Okabe-Ito Palette: Why It's the Gold Standard for Scientific Figures

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 — 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 → — pick Okabe-Ito or any CVD-safe palette and copy hex codes in one click.

Who Made the Okabe-Ito Palette (and Why)

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 — genetic linkage maps, fluorescence channels, microscopy overlays — 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:

• Protanopia — missing the L (red) cone; the most common type
• Deuteranopia — missing the M (green) cone
• Tritanopia — missing the S (blue) cone; rare but still meaningful

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" — but the original work is Okabe and Ito's.

The Eight Colors and Their Design Rationale

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 — 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.

How the Okabe-Ito Palette Solves the Color Vision Problem

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 — still common in print journals and lab notebooks.

The Okabe-Ito palette solves this three ways:

1. No pure red, no pure green. Vermillion (#D55E00) and bluish green (#009E73) sit far enough off the red-green axis to remain distinguishable under all three CVD types.
2. Wide luminance variation. Yellow at L* ≈ 88 and blue at L* ≈ 40 give a four-step luminance ramp, so the colors still separate when desaturated or printed in grayscale.
3. Maximum hue rotation. The set is roughly evenly spaced around the hue wheel, so even when two adjacent series share luminance, their hue contrast carries the distinction.

You can verify this with a CVD simulator like Coblis or the dichromat R package — drop an Okabe-Ito chart in and the eight series stay identifiable in every mode.

When to Use the Okabe-Ito Palette (and When Not To)

The Okabe-Ito palette is built for categorical data — discrete groups with no inherent order. It is not a sequential or diverging palette. The decision tree:

• Categorical, ≤ 8 groups, accessibility matters → Okabe-Ito ✓
• Categorical, ≥ 9 groups → Paul Tol's "muted" (9 colors), or facet your data into smaller subplots
• Sequential / ordered data (a continuous variable) → Viridis, Cividis, Magma
• Diverging (positive vs negative around zero) → ColorBrewer RdBu, PiYG, or Tol's "sunset"
• Heatmap / spatial → Viridis, Inferno
• Brand or marketing materials → not Okabe-Ito; the colors are tuned for distinctness, not emotion

A common misuse is treating the Okabe-Ito palette as a gradient by picking 3–5 of its colors as a low/medium/high scale. They aren't designed for that — they're maximally different, not stepwise. Reach for Viridis or Cividis when you need an ordered scale that also happens to be colorblind-safe.

Okabe-Ito Palette vs Other Colorblind-Safe Palettes

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 ≤ 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 — 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 — Okabe-Ito and Tol's palettes scored highest.

Where the Okabe-Ito Palette Is Built In Today

You no longer have to type the hex codes by hand. Major scientific stacks ship Okabe-Ito as a first-class option:

• R 4.0+ — palette.colors() returns Okabe-Ito by default
• ggplot2 via easystats see — scale_color_okabeito()
• ggplot2 via ggokabeito — scale_color_okabe_ito()
• Python — registered in colorcet, palettable, and cmcrameri
• Python seaborn — pass the hex list to sns.set_palette()
• Observable / D3 — available in d3-scale-chromatic
• CSS variables — easy to hand-roll; see the hex codes reference

A 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.

Five Common Mistakes When Using the Okabe-Ito Palette

The palette is robust, but it can still be misused:

1. Treating it as ordered. The colors are designed to be maximally different, not stepwise. Don't pick three as a low/medium/high scale — use Viridis.
2. Mixing in non-palette colors. Adding a "ninth" color from outside the set (like a brand red) reintroduces CVD problems and breaks the perceptual balance.
3. Reducing saturation for "softness." Lower saturation collapses the distinctness margin. If a figure feels too loud, reduce the number of categories instead.
4. Ignoring the recommended order. Okabe and Ito specified an order optimized for first-encounter contrast. ggplot2 and base R both respect it — overriding it loses some safety margin.
5. Skipping the grayscale check. Even with a CVD-safe palette, always preview the figure in grayscale. If two series still collide, the issue is overlapping lines, not the colors.

Frequently Asked Questions

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 — 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 ≤ 8 series, switch to Paul Tol's "muted" palette (9 colors), or — if your data is actually ordered — 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 — 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.