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scientific-visualization

Create publication-ready scientific figures with matplotlib, seaborn, and plotly.

Updated Aug 28, 2026
Search Tags:#data visualization#plotly#matplotlib#seaborn#publication#colorblind-safe#scientific figures
One-click install
npx skills add https://github.com/listentomi/Orcajack --skill scientific-visualization-listentomi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/listentomi/Orcajack/tree/main/skills/science/scientific-visualization
Command: npx skills add https://github.com/listentomi/Orcajack --skill scientific-visualization-listentomi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, seaborn, plotly, numpy, pandas, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve? Scientific figures often get rejected or require revision because they fail journal requirements on resolution, colorblind accessibility, font sizes, and file formats. This Skill guides the creation of journal-compliant plots so figures meet publication standards on the first submission. ## Core Features & Use Cases - Journal-Specific Styling: Pre-configured matplotlib style files and presets for Nature, Science, and Cell, with correct column widths, fonts, and DPI settings. - Colorblind-Safe Palettes: Okabe-Ito and perceptually uniform colormaps (viridis, RdBu_r) with grayscale compatibility guidance. - Automated Export Utilities: Helper scripts save figures as PDF/EPS/TIFF/PNG at correct DPI and verify dimensions against journal specifications. - Use Case: A researcher preparing a multi-panel figure for Nature applies the nature.mplstyle preset, builds a 2x2 GridSpec layout with bold panel labels and error bars, then exports at 300 DPI in PDF and PNG formats. ## Quick Start Create a publication-quality line plot for Nature with colorblind-safe colors, axis labels with units, error bars, and export it as a 300 DPI PDF.

Frequently Asked Questions about scientific-visualization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I make publication-quality figures in matplotlib?▼

Apply a publication style preset, set figure size to the target journal's column width, use colorblind-safe colors, label axes with units, and remove top and right spines. Export as PDF or PNG at 300 DPI or higher using a save helper that handles format settings.

What DPI and file format do journals require for figures?▼

Raster images need 300-600 DPI and line art needs 600-1200 DPI or vector format. Preferred formats are PDF, EPS, or SVG for plots and TIFF or PNG for images; JPEG should never be used for scientific data.

Matplotlib vs seaborn for scientific plots?▼

Matplotlib gives the most control over multi-panel layouts and fine publication details. Seaborn is better for statistical plots since it computes confidence intervals automatically and supports faceting, while remaining fully compatible with matplotlib customization.

How do I make colorblind-friendly plots in Python?▼

Use the Okabe-Ito palette or seaborn's built-in colorblind palette, and perceptually uniform colormaps like viridis or cividis for heatmaps. Add redundant encoding with line styles and markers, and test the figure in grayscale.

Why does my figure text look too small in the published paper?▼

Font sizes shrink when a large figure is scaled down to column width. Create the figure at its final print size and keep axis labels at 7-9 pt and tick labels at 6-8 pt so text stays readable.

Can I export plotly figures for journal submission?▼

Yes, plotly figures can be exported as static images using write_image with a scale factor of about 3 to reach roughly 300 DPI. Configure a sans-serif font and white background to match publication styling.

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