bio-data-visualization-color-palettes
Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps, viridis/cividis/magma, Okabe-Ito categorical, ColorBrewer, and the rainbow/jet critique. Use when choosing palettes for heatmaps, scatter, networks, or any encoding where color carries quantitative or categorical meaning.
npx skills add BioTender-max/awesome-bio-agent-skills --skill color-palettes --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: viridis 0.6+, RColorBrewer 1.1+, scico 1.5+ (Crameri colormaps in R), khroma 1.12+ (Tol/Crameri palettes in R), matplotlib 3.8+, colorcet 3.0+, ggsci 3.0+, colorspace 2.1+. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Color Palettes for Scientific Visualization **"Pick a color palette"** -> Choose a colormap that (a) is perceptually uniform along the relevant data axis, (b) remains interpretable under common color-vision deficiencies, (c) prints correctly to grayscale, and (d) matches the data type — sequential, diverging, cyclic, or qualitative. - R: `viridis::viridis`, `scico::scale_color_scico`, `khroma::color`, `RColorBrewer::brewer.pal` - Python: `matplotlib.colormaps`, `colorcet`, `seaborn.color_palette`, `cmcrameri.cm` ## The Three Modern Standards 1. **Perceptual uniformity
- Version Compatibility
- The Three Modern Standards
- Palette Type by Data Type
- The Crameri Scientific Colormaps
- viridis Family (matplotlib default since 3.0)
- Okabe-Ito Categorical Palette (Wong 2011)
- ColorBrewer (Harrower & Brewer 2003)
- Scientific Journal Brand Palettes
- CVD Simulation -- The Mandatory Check
- Grayscale Monotonicity Test
- Diverging Palette Setup (LFC, z-score)
- Custom Palette Construction
- Common Failure Modes
- Asymmetric bounds on diverging data
What does the bio-data-visualization-color-palettes skill do?
Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps, viridis/cividis/magma, Okabe-Ito categorical, ColorBrewer, and the rainbow/jet critique. Use when choosing palettes for heatmaps, scatter, networks, or any encoding where color carries quantitative or categorical meaning.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill color-palettes --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.