skills.rest
brainworkup
brainworkupCOMMUNITY
Explore all skills by this author →
Website𝕏@brainworkupGitHub

scientific-visualization

Generate publication-ready multi-panel figures with journal-specific styles and colorblind-safe palettes.

1|Updated Apr 8, 2026
Search Tags:#visualization#matplotlib#publication#figures#colorblind#multi-panel
One-click install
npx skills add https://github.com/brainworkup/skills --skill scientific-visualization-brainworkup
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/brainworkup/skills/tree/main/neuropsych-reports/references/luria-related-complement-skills/scientific-visualization
Command: npx skills add https://github.com/brainworkup/skills --skill scientific-visualization-brainworkup

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Creating publication-ready figures from data with consistent styling and journal-specific requirements.

Core Features & Use Cases

  • Multi-panel figure composition for manuscripts with consistent layout
  • Colorblind-friendly palettes and publication-ready styling across Matplotlib, Seaborn, and Plotly
  • Journal-specific exports (Nature/Science/Cell etc.) with vector and high-DPI formats
  • Reusable presets for typography, color, and layout to accelerate figure production

Quick Start

Create a publication-ready multi-panel figure from your data using the default publication style and color palette.

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 in Matplotlib for a Nature or Science manuscript?▼

Publication-ready figures can be created in Matplotlib using preconfigured styles that enforce journal-specific guidelines, embedded fonts, vector formats, and high DPI exports. Reusable presets handle typography, color, and layout to meet Nature, Science, and Cell standards.

How do I build multi-panel figure layouts in Matplotlib for manuscripts?▼

Multi-panel figure layouts are built using publication-style presets that ensure consistent layout and styling across panels. Reusable presets for typography and color accelerate multi-panel figure composition for manuscripts.

Can I use colorblind-friendly palettes with Seaborn and Plotly for scientific visualization?▼

Yes, colorblind-friendly palettes are supported across Matplotlib, Seaborn, and Plotly. These publication-ready styling presets ensure accessibility considerations are met without manual color configuration.

Does this approach handle high DPI and vector format exports for journal submissions?▼

Journal-ready export requirements are handled directly through preconfigured styles that output high DPI and vector formats. Embedded fonts and journal-specific export settings satisfy manuscript submission guidelines.

What's the best way to add significance annotations to scientific figures?▼

Significance annotations are applied using publication-style presets designed for manuscript figures. These presets integrate annotations within multi-panel layouts while maintaining adherence to journal guidelines.

Are there reusable Matplotlib presets for typography and color in scientific figures?▼

Reusable presets for typography, color, and layout are available to accelerate figure production. These presets provide consistent publication-ready styling across Matplotlib, Seaborn, and Plotly.

Related Skills

Explore More Content & Communication Skills→
sagunkayasthasagunkayastha

timesfm-forecasting

Forecast univariate time series with Google's TimesFM foundation model.

Community
Intermediate
qinyan-aiqinyan-ai
48

scientific-visualization

Create publication-ready multi-panel scientific figures with journal-specific styling.

Community
Advanced
jkitchinjkitchin
34

python-plotting

Select and apply Matplotlib, Seaborn, or Plotly for Python visualizations.

Community
Intermediate
bahayonghangbahayonghang
16

academic-figure

Generate journal-compliant academic figures from raw data using Matplotlib, Seaborn, or Plotly.

Community
Advanced