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scientific-visualization

Create publication-ready multi-panel figures from data using matplotlib, seaborn, or plotly.

3|Updated Jan 1, 2026
Search Tags:#visualization#plotly#matplotlib#seaborn#publication#colorblind#multPanel
One-click install
npx skills add https://github.com/kjgarza/marketplace-claude --skill scientific-visualization-kjgarza
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/kjgarza/marketplace-claude/tree/main/plugins/scholarly-comms-researcher/skills/scientific-visualization
Command: npx skills add https://github.com/kjgarza/marketplace-claude --skill scientific-visualization-kjgarza

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Researchers often struggle to turn raw data into publication-ready figures that satisfy journal guidelines and accessibility standards. This skill streamlines the process by generating polished, consistent figures directly from data.

Core Features & Use Cases

  • Multi-panel figure creation with consistent styling across panels for manuscripts.
  • Colorblind-safe palettes and accessible typography to ensure readability in color and grayscale.
  • High-quality exports to vector and raster formats (PDF, EPS, TIFF, PNG) with publication-grade DPI.
  • Use Case: A biologist creates a 2x2 panel figure combining a time-series, a box plot, and a heatmap, then exports ready-for-submission files.

Quick Start

Provide your dataset and specify the target journal, then generate a publication-ready figure and export it as PDF and PNG.

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 with colorblind-safe palettes using Python?▼

Multi-panel figures for journals are created by applying consistent styling across all subplots using matplotlib and seaborn, ensuring accessible typography and colorblind-safe palettes for readability in color and grayscale.

Can I export matplotlib figures to PDF, EPS, TIFF, and PNG formats for manuscript submission?▼

Yes, you can export matplotlib figures to PDF, EPS, TIFF, and PNG formats, generating high-resolution outputs with publication-grade DPI that meet standard manuscript submission requirements.

Does this scientific visualization skill support multi-panel figure layouts for academic journals?▼

Yes, this scientific visualization skill supports multi-panel figure layouts, allowing you to combine plots like time-series, box plots, and heatmaps with consistent styling tailored for academic journals.

Do I need matplotlib installed to generate colorblind-safe plots for publication?▼

Yes, you need matplotlib installed as the primary dependency, alongside standard Python packages like seaborn and plotly, to generate colorblind-safe plots and validate figure sizes for publication.

What is the best way to ensure my research plots are accessible in both color and grayscale?▼

The best way to ensure research plots are accessible in color and grayscale is to apply colorblind-safe palettes and accessible typography during the figure generation process using seaborn and matplotlib styling scripts.

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