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scientific-visualization

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

Updated Feb 27, 2026
Search Tags:#plotly#matplotlib#publication figures#seaborn#scientific visualization#journal formatting#colorblind palette
One-click install
npx skills add https://github.com/gracefullight/cnn --skill scientific-visualization-gracefullight
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/gracefullight/cnn/tree/main/.agents/skills/scientific-visualization
Command: npx skills add https://github.com/gracefullight/cnn --skill scientific-visualization-gracefullight

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, seaborn, plotly, numpy, pandas, scipy, PyPDF2, 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: wrong resolution, non-colorblind-safe colors, incorrect dimensions, or missing statistical annotations. This Skill guides the creation of figures that meet the technical specifications of journals like Nature, Science, Cell, and PLOS from the start. ## Core Features & Use Cases - Journal-Specific Formatting: Pre-configured matplotlib styles and export utilities matching Nature, Science, Cell, PLOS, ACS, and IEEE requirements for dimensions, DPI, and file formats. - Colorblind-Safe Palettes: Built-in Okabe-Ito, Wong, and Paul Tol palettes plus guidance on perceptually uniform colormaps (viridis, cividis) and safe diverging maps. - Automated Export & Validation: Helper scripts save figures in multiple formats at correct DPI and verify figure dimensions against journal column widths. - Use Case: A researcher preparing a manuscript for Nature needs a multi-panel figure with error bars, significance markers, and lowercase panel labels at exactly 89 mm width. The Skill configures the Nature style, builds the panels, checks size compliance, and exports PDF at 1000 DPI. ## Quick Start Create a publication-ready multi-panel figure for Nature journal submission with colorblind-safe colors, error bars, and panel labels, then export it as PDF and PNG at 300 DPI.

Frequently Asked Questions about scientific-visualization

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

FAQPage Schema
How do I create publication-ready figures in matplotlib?▼

Apply a publication style preset, set figure size to the target journal's column width, use colorblind-friendly colors, label axes with units, and remove top and right spines. Export as PDF or EPS at 300-1000 DPI using savefig with bbox_inches='tight'.

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

Most journals require 300 DPI minimum for photographs, 600 DPI for combination figures, and 1000 DPI for line art. Vector formats like PDF and EPS are preferred for graphs, while TIFF or PNG are used for images. Never use JPEG for plots.

Which color palettes are colorblind-friendly for scientific plots?▼

The Okabe-Ito palette is the most widely recommended colorblind-safe categorical palette, distinguishable by all forms of color vision deficiency. For continuous data, use perceptually uniform colormaps like viridis, plasma, or cividis, and avoid jet and red-green diverging maps.

Seaborn vs matplotlib for scientific publication figures?▼

Seaborn provides high-level statistical plotting with automatic confidence intervals and faceting, ideal for box plots, violin plots, and heatmaps. Matplotlib offers finer control for complex multi-panel layouts. Both integrate since seaborn is built on matplotlib.

How do I check if my figure size meets Nature requirements?▼

Nature requires 89 mm width for single column and 183 mm for double column figures, with a maximum height of 247 mm. The check_figure_size function in figure_export.py verifies your matplotlib figure dimensions against these specifications automatically.

Why should I avoid JPEG format for scientific graphs?▼

JPEG uses lossy compression that introduces visible artifacts around text and sharp lines, degrading figure quality. Use vector formats (PDF, EPS, SVG) for plots or lossless raster formats (TIFF, PNG) for photographs and microscopy images.

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