scientific-visualization

Generate publication-ready scientific figures with matplotlib, seaborn, and plotly.

Updated May 4, 2026

Search Tags:#publication figures #scientific visualization #journal formatting #error bars #colorblind palettes

Ready to run

npx skills add https://github.com/luokai25/luo-ai-skills-market --skill scientific-visualization-luokai25

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/luokai25/luo-ai-skills-market/tree/main/09-data-and-ai%20%28by%20Luo%20Kai%29/10-research-analysis/scientific-visualization
Command: npx skills add https://github.com/luokai25/luo-ai-skills-market --skill scientific-visualization-luokai25

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides tools to create high-quality, publication-ready figures that adhere to journal specifications and are accessible to all readers, including those with color vision deficiencies.

Core Features & Use Cases

Multi-panel Layouts: Generate figures with multiple panels, ensuring consistent styling and alignment.
Error Bars and Significance: Add error bars and significance markers to your plots.
Colorblind-Friendly Palettes: Use predefined palettes that are distinguishable by all types of color blindness.
Journal Formatting: Apply specific journal formatting, including dimensions, colors, and fonts.
Use Case: If you are preparing figures for a Nature journal submission and need to create a multi-panel figure with error bars and a specific journal's style, this Skill can help you achieve that.

Quick Start

Create a multi-panel figure with error bars and a significance marker using the following command:

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.errorbar([1, 2, 3], [4, 5, 6], yerr=[0.1, 0.2, 0.3], label='Data')
ax.legend()
plt.show()

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 error bars for a Nature journal submission?▼

You can create publication-ready figures by orchestrating matplotlib and seaborn to generate multi-panel layouts with error bars and significance annotations, applying specific journal formatting like Nature to ensure compliance.

What is the best way to make colorblind-friendly scientific visualizations?▼

The best way to make colorblind-friendly scientific visualizations is to use predefined colorblind-safe palettes that ensure your plots are distinguishable and accessible to readers with all types of color vision deficiencies.

Does this tool support multi-panel layouts with consistent styling for scientific publications?▼

Yes, this tool supports multi-panel layouts by orchestrating matplotlib, seaborn, and plotly to generate figures with multiple panels, ensuring consistent styling and alignment across all visual elements.

Can I use plotly to apply specific journal formatting like Cell or Science?▼

Yes, you can use plotly alongside matplotlib and seaborn to apply specific journal formatting like Cell or Science, handling dimensions, colors, and fonts to meet exact journal specifications.

Do I need matplotlib and style_presets to generate scientific publication figures?▼

Yes, you need matplotlib, seaborn, plotly, and style_presets installed as dependencies to execute the generation of publication-ready figures and apply the necessary journal formatting.

How do I add significance annotations to error bar plots?▼

You can add significance annotations to error bar plots by utilizing the built-in scientific visualization capabilities that handle both error bars and significance markers automatically during figure generation.