scientific-visualization_skill

This skill helps you create publication-ready scientific visualizations with matplotlib/ seaborn/ plotly, including multi-panel layouts and colorblind-safe

Python

2

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill kjgarza/marketplace-claude --skill scientific-visualization

SKILL.md24.7 KB

Overview

This skill produces publication-ready scientific figures using matplotlib, seaborn, and plotly. It focuses on multi-panel layouts, colorblind-safe palettes, error bars, significance markers, and correct typography. Export options include vector and high-resolution raster formats (PDF, EPS, TIFF) tailored for journal submission.

How this skill works

Apply curated publication styles and journal presets to configure fonts, sizes, and figure dimensions. Build plots with helpers for multi-panel GridSpec layouts, color palettes, error bars and statistical annotations, then export with the correct DPI and file formats. The tool includes colorblind-safe palettes, grayscale testing, and utilities to check journal column widths and labeling conventions.

When to use it

Preparing figures for manuscript submission (Nature, Science, Cell, PLOS, etc.)
Creating multi-panel figures with consistent styling and alignment
Exporting vector or high-resolution raster files to meet journal specs
Ensuring colorblind accessibility and grayscale readability
Adding error bars, sample sizes, and significance markers for statistical rigor

Best practices

Use vector formats (PDF/EPS/SVG) for line art; 600+ DPI for high-detail raster exports
Choose colorblind-safe palettes (Okabe-Ito, viridis/cividis for continuous data) and test in grayscale
Set fonts and point sizes at final print dimensions (axis labels ~7–9 pt, ticks ~6–8 pt)
Label panels with bold letters, align panels, and keep consistent margins and white space
Show individual data points when possible and always state error bar type (SD, SEM, CI) and n

Example use cases

Single-column line plot with confidence intervals and publication fonts exported as PDF
Two-by-two multi-panel figure with shared styling, bold panel labels, and aligned axes
Seaborn boxplot with individual points, colorblind palette, and significance stars annotated
Heatmap for correlation matrices using a perceptually uniform colormap and annotated colorbar
Convert an existing figure to meet journal DPI and file-format requirements

FAQ

Prefer vector formats (PDF, EPS, SVG) for plots and 300–600+ DPI TIFF for raster images like microscopy; avoid JPEG.

How do I ensure figures are colorblind-friendly?

Use approved palettes (Okabe-Ito) or perceptually uniform colormaps and add redundant encodings (markers, line styles); test in grayscale or with a colorblind simulator.