Publication-grade and colorblind-safe color palettes for scientific figures, with HEX values, journal conventions, and use-case tips for plots and posters.
SciFig Team
Scientific Illustration Experts
Color is the fastest way to make a scientific figure look professional — and the fastest way to make it look amateur. The same data, plotted with a default Excel rainbow versus a restrained four-color palette, reads as either a high-school project or a Nature submission. Reviewers form that impression in the first second, before they read a single axis label.
This guide gives you publication-grade color palettes with HEX values you can copy, the journal conventions worth knowing, and use-case guidance for plots, heatmaps, posters, and pathways. It also covers the one constraint too many figures still ignore: roughly 8% of men and 0.5% of women have some form of color vision deficiency, which means a palette that fails colorblind-safe testing is unreadable to a meaningful slice of your audience — including reviewers.
A grid of scientific color palette swatches with HEX values, spanning sequential, diverging, and categorical schemes (Figure generated with SciFig)
Why Color Decides Figure Readability and Acceptance
Color in a scientific figure is not decoration — it's an encoding channel, and it either carries information or it adds noise. A well-chosen palette makes categories instantly distinguishable, guides the eye to the key result, and survives both grayscale printing and colorblind viewing. A poorly chosen one forces the reader to work, and a tired reviewer who has to work tends to disengage.
Three failure modes recur. The rainbow palette (the old jet colormap) creates false boundaries where the data is continuous, because the human eye doesn't perceive its color steps as equal. Red-green encoding is invisible to the most common form of color blindness, yet it's still the default for "good vs bad." And too many categories — more than about eight distinct colors — exceeds what anyone can hold in working memory, so a legend with twelve colors becomes a lookup table the reader abandons. The palettes below are chosen to avoid all three.
Top journals don't mandate exact palettes, but their published figures converge on a recognizable house style: muted, low-saturation colors with strong value contrast. Matching that style signals "this belongs here" before a reviewer reads the caption. Below are starting palettes aligned to common journal aesthetics.
Palette
Use case
Example HEX values
Muted categorical
Up to 6 groups in bar/line plots
#4E79A7#F28E2B#59A14F#E15759#B07AA1#76B7B2
Nature-style cool
Two-to-three group comparisons
#386CB0#7FC97F#BEAED4
Sequential (single hue)
Ordered data, density, magnitude
#F7FBFF#9ECAE1#4292C6#08519C
Diverging
Data with a meaningful midpoint (e.g. log-fold change)
#B2182B#F4A582#F7F7F7#92C5DE#2166AC
Two rules carry most of the benefit. Use sequential palettes (light-to-dark of one hue) for ordered or continuous data, and diverging palettes for data centered on a meaningful zero. Reserve categorical palettes for unordered groups, and cap the count near six — beyond that, distinguish groups with shape or texture, not more colors.
Side-by-side journal-style palettes — muted categorical, sequential, and diverging — applied to the same bar chart and heatmap (Figure generated with SciFig)
A colorblind-safe palette isn't an accessibility nicety — it's a requirement for your figure to be read correctly by everyone, and several journals now explicitly recommend it. The two best-tested options are the Okabe-Ito palette and Wong's palette, both designed so that every pair of colors stays distinguishable across the common forms of color vision deficiency.
The Okabe-Ito eight-color set is the most widely cited: #000000 (black), #E69F00 (orange), #56B4E9 (sky blue), #009E73 (bluish green), #F0E442 (yellow), #0072B2 (blue), #D55E00 (vermillion), and #CC79A7 (reddish purple). It's intentionally not a rainbow — the colors were selected for contrast under deficiency, not visual prettiness, which is exactly why it works.
Two habits make any palette safer. First, never encode meaning with red-vs-green alone — pair color with a second channel like position, shape, or a direct label. Second, test in grayscale: if your figure still reads when desaturated, it will survive both colorblind viewing and the black-and-white printing some journals still use. The example below shows the same figure in a red-green encoding versus an Okabe-Ito encoding, simulated under deuteranopia.
A figure shown twice — red-green encoding versus Okabe-Ito palette — each simulated under deuteranopia to show which stays readable (Figure generated with SciFig)
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The right palette depends on what the figure does, not on personal taste. Matching palette type to data type is the single highest-leverage decision in figure color.
Line and bar plots — a muted categorical palette, capped at about six colors. If you have more series than that, split into small multiples rather than adding colors.
Box plots and grouped comparisons — two to four high-contrast categorical colors; keep the within-group fill light so the median line stays visible.
Heatmaps — a sequential palette for one-directional magnitude, or a diverging palette when there's a meaningful center (zero, baseline, control). Avoid rainbow heatmaps entirely.
Pathway and mechanism diagrams — a small palette where color means something (one hue per molecule class, for example), not a different color per object. Consistency across the figure matters more than variety.
Pie charts — if you must use one, cap at five slices and order them; beyond five, a bar chart communicates better.
The unifying principle: color should reduce the reader's work, not add to it. When a palette makes you reach for the legend repeatedly, it's doing the opposite of its job. For the broader set of avoidable figure errors, see 5 common mistakes when creating scientific figures.
Poster-Specific Color Palettes
Posters change the color math because the viewing distance changes. A palette that's elegant at journal-figure scale can wash out across a two-meter poster hall, so posters favor slightly higher contrast and a tighter palette — often just two or three colors plus neutrals. A common winning scheme pairs a deep navy or teal for structure with a single accent color reserved for the key finding, on a white or very light background.
The discipline is the same as for figures, only stricter: fewer colors, more contrast, and color reserved for meaning. For the full anatomy of a poster that works — hierarchy, hero figure, and how color supports both — see our guide to scientific poster examples.
Apply Palettes in One Click with SciFig
Choosing the right palette is one problem; applying it consistently across every figure in a paper is another. Manually re-coloring a multi-panel figure to a journal palette, then re-checking it for colorblind safety, is the kind of tedious work that gets skipped under deadline. SciFig's figure-enhancer handles the recoloring step: it applies a chosen palette across a figure's elements and keeps the scheme consistent, so you're not hand-matching HEX values panel by panel.
The workflow is straightforward — generate or import your figure, choose a palette (journal-aligned or colorblind-safe), and let the tool apply it uniformly. The figures stay publication-ready without the manual color bookkeeping. To see how real figures look with disciplined palettes applied, browse the inspiration gallery.
Before and after: a figure recolored from a default rainbow palette to a colorblind-safe journal palette in SciFig's figure-enhancer (Figure generated with SciFig)
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