scientific-visualization

Create publication-ready scientific figures with journal-grade styling and export formats.

Updated Mar 21, 2026

Search Tags:#visualization #plotly #matplotlib #seaborn #publication #colorblind #journal-formatting

Ready to run

npx skills add https://github.com/ManfronEnrico/thesis-manifold --skill scientific-visualization-manfronenrico

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, numpy, PyPDF2, and includes scripts (resource) and assets (resource) components.

What problem does it solve?

Publication workflows for scientific figures are labor-intensive and error-prone; this skill consolidates styling, accessibility, and export settings into a reusable framework.

Core Features & Use Cases

Multi-panel figure composition with consistent styling across Matplotlib, Seaborn, and Plotly.
Publication-ready styling presets for Nature, Science, Cell, and other journals.
Colorblind-friendly palettes and accessible color maps with font embedding and sizing controls.
Automated exports to vector and raster formats (PDF, EPS, TIFF, PNG) at publication-grade DPI.
Real-world use case: a researcher quickly generates a 3-panel figure with results and saves all required files for manuscript submission.

Quick Start

Configure publication styles, design a publication-ready figure with colorblind-safe palettes, and export high-resolution vectors and rasters 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 create publication-ready figures for journal submission using Matplotlib?▼

You can generate publication-ready figures by applying journal-specific styling presets across Matplotlib, ensuring font embedding, colorblind-friendly palettes, and exporting to vector or raster formats at publication-grade DPI.

What's the best way to build multi-panel scientific figures with consistent styling?▼

Multi-panel scientific figures with consistent styling are built using a reusable framework that unifies aesthetics across Matplotlib, Seaborn, and Plotly, ensuring uniform typography and accessible color maps for academic research.

Can I use colorblind-friendly palettes and specific journal formatting presets with Plotly?▼

Yes, colorblind-friendly palettes and specific journal formatting presets are supported across Plotly, Seaborn, and Matplotlib, allowing you to maintain accessibility and meet publisher guidelines for scientific visualizations.

Does this approach support automated exports to vector and raster formats like TIFF and EPS?▼

Yes, automated exports to vector and raster formats including PDF, EPS, TIFF, and PNG are fully supported, ensuring all required files are generated at publication-grade DPI for manuscript submission.

Why do my scientific figures fail journal style checks for fonts and color accessibility?▼

Scientific figures fail journal style checks when font embedding is missing or colors are inaccessible; applying this framework enforces font embedding and colorblind-friendly palettes aligned to publisher guidelines for Nature, Science, and Cell.