Does it support colorblind-accessible palettes?

Absolutely. It defaults to the Okabe-Ito palette and other perceptually uniform colormaps like viridis and plasma to ensure accessibility for all readers.

Can this skill format figures for specific journals?

Yes, it includes pre-configured style files and helper scripts specifically designed for journals like Nature, Science, and Cell, automatically adjusting widths, fonts, and margins.

Can I add statistical significance markers automatically?

Yes, the skill includes patterns for adding error bars (SEM, SD, CI) and significance annotations to plots while maintaining a clean, professional aesthetic.

What file formats are supported for figure export?

The skill provides tools to export in high-quality vector formats (PDF, EPS, SVG) and high-resolution raster formats (TIFF, PNG) at the DPI required by publishers.

How does it handle multi-panel figure layouts?

It utilizes GridSpec and standardized labeling functions to create complex multi-panel figures (A, B, C) with consistent alignment and shared styling across all panels.

Scientific Visualization & Figure Design

Name: Scientific Visualization & Figure Design
Author: Sologa

bySologa

数据科学与机器学习

Generates publication-ready scientific figures and multi-panel layouts optimized for top-tier academic journals like Nature and Science.

关于

This skill provides specialized guidance for creating journal-quality visualizations that meet the rigorous technical requirements of scientific publishing. It orchestrates Python libraries such as Matplotlib, Seaborn, and Plotly to implement essential publication standards, including colorblind-safe palettes (Okabe-Ito), precise typography, and high-resolution exports (PDF, EPS, TIFF). Whether you are building complex multi-panel arrangements or adding statistical significance markers, this skill ensures your data is presented with the clarity, accuracy, and professional aesthetics required for manuscript submission.

主要功能

Automated multi-panel layout orchestration with standardized labeling
High-resolution export configurations (300-1200 DPI) for vector and raster formats
0 GitHub stars
Standardized implementation of statistical markers, error bars, and units
Journal-specific presets for Nature, Science, Cell, and PLOS guidelines
Colorblind-accessible palette enforcement and grayscale compatibility testing

使用场景

Preparing multi-panel figures for peer-reviewed journal submissions
Ensuring figure compliance with publisher-specific technical and formatting guidelines
Converting exploratory plots into accessible, high-resolution scientific visualizations

关于

主要功能

Automated multi-panel layout orchestration with standardized labeling
High-resolution export configurations (300-1200 DPI) for vector and raster formats
0 GitHub stars
Standardized implementation of statistical markers, error bars, and units
Journal-specific presets for Nature, Science, Cell, and PLOS guidelines
Colorblind-accessible palette enforcement and grayscale compatibility testing

使用场景

Preparing multi-panel figures for peer-reviewed journal submissions
Ensuring figure compliance with publisher-specific technical and formatting guidelines
Converting exploratory plots into accessible, high-resolution scientific visualizations