be archived and adhere to FAIR guiding principles | 1–5 |
| 2 | Archived data corresponds with the data reported in the manuscript | 6 |
| 3 | Code must be archived and adhere to FAIR guiding principles | 7–11 |
| 4 | Archived code corresponds with the workflow reported in the manuscript | 12 |
| 5 | Archived code runs with the archived data | 13 |
| 6 | Results can be computationally reproduced by running the archived code | 14–15 |
Stages 1 and 3 cover accessibility in an open repository with a persistent DOI, licensing, completeness, interoperable (non-proprietary) file formats, and adequate metadata. Stages 2 and 4 check correspondence between what is archived and what the manuscript claims. Stages 5 and 6 cover execution and computational reproducibility of numeric results and figures, with an option to record the tolerance applied — for example the percentage discrepancy measure of Hardwicke et al. (2021).
DCQC is not on CRAN. Install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("SORTEE/DCQC")Export renders an R Markdown template to PDF, which needs Pandoc and a LaTeX distribution in addition to the declared package dependencies:
install.packages("rmarkdown")
# If you do not already have LaTeX installed:
install.packages("tinytex")
tinytex::install_tinytex()The package exports one function:
library(DCQC)
DCQC()This launches the Shiny app. The workflow is:
- Set up the review. A modal collects the paper title, your name as reviewer, and the journal, then asks which stages to review. At least one stage must be selected.
- Work through the checklist. Each item shows the full guideline text, a Yes / No toggle, and a comment box. Comments are where you record what was missing, what you fixed, and what the authors need to address — they carry most of the value in the final report.
- Export. Download Text Report renders a dated report containing every item you answered, its verdict, and its comment, headed with the manuscript, journal, and reviewer details.
Nothing is transmitted anywhere: the app runs locally and the report is written to your machine.
The exported report is generated from inst/rmd/DCQCreport.Rmd and contains:
- Manuscript title, journal, reviewer name, and report date
- One section per answered checklist item, with the guideline text, the Yes/No response, and the reviewer's comment (or "No comment provided.")
Items belonging to unselected stages, and items left unanswered, are omitted rather than reported as failures.
bslib, shiny, shinyalert, shinyjs, shinyWidgets, tippy (declared), plus rmarkdown and a LaTeX engine at export time.
Bug reports, feature requests, and pull requests are welcome via GitHub Issues. Because the checklist text is a direct implementation of a published, community-agreed standard, changes to the wording of the items themselves should be raised as an issue for discussion first — the app should track the guidelines rather than diverge from them.
If you use DCQC in an editorial workflow or in research, please cite the guidelines it implements:
Pick, J. L., Allen, B. J., Bachelot, B., Bairos-Novak, K. R., Brand, J. A., Class, B., Dallas, T., D'Amelio, P. B., Fenollosa, E., Fernández-Juricic, E., Gomes, D. G. E., Grainger, M. J., Guillemaud, T., John, C., Krasnow, R., Lagisz, M., Lequime, S., Maynard, D. S., Nakagawa, S., O'Dea, R. E., Paquet, M., Petitjean, Q., Sánchez-Tójar, A., van Dis, N. E., Wilson, L. A. B., & Ivimey-Cook, E. R. (2026). The SORTEE guidelines for data and code quality control in ecology and evolutionary biology. Peer Community Journal, 6, e20. https://doi.org/10.24072/pcjournal.687
and the software:
Ivimey-Cook, E. R., & Pick, J. L. (2026). DCQC: Data and code quality control for ecology and evolutionary biology. R package version 0.0.0.9000. https://github.com/SORTEE/DCQC
A machine-readable CITATION.cff is included, so GitHub's
"Cite this repository" button gives formatted APA and BibTeX.
- Pick, J. L., et al. (2026). The SORTEE guidelines for data and code quality control in ecology and evolutionary biology. Peer Community Journal, 6, e20. https://doi.org/10.24072/pcjournal.687 (preprint: https://doi.org/10.32942/X24P8S)
- Hardwicke, T. E., Bohn, M., MacDonald, K., Hembacher, E., Nuijten, M. B., Peloquin, B. N., deMayo, B. E., Long, B., Yoon, E. J., & Frank, M. C. (2021). Analytic reproducibility in articles receiving open data badges at the journal Psychological Science: an observational study. Royal Society Open Science, 8(1), 201494. https://doi.org/10.1098/rsos.201494
MIT © Edward R. Ivimey-Cook and Joel L. Pick. See LICENSE.
Developed by the Society for Open, Reliable, and Transparent Ecology and Evolutionary Biology (SORTEE).