Which Python libraries does this skill support?

The skill is optimized for Matplotlib, Seaborn, and Plotly, providing specialized helpers for each to ensure publication standards are met.

How does it handle accessibility and colorblindness?

It defaults to perceptually uniform colormaps like viridis and provides easy access to the Okabe-Ito palette, while encouraging redundant encoding like line styles and markers.

Does it include specific formatting for major journals?

Yes, it contains pre-configured styles for Nature, Science, and Cell, automatically adjusting figure widths, font sizes, and label cases to match their guidelines.

Can I export figures in vector formats for journals?

Yes, the skill includes scripts to export figures in PDF, EPS, and SVG vector formats, as well as high-resolution TIFF and PNG files (300-1200 DPI).

Scientific Visualization

Name: Scientific Visualization
Author: K-Dense-AI

byK-Dense-AI

•

3,719

数据科学与机器学习

Generates publication-quality scientific figures and multi-panel layouts using Python libraries while adhering to journal-specific standards.

关于

Scientific Visualization is a specialized skill designed for researchers and data scientists to create journal-ready figures directly within Claude. It automates the generation of high-resolution, colorblind-safe visualizations using Matplotlib, Seaborn, and Plotly, ensuring every plot meets the rigorous specifications of top-tier journals like Nature, Science, and Cell. From handling complex multi-panel GridSpec layouts to applying precise typography and statistical significance markers, this skill streamlines the transition from raw data to submission-ready manuscripts.

主要功能

Journal-specific style presets (Nature, Science, Cell, PLOS)
Colorblind-safe palettes and grayscale compatibility testing
3,719 GitHub stars
Integrated statistical rigor with error bars and significance markers
High-resolution vector and raster export (PDF, EPS, TIFF, 300+ DPI)
Automated multi-panel figure alignment and bold labeling

使用场景

Standardizing data visualization styles across large research projects
Transforming exploratory data plots into accessible, inclusive visuals
Preparing professional figures for academic manuscript submission

关于

主要功能

Journal-specific style presets (Nature, Science, Cell, PLOS)
Colorblind-safe palettes and grayscale compatibility testing
3,719 GitHub stars
Integrated statistical rigor with error bars and significance markers
High-resolution vector and raster export (PDF, EPS, TIFF, 300+ DPI)
Automated multi-panel figure alignment and bold labeling

使用场景

Standardizing data visualization styles across large research projects
Transforming exploratory data plots into accessible, inclusive visuals
Preparing professional figures for academic manuscript submission