Tracking the Shift from Bar Graphs to Informative Plots in Biomedical Journals. Impact of Journal Policies on the Visualization of Continuous Data.
Authors/Creators
- 1. Universidade de Coimbra Instituto de Investigação Clínica e Biomédica de Coimbra (CIBB)
-
2.
Berlin Institute of Health at Charité - Universitätsmedizin Berlin
Description
This dashboard tracks how data-visualization practices in scientific publishing have evolved over time, focusing on the use of bar charts versus more informative alternatives. Visualization types are classified by barzooka, an automated deep-learning tool that screens PDF figures.
- Bar charts - the conventional mean-and-error bar format
- Informative charts - bars with dots, box plots, dot plots, histograms, or violin plots
Data are organized across 12 Research Fields:
- Cardiac & Cardiovascular Systems
- Clinical Neurology
- Endocrinology & Metabolism
- Genetics & Heredity
- Immunology
- Neurosciences
- Oncology
- Orthopedics
- Pharmacology & Pharmacy
- Physiology
- Rheumatology
- Urology & Nephrology
The dataset covers 213 biomedical journals (2010–2025), 71 of which adopted an editorial figure-type policy, enabling comparison of policy vs. non-policy journals and before/after policy adoption.
Unless otherwise specified, percentages were calculated in comparison with the total eligible articles, meaning articles containing at least one bar graph or an informative plot.
Live dashboard: https://excelscior-uc.github.io/journal-policy-dashboard/
Source code: https://github.com/excelscior-uc/journal-policy-dashboard/
Files
journal-policy-dashboard-1.0.0.zip
Files
(1.7 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:385ea004f035432464cc9408f4166373
|
1.7 MB | Preview Download |
Additional details
Funding
Software
- Repository URL
- https://github.com/excelscior-uc/journal-policy-dashboard
- Programming language
- JavaScript , HTML , CSS , Python
- Development Status
- Active