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danieldekay
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scientific-visualization

Automates publication-ready figure creation from data across Matplotlib, Seaborn, and Plotly.

1|2|Updated Jan 7, 2026
Search Tags:#visualization#plotly#matplotlib#seaborn#publication#colorblind#journal-guidelines
One-click install
npx skills add https://github.com/danieldekay/copilot-config-promptops --skill scientific-visualization-danieldekay
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/danieldekay/copilot-config-promptops/tree/main/skills/scientific-visualization
Command: npx skills add https://github.com/danieldekay/copilot-config-promptops --skill scientific-visualization-danieldekay

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Automates the production of publication-ready figures from data, aligning with journal guidelines and accessibility standards.

Core Features & Use Cases

  • Multi-panel figure Layouts with consistent styling
  • Colorblind-friendly palettes and accessible color choices by default
  • Export in vector and raster formats (PDF/EPS/SVG/TIFF/PNG) with publication-grade DPI
  • Reproducible workflows for journal submissions (Nature, Science, Cell, etc.)

Quick Start

Create a publication-ready figure by configuring publication styles, building your plots with Matplotlib/Seaborn/Plotly, and exporting to PDF/SVG/TIFF at the target DPI.

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 that meet journal guidelines in Matplotlib?▼

Publication-ready figures in Matplotlib require configuring multi-panel layouts, colorblind-safe palettes, and high-DPI vector exports with embedded fonts. This automates styling and export to journal-specific formats like PDF, EPS, and SVG.

Can I export Seaborn plots to vector formats with 300 DPI for Nature or Science submissions?▼

Yes, Seaborn plots can be exported to vector formats with 300 to 1200 DPI. This automates generating PDF, EPS, and SVG files with embedded fonts and publication-grade resolution for reproducible journal workflows.

What's the best way to apply colorblind-friendly palettes across multi-panel Plotly figures?▼

The best way to apply colorblind-friendly palettes across multi-panel Plotly figures is using automated styling tools. This applies accessible color choices by default and ensures consistent styling across all subplots before export.

Does this publication workflow support both vector and raster outputs like TIFF and PNG?▼

Yes, the publication workflow supports vector and raster outputs including TIFF and PNG. It exports figures at publication-grade DPI with embedded fonts, ensuring accessibility and compliance with journal guidelines.

Why do I need to embed fonts in PDF or SVG files for journal submission?▼

Embedding fonts in PDF or SVG files is required for journal submission to ensure figures render consistently across systems. This automates font embedding and applies journal-specific export formats to meet publication standards.

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