scientific-visualizationlisted

# 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 ```python import matplotlib.pyplot as plt import numpy as np # Apply publication style (from scripts/style_presets.py) from style_presets import apply_publication_style apply_publication_style('default') # Create figure with appropriate size (single column = 3.5 inches) fig, ax = plt.subplots(figsize=(3.5, 2.5)) # Plot data x = np.linspace(0, 10, 100) ax.plot(x, np.sin(x), label='sin(x)') ax.plot(x, np.cos(x), label='cos(x)') # Proper labeling with units ax.set_xlabel('Time (seconds)') ax.set_ylabel('Amplitude (mV)') ax.legend(frameon=False) # Remove unnecessary spines ax