FigureCheck v1.0.4
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Getting started

Create your first scientific figure

FigureCheck turns a table of measurements into an editable Matplotlib figure, with fitting, statistical checks, and reproducible exports available when you need them.

Hands-on example

Try this in FigureCheck

Open a ready-made table, choose the axes, and turn it into your first editable figure.

Opens a small synthetic dataset with a step-by-step guide in a new editable session.

The basic workflow

1Open or enter dataImport CSV, TSV, or XLSX data, or start with the built-in blank spreadsheet.
2Choose X and YSelect the columns that belong on each axis. The figure updates as graph controls change.
3Refine the graphSet the graph type, title, labels, colors, axis scales, and final image dimensions.
4Inspect the resultAdd a fit, check residuals and statistics, and review any suggested quality issues.
5Check unusual pointsHighlight possible outliers and decide whether they should remain in the working data.
6ExportDownload the figure, reproducible Python code, or statistical results.

Simple mode and Pro mode

Simple mode keeps the most common plotting, table, outlier, and export tools visible. Pro mode adds advanced series controls, fitting diagnostics, and statistical comparisons.

You can change modes later in Settings without discarding the current table or graph.

What FigureCheck does not decide for you

FigureCheck helps you inspect evidence; it does not determine which scientific model is true. A high R², an outlier suggestion, or a machine-learning warning must be interpreted in the context of the experiment.

Removing a point changes only FigureCheck's working copy. Keep the original data and document a scientific reason for every exclusion.

FigureCheck Web v1.0.4 (20260805-002332) Next: Data and series
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