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scientific-visualization

Generate publication-quality multi-panel figures from data using Python visualization libraries.

1|Updated Oct 2, 2022
Search Tags:#visualization#plotly#matplotlib#seaborn#publication#colorblind-friendly#multi-panel
One-click install
npx skills add https://github.com/danburonline/public-writings --skill scientific-visualization-danburonline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/danburonline/public-writings/tree/main/.cursor/skills/scientific-visualization
Command: npx skills add https://github.com/danburonline/public-writings --skill scientific-visualization-danburonline

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Researchers spend substantial time formatting and exporting figures to meet journal guidelines; this Skill streamlines creating high-quality publication-ready visuals from data using Python libraries.

Core Features & Use Cases

  • Multi-panel figure assembly with consistent styling across panels.
  • Colorblind-friendly palettes and grayscale accessibility checks.
  • Journal-ready exports in PDF, EPS, SVG, TIFF, and PNG with precise DPI control.
  • Quick workflows for common scientific plots (line plots, heatmaps, box plots) using Matplotlib, Seaborn, and Plotly.

Quick Start

Install the required Python packages (matplotlib, seaborn, plotly, numpy) and configure the publication style using the included style presets. Then import your data, build figures across one or more panels, and export using the helper functions to vector and raster formats suitable for submission.

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 Matplotlib for journal submission?▼

Publication-ready figures with Matplotlib are created by applying style presets for consistent formatting, assembling multi-panel layouts, and exporting to vector formats like PDF or EPS with precise DPI control and embedded fonts.

Can I generate colorblind-friendly scientific plots using Python?▼

Colorblind-friendly scientific plots in Python are generated by applying specialized palettes and running grayscale accessibility checks to ensure visuals remain readable and compliant with accessibility standards.

What's the best way to export Matplotlib figures to PDF, SVG, or TIFF formats?▼

Exporting Matplotlib figures to PDF, SVG, or TIFF formats is best done using helper functions that manage vector and raster outputs, enforce precise DPI settings, and ensure font embedding for journal compliance.

Does this approach support multi-panel figure assembly with Matplotlib and Seaborn?▼

Multi-panel figure assembly with Matplotlib and Seaborn is fully supported, applying consistent styling across all panels to streamline the creation of complex scientific visuals like heatmaps and box plots.

Do I need Numpy installed to build scientific plots with these Python visualization libraries?▼

Numpy is required to build scientific plots with these Python visualization libraries, serving as a core dependency for data handling and numerical operations required by the plotting scripts.

Why do my Matplotlib figures lose formatting when exported for scientific manuscripts?▼

Figures lose formatting during scientific manuscript export if style presets are unapplied and DPI settings are unmanaged, which this Skill resolves by standardizing styling and enforcing font embedding during export.

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