Scientific Visualization

Overview

Scientific visualization transforms data into clear, accurate figures for publication. Create journal-ready plots with multi-panel layouts, error bars, significance markers, and colorblind-safe palettes. Export as PDF/EPS/TIFF using matplotlib, seaborn, and plotly for manuscripts.

When to Use This Skill

This skill should be used when:

• - Creating plots or visualizations for scientific manuscripts
• Preparing figures for journal submission (Nature, Science, Cell, PLOS, etc.)
• Ensuring figures are colorblind-friendly and accessible
• Making multi-panel figures with consistent styling
• Exporting figures at correct resolution and format
• Following specific publication guidelines
• Improving existing figures to meet publication standards
• Creating figures that need to work in both color and grayscale

Quick Start Guide

Basic Publication-Quality Figure

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Using Pre-configured Styles

Apply journal-specific styles using the matplotlib style files in assets/:

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Quick Start with Seaborn

For statistical plots, use seaborn with publication styling:

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Core Principles and Best Practices

1. Resolution and File Format

Critical requirements (detailed in references/publication_guidelines.md):

• - Raster images (photos, microscopy): 300-600 DPI
• Line art (graphs, plots): 600-1200 DPI or vector format
• Vector formats (preferred): PDF, EPS, SVG
• Raster formats: TIFF, PNG (never JPEG for scientific data)

Implementation:
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2. Color Selection - Colorblind Accessibility

Always use colorblind-friendly palettes (detailed in references/color_palettes.md):

Recommended: Okabe-Ito palette (distinguishable by all types of color blindness):
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For heatmaps/continuous data:

• - Use perceptually uniform colormaps: viridis, plasma, INLINECODE5
• Avoid red-green diverging maps (use PuOr, RdBu, BrBG instead)
• Never use jet or rainbow colormaps

Always test figures in grayscale to ensure interpretability.

3. Typography and Text

Font guidelines (detailed in references/publication_guidelines.md):

• - Sans-serif fonts: Arial, Helvetica, Calibri
• Minimum sizes at final print size:

• - Sentence case for labels: "Time (hours)" not "TIME (HOURS)"
• Always include units in parentheses

Implementation:
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4. Figure Dimensions

Journal-specific widths (detailed in references/journal_requirements.md):

• - Nature: Single 89 mm, Double 183 mm
• Science: Single 55 mm, Double 175 mm
• Cell: Single 85 mm, Double 178 mm

Check figure size compliance:
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5. Multi-Panel Figures

Best practices:

• - Label panels with bold letters: A, B, C (uppercase for most journals, lowercase for Nature)
• Maintain consistent styling across all panels
• Align panels along edges where possible
• Use adequate white space between panels

Example implementation (see references/matplotlib_examples.md for complete code):
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Common Tasks

Task 1: Create a Publication-Ready Line Plot

See references/matplotlib_examples.md Example 1 for complete code.

Key steps:

1. 1. Apply publication style
2. Set appropriate figure size for target journal
3. Use colorblind-friendly colors
4. Add error bars with correct representation (SEM, SD, or CI)
5. Label axes with units
6. Remove unnecessary spines
7. Save in vector format

Using seaborn for automatic confidence intervals:
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Task 2: Create a Multi-Panel Figure

See references/matplotlib_examples.md Example 2 for complete code.

Key steps:

1. 1. Use GridSpec for flexible layout
2. Ensure consistent styling across panels
3. Add bold panel labels (A, B, C, etc.)
4. Align related panels
5. Verify all text is readable at final size

Task 3: Create a Heatmap with Proper Colormap

See references/matplotlib_examples.md Example 4 for complete code.

Key steps:

1. 1. Use perceptually uniform colormap (viridis, plasma, cividis)
2. Include labeled colorbar
3. For diverging data, use colorblind-safe diverging map (RdBu_r, PuOr)
4. Set appropriate center value for diverging maps
5. Test appearance in grayscale

Using seaborn for correlation matrices:
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Task 4: Prepare Figure for Specific Journal

Workflow:

1. 1. Check journal requirements: INLINECODE23
2. Configure matplotlib for journal:

   from style_presets import configure_for_journal
   configure_for_journal('nature', figure_width='single')
   

1. 3. Create figure (will auto-size correctly)
2. Export with journal specifications:

Task 5: Fix an Existing Figure to Meet Publication Standards

Checklist approach (full checklist in references/publication_guidelines.md):

1. 1. Check resolution: Verify DPI meets journal requirements
2. Check file format: Use vector for plots, TIFF/PNG for images
3. Check colors: Ensure colorblind-friendly
4. Check fonts: Minimum 6-7 pt at final size, sans-serif
5. Check labels: All axes labeled with units
6. Check size: Matches journal column width
7. Test grayscale: Figure interpretable without color
8. Remove chart junk: No unnecessary grids, 3D effects, shadows

Task 6: Create Colorblind-Friendly Visualizations

Strategy:

1. 1. Use approved palettes from INLINECODE25
2. Add redundant encoding (line styles, markers, patterns)
3. Test with colorblind simulator
4. Ensure grayscale compatibility

Example:
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Statistical Rigor

Always include:

• - Error bars (SD, SEM, or CI - specify which in caption)
• Sample size (n) in figure or caption
• Statistical significance markers (, , )
• Individual data points when possible (not just summary statistics)

Example with statistics:
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Working with Different Plotting Libraries

Matplotlib

• - Most control over publication details
• Best for complex multi-panel figures
• Use provided style files for consistent formatting
• See references/matplotlib_examples.md for extensive examples

Seaborn

Seaborn provides a high-level, dataset-oriented interface for statistical graphics, built on matplotlib. It excels at creating publication-quality statistical visualizations with minimal code while maintaining full compatibility with matplotlib customization.

Key advantages for scientific visualization:

• - Automatic statistical estimation and confidence intervals
• Built-in support for multi-panel figures (faceting)
• Colorblind-friendly palettes by default
• Dataset-oriented API using pandas DataFrames
• Semantic mapping of variables to visual properties

Quick Start with Publication Style

Always apply matplotlib publication styles first, then configure seaborn:

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Common Plot Types for Publications

Statistical comparisons:
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Distribution analysis:
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Correlation matrices:
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Time series with confidence bands:
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Multi-Panel Figures with Seaborn

Using FacetGrid for automatic faceting:
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Combining seaborn with matplotlib subplots:
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Color Palettes for Publications

Seaborn includes several colorblind-safe palettes:

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Choosing Between Axes-Level and Figure-Level Functions

Axes-level functions (e.g., scatterplot, boxplot, heatmap):

• - Use when building custom multi-panel layouts
• Accept ax= parameter for precise placement
• Better integration with matplotlib subplots
• More control over figure composition

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Figure-level functions (e.g., relplot, catplot, displot):

• - Use for automatic faceting by categorical variables
• Create complete figures with consistent styling
• Great for exploratory analysis
• Use height and aspect for sizing

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Statistical Rigor with Seaborn

Seaborn automatically computes and displays uncertainty:

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Best Practices for Publication-Ready Seaborn Figures

1. 1. Always set publication theme first:

1. 2. Use colorblind-safe palettes:

1. 3. Remove unnecessary elements:

1. 4. Control figure size appropriately:

1. 5. Show individual data points when possible:

1. 6. Include proper labels with units:

1. 7. Export at correct resolution:

Advanced Seaborn Techniques

Pairwise relationships for exploratory analysis:
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Hierarchical clustering heatmap:
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Joint distributions with marginals:
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Common Seaborn Issues and Solutions

Issue: Legend outside plot area
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Issue: Overlapping labels
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Issue: Text too small at final size
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Additional Resources

For more detailed seaborn information, see:

• - scientific-packages/seaborn/SKILL.md - Comprehensive seaborn documentation
• INLINECODE37 - Practical use cases
• INLINECODE38 - Complete API reference
• INLINECODE39 - Modern declarative API

Plotly

• - Interactive figures for exploration
• Export static images for publication
• Configure for publication quality:

Resources

References Directory

Load these as needed for detailed information:

• - publication_guidelines.md: Comprehensive best practices

• - color_palettes.md: Color usage guide

• - journal_requirements.md: Journal-specific specifications

• - matplotlib_examples.md: Practical code examples

Scripts Directory

Use these helper scripts for automation:

• - figure_export.py: Export utilities

• - style_presets.py: Pre-configured styles

Assets Directory

Use these files in figures:

• - color_palettes.py: Importable color definitions

• - Matplotlib style files: Use with INLINECODE56

Workflow Summary

Recommended workflow for creating publication figures:

1. 1. Plan: Determine target journal, figure type, and content
2. Configure: Apply appropriate style for journal

   from style_presets import configure_for_journal
   configure_for_journal('nature', 'single')
   

1. 3. Create: Build figure with proper labels, colors, statistics
2. Verify: Check size, fonts, colors, accessibility

   from figure_export import check_figure_size
   check_figure_size(fig, journal='nature')
   

1. 5. Export: Save in required formats

   from figure_export import save_for_journal
   save_for_journal(fig, 'figure1', 'nature', 'combination')
   

1. 6. Review: View at final size in manuscript context

Common Pitfalls to Avoid

1. 1. Font too small: Text unreadable when printed at final size
2. JPEG format: Never use JPEG for graphs/plots (creates artifacts)
3. Red-green colors: ~8% of males cannot distinguish
4. Low resolution: Pixelated figures in publication
5. Missing units: Always label axes with units
6. 3D effects: Distorts perception, avoid completely
7. Chart junk: Remove unnecessary gridlines, decorations
8. Truncated axes: Start bar charts at zero unless scientifically justified
9. Inconsistent styling: Different fonts/colors across figures in same manuscript
10. No error bars: Always show uncertainty

Final Checklist

Before submitting figures, verify:

• - [ ] Resolution meets journal requirements (300+ DPI)
• [ ] File format is correct (vector for plots, TIFF for images)
• [ ] Figure size matches journal specifications
• [ ] All text readable at final size (≥6 pt)
• [ ] Colors are colorblind-friendly
• [ ] Figure works in grayscale
• [ ] All axes labeled with units
• [ ] Error bars present with definition in caption
• [ ] Panel labels present and consistent
• [ ] No chart junk or 3D effects
• [ ] Fonts consistent across all figures
• [ ] Statistical significance clearly marked
• [ ] Legend is clear and complete

Use this skill to ensure scientific figures meet the highest publication standards while remaining accessible to all readers.