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data-visualization

Generate journal-ready charts with colorblind-safe palettes and PDF/TIFF exports.

33|6|Updated Mar 17, 2026
Search Tags:#data visualization#matplotlib#publication figures#seaborn#ggplot2#colorblind-safe
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill data-visualization-xjtulyc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/00-universal/data-visualization
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill data-visualization-xjtulyc

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you avoid time-consuming trial-and-error when producing research figures that meet journal requirements for layout, typography, color safety, and export formats.

Core Features & Use Cases

  • Publication-quality styling: Applies consistent font sizes, line widths, tick sizing, and export settings to produce figures that look acceptable across common journal workflows.
  • Colorblind-safe palettes: Uses established qualitative palettes (e.g., Okabe–Ito) and optional ColorBrewer palettes to reduce accessibility issues.
  • Common figure types: Supports scatter/regression, violin+strip distributions, and multi-panel layouts suitable for papers and posters.
  • Cross-platform plotting: Provides both Python (matplotlib/seaborn) and R (ggplot2) templates for the same publication-grade intent.
  • Export for submission: Targets standard formats like PDF/SVG for vector edits and TIFF/EPS for journal submission conventions.

Quick Start

Use the data-visualization skill to generate a journal-ready scatter plot with a regression line and export it as both PDF and TIFF.

Frequently Asked Questions about data-visualization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create publication-quality figures with matplotlib that meet journal typography and export standards?▼

To create publication-quality figures with matplotlib, apply deterministic styling parameters like specific font sizes, line widths, and DPI settings, then export to submission-friendly formats such as PDF or TIFF to satisfy journal standards.

What is the best way to make colorblind-safe scatter plots for a research paper?▼

The best way to make colorblind-safe scatter plots is using established qualitative palettes like Okabe–Ito or ColorBrewer within Python seaborn or R ggplot2 to ensure visual accessibility for journal reporting.

Can I use ggplot2 to generate multi-panel layouts suitable for journal submission?▼

Yes, you can use ggplot2 to generate multi-panel layouts with publication-grade styling, applying consistent typography and exporting the final figures to vector formats like PDF or SVG for journal submission.

How do I export seaborn distribution plots to TIFF format for journal requirements?▼

To export seaborn distribution plots to TIFF, configure deterministic export settings within your Python workflow, targeting standard submission formats like TIFF or EPS alongside vector edits in PDF.

Does this data visualization approach support both Python and R for scientific reporting workflows?▼

Yes, this data visualization approach supports both Python and R, providing matplotlib/seaborn and ggplot2 templates respectively to produce consistent publication-grade charts for scientific reporting.

Why do my matplotlib figures fail journal figure checks for typography and color accessibility?▼

Your matplotlib figures may fail journal checks if they lack deterministic styling parameters for fonts and ticks, or fail to use colorblind-safe palettes like Okabe–Ito required for accessibility compliance.

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