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Scientific Visualization | Skills Pool

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Scientific Visualization

Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.

aiming-lab11,332 스타2026. 3. 31.

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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

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Scientific Visualization

npx skillvault add aiming-lab/aiming-lab-autoresearchclaw-claude-skills-scientific-visualization-skill-md

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작성자: aiming-lab
스타: 11,332
업데이트: 2026. 3. 31.
직업

이 페이지의 내용

01Scientific Visualization Best Practice

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() with constrained_layout=True for 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

02

Figure Design Principles

03Journal Figure Sizing

04Colorblind-Safe Design

05Multi-Panel Layouts

06Statistical Annotations on Figures

07Export and Quality Checklist

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Visualization Expert

Chart selection and data visualization guidance for effective data communication. Use when: creating visualizations, choosing chart types, designing dashboards, or when user mentions data visualization, charts, graphs, or needs help presenting data visually.

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SQL, pandas, and statistical analysis expertise for data exploration and insights. Use when: analyzing data, writing SQL queries, using pandas, performing statistical analysis, or when user mentions data analysis, SQL, pandas, statistics, or needs help exploring datasets.

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Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.

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Coordinate parallel code reviews across multiple quality dimensions with finding deduplication, severity calibration, and consolidated reporting. Use this skill when organizing multi-reviewer code reviews, calibrating finding severity, or consolidating review results.

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Startup Financial Modeling

Build comprehensive 3-5 year financial models with revenue projections, cost structures, cash flow analysis, and scenario planning for early-stage startups. Use this skill when creating financial projections, calculating burn rate or runway, modeling fundraising scenarios, or preparing investor-ready financials for a seed or Series A raise.

데이터 과학자