scientific-figures

This Skill generates publication-ready figures from raw scientific data across Python and R environments. It supports histograms, box plots, Kaplan–Meier plots, and heatmaps while enforcing DPI, fonts, colorblind-safe palettes, and reproducible scripting for journal submissions.

Yes, this Skill supports creating Kaplan–Meier plots using the lifelines package. It integrates end-to-end workflows for survival analysis visualization, ensuring the output meets publication standards for manuscripts and conference materials.

Yes, you can use ggplot to create multi-panel figures for manuscripts. This Skill provides recommended styles, layouts, and templates for multi-panel figures, ensuring consistent styling and accessible color palettes across all panels.

The best way to ensure figures meet journal accessibility requirements is to use this Skill's built-in validation. It automatically enforces colorblind-safe palettes, appropriate DPI settings, and readable fonts, while providing captions and reproducible scripts to meet strict journal guidelines.

You need to provide raw scientific data in a format compatible with Python or R environments. This Skill handles the end-to-end workflow from data input to final figure output, applying necessary styling, layouts, and accessibility checks automatically.

To write reproducible scripts for scientific data visualization, use the example scripts provided in the analysis/scripts directory. This Skill ensures reproducibility by integrating figure-generation workflows that enforce consistent fonts, DPI settings, and color accessibility standards.