ColorBrewer Palettes for Scientific Figures

ColorBrewer provides tested sequential, diverging, and qualitative palettes.

It began as a cartography tool from Cynthia Brewer and Mark Harrower at Penn State, and it remains the most widely used palette library in scientific plotting because it does one thing well: it separates palettes by the kind of data they encode, then tells you which ones survive colorblindness and grayscale printing.

Pick the Palette Type First

Almost every bad color choice in a scientific figure is a type error rather than a taste error. Match the palette family to the data before comparing individual colors.

Using a diverging palette for sequential data invents a midpoint that is not there. Using a sequential palette for categories implies a ranking that does not exist. Both mislead readers before they read a single axis label.

Qualitative Palettes

A caution on Paired: only the pairing is meaningful. Do not use it for twelve unrelated categories, because readers will infer relationships from the pairing that your data does not support.

Sequential and Diverging Palettes

For diverging maps, anchor the neutral color to the meaningful midpoint. If your log fold change runs from -1 to +4 and you let the software center the scale on the data range, the neutral color lands at +1.5 and the figure will read as though most genes are downregulated.

Colorblind-Safe Subset

Not every ColorBrewer palette is colorblind safe, and the qualitative family is the weakest. As a practical rule:

• Sequential single-hue palettes (Blues, Greens, Purples) are safe by construction, since they vary mainly in lightness.
• Set2 and Dark2 hold up well at three to four classes and degrade beyond that.
• Paired is not reliable, because the light/dark pairing that carries meaning compresses under deficiency.

Related Reading

Related reading: Best Color Palettes for Scientific Figures (2026), How to Add Figures to a Research Paper.

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