---
name: bio-reporting-figure-export
description: Exports publication-ready figures in various formats with proper resolution, sizing, and typography. Use when preparing figures for journal submission, creating vector graphics for presentations, or ensuring consistent figure styling across analyses.
tool_type: mixed
primary_tool: matplotlib
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
  - read_file
  - run_shell_command
---

<!--
# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
#
# Provenance: Authenticated by MD BABU MIA

-->


# Publication-Ready Figure Export

## Python (matplotlib)

```python
import matplotlib.pyplot as plt

# Set publication defaults
plt.rcParams.update({
    'font.size': 8,
    'font.family': 'Arial',
    'axes.linewidth': 0.5,
    'lines.linewidth': 1,
    'figure.dpi': 300
})

fig, ax = plt.subplots(figsize=(3.5, 3))  # Single column width
# ... create plot ...

# Save in multiple formats
fig.savefig('figure1.pdf', bbox_inches='tight', dpi=300)
fig.savefig('figure1.png', bbox_inches='tight', dpi=300)
fig.savefig('figure1.svg', bbox_inches='tight')
```

## R (ggplot2)

```r
library(ggplot2)

p <- ggplot(data, aes(x, y)) + geom_point() +
  theme_classic(base_size = 8) +
  theme(text = element_text(family = 'Arial'))

# PDF for vector graphics
ggsave('figure1.pdf', p, width = 3.5, height = 3, units = 'in')

# High-res PNG
ggsave('figure1.png', p, width = 3.5, height = 3, units = 'in', dpi = 300)

# TIFF (some journals require)
ggsave('figure1.tiff', p, width = 3.5, height = 3, units = 'in',
       dpi = 300, compression = 'lzw')
```

## Journal Requirements

| Journal Type | Format | Resolution | Width |
|--------------|--------|------------|-------|
| Most journals | PDF/EPS | Vector | 3.5" (1-col), 7" (2-col) |
| Online-only | PNG | 300 DPI | Variable |
| Print | TIFF | 300-600 DPI | Column width |

## Multi-panel Figures

```python
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

fig = plt.figure(figsize=(7, 5))  # Two-column width
gs = GridSpec(2, 3, figure=fig)

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

# Add panel labels
for ax, label in zip([ax1, ax2, ax3], ['A', 'B', 'C']):
    ax.text(-0.1, 1.1, label, transform=ax.transAxes,
            fontsize=10, fontweight='bold')

fig.savefig('figure_multipanel.pdf', bbox_inches='tight')
```

## Color Considerations

- Use colorblind-friendly palettes (viridis, cividis)
- Ensure sufficient contrast for grayscale printing
- Maintain consistency across all figures

## Related Skills

- data-visualization/ggplot2-fundamentals - Creating plots in R
- data-visualization/heatmaps-clustering - Complex visualizations
- data-visualization/multipanel-figures - Figure composition


<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

---
Source attribution: GPTomics
Original project: https://github.com/GPTomics/bioSkills
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 FridrichMethod

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

---

NOTE ON BUNDLED CONTENT

This repository aggregates skills from multiple upstream projects (see the
README for the source list). Each `skills/<name>/` directory may carry its
own license inherited from its upstream source. The MIT license above
applies to this repository's curation and the scripts/workflows in `/`,
`/scripts/`, and `/.github/` — not to the upstream skill content itself.
Consult the upstream repository linked in the README and any LICENSE
file inside a skill directory before reusing skill content commercially.



# Mega AI Hub resource metadata

Creator / publisher: GPTomics
Original project: https://github.com/GPTomics/bioSkills
Attribution basis: Exact package in biomedical source index and publisher source table

Fields of work: Data & Analytics, Education & Research
Roles: Business Intelligence Analyst, Data Analyst, Researcher
Specialties: Backend, Frontend, PowerPoint
Tools: Ggplot2, Matplotlib
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: cddc9eaf71f91db3052f7fa1fbef0dba6b0de25712f86c1acb98059382e98719
Metadata reviewed: 2026-10-27
This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.