Just because it's black in the dark,
Oh, doesn't mean there's no colors.
— Laleh (Colors)
Authentic and color-accurate images of Ishihara's test plates for colour deficiency.
I provide high-resolution bitmaps and SVG files for each plate. I also provide the position, size, and color of each circle on each test plate.
Can this information be used to make fakes? Yes, but at least they'll be really good ones. Also, please don't.
If you're interested to learn more about colorblindess and the mathematics behind it, see my Designing for Color Blindness, Palettes for Color Blindness and Math of Color Blindness.
And turn those lines of confusion into understanding!
Below are images of each of the 38 Ishihara test plates. These images are derived from photos that have been carefully color-corrected. Read these details if you're wondering why the background isn't white.
If your monitor is calibrated, the colors will be accurate.
For each plate, I show a simulation of how it would appear to a deuteranope and a protanope.
Simulation results (particularly luminance) depend on which color matrices are used (colorblindness math and simulation details.
There are 2 demonstration plates (1, 38).
There are 12 transformation plates (2–9, 34–37).
There are 12 vanishing plates (10–17, 30–33).
There are 6 hidden plates (18–21, 28–29).
There are 6 diagnostic plates (22–27).
It is not certain that everything is uncertain. —Blaise Pascal
We have already explored how we can mitigate bias caused by confounding variables in observational studies using propensity score (PS) matching (PSM) and propensity score weighting (PSW). However, any statistical model is only as good as its assumptions and, if it is specified incorrectly, it can itself produce biased estimates of the treatment effect.
This month, we explore double robustness, a powerful statistical concept that provides a valuable “safety net” against the risk of an incorrect model. It offers two opportunities, instead of just one, to obtain a valid estimate of the treatment effect — making it possible to draw credible causal inferences from observational data without having to depend on a single set of modeling assumptions.
Kurz, C.F., Krzywinski, M. & Altman, N. (2026) Points of significance: Double Robustness. Nat. Methods 23:868–869.
My cover design on the 7 April 2026 Nature Biotechnology issue shows the dendrogram that represents a cluster of uniquely expressed (or downregulated) genes in human naive stem cells induced from such cells. Within each dendrogram block, the genomic barcode sequence (sampled from Supplementary Table 1) is depicted with a Code 39 barcode. The highlighted barcode is one of those used for cell isolation.
Ishiguro S. et al. A multi-kingdom genetic barcoding system for precise clone isolation (2026) Nature Biotechnology 44:616–629.
Browse my gallery of cover designs.
Celebrate π Day (March 14th) and enjoy the art — but only if you're part of the 5%.
Go ahead, see what you can't see.
Authentic and accurate images of Ishihara's test plates photographed (and lovingly color-corrected) from the 38-plate Ishihara's Tests for Colour Deficiency.
I also provide the position, size, and color of each circle on each test plate.
What immortal hand or eye, could frame thy fearful symmetry? — William Blake, "The Tyger"
This month, we look at symmetric regression, which, unlike simple linear regression, it is reversible — remaining unaltered when the variables are swapped.
Simple linear regression can summarize the linear relationship between two variables `X` and `Y` — for example, when `Y` is considered the response (dependent) and `X` the predictor (independent) variable.
However, there are times when we are not interested (or able) to distinguish between dependent and independent variables — either because they have the same importance or the same role. This is where symmetric regression can help.
Luca Greco, George Luta, Martin Krzywinski & Naomi Altman (2025) Points of significance: Symmetric alternatives to the ordinary least squares regression. Nat. Methods 22:1610–1612.