Can I export figures in vector format?

Yes, the skill prioritizes vector formats such as PDF, EPS, and SVG to ensure infinite scalability and professional print quality.

Is it compatible with Seaborn?

Absolutely. It leverages Seaborn for high-level statistical graphics while applying custom matplotlib styles to ensure the output meets publication standards.

Which journals does this skill support?

It includes specific presets and styling for major journals like Nature, Science, and Cell, while providing general templates adaptable to any peer-reviewed publication.

How does it ensure figure accessibility?

The skill enforces the use of colorblind-friendly palettes like Okabe-Ito and encourages redundant encoding through varied line styles and markers.

Does it handle statistical significance markers?

Yes, it provides implementation patterns for adding significance markers (asterisks), error bars (SD, SEM, or CI), and individual data points to maintain statistical rigor.

Scientific Visualization

Name: Scientific Visualization
Author: pur3v4d3r

bypur3v4d3r

•

Data Science & ML

Generates publication-quality scientific figures and multi-panel plots adhering to journal standards and accessibility guidelines.

About

This skill empowers Claude to create professional, journal-ready visualizations using Matplotlib, Seaborn, and Plotly. It automates the complex process of meeting strict publication requirements, including specific DPI resolutions, vector formats like PDF and EPS, and colorblind-safe palettes like Okabe-Ito. By providing domain-specific guidance on multi-panel layouts, statistical rigor (error bars and significance markers), and typography, it ensures that data is represented accurately and accessibly for manuscripts in high-impact journals such as Nature, Science, and Cell.

Key Features

Colorblind-accessible palette integration including Okabe-Ito and perceptually uniform maps
High-resolution export in vector (PDF/EPS) and raster (TIFF/PNG) formats
Multi-panel figure layout management using GridSpec and consistent alignment
1 GitHub stars
Automated journal-specific styling for Nature, Science, and Cell
Statistical visualization tools for error bars, significance markers, and sample sizes

Use Cases

Transforming raw experimental data into multi-panel manuscript illustrations
Preparing figures for submission to peer-reviewed scientific journals
Ensuring data visualizations meet accessibility and colorblindness standards

About

Key Features

Colorblind-accessible palette integration including Okabe-Ito and perceptually uniform maps
High-resolution export in vector (PDF/EPS) and raster (TIFF/PNG) formats
Multi-panel figure layout management using GridSpec and consistent alignment
1 GitHub stars
Automated journal-specific styling for Nature, Science, and Cell
Statistical visualization tools for error bars, significance markers, and sample sizes

Use Cases

Transforming raw experimental data into multi-panel manuscript illustrations
Preparing figures for submission to peer-reviewed scientific journals
Ensuring data visualizations meet accessibility and colorblindness standards