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scientific-visualization

Create publication-ready scientific figures from raw data in matplotlib, seaborn, and plotly.

74|5|Updated Dec 10, 2025
Search Tags:#matplotlib#publication figures#seaborn#scientific visualization#journal formatting#figure export#colorblind palettes
One-click install
npx skills add https://github.com/dralkh/seerai --skill scientific-visualization-dralkh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/dralkh/seerai/tree/main/skills/scientific-visualization
Command: npx skills add https://github.com/dralkh/seerai --skill scientific-visualization-dralkh

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?

This Skill turns rough plots and raw data into journal-ready scientific figures, removing the manual work of matching publisher specs, fixing styling issues, and making visuals accessible for readers and reviewers.

Core Features & Use Cases

  • Publication styling: Applies clean, consistent figure settings for manuscripts and presentations.
  • Journal compliance: Helps match Nature, Science, Cell, PLOS, ACS, and IEEE size, format, and resolution requirements.
  • Accessible design: Uses colorblind-safe palettes, grayscale-friendly choices, and clear typography.
  • Figure export and validation: Saves figures in publication formats and checks dimensions before submission.
  • Use case: Prepare a multi-panel result figure with significance markers, export it as PDF and PNG, and verify it fits the target journal column width.

Quick Start

Create a publication-ready figure from my data, apply an appropriate journal style, and export it in the correct formats 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 scientific figures in matplotlib that meet journal submission requirements?▼

You can create publication-ready scientific figures by applying journal-specific sizing, resolution, and typography settings to your raw plots. This Skill enforces column width constraints and export formats required by publishers like Nature, Science, and Cell.

What is the best way to format multi-panel layouts with significance annotations for a manuscript?▼

The best way to format multi-panel layouts with significance annotations is by applying consistent figure settings across all panels. This Skill adds significance markers and verifies final dimensions match your target journal's column width before export.

Can I use seaborn and plotly workflows to generate colorblind-safe palettes for scientific visualization?▼

Yes, seaborn and plotly workflows are supported for generating scientific visualizations with colorblind-safe palettes. The Skill applies grayscale-friendly color choices and clear typography to ensure visuals are accessible for readers and reviewers.

Does matplotlib export validation check PDF and PNG dimensions for ACS and IEEE journals?▼

Yes, matplotlib export validation checks PDF and PNG dimensions to ensure compliance with ACS and IEEE journal requirements. The Skill saves figures in publication formats and validates dimensions before submission to prevent formatting rejections.

Why do my journal figures fail submission resolution and sizing checks?▼

Journal figures fail submission checks when dimensions and resolution do not match publisher specifications. This Skill enforces journal sizing, resolution, and typography requirements, then validates exported PDF and PNG files against target journal constraints.

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