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Ritabrata-Chakraborty
Ritabrata-ChakrabortyCOMMUNITY
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scientific-visualization

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

Updated Jun 6, 2026
Search Tags:#data visualization#publication figures#scientific visualization#journal formatting#plot creation
One-click install
npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill scientific-visualization-ritabrata-chakraborty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/Ritabrata-Chakraborty/Claude-Setup/tree/main/skills/scientific-visualization
Command: npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill scientific-visualization-ritabrata-chakraborty

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the process of creating publication-ready scientific figures, ensuring clarity, accuracy, and accessibility.

Core Features & Use Cases

  • Publication Formatting: Adhere to journal-specific formatting and colorblind-friendly palettes.
  • Multiple Plot Types: Generate a wide range of plots including line, bar, scatter, heatmap, and more.
  • Interactive Visualization: Export plots for interactive exploration with tools like Plotly.
  • Use Case: You have a dataset from an experiment that you need to visualize for a journal submission. Use this Skill to create a multi-panel figure with error bars, colorblind-safe palettes, and consistent styling to meet publication standards.

Quick Start

Use the scientific-visualization skill to create a multi-panel figure with error bars for your dataset, saved as 'figure1.pdf'.

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 scientific figures for journal submission?▼

To create publication-ready scientific figures, you generate plots using matplotlib, seaborn, and plotly while applying journal-specific formatting and colorblind-friendly palettes. This process ensures your research data visualizations meet strict publication standards.

Does this Skill support interactive visualization with plotly?▼

Yes, this Skill supports interactive visualization by exporting plots for interactive exploration using plotly. You can generate various plot types and export them to allow dynamic data exploration alongside standard static publication figures.

What is the best way to ensure my research plots use colorblind-friendly palettes?▼

The best way to ensure your research plots use colorblind-friendly palettes is to apply the built-in accessibility compliance features during plot creation. This automatically adjusts your matplotlib and seaborn visualizations to be accessible for all readers.

Do I need pandas and numpy installed to generate multi-panel figures with error bars?▼

Yes, you need pandas and numpy installed as they are required dependencies. This Skill relies on these libraries alongside matplotlib, seaborn, and plotly to process your dataset and generate multi-panel figures with error bars.

Can I export scientific visualizations in multiple formats for educational materials?▼

Yes, you can export scientific visualizations in multiple formats for educational materials. The Skill allows you to visualize and export your figures, such as saving a multi-panel plot as a PDF file for reports or journal submissions.

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