Publication-Quality 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. This skill orchestrates matplotlib, seaborn, and plotly with publication styles for manuscripts.

When to Use

• Creating plots for scientific manuscripts
• Preparing figures for journal submission (Nature, Science, Cell, PLOS, etc.)
• Ensuring colorblind-friendly and accessible figures
• Making multi-panel figures with consistent styling
• Exporting figures at correct resolution and format
• Following specific publication guidelines

Quick Start

Basic Publication-Quality Figure

import matplotlib.pyplot as plt
import numpy as np

# Create figure with appropriate size (single column = 3.5 inches)
fig, ax = plt.subplots(figsize=(3.5, 2.5))

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.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Save in publication formats
plt.savefig('figure1.pdf', bbox_inches='tight')
plt.savefig('figure1.png', dpi=300, bbox_inches='tight')

With Seaborn

import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style='ticks', context='paper', font_scale=1.1)
sns.set_palette('colorblind')

fig, ax = plt.subplots(figsize=(3.5, 3))
sns.boxplot(data=df, x='treatment', y='response',
            order=['Control', 'Low', 'High'], palette='Set2', ax=ax)
sns.stripplot(data=df, x='treatment', y='response',
              order=['Control', 'Low', 'High'],
              color='black', alpha=0.3, size=3, ax=ax)
ax.set_ylabel('Response (uM)')
sns.despine()

Core Principles

1. Resolution and File Format

Never use JPEG for scientific data plots -- it creates compression artifacts.

2. Colorblind-Safe Color Selection

Recommended: Okabe-Ito palette (distinguishable by all types of color blindness):

okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
             '#0072B2', '#D55E00', '#CC79A7', '#000000']
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=okabe_ito)

For heatmaps/continuous data:

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

Always test figures in grayscale to ensure interpretability.

3. Typography

Font guidelines:

• Sans-serif fonts: Arial, Helvetica, Calibri
• Minimum sizes at final print size:
  • Axis labels: 7-9 pt
  • Tick labels: 6-8 pt
  • Panel labels: 8-12 pt (bold)
• Sentence case for labels: "Time (hours)" not "TIME (HOURS)"
• Always include units in parentheses

import matplotlib as mpl
mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.sans-serif'] = ['Arial', 'Helvetica']
mpl.rcParams['font.size'] = 8
mpl.rcParams['axes.labelsize'] = 9
mpl.rcParams['xtick.labelsize'] = 7
mpl.rcParams['ytick.labelsize'] = 7

4. Journal-Specific Dimensions

Nature specifics:

• Panel labels: a, b, c (lowercase, bold) in top-left corner
• Scale bars required for microscopy images
• Statistics: mark significance; define symbols in legend

Science specifics:

• Panel labels: (A), (B), (C) in parentheses
• Minimal text within figures (details in caption)
• Error bars required; define in caption

Cell specifics:

• Panel labels: A, B, C (uppercase, bold)
• Font 6-8 pt at final size

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

from string import ascii_uppercase

fig = plt.figure(figsize=(7, 4))
gs = fig.add_gridspec(2, 2, hspace=0.4, wspace=0.4)

ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
ax3 = fig.add_subplot(gs[1, 0])
ax4 = fig.add_subplot(gs[1, 1])

# Add panel labels
for i, ax in enumerate([ax1, ax2, ax3, ax4]):
    ax.text(-0.15, 1.05, ascii_uppercase[i], transform=ax.transAxes,
            fontsize=10, fontweight='bold', va='top')

Multi-Panel with Seaborn

FacetGrid for automatic faceting:

g = sns.relplot(data=df, x='dose', y='response',
                hue='treatment', col='cell_line', row='timepoint',
                kind='line', height=2.5, aspect=1.2,
                errorbar=('ci', 95), markers=True)
g.set_axis_labels('Dose (uM)', 'Response (AU)')
g.set_titles('{row_name} | {col_name}')
sns.despine()

Seaborn with matplotlib subplots:

fig, axes = plt.subplots(2, 2, figsize=(7, 6))

sns.regplot(data=df, x='predictor', y='response', ax=axes[0, 0])
axes[0, 0].text(-0.15, 1.05, 'A', transform=axes[0, 0].transAxes,
                fontsize=10, fontweight='bold')

sns.violinplot(data=df, x='group', y='value', ax=axes[0, 1])
axes[0, 1].text(-0.15, 1.05, 'B', transform=axes[0, 1].transAxes,
                fontsize=10, fontweight='bold')

sns.heatmap(correlation_data, cmap='viridis', ax=axes[1, 0])
axes[1, 0].text(-0.15, 1.05, 'C', transform=axes[1, 0].transAxes,
                fontsize=10, fontweight='bold')

sns.lineplot(data=timeseries, x='time', y='signal',
             hue='condition', ax=axes[1, 1])
axes[1, 1].text(-0.15, 1.05, 'D', transform=axes[1, 1].transAxes,
                fontsize=10, fontweight='bold')

plt.tight_layout()
sns.despine()

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)

# Show individual points with summary statistics
ax.scatter(x_jittered, individual_points, alpha=0.4, s=8)
ax.errorbar(x, means, yerr=sems, fmt='o', capsize=3)

# Mark significance
ax.text(1.5, max_y * 1.1, '***', ha='center', fontsize=8)

Seaborn automatic uncertainty:

# Line plot: shows mean +/- 95% CI by default
sns.lineplot(data=df, x='time', y='value', hue='treatment',
             errorbar=('ci', 95))

# Bar plot: bootstrapped CI
sns.barplot(data=df, x='treatment', y='response',
            errorbar=('ci', 95), capsize=0.1)

Plotly for Publication Export

fig.update_layout(
    font=dict(family='Arial, sans-serif', size=10),
    plot_bgcolor='white',
)
fig.write_image('figure.png', scale=3)  # scale=3 gives ~300 DPI

Workflow Summary

1. Plan: Determine target journal, figure type, and content
2. Configure: Apply appropriate style for journal
3. Create: Build figure with proper labels, colors, statistics
4. Verify: Check size, fonts, colors, accessibility
5. Export: Save in required formats
6. Review: View at final size in manuscript context

Submission Checklist

• 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

Common Pitfalls