Distractions and amusements, with a sandwich and coffee.
Here, I help you understand color blindness and describe a process by which you can make good color choices when designing for accessibility.
The opposite of colorblindness is seeing all the colors and I can help you find 1,000 (or more) maximally distinct colors.
You can also delve into the mathematics behind the color blindness simulations and learn about copunctal points (the invisible color!) and lines of confusion.
Download all palettes
plain text, v11 20 May 2020
Download slides
PDF, v11 20 May 2020
In this section, I cover how to make good color choices when considering audiences with color blindness.
With the exception of the 8-color palette, all palettes have been created using a process (read below) that tries to maintain perceptual luminance uniformity in color blind space.
This 8-color palette is adapted from Nature Method's Points of View: Color blindness by Bang Wong. Note that in that original source the RGB values listed in the table did not exactly correspond to the RGB swatches—probably an RGB vs CMYK conversion mixup.
This palette is suitable for categorical color encoding—the colors do not, as a whole, have a natural order and none is substantially more salient than another.
You can download these colors as plain text list of HEX and RGB values.
For more tips about designing with color blindness in mind, see Color Universal Design (CUD) — How to make figures and presentations that are friendly to people with color blindess.
To people with color blindness, some colors appear the same. This equivalence can be used to identify colors that are distinct to those with normal as well as to those with color blindness.
For a given RGB color we can simulate how it would appear to someone with color blindess and identify groups of RGB colors that appear indistinguishable in color blindness.
These equivalencies can be used to construct color palettes—lists of colors that are distinguishable to deuteranopes and those with normal vision.
Since deuteranopia is the most common, this is the condition that I use for color selection.
The exact luminance (perceived brightness) of the simulated color varies depending on the color blindness algorithm. Each row in the squares above should look identical using any color blindness simulation (e.g. Color Oracle, Photoshop, etc) but brightness of the rows may be slightly different than shown here.
This palette maps four colors onto each of the two color dimensions in deuteranopes and four onto greyscale. This palette is very useful for designing transit and subway maps.
Color names are playful selections from my list of 10,000 color names.
You can download these colors as plain text list of HEX and RGB values.
You can download these colors as plain text list of HEX and RGB values.
Even more color choices for color blindess, including colors that map onto greys. For these, I don't have RGB/HEX values handy.
You can download these colors as plain text list of HEX and RGB values.
You can create your own color palettes using the figure below.
For a given color blindness type (e.g. deuteranopia) and channel (e.g. blue), the rows represent reasonably uniform steps in LCH luminance of the simulated color and a rich (high chroma) simulation at that luminance.
Love's the only engine of survival. —L. Cohen
We begin a series on survival analysis in the context of its two key complications: skew (which calls for the use of probability distributions, such as the Weibull, that can accomodate skew) and censoring (required because we almost always fail to observe the event in question for all subjects).
We discuss right, left and interval censoring and how mishandling censoring can lead to bias and loss of sensitivity in tests that probe for differences in survival times.
Dey, T., Lipsitz, S.R., Cooper, Z., Trinh, Q., Krzywinski, M & Altman, N. (2022) Points of significance: Survival analysis—time-to-event data and censoring. Nature Methods 19:906–908.
See How Scientists Put Together the Complete Human Genome.
My graphic in Scientific American's Graphic Science section in the August 2022 issue shows the full history of the human genome assembly — from its humble shotgun beginnings to the gapless telomere-to-telomere assembly.
Read about the process and methods behind the creation of the graphic.
See all my Scientific American Graphic Science visualizations.
My poster showing the genome structure and position of mutations on all SARS-CoV-2 variants appears in the March/April 2022 issue of American Scientist.
An accompanying piece breaks down the anatomy of each genome — by gene and ORF, oriented to emphasize relative differences that are caused by mutations.
My cover design on the 11 April 2022 Cancer Cell issue depicts depicts cellular heterogeneity as a kaleidoscope generated from immunofluorescence staining of the glial and neuronal markers MBP and NeuN (respectively) in a GBM patient-derived explant.
LeBlanc VG et al. Single-cell landscapes of primary glioblastomas and matched explants and cell lines show variable retention of inter- and intratumor heterogeneity (2022) Cancer Cell 40:379–392.E9.
Browse my gallery of cover designs.
My cover design on the 4 April 2022 Nature Biotechnology issue is an impression of a phylogenetic tree of over 200 million sequences.
Konno N et al. Deep distributed computing to reconstruct extremely large lineage trees (2022) Nature Biotechnology 40:566–575.
Browse my gallery of cover designs.