Does this skill support specific journal formats?

Yes, it includes pre-configured styles and helper functions for major journals like Nature, Science, and Cell to ensure compliance with their specific layout, font, and resolution requirements.

Can it handle colorblind-accessible plots?

Absolutely. The skill prioritizes accessibility by utilizing approved palettes like Okabe-Ito and perceptually uniform colormaps like viridis and plasma.

How does it handle statistical markers?

It provides implementation patterns for adding significance markers (asterisks), error bars (SD, SEM, or CI), and jittered individual data points to ensure statistical rigor in your visualizations.

What file formats are supported for figure export?

The skill supports high-resolution exports in vector formats such as PDF, EPS, and SVG, as well as publication-grade raster formats like TIFF and PNG at 300-600 DPI.

Scientific Data Visualization

Name: Scientific Data Visualization
Author: K-Dense-AI

byK-Dense-AI

•

2,188

데이터 과학 및 ML

Generates publication-ready scientific figures and multi-panel layouts using Matplotlib, Seaborn, and Plotly.

소개

The Scientific Visualization skill empowers researchers and developers to create high-resolution, journal-quality plots directly within Claude Code. It provides pre-configured styles for major publications like Nature, Science, and Cell, ensuring figures meet strict requirements for resolution, typography, and colorblind accessibility. Whether you are building complex multi-panel layouts with statistical markers or exporting vector graphics for manuscripts, this skill automates the technical aspects of figure preparation while maintaining rigorous scientific standards.

주요 기능

Journal-ready style presets for Nature, Science, and Cell compliance
Standardized statistical markers, error bars, and significance notation
Automated multi-panel figure generation with GridSpec and panel labeling
2,188 GitHub stars
High-resolution exports in vector (PDF, EPS) and raster (TIFF, PNG) formats
Colorblind-safe palettes and perceptually uniform colormaps

사용 사례

Automating the layout of complex, multi-panel plots from raw datasets
Creating accessible, colorblind-friendly data visualizations for research
Preparing high-resolution figures for peer-reviewed journal submissions

소개

주요 기능

Journal-ready style presets for Nature, Science, and Cell compliance
Standardized statistical markers, error bars, and significance notation
Automated multi-panel figure generation with GridSpec and panel labeling
2,188 GitHub stars
High-resolution exports in vector (PDF, EPS) and raster (TIFF, PNG) formats
Colorblind-safe palettes and perceptually uniform colormaps

사용 사례

Automating the layout of complex, multi-panel plots from raw datasets
Creating accessible, colorblind-friendly data visualizations for research
Preparing high-resolution figures for peer-reviewed journal submissions