T.-J. Lin, M.P. Landry*. Quantifying Data Distortion in Bar Graphs in Biological Research. bioRxiv (2024). DOI: 10.1101/2024.09.20.609464
bar_constants.py- Order: 1
- Plot settings and constants
bar_util.py- Order: 2
- Utility functions and custom statistics
bar-graph-classification.ipynb- Order: 3
- Classification of articles
- Graph-level bias analysis
- Author number analysis
bar-graph-examples.ipynb- Order: 0
- Examples of correct and incorrect bar graphs in Cartesian coordinates
lie-factor-quantification.ipynb- Order: 4
- Quantify extend of data distortion with data distortion metrics by mistake types
- No grouping
- Grouped by absolute/relative measurands
- Grouped by measurand identity
- Grouped by journals
polar-bar-plot.ipynb- Order: 0
- Examples of bar graphs in polar coordinates
misused_bar_graph_annotation- Order: 3
- The raw annotation data file from WebPlotDigitizer v5 stored in folders by journals and mistake types
- Each article has a visualization value file (viz) and a true value file (val)
misused_bar_graph_data- Order: 4
- CSV data file from WebPlotDigitizer v5 stored in folders by journals and mistake types
- Each article has a visualization value file (viz) and a true value file (val)
misused_bar_graph_figures- Order: 2
- Misused bar graphs are screenshotted and stored in folders by journals and mistake types
- Each folder contains a CSV file storing the manually labeled metadata of each figure
processed_data- Order: 5
zotero_data- Order: 1
- CSV of Zotero export of articles included in the study
- "Manual Tags" column contains the categorization of each article