# Scientific Visualization

Meta-skill for publication-ready figures.

- Repository: https://github.com/zLanqing/codex-claude-academic-skills (5550 stars)
- Category: Engineering
- License: MIT license
- Last updated: 2026-05-14
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add zLanqing/codex-claude-academic-skills --skill scientific-visualization -g -a claude-code -y`
Codex: `npx skills add zLanqing/codex-claude-academic-skills --skill scientific-visualization -g -a codex -y`
Cursor: `npx skills add zLanqing/codex-claude-academic-skills --skill scientific-visualization -g -a cursor -y`
Gemini CLI: `npx skills add zLanqing/codex-claude-academic-skills --skill scientific-visualization -g -a gemini-cli -y`

## Description

Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Scientific Visualization

## Overview

Scientific visualization transforms data into clear, accurate figures for publication. Create journal-ready plots with multi-panel layouts, error bars, significance markers, and colorblind-safe palettes. Export as PDF/EPS/TIFF using matplotlib, seaborn, and plotly for manuscripts.

## When to Use This Skill

This skill should be used when:
- Creating plots or visualizations for scientific manuscripts
- Preparing figures for journal submission (Nature, Science, Cell, PLOS, etc.)
- Ensuring figures are colorblind-friendly and accessible
- Making multi-panel figures with consistent styling
- Exporting figures at correct resolution and format
- Following specific publication guidelines
- Improving existing figures to meet publication standards
- Creating figures that need to work in both color and grayscale

## Quick Start Guide

### Basic Publication-Quality Figure

```python
import matplotlib.pyplot as plt
import numpy as np

# Apply publication style (from scripts/style_presets.py)
from style_presets import apply_publication_style
apply_publication_style('default')

# Create figure with appropriate size (single column = 3.5 inches)
fig, ax = plt.subplots(figsize=(3.5, 2.5))

# Plot data
x = np.linspace(0, 10, 100)
ax.plot(x, np.sin(x), label='sin(x)')
ax.plot(x, np.cos(x), label='cos(x)')

# Proper labeling with units
ax.set_xlabel('Time (seconds)')
ax.set_ylabel('Amplitude (mV)')
ax.legend(frameon=False)

# Remove unnecessary spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Save in publication formats (from scripts/figure_export.py)
from figure_export import save_publication_figure
save_publication_figure(fig, 'figure1', formats=['pdf', 'png'], dpi=300)
```

### Using Pre-configured Styles

Apply journal-specific styles using the matplotlib style files in `assets/`:

```python
import matplotlib.pyplot as plt

# Option 1: Use style file directly
plt.style.use('assets/nature.mplstyle')

The rest: https://github.com/zLanqing/codex-claude-academic-skills/blob/HEAD/scientific-toolkit-skill/references/scientific-skills/scientific-visualization/SKILL.md