Free Scientific Color Palette Generator

A scientific color palette must satisfy three criteria: the colors must be perceptually distinct so readers can tell data series apart, they must remain distinguishable when converted to grayscale (for print), and they must be accessible to readers with color vision deficiencies. The built-in colorblind-safe palettes in SciFig's scientific color palette generator — Okabe–Ito, Wong, and Paul Tol's bright and muted sets — are designed to keep colors distinct for readers with common color vision deficiencies.

For a colorblind-friendly or daltonic-friendly palette, pick one that has at least as many colors as the categories you need to distinguish. SciFig's scientific color palette generator offers established colorblind-safe palettes — Okabe–Ito, Wong, and Paul Tol's bright and muted sets — designed to stay distinguishable for readers with the common red–green color vision deficiencies, and you receive the hex code for each color.

Nature's figure guidelines ask authors to use an accessible colour palette and to avoid colour combinations that readers with colour blindness cannot tell apart, citing the Wong palette as an example. SciFig's scientific color palette generator offers the Wong and Okabe–Ito colorblind-safe palettes, plus a Nature-style palette inspired by figures in Nature journals: #E64B35, #4DBBD5, #00A087, #3C5488, #F39B7F, #8491B4, #91D1C2, #DC0000, #7E6148 and #B09C85. For colorblind-safe figures, choose Wong or Okabe–Ito.

Daltonism usually refers to red–green color vision deficiency, named after John Dalton, who described his own. Inherited red–green deficiency affects approximately 8% of males and 0.5% of females of Northern European ancestry. A daltonic-friendly color palette — also called a colorblind-safe or colorblind-accessible palette — avoids red-green combinations that are indistinguishable to people with deuteranopia or protanopia. Using a daltonic-friendly palette for science figures ensures your data is readable by the broadest possible audience.

For categorical data in scientific figures, palettes of 4–8 colors are most effective — beyond 8 categories, readers struggle to reliably distinguish series even with optimal palettes. SciFig's colorblind-safe palettes provide seven to nine colors; for datasets with more categories, combine color with alternative visual encodings such as shape or pattern.

SciFig provides hex codes for every color in your scientific palette, which you can paste directly into figure creation tools — including the SciFig figure editor, matplotlib, R ggplot2, Illustrator, or any other software that accepts hex color values. Colors are listed in the palette's own order, so you can assign them to data series from the first color onward. When you are choosing a palette for scientific diagrams rather than data plots — pathway maps, schematics, or mechanism figures — assign the most prominent colors to the elements readers should notice first, and leave the remaining colors for supporting structures. The palette tool below the input previews the built-in catalogue and copies the selected hex values or ready-to-use Python, R and CSS code. Save the settings as JSON and export the preview as SVG or PNG.