Does it handle statistical significance markers?

Yes, it provides implementation patterns for adding error bars (SD, SEM, or CI) and standard significance markers (*, **, ***) to your plots.

Can I export figures in vector formats?

Yes, the skill automates exports in high-quality vector formats including PDF, EPS, and SVG, ensuring lossless scaling for print.

Can it create multi-panel figures?

Yes, it uses advanced layouts like GridSpec to create complex, aligned multi-panel figures with automated bold labeling (A, B, C).

Which journals are supported by this skill?

It includes built-in presets for major publishers like Nature, Science, Cell, and PLOS, while allowing custom configurations for any specific journal guideline.

Is it accessible for colorblind readers?

Absolutely. The skill defaults to perceptually uniform colormaps like viridis and uses the Okabe-Ito palette to ensure accessibility for all readers.

Scientific Visualization for Publication

Name: Scientific Visualization for Publication
Author: ricable

byricable

•

Ciencia de Datos y ML

Generates journal-ready, publication-grade figures and statistical visualizations using colorblind-safe palettes and professional layouts.

Acerca de

This skill empowers researchers and data scientists to transform raw data into polished, peer-reviewed quality visualizations directly within their workflow. It automates the complex requirements of high-impact journals, including specific DPI settings, vector formats (PDF/EPS), consistent typography, and accessible color palettes. Whether you need complex multi-panel grids, statistical error bars, or significance markers, this tool provides standardized templates and best practices to ensure your data is communicated clearly, accurately, and professionally for manuscripts and presentations.

Características Principales

High-resolution exports in vector (PDF, EPS) and raster (TIFF, PNG) formats
Automated multi-panel figure generation with consistent alignment and labeling
Integration with Matplotlib, Seaborn, and Plotly for advanced statistical plotting
1 GitHub stars
Colorblind-accessible palettes (Okabe-Ito) and grayscale compatibility testing
Journal-specific style presets for Nature, Science, Cell, and PLOS

Casos de Uso

Preparing high-resolution figures for submission to peer-reviewed scientific journals
Converting exploratory data analysis plots into accessible, publication-quality assets
Standardizing data visualization styles across a research team or laboratory project

Acerca de

Características Principales

High-resolution exports in vector (PDF, EPS) and raster (TIFF, PNG) formats
Automated multi-panel figure generation with consistent alignment and labeling
Integration with Matplotlib, Seaborn, and Plotly for advanced statistical plotting
1 GitHub stars
Colorblind-accessible palettes (Okabe-Ito) and grayscale compatibility testing
Journal-specific style presets for Nature, Science, Cell, and PLOS

Casos de Uso

Preparing high-resolution figures for submission to peer-reviewed scientific journals
Converting exploratory data analysis plots into accessible, publication-quality assets
Standardizing data visualization styles across a research team or laboratory project