From analysis output to a readable scientific figure
Choose the visual comparison first, then prepare a reproducible export.
ScienceSkills editorial guide · AI-assisted planning material, not a tested execution report.
1. State the comparison
Write one sentence describing what the figure should help readers compare. Identify variables, units and the observation unit before choosing a chart type.
2. Prepare the plotting data
Keep the raw observations separate from summarised data. State how missing values, outliers and repeated measurements are handled.
3. Design for interpretation
Use legible labels, accessible colours and consistent scales. Identify whether intervals represent variability or uncertainty. Do not use a decorative effect that distorts the comparison.
4. Export and inspect
Export the format required by the destination. Check the figure at its final physical size and keep the plotting script, data version and caption together.
Expected deliverables
An editable plotting script, a source-data table, a publication-size figure and a caption explaining the data and uncertainty.
A starter prompt
Help me design a figure for [comparison] using [variables and units]. Ask about sample structure and uncertainty. Suggest a chart, caption and export settings. Keep data transformation steps explicit.
Confirm details in the linked skill documentation and underlying tool references before use.
Creates and customizes scientific plots with Matplotlib. Used for fine-grained control over plot elements, novel plot types, and scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
Creates Seaborn statistical visualizations with pandas integration for distributions, relationships, categorical comparisons, regression displays, pair plots, and heatmaps. Supports function and objects interfaces with explicit aggregation, uncertainty, and missing-data handling. Best suited to static exploratory plots; plotly covers interactive figures and scientific-visualization covers publication styling.
Creates and audits truthful, accessible, publication-ready scientific figures with Matplotlib, Seaborn, or Plotly. Use it for figure design, multi-panel layouts, uncertainty and missing-data displays, color/contrast review, image metadata validation, and journal export planning.
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