scientific-visualization

Topics in the source

• Scientific Visualization
• Overview
• When to Use This Skill
• Quick Start Guide
• Basic Publication-Quality Figure
• Using Pre-configured Styles
• Quick Start with Seaborn
• Core Principles and Best Practices

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. 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 Apply journal-specific styles using the matplotlib style files in assets/: For statistical plots, use seaborn with publication styling: Critical requirements (detailed in references/publicationguidelines.md): Raster images (photos, microscopy): 300-600 DPI Line art (graphs, plots): 600-1200 DPI or vector format Vector formats (preferred): PDF, EPS, SVG Raster formats: TIFF, PNG (never JPEG for scientific data) Implementation:

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