scientific-visualization
Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.
pinned to #ea77ec1updated 3 months ago
Ask your AI client: “install skills/scientific-visualization”.
Requires the metahub MCP server installed in your client. Set up MCP.
mh install skills/scientific-visualizationmetahub onboarded this repo on the author's behalf.
If you own github.com/aiming-lab/AutoResearchClaw on GitHub, claim the listing to take over publishing. Your claim preserves the existing eval history and badges; only the curator label is replaced with verified-publisher on your next publish.
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- #autonomous-research
- #citation-verification
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- #metaclaw
- #multi-agent-debate
- #openclaw
- #paper-generation
- #scientific-discovery
- #self-evolving
Automated checks the publisher passed at publish time — structure, docs, safety, and whether the artifact behaves as claimed.ea77ec1· 3 months ago
Behavioral checks ran but aren't published for this artifact; the static checks above ran at publish time.
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found at .claude/skills/scientific-visualization/SKILL.md · frontmatter source: SKILL.md
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351 words · 2,315 chars · 7 sections
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Release history
1- releasecurrentea77ec1warn3 months ago
Contents
Scientific Visualization Best Practice
Figure Design Principles
- Every figure must have a clear, self-contained message
- Minimize chartjunk: remove gridlines, background shading, and 3D effects
- Use direct labeling instead of legends when possible
- Remove top and right spines for cleaner appearance
- Ensure all text is readable at final print size (minimum 6pt font)
Journal Figure Sizing
- Single column: 3.3-3.5 inches (85-89 mm) wide
- 1.5 column: 4.5-5.5 inches (114-140 mm) wide
- Double column / full width: 6.5-7.1 inches (165-180 mm) wide
- Resolution: 300 DPI minimum for raster; prefer vector formats (PDF, EPS, SVG)
- Check target journal author guidelines for exact specifications
Colorblind-Safe Design
- Use colorblind-friendly palettes: seaborn "colorblind", Okabe-Ito, viridis, cividis
- NEVER rely on color alone — combine with shape, pattern, or line style
- Avoid red-green combinations; prefer blue-orange or blue-yellow contrasts
- Test figures with a colorblind simulator before submission
- Ensure figures work in grayscale for print journals
Multi-Panel Layouts
- Label panels with uppercase letters: (A), (B), (C) in bold, top-left corner
- Use consistent axis scales across panels when comparing related data
- Share axes where appropriate to reduce redundancy
- Maintain consistent font sizes and line widths across all panels
- Use
plt.subplots()withconstrained_layout=Truefor automatic spacing
Statistical Annotations on Figures
- Show individual data points alongside summary statistics (box + strip plots)
- Always include error bars; specify type in caption (SEM, SD, 95% CI)
- Use significance brackets with stars: * p<.05, ** p<.01, *** p<.001
- Annotate effect sizes or key statistics directly on the figure when helpful
- Never use bar charts for small-n data — use dot plots or box plots instead
Export and Quality Checklist
- Save in vector format (PDF/SVG) for line art; TIFF/PNG for photographs
- Embed fonts or convert text to outlines for cross-platform consistency
- Verify axis labels include units in parentheses: "Time (s)", "Force (N)"
- Ensure figure caption fully explains all symbols, abbreviations, and panels
- Check that color-coded elements match between figure and caption
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Related
Verification Before Completion
Evidence before assertions, always
Writing Plans
Turn specs into phased implementation plans
Test-Driven Development
Red → green → refactor discipline for any feature or bugfix
mh install skills/scientific-visualization