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scientific-visualization

Create publication-ready scientific figures with matplotlib, seaborn, and plotly for journal submission.

Updated Aug 12, 2026
Search Tags:#plotly#matplotlib#publication figures#seaborn#scientific visualization#journal formatting#colorblind palette
One-click install
npx skills add https://github.com/littlt-momo-c-yfc/skills --skill scientific-visualization-littlt-momo-c-yfc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/littlt-momo-c-yfc/skills/tree/main/skills/scientific-toolkit-skill/references/scientific-skills/scientific-visualization
Command: npx skills add https://github.com/littlt-momo-c-yfc/skills --skill scientific-visualization-littlt-momo-c-yfc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, seaborn, plotly, numpy, pandas, scipy, PyPDF2, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve? Preparing figures that meet strict journal requirements (resolution, dimensions, fonts, colorblind accessibility) is tedious and error-prone, often causing manuscript rejections or revision delays. ## Core Features & Use Cases - Journal-Specific Formatting: Pre-configured styles and export settings for Nature, Science, Cell, PLOS, ACS, and IEEE, including correct column widths and DPI. - Colorblind-Safe Palettes: Built-in Okabe-Ito, Wong, and Paul Tol palettes plus perceptually uniform colormaps for accessible figures. - Automated Export & Validation: Helper scripts save figures in multiple formats (PDF, EPS, TIFF, PNG) at correct DPI and verify figure dimensions against journal specs. - Use Case: A researcher finishing a Nature submission uses the skill to apply the Nature style preset, build a multi-panel figure with panel labels and error bars, check the 89 mm single-column width, and export a 1000 DPI PDF. ## Quick Start Ask the agent to create a publication-ready multi-panel figure for Nature from your dataset, using colorblind-safe colors and exporting it as a 300 DPI 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 figures in matplotlib?▼

Apply a publication style preset, set the figure size to the target journal's column width, use colorblind-friendly colors, label axes with units, and remove top and right spines. Export as PDF or PNG at 300 DPI or higher using a save helper function.

What DPI and file format do journals require for figures?▼

Most journals require 300 DPI for photographs, 600 DPI for combination figures, and 1000 DPI for line art. Vector formats like PDF and EPS are preferred for graphs, while TIFF or PNG are used for images; JPEG should be avoided for scientific data.

Seaborn vs matplotlib for scientific plots, which should I use?▼

Seaborn provides high-level statistical plots with automatic confidence intervals and faceting, ideal for quick statistical visualization. Matplotlib offers finer control for complex multi-panel layouts, and both can be combined since seaborn is built on matplotlib.

How do I make colorblind-friendly scientific figures?▼

Use the Okabe-Ito or Wong palettes for categorical data and perceptually uniform colormaps like viridis or cividis for continuous data. Avoid red-green combinations and jet colormaps, and test figures in grayscale to confirm interpretability.

What figure size does Nature require for submissions?▼

Nature requires 89 mm width for single-column figures and 183 mm for double-column figures, with a maximum height of 247 mm. Fonts should be Arial or Helvetica at 5-7 pt minimum at final print size.

Why should I avoid JPEG format for scientific plots?▼

JPEG uses lossy compression that introduces artifacts around text and lines, degrading figure quality. Use vector formats like PDF or EPS for plots, or lossless raster formats like TIFF and PNG for images.

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