
The Okabe-Ito palette is a set of eight colors (plus an optional neutral grey) designed by Masataka Okabe and Kei Ito to remain distinguishable to viewers with all three common types of color vision deficiency. Introduced through the Color Universal Design project in 2002 and popularized by Bang Wong in Nature Methods in 2011, it is the de facto standard for colorblind-safe categorical scientific figures β defaulted in R 4.0+, baked into journal templates, and recommended by thousands of style guides.
This guide goes one layer deeper than a hex code list: where the Okabe-Ito palette actually came from, why those exact eight colors were chosen, when to use it versus the alternatives that have appeared since, and the common mistakes that quietly defeat its colorblind safety.
Building a colorblind-safe figure right now? Try the Scientific Color Palette Generator β β pick Okabe-Ito or any CVD-safe palette and copy hex codes in one click.

The palette comes from the Color Universal Design (CUD) project led by Masataka Okabe at Jikei Medical School and Kei Ito at the University of Tokyo. Their motivation was concrete: medical figures β genetic linkage maps, fluorescence channels, microscopy overlays β routinely looked indistinguishable to colorblind viewers. About 8% of men and 0.5% of women of Northern European descent have some form of color vision deficiency (CVD), roughly 300 million people worldwide.
Rather than guess at color choices, Okabe and Ito ran candidate colors through CVD simulators and verified distinctness across the three main forms of color vision deficiency:
The eight colors that survived all three tests became the published palette. Three years later, designer Bang Wong republished the same set in his Nature Methods "Points of View" column (Wong, 2011), giving it a global audience. That single citation is why the palette is sometimes called the "Wong palette" β but the original work is Okabe and Ito's.
The Okabe-Ito palette is small on purpose. Going past eight categories makes any palette unreliable for CVD viewers, so the team stopped at eight:
| # | Color | Hex | RGB | Why it's in the set |
|---|---|---|---|---|
| 1 | Orange | #E69F00 | rgb(230, 159, 0) | High-luminance warm color, distinguishable from green under all CVD types |
| 2 | Sky Blue | #56B4E9 | rgb(86, 180, 233) | Light cool color, stays distinct from yellow under tritanopia |
| 3 | Bluish Green | #009E73 | rgb(0, 158, 115) | Replacement for "pure green" that collides with red under protanopia |
| 4 | Yellow | #F0E442 | rgb(240, 228, 66) | The most luminant value in the set β survives grayscale |
| 5 | Blue | #0072B2 | rgb(0, 114, 178) | The only color all three CVD types perceive cleanly |
| 6 | Vermillion | #D55E00 | rgb(213, 94, 0) | A "red" that retains its warmth under protanopia |
| 7 | Reddish Purple | #CC79A7 | rgb(204, 121, 167) | Magenta variant that doesn't collapse into gray under deuteranopia |
| 8 | Black | #000000 | rgb(0, 0, 0) | Reference / outline color |
There's a quiet ninth slot too: a neutral grey (#999999) the CUD page recommends adding when you need a background series. Most ports drop it, but Wong's Nature Methods version and R's base implementation kept it.
For a copy-ready reference with RGB values and code snippets in four languages, see our Okabe-Ito Palette Hex Codes guide. The rest of this article focuses on when and how to use the palette, not the codes themselves.
About 1 in 12 men has some form of CVD. The classic failure mode in scientific figures is the red-green collision: under protanopia and deuteranopia, the canonical "red line, green line" comparison looks nearly identical. The figure also fails in monochrome printouts β still common in print journals and lab notebooks.
The Okabe-Ito palette solves this three ways:
#D55E00) and bluish green (#009E73) sit far enough off the red-green axis to remain distinguishable under all three CVD types.You can verify this with a CVD simulator like Coblis or the dichromat R package β drop an Okabe-Ito chart in and the eight series stay identifiable in every mode.

The Okabe-Ito palette is built for categorical data β discrete groups with no inherent order. It is not a sequential or diverging palette. The decision tree:
A common misuse is treating the Okabe-Ito palette as a gradient by picking 3β5 of its colors as a low/medium/high scale. They aren't designed for that β they're maximally different, not stepwise. Reach for Viridis or Cividis when you need an ordered scale that also happens to be colorblind-safe.
The CVD-safe palette space has grown since 2008. Here is how the Okabe-Ito palette stacks up against the modern alternatives most cited in scientific style guides:
| Palette | Type | # Colors | Strength | Limitation |
|---|---|---|---|---|
| Okabe-Ito | Categorical | 8 | Most widely adopted; saturated; journal-ready | Hard cap at 8 |
| Tol "muted" | Categorical | 9 | One extra color; explicit grey for "missing data" | Lower saturation; harder to project |
| Tol "bright" | Categorical | 7 | Higher contrast than muted | Fewer slots |
| IBM Design Library | Categorical | 5 | Web-tuned; designed for dashboards | Only 5 colors |
| Viridis | Sequential | Continuous | Perceptually uniform; CVD-safe across the gradient | Not for categorical data |
| ColorBrewer Set2 | Categorical | 8 | Soft pastel feel | Not CVD-tested as rigorously |
For categorical scientific figures with β€ 8 groups, the Okabe-Ito palette remains the strongest default. For dashboards where the palette competes with brand colors, IBM's set integrates more easily. For more than 8 categories, Paul Tol's "muted" buys you one slot and a clean grey for missing data β at the cost of saturation.
A useful third source: the R Journal 2023 article "Coloring in R's Blind Spot" formally tested several palettes for distinctness under all three CVD types β Okabe-Ito and Tol's palettes scored highest.

You no longer have to type the hex codes by hand. Major scientific stacks ship Okabe-Ito as a first-class option:
palette.colors() returns Okabe-Ito by defaultsee β scale_color_okabeito()ggokabeito β scale_color_okabe_ito()colorcet, palettable, and cmcramerisns.set_palette()d3-scale-chromaticA minimal Python example with seaborn:
import seaborn as sns
okabe_ito = ["#E69F00", "#56B4E9", "#009E73", "#F0E442",
"#0072B2", "#D55E00", "#CC79A7", "#000000"]
sns.set_palette(sns.color_palette(okabe_ito))
Several journal-figure tools also ship it built in, including BioRender, Inkscape's accessible swatches extension, and our own Scientific Color Palette Generator.
The palette is robust, but it can still be misused:
What is the Okabe-Ito palette?
A set of eight colors (plus an optional grey) designed by Masataka Okabe and Kei Ito in 2002 to remain distinguishable across the three main forms of color vision deficiency. It became the de facto standard for categorical scientific figures after Bang Wong cited it in Nature Methods in 2011.
Is the Okabe-Ito palette colorblind friendly?
Yes. It was designed specifically for CVD safety and empirically tested across protanopia, deuteranopia, and tritanopia. It also survives grayscale printing because the eight colors span a wide luminance range.
What are the 8 colors of Okabe-Ito?
Orange (#E69F00), sky blue (#56B4E9), bluish green (#009E73), yellow (#F0E442), blue (#0072B2), vermillion (#D55E00), reddish purple (#CC79A7), and black (#000000). The set also includes a recommended neutral grey (#999999).
Is the Okabe-Ito palette the same as the Wong palette?
Yes β they are the same eight colors. "Wong palette" is a common nickname because Bang Wong reused Okabe and Ito's set in his 2011 Nature Methods article, which reached a much wider audience than the original CUD webpage.
Can I use the Okabe-Ito palette commercially?
Yes. Okabe and Ito published the palette as a public design recommendation with no usage restrictions. You don't need attribution, though citing the original CUD page or Wong (2011) is good practice in academic work.
What if I need more than 8 colors?
Either split your data into facets so each subplot shows β€ 8 series, switch to Paul Tol's "muted" palette (9 colors), or β if your data is actually ordered β use a sequential palette like Viridis. Don't try to extend Okabe-Ito with extra colors; it breaks the CVD-safety guarantee.
Does the 60/30/10 rule apply to the Okabe-Ito palette?
No. The 60/30/10 (sometimes 60/30/10/10) rule is a brand and interior design guideline β pick one dominant color (60%), one secondary (30%), and one accent (10%). The Okabe-Ito palette is a categorical scheme where every series gets equal visual weight on purpose: the goal is maximum distinctness across data groups, not aesthetic balance. Use the 60/30/10 rule for slide templates or UI, not for scientific figures.
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