What is the best color palette for heatmaps?

Perceptually uniform colormaps are the right choice for heatmaps and any continuously varying data. Viridis, Plasma, Magma, and Cividis all share the property that equal steps in data value produce equal steps in perceived lightness, so readers can judge magnitudes accurately across the full range. Viridis, running from dark purple to bright yellow, is the most widely adopted and ships as the default in many scientific plotting libraries.

How do I make my figures colorblind-friendly?

Start with a palette designed for accessibility: Okabe-Ito works well for categorical groupings, while Viridis suits continuous scales. Steer clear of red-green pairings, which cause confusion for the most common forms of color deficiency. Layer in redundant visual channels by varying line styles (solid, dashed, dotted) and marker shapes alongside color. Before submission, run your figures through a colorblind simulation tool such as Color Oracle or Coblis to check the result.

What colors does Nature journal recommend?

Nature does not prescribe a mandatory palette, but it encourages authors to choose colors that remain distinguishable under color vision deficiency. Figures in the journal frequently feature tones such as deep blue (#1f77b4), coral red (#d62728), forest green (#2ca02c), and muted orange (#ff7f0e). The practical test is whether the figure reads clearly when converted to grayscale, because many readers encounter printed versions.

Can I use these palettes in Excel and PowerPoint?

Yes. Take any hex code from the examples, open the custom color dialog in Excel or PowerPoint (Format, Fill, More Colors, Custom), and paste the value directly. You can also bundle your chosen palette into a custom Office theme so the colors are available in one click across every slide deck or spreadsheet you create.

What is the difference between sequential and categorical palettes?

Categorical (qualitative) palettes assign visually distinct colors to groups that have no natural ordering, such as experimental treatments or taxonomic categories. Sequential palettes encode a progression from low to high values through a smooth gradient, making them the right tool for heatmaps or concentration maps. Diverging palettes extend outward in two directions from a meaningful midpoint, useful whenever both positive and negative deviations from a baseline matter.

Why is the jet or rainbow colormap considered bad for figures?

Because its lightness is not monotonic. Perceived brightness rises and falls across a rainbow rather than climbing steadily, creating false edges where the data is smooth (most visibly around cyan and yellow) while compressing real variation elsewhere, so the figure suggests structure the data does not contain. Rainbow ramps also collapse under red-green color vision deficiency and lose meaning in grayscale. Perceptually uniform maps such as Viridis or Cividis avoid all three.

Do these palettes still work when the journal prints in grayscale?

Not automatically. Colorblind-safe and grayscale-safe are different properties: Okabe-Ito keeps its hues distinguishable under color vision deficiency, but several colors sit at similar lightness and merge in black and white. Viridis survives grayscale because it varies lightness by design. If your figure may be printed in monochrome, convert a copy and look at it, then add redundant cues such as line styles or marker shapes wherever two series blur together.

For Researchers

Scientific Color Palette Generator

Build colorblind-friendly palettes for scientific figures and research visualizations. Grab hex codes for Okabe-Ito, Viridis, and journal-recommended color sets in seconds.

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Color Palette Examples

Explore scientific color palettes and chart examples complete with hex codes ready to copy

Okabe-Ito Categorical Palette

Eight perceptually distinct colors chosen to remain separable under all major types of color vision deficiency.

okabe-itocolorblind-friendlycategorical

Viridis Sequential Palette

A brightness-monotone gradient well suited to heatmaps and any chart encoding magnitude along a continuous scale.

viridissequentialheatmap

Nature Journal Style

A refined set of colors matching the restrained, high-impact aesthetic common in top-tier journal figures.

naturejournalpublication

Bar Chart Color Scheme

A practical walkthrough of how accessible colors look when applied to a grouped bar chart.

bar-chartexamplecategorical

Line Graph Colors

Multi-series time course chart combining accessible colors with varied stroke patterns for maximum legibility.

line-graphtime-seriesexample

Heatmap Color Scale

Correlation matrix example showing how a perceptually uniform scale and labeled color bar support accurate reading of continuous values.

heatmapviridiscorrelation

Prompt templates you can copy

Start with one of these examples, then adapt the subject, labels, data, or layout for your own use.

Okabe-Ito Categorical Palette

Eight perceptually distinct colors chosen to remain separable under all major types of color vision deficiency.

Create a professional color palette showcase displaying the Okabe-Ito colorblind-friendly color scheme. Show 8 color swatches in a clean horizontal row: orange (#E69F00), sky blue (#56B4E9), bluish green (#009E73), yellow (#F0E442), blue (#0072B2), vermillion (#D55E00), reddish purple (#CC79A7), and black (#000000). Each swatch should be a rounded rectangle with the hex code displayed below. Include "Okabe-Ito Palette" as the title. Clean minimalist design, white background, professional scientific visualization style.

Viridis Sequential Palette

A brightness-monotone gradient well suited to heatmaps and any chart encoding magnitude along a continuous scale.

Create a professional color palette showcase displaying the Viridis colormap for scientific data visualization. Show a smooth gradient bar from dark purple (#440154) through blue (#31688e), teal (#21918c), green (#35b779), to bright yellow (#fde725). Include 7-9 discrete color swatches below showing key colors in the gradient with hex codes. Title: "Viridis Sequential Palette". Clean minimalist design, white background, suitable for research publications.

Nature Journal Style

A refined set of colors matching the restrained, high-impact aesthetic common in top-tier journal figures.

Create a professional color palette showcase inspired by Nature journal scientific figures. Display 6 sophisticated colors commonly used in high-impact publications: deep blue (#1f77b4), coral red (#d62728), forest green (#2ca02c), purple (#9467bd), burnt orange (#ff7f0e), and teal (#17becf). Show each color as an elegant swatch with hex code. Include "Nature Journal Style" title. Professional academic design, clean layout, white background.

Bar Chart Color Scheme

A practical walkthrough of how accessible colors look when applied to a grouped bar chart.

Create a sample bar chart demonstrating colorblind-friendly colors for scientific figures. Show 5 categories (Groups A-E) with bars using Okabe-Ito colors: orange, sky blue, green, yellow, blue. Include proper axis labels, a legend with color swatches and hex codes (#E69F00, #56B4E9, #009E73, #F0E442, #0072B2). Y-axis shows "Measurement (units)", X-axis shows "Experimental Groups". Clean academic publication style, white background, high quality data visualization.

What is a Scientific Color Palette?

A scientific color palette is a rigorously curated set of colors built around the demands of data visualization in research contexts. Where decorative palettes chase aesthetics, scientific palettes are engineered for three properties: accessibility for readers with color vision deficiency, perceptual uniformity so every hue carries equal visual weight, and stable reproduction across monitors, projectors, and print. The workhorses of this field include Okabe-Ito for labeling discrete categories, Viridis for encoding continuous magnitude, and the family of schemes favored by flagship journals such as Nature, Science, and Cell. For a deeper walkthrough of each scheme with side-by-side figure examples, see our complete guide to scientific color palettes.

Why Use Colorblind-Friendly Palettes?

Roughly 8% of men and 0.5% of women experience some degree of color vision deficiency

A growing number of journals now formally require or recommend accessible color choices

Accessible palettes communicate clearly to every reader, not only those with color vision differences

Established schemes like Okabe-Ito signal immediately that care went into the figure design

Figures that more readers can interpret tend to accumulate more citations over time

Well-chosen palettes also hold up when figures are reproduced in black-and-white print

Popular Scientific Color Palettes

Okabe-Ito: 8 colors tuned for every major category of color blindness, the default choice for bar charts and group comparisons

Viridis: A brightness-monotone purple-to-yellow ramp for heatmaps and any continuously varying data

Plasma: Shares Viridis structure but spans a magenta-orange-yellow range for a warmer feel

Cividis: Engineered explicitly for deuteranomaly and protanomaly, the two most prevalent deficiency types

ColorBrewer: A systematic library of qualitative, sequential, and diverging sets for web and print

Nature/Science style: Muted, high-contrast hues that match the restrained visual language of top journals

How to Use These Hex Codes

Copy any hex code from the palette examples and paste it directly into your software. In Excel: open Format, choose Shape Fill, select More Colors, switch to Custom, and enter the hex value. In PowerPoint: open Format Shape, go to Fill, click Color, then More Colors and Custom. In R, supply the hex strings to ggplot2 via scale_color_manual() or scale_fill_manual(). In Python, hand the hex code to the color argument of any matplotlib plotting call. In Illustrator, double-click the fill or stroke swatch and type the hex code into the color picker.

Sequential, Diverging, or Categorical: Match the Palette to Your Data

Start with one question: what shape is your data? Categorical sets like Okabe-Ito give unrelated hues to groups with no inherent order, right for treatment arms in a bar chart or box plot. Sequential ramps like Viridis or Cividis climb from low to high through steadily increasing lightness, fitting concentration or count data in a heatmap. Diverging schemes such as ColorBrewer RdBu or Crameri vik set a neutral tone at a meaningful midpoint, suiting log fold changes or correlations. One rule outweighs the palette itself: a diverging scale needs limits centered on that midpoint. Autoscale data running from -1 to 3 and zero lands off-center, so the neutral color misreports the baseline.

Why Rainbow and Jet Colormaps Are Discouraged

Jet and other rainbow ramps ruled scientific software for decades and still get figures flagged. Lightness is not monotonic across a rainbow: it rises and falls, peaking near cyan and yellow, so the eye reads sharp boundaries even where data changes smoothly, while real gradients through the green band flatten out. A rainbow invents structure your data lacks and hides structure it has. Rainbows also fail under color vision deficiency and in grayscale, where very different values share a brightness. Perceptually uniform maps fix this: Viridis is the matplotlib default because equal steps in value give equal steps in perceived lightness. More in our data visualization best practices guide.

Designing for Every Type of Color Vision Deficiency

Color vision deficiency is not one condition, and a palette that handles one form can fail another. Deuteranomaly and protanomaly, the red-green types, are the most common, which is why red-versus-green encoding is the riskiest choice in a figure. Tritanomaly affects blue-yellow discrimination and is rarer, but it rules out leaning on blue-to-yellow alone. Okabe-Ito stays separable across all of these at once; our Okabe-Ito hex code reference lists all eight values with Python, R, and CSS snippets. Two habits still beat any palette: never let color be the only channel carrying meaning, so vary line style and marker shape in a line chart. And treat "colorblind-safe" precisely — hues stay distinguishable, but several Okabe-Ito colors merge in grayscale.

Contrast, Labels, and Common Mistakes That Get Figures Returned

Using more than about seven categorical colors, past which readers stop matching legend to plot

Encoding ordered data with a categorical palette, throwing away the ordering the reader needs

Setting axis labels in low-contrast gray; WCAG asks 4.5:1 for normal text and 3:1 for large text and graphical elements, a sensible floor for figure labels and thin plot lines

Placing text over the mid-tones of a Viridis heatmap, where neither white nor black labels stay legible

Mixing palettes across panels, so the same color means different things in different places

Forgetting that journals specify sizing, resolution, and color mode too: see our Nature figure guidelines and poster typography handbook

Frequently asked questions

The Okabe-Ito palette is an 8-color set developed by Masataka Okabe and Kei Ito with color vision accessibility as the primary design goal. The eight hues are: orange (#E69F00), sky blue (#56B4E9), bluish green (#009E73), yellow (#F0E442), blue (#0072B2), vermillion (#D55E00), reddish purple (#CC79A7), and black (#000000). It has become the recommended default in many scientific communities and journals.

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