scientific-visualization

# scientific-visualization ## Overview Effective scientific visualization communicates data clearly, honestly, and accessibly. Poor chart choices, misleading axes, or inaccessible color palettes can obscure findings or introduce bias. This guide covers the full workflow of scientific figure preparation: from selecting the right chart type for your data structure through color theory, accessibility, and journal submission formatting requirements. ## Key Concepts ### Chart Type and Data Type Alignment Every chart type is optimized for a specific data structure. Mismatches (e.g., pie charts for continuous distributions, bar charts for time series) hide structure and distort perception. | Data Type | Recommended Chart | Avoid | |-----------|------------------|-------| | Continuous distribution (1 group) | Histogram, violin plot, ridge plot | Bar chart with mean only | | Continuous distribution (2–5 groups) | Violin + boxplot overlay, beeswarm | Grouped bar chart | | Two continuous variables, correlation | Scatter plot, hexbin (large N) | Line chart without temporal order | | Categorical counts / proportions | Bar chart (horizontal for long labels) | Pie chart (>4 categories) | | Change over time (continuous) | Line chart | Bar chart | | Change over time (sparse events) | Step chart, event raster | Connected scatter | | Part-to-whole (≤5 parts) | Stacked bar, waffle chart | 3D pie chart | | High-dimensional (>5 variables) | Heatmap (clustered), parallel coordinates | 3D scat...