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scientific-visualization

Create and audit publication-ready scientific figures with Matplotlib, Seaborn, and Plotly.

Updated Aug 12, 2026
Search Tags:#color palettes#data visualization#accessibility#matplotlib#journal submission#scientific figures#wcag contrast
One-click install
npx skills add https://github.com/Mzane0803/latent-minds-skills-marketplace --skill scientific-visualization-mzane0803
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/Mzane0803/latent-minds-skills-marketplace/tree/main/plugins/research-writing/skills/scientific-visualization
Command: npx skills add https://github.com/Mzane0803/latent-minds-skills-marketplace --skill scientific-visualization-mzane0803

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, seaborn, plotly, kaleido, pillow, pypdf, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve? Scientific figures often distort data, fail accessibility checks, or get rejected at journal submission because of wrong dimensions, DPI, formats, or fonts. This Skill guides the design, export, and inspection of truthful, accessible figures while keeping raw data, transformations, and provenance explicit. ## Core Features & Use Cases - Honest figure design: Enforces integrity guardrails for baselines, uncertainty, missing data, log axes, binning, normalization, and dual axes before any plotting code is written. - Deterministic CLI helpers: Bundled network-free scripts inspect raster/SVG/PDF/EPS metadata, audit palette contrast against WCAG thresholds, plan publisher exports from dated profiles, and export Matplotlib figures atomically with manifests. - Accessibility and journal screening: Audits color contrast and grayscale separation, checks effective DPI at final width, and compares files against dated publisher snapshots without claiming compliance. - Use Case: A researcher preparing a Nature submission uses the export planner to confirm single-column width and format rules, builds a multi-panel Matplotlib figure with an Okabe-Ito palette, exports PDF and TIFF with a provenance manifest, then screens the output file's DPI, size, and fonts before upload. ## Quick Start Ask the AI to create a publication-ready multi-panel figure from your data with an accessible palette, then export it as PDF and PNG at 600 DPI and inspect the output against the target journal's requirements.

Frequently Asked Questions about scientific-visualization

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

FAQPage Schema
How do I create a publication-ready figure with Matplotlib?▼

Use Matplotlib's object-oriented API with a scoped style context, set exact physical dimensions in inches, and export with explicit DPI and format settings. The bundled export_figure helper writes files atomically, preserves page dimensions, and records a provenance manifest.

How do I check if my figure meets journal DPI and size requirements?▼

Run the export_plan.py CLI with the publisher, figure type, width, and submission phase to build a plan from dated official-source snapshots. Add --input with your exported file to screen its format, effective DPI, dimensions, and file size against that plan.

What color palettes are colorblind-safe for scientific plots?▼

The Okabe-Ito and Paul Tol qualitative schemes are widely used starting points, and a five-color Okabe-Ito subset meets 3:1 contrast against white. No palette alone guarantees accessibility, so audit rendered contrast with palette_audit.py and add redundant encodings like markers or line styles.

Does Plotly static export still work with Kaleido v1?▼

Kaleido 1.3.0 supports PNG, JPEG, WebP, SVG, and PDF static export but requires a separate Chrome or Chromium installation since it no longer bundles one. EPS export is only available in Kaleido v0, and the deprecated engine parameter and Orca are removed.

Why should I avoid bbox_inches tight when exporting figures?▼

bbox_inches='tight' crops the canvas to the drawn artists, which changes the figure's physical page dimensions and can break exact journal width requirements. Use it only when that size change is intentional; otherwise export with bbox_inches=None to preserve the planned page size.

Can automated checks certify my figure is journal-compliant or accessible?▼

No. The bundled tools screen only machine-readable properties like format, DPI, dimensions, and contrast ratios against dated snapshots. They cannot verify visual quality, embedded raster resolution in vector files, scientific integrity, or actual publisher acceptance.

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