scientific-visualization

This skill acts as a comprehensive orchestration layer for creating high-fidelity, journal-ready scientific figures. It is designed for researchers, bioinformaticians, and data scientists preparing manuscripts for high-impact journals like Nature, Science, and Cell. By automating styling, layout, and export settings, the skill ensures that graphical outputs meet strict aesthetic and technical requirements, including colorblind-friendly palettes, appropriate resolution (DPI), vector format exports (PDF/EPS), and standardized typography.

The skill provides deep integration with the Python data visualization stack. It streamlines the process of converting raw data from exploration into professional-grade final figures. Whether you need to arrange multi-panel figures for a complex genomic study or generate statistical comparison plots with precise significance annotations, this skill provides the necessary boilerplate, best-practice presets, and export utilities.

• Orchestrates Matplotlib, Seaborn, and Plotly to ensure consistent publication styling.

• Automates journal-specific layout configurations (e.g., single/double column widths for Nature, Science, Cell).

• Provides validated color palettes such as Okabe-Ito, ensuring accessibility for colorblind readers.

• Handles figure export requirements including DPI settings for raster images and vector preservation for line art.

• Includes helper scripts for applying standard fonts (Arial, Helvetica), removing unnecessary spines, and managing legend placement.

• Features pre-configured style presets that can be applied globally to maintain visual consistency across an entire paper.

• Integrates with data analysis workflows to provide automated figure checking and size compliance validation.

• Facilitates the creation of combination figures, multi-panel compositions, and complex statistical visualizations.

• Input: Data frames, statistical results, or plot parameters defined by the user.

• Output: High-resolution files (PDF, EPS, TIFF, PNG) ready for submission.

• Usage Tip: Use individual library plotting for initial exploratory data analysis, then invoke this skill once the figure structure is finalized for publication formatting.

• Constraint: Ensure that your environment has access to the repository's assets directory to load required style files and helper scripts.