Module 16: Scientific Visualization for Connectomics

📽 Principles of Scientific Visualization

Clear visual communication is essential to science. Understand the principles of effective figure design and when to use specific chart types.

• Color choice and accessibility
• Balancing detail vs. clarity
• Designing multi-panel figures

📊 Tools for Visualization

From Python to 3D rendering engines, learn to use available tools for compelling visual outputs.

• matplotlib and seaborn for publication-ready figures
• Plotly and dash for interactive dashboards
• Neuroglancer for 3D brain data rendering

🖌️ Telling a Visual Story

Figures should do more than decorate—they should lead to insight. This section explores storytelling through graphics.

• Title and caption design
• Ordering panels for narrative flow
• Designing figures for posters and papers

🌟 COMPASS Integration

• Knowledge: Principles of data presentation
• Skills: Graphing, color theory, composition
• Character: Aesthetic judgment, clarity
• Meta-Learning: Revising and improving figures over time

📚 References & Resources

• Rougier et al., 2014. Ten Simple Rules for Better Figures. PLoS Comp Bio.
• Ware, 2013. Information Visualization: Perception for Design.
• Colab: "Data Visualization with Seaborn and Plotly for Connectomics"
• Notebook: Mesh Example (source)
• Notebook: Render 3D Scale Bar (source)

✅ Assessment

• Create a figure to summarize results from a previous module
• Design a multi-panel layout for a hypothetical paper
• Critique sample visualizations for clarity and impact