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scientific-visualization

Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.

Author

@K-Dense-AI

Category

Image Processing

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Scientific Visualization - Publication-quality Scientific Figure Production

Skill Overview


Scientific Visualization is a meta-skill for creating scientific figures that meet top-journal submission standards. It supports producing publication-quality figures with matplotlib, seaborn, and plotly, including multi-panel layouts, colorblind-friendly palettes, error bars, and significance annotations.

Use Cases


  • Journal submission figure creation - Create figures that comply with the formatting requirements of top journals like Nature, Science, Cell, including correct resolution, file formats, and size specifications.

  • Statistical data visualization - Use seaborn to create statistical plots with confidence intervals, error bars, and significance markers to display distributions and differences in experimental data.

  • Complex multi-panel figures - Build composite figures containing multiple subplots while maintaining consistent style, font settings, and color schemes.

    • Core Features


  • Journal style presets - Built-in configurations for journals like Nature, Science, and Cell that apply the correct fonts, sizes, and layout settings with one click.

  • Colorblind-friendly palettes - Provide scientifically validated palettes such as Okabe-Ito to ensure figures are clearly readable for users with all types of color vision deficiencies.

  • Smart figure export - Automatically export figures with the correct DPI (300–1200) and formats (PDF/EPS/SVG) to meet different journals' technical requirements.

    • Frequently Asked Questions

    How do I create figures that meet Nature's submission requirements?


    Use the built-in journal configuration feature to apply Nature's settings with one command: configure_for_journal('nature', figure_width='single') will automatically apply the correct figure size (89 mm single-column), font settings, and styles. Ensure you export line plots in vector formats (PDF/EPS) and set DPI to 600–1200; photographic images should be exported as TIFF at 300–600 DPI.

    What DPI should paper figures be set to?


    Line plots (charts, curves) require 600–1200 DPI; photos and microscope images require 300–600 DPI. The safest approach is to export all line plots in vector formats (PDF, EPS, SVG), which can be scaled indefinitely without loss of quality. The export tool in this skill will automatically choose the correct settings based on the figure type.

    What are colorblind-friendly scientific figure color schemes?


    Colorblind-friendly color schemes use validated palettes (such as Okabe-Ito) to ensure that users with red–green color blindness (the most common type) can distinguish all data series. It is recommended to use perceptually uniform sequential colormaps like viridis, plasma, and cividis, and to avoid jet/rainbow and red–green gradients. This skill includes these palettes for direct use.