scientific-visualization

Name: scientific-visualization
Availability: InStock
Author: Engstrom

Create publication-ready multi-panel scientific figures with Matplotlib, Seaborn, or Plotly.

18|1|Updated Dec 27, 2025

Search Tags:#visualization #plotly #matplotlib #seaborn #publication #colorblind #mult-panel

Ready to run

npx skills add https://github.com/LogauaEngstrom/claude-scientific-skills --skill scientific-visualization-logauaengstrom

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/LogauaEngstrom/claude-scientific-skills/tree/main/scientific-skills/scientific-visualization
Command: npx skills add https://github.com/LogauaEngstrom/claude-scientific-skills --skill scientific-visualization-logauaengstrom

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Creating publication-quality figures is time-consuming and error-prone without standardized styling, color palettes, and export pipelines.

Core Features & Use Cases

Multi-panel figure creation with consistent layout and panel labeling.
Colorblind-friendly palettes and grayscale testing to ensure accessibility.
Export of figures in vector and high-resolution raster formats (PDF, EPS, TIFF, SVG) with embedded fonts.
Reusable styles and presets for Matplotlib/Seaborn/Plotly workflows to meet journal guidelines.

Quick Start

Generate a publication-ready figure by configuring the publication style, assembling a multi-panel plot, and exporting to PDF/TIFF using the provided scripts.

Frequently Asked Questions about scientific-visualization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I export Matplotlib figures to vector formats like PDF and SVG with embedded fonts for journal submission?▼

Export Matplotlib figures to vector formats like PDF and SVG with embedded fonts using standardized styling scripts. This ensures your publication-ready figures meet journal guidelines for high-resolution graphics and preserve text rendering across biology, chemistry, and medicine submissions.

How do I create multi-panel layouts in Matplotlib with consistent styling and panel labeling?▼

Create multi-panel layouts in Matplotlib with consistent styling and panel labeling by applying reusable publication presets. These scripts automate layout assembly to ensure uniform spacing and labeling across all subplots for high-resolution scientific figures.

How do I apply colorblind-friendly palettes and test scientific plots for grayscale accessibility?▼

Apply colorblind-friendly palettes and test scientific plots for grayscale accessibility by using integrated color-safe presets. The grayscale testing feature ensures your visualizations remain readable for all audiences and meet publication accessibility standards.

Does this publication styling workflow support Plotly and Seaborn alongside Matplotlib?▼

Yes, the publication styling workflow supports Plotly and Seaborn alongside Matplotlib. You can apply reusable styles, color-safe palettes, and export presets across these frameworks to generate journal-ready figures consistently.

Why does my scientific figure fail journal guidelines without standardized styling and export pipelines?▼

Scientific figures often fail journal guidelines without standardized styling and export pipelines due to inconsistent fonts, incorrect color palettes, and unsupported resolutions. The Skill automates these requirements to generate compliant vector and high-resolution raster outputs.

Can I use these visualization scripts for high-resolution TIFF exports required by medical and biology journals?▼

Yes, you can use these visualization scripts for high-resolution TIFF exports required by medical and biology journals. The export pipeline supports TIFF, PDF, EPS, and SVG formats with embedded fonts to meet strict publication standards.