# Foundational Reading

Canonical URL: https://colorcurio.us/library/collections/foundational-reading
Source type: Collection

> The papers and books the rest of the library keeps quoting, in roughly the order they are worth reading. Albers teaches you to distrust a swatch seen in isolation; Brewer and Harrower turned that…

## Summary

The papers and books the rest of the library keeps quoting, in roughly the order they are worth reading. Albers teaches you to distrust a swatch seen in isolation; Brewer and Harrower turned that into schemes for maps; Borland and Taylor, Light and Bartlein, and Crameri made the case against the rainbow; Kovesi and Moreland showed how to build something better; Ottosson gave front-end color a space that behaves. The papers are short, the books reward a slow read, and each one changes what you notice.

## Entries

1. [Interaction of Color](https://colorcurio.us/library/interaction-of-color): Albers’ course in seeing color relatively, in Yale’s complete digital edition with interactive plates. ([source](https://interactionofcolor.com/))
2. [A Field Guide to Digital Color](https://colorcurio.us/library/field-guide-digital-color): Color from perception through displays, print, and design — a bridge between color science and practice. ([source](https://www.routledge.com/A-Field-Guide-to-Digital-Color/Stone/p/book/9781568811611))
3. [Color Sequences for Univariate Maps: Theory, Experiments and Principles](https://colorcurio.us/library/ware-color-sequences): Experiments separating reading values from reading shape: lightness carries form, hue helps read values. ([source](https://scholars.unh.edu/ccom/905/))
4. [Color Use Guidelines for Mapping and Visualization](https://colorcurio.us/library/brewer-color-use-guidelines): The chapter that sorted map schemes into sequential, diverging, and qualitative — the typology ColorBrewer uses. ([source](https://doi.org/10.1016/B978-0-08-042415-6.50014-4))
5. [ColorBrewer.org: An Online Tool for Selecting Colour Schemes for Maps](https://colorcurio.us/library/colorbrewer-paper): Why ColorBrewer exists, and how its schemes were tested on real maps rather than swatches. ([source](https://doi.org/10.1179/000870403235002042))
6. [Escaping RGBland: Selecting Colors for Statistical Graphics](https://colorcurio.us/library/escaping-rgbland): The case for building statistical palettes in HCL rather than RGB, and the basis of R’s colorspace package. ([source](https://doi.org/10.1016/j.csda.2008.11.033))
7. [Rainbow Color Map (Still) Considered Harmful](https://colorcurio.us/library/rainbow-colormap-harmful): The classic case against rainbow maps: no perceptual order, hidden structure, and invented edges. ([source](https://doi.org/10.1109/mcg.2007.323435))
8. [How NOT to Lie with Visualization](https://colorcurio.us/library/how-not-to-lie-with-visualization): The rainbow draws contours the data lacks; match the colormap to the task and the data’s spatial frequency. ([source](https://doi.org/10.1063/1.4822401))
9. [The End of the Rainbow? Color Schemes for Improved Data Graphics](https://colorcurio.us/library/end-of-the-rainbow): An early plea to earth scientists to drop rainbow maps for ColorBrewer-style schemes that CVD readers can use. ([source](https://doi.org/10.1029/2004EO400002))
10. [The Misuse of Colour in Science Communication](https://colorcurio.us/library/misuse-of-colour): How rainbow and red–green maps distort scientific figures, and what to use instead. ([source](https://www.nature.com/articles/s41467-020-19160-7))
11. [Good Colour Maps: How to Design Them](https://colorcurio.us/library/good-colour-maps): The paper behind ColorCET: a test image that exposes flat spots in a colormap, and a recipe for uniform ones. ([source](https://arxiv.org/abs/1509.03700))
12. [Diverging Color Maps for Scientific Visualization](https://colorcurio.us/library/moreland-diverging): The paper behind the cool-warm map: interpolate diverging scales in Msh so the midpoint stays neutral. ([source](https://www.kennethmoreland.com/color-maps/))
13. [True Colors of Oceanography](https://colorcurio.us/library/true-colors-of-oceanography): The paper introducing cmocean: one colormap per ocean variable, chosen to match how each quantity behaves. ([source](https://doi.org/10.5670/oceanog.2016.66))
14. [How We Designed the New Color Palettes in Tableau 10](https://colorcurio.us/library/tableau-10-palettes): How Tableau’s default categorical palette was rebuilt: balanced lightness, nameable hues, and a default’s tradeoffs. ([source](https://www.tableau.com/blog/colors-upgrade-tableau-10-56782))
15. [Choosing Colors for Data Visualization](https://colorcurio.us/library/choosing-colors-for-data-viz): A primer: color’s first jobs are to label and group, and axes and background are part of the palette too. ([source](https://www.perceptualedge.com/articles/b-eye/choosing_colors.pdf))
16. [Color Naming Models for Color Selection, Image Editing and Palette Design](https://colorcurio.us/library/color-naming-models): A model of the names people give colors, used to test whether a palette’s swatches are nameably distinct. ([source](http://vis.stanford.edu/papers/color-naming-models))
17. [Selecting Semantically-Resonant Colors for Data Visualization](https://colorcurio.us/library/semantically-resonant-colors): Colors that match what a category means — blue for water, yellow for banana — make charts faster to read. ([source](http://vis.stanford.edu/papers/semantically-resonant-colors))
18. [Modeling Color Difference for Visualization Design](https://colorcurio.us/library/modeling-color-difference): How far apart two colors must be to look different depends on the mark: bars, points, and lines differ. ([source](https://doi.org/10.1109/TVCG.2017.2744359))
19. [Mapping Color to Meaning in Colormap Data Visualizations](https://colorcurio.us/library/mapping-color-to-meaning): Viewers read darker as more — unless a map looks semi-transparent, and then more opaque reads as more. ([source](https://schlosslab.discovery.wisc.edu/wp-content/uploads/2018/09/SchlossGramazioSilvermanParkerWanginPress.pdf))
20. [Somewhere Over the Rainbow: An Empirical Assessment of Quantitative Colormaps](https://colorcurio.us/library/somewhere-over-the-rainbow): Nine colormaps tested on distance judgments: viridis holds up at every span; single-hue blues blur small gaps. ([source](https://idl.uw.edu/papers/quantitative-color))
21. [A Perceptual Color Space for Image Processing](https://colorcurio.us/library/oklab-color-space): The post introducing OKLab: where CIELAB’s hue and lightness slip, and the transform that fixes them. ([source](https://bottosson.github.io/posts/oklab/))
22. [Frequently Asked Questions About Color](https://colorcurio.us/library/poynton-color-faq): The Color and Gamma FAQs: luminance versus lightness, gamma, and the CIE terms color code often gets wrong. ([source](https://poynton.ca/faq/))
23. [Color Appearance Models](https://colorcurio.us/library/color-appearance-models): The textbook on appearance models beyond CIELAB — CIECAM02 and kin — and the viewing effects they predict. ([source](https://www.wiley.com/en-us/Color+Appearance+Models%2C+3rd+Edition-p-9781119967033))
24. [Information Visualization: Perception for Design](https://colorcurio.us/library/ware-information-visualization): The textbook on vision for visualization design, with chapters tying color theory to rules for coding data. ([source](https://shop.elsevier.com/books/information-visualization/ware/978-0-12-812875-6))