The misuse of colour in science communication
nature.com
nature.com
My only minor quibble is the use of "scientifically" in phrases like "scientifically derived color maps." Since there's no general "scienctific" concept, and it's actually about the color values perceptual consistency, using a term that reflects that specific "scientific" aspect would be a lot clearer. The terms "scientific" and "unscientific" are highly subjective and do not convey much useful meaning, IMHO. And when an article is read by non-scientists, which I hope this article will be, using terms like "unscientific" reinforces the false popular perception that scientists are commonly dividing the world into "scientific" and "unscientific" like a 1950s Time magazine article. But again, very minor quibble in what is a very important message.
Yeah batlow looks better than jet on a photograph, but that doesn't mean it's necessarily better for communicating data that doesn't share the underlying features of photographs. I'd like to see whether jet or batlow has better accuracy when asking readers to eyeball data values from -1 to 1 based on color.
I personally avoid jet etc, but the motivations they give here seem to miss the point of why these colormaps are used.
I wouldn't describe a rainbow colormap as divergent.
A divergent colormap transitions from one color to another, with neutral color in the middle (e.g., from blue to red, passing through white). A rainbow color map transitions between a series of spectrally-orderd distinct colors (red, orange, yellow, green, blue, indigo, violet); this gives rise to apparent discontinuities (e.g. [0]).
If you want a divergent color scheme to highlight the extreme values, then use one (see, e.g. [1]) rather than a rainbow scheme.
[0]: http://sci.utah.edu/~vdl/papers/2019_eurovis_implicit-discre...
Even worse, you can can see in the visualisation of various types of colourblindness that with protanopia only the cyan peak is visible, and with tritanopia only the yellow peak is visible.
I understand that jet is poor for accessiblity, and I don't use it for that reason, but in my experience jet is used frequently for divergent data and highlighting extreme values. The article's argument for perceptually uniform colormaps in this context is weak to non-existent. Surely roma wouldn't look very good on a photo of Curie.
In a deleted reply to my comment, a user referenced a paper by Borkin [1], that actually compares the quality of decisions made using jet vs other divergent colormaps. Jet loses. It's a shame this sort of argument wasn't included in the ncomms article, because imo it would do a much better job of convincing people who like jet.
[1] https://www.eecs.harvard.edu/~kgajos/papers/2011/borkin11-in...
I think the same thing happens in the arteries example in that SciPy talk. Sure it could be useful in some scenarios to highlight specific parts of the spectrum (like you mentioned with small deviations from the zero point) but at the end of the day the colourmap doesn't know anything about the data and shouldn't make any assumptions. Highlighting certain small intervals in the data could be better achieved with preprocessing, i.e. some polynomial scaling function.
IMO roma is actually the worst of the bunch, despite being the most 'scientific.' Whoever decided that deep, saturated blue and dull brown-red are equally distinct from beige needs to reevalute their model of color perception.
I agree, in the "this should be so obvious that it doesn't need an article written about it" sense. But the continued prevalence of jet used as a sequential colour map suggests that the comparison is necessary.
I think one of the issues is that it doesn't actually highlight the extremes, but rather some arbitrary values in the middle: The values with the brightest colours as well as the steepest increases in brightness are in the cyan and yellow sections in the middle, whereas the extremes are dark red and dark blue - so attention is actually pulled away from the extremes.
Likewise, it highlights some contours but will obscure others - and it's difficult to predict which contours will be highlighted and which will be obscured.
Sometimes I have tried using side-by-side figures with other colormap, usually to prove some specific point, but changing the habits of a whole field is really difficult. It is even more difficult when there is not a clear alternative, but several to choose from; I have seen a few other people using alternative colormaps too and they did not use the same as me (or I did not use the same as them).
To give a more extreme example: if a doctor has to check my cancer tests and he has been diagnosing tumors using the jet colormap for decades, I would not feel comfortable if my results were given to him using the much better cividis colormap.
The message is fine, as far as it goes.
However, I would argue that perceptual neutrality is not the only measure of a colour scale.
I hate jet as much as the next person, but I still do use the similar turbo scheme (which is less 'jagged' in perceptual space) when I teach. Why? Because it lets me show an image of a complex field (perhaps sea-surface temperature) and point to a spot of some value, and ask students to visually trace nearby spots of the same hue. It doesn't really matter if some students perceive the same hue for two different actual hues. So long as the neighbouring hues differ, the students can trace a local contour. I need that to explain certain topics.
Using a colour scheme with just a couple of hues makes it very, very, difficult to trace contours. It's even worse when an image is projected onto a screen, in a classroom or at a meeting. Speaker: "See this slightly yellowish green on the colourbar, at 10C? Where is it in the map?" Audience: "I dunno, when's the next coffee break?"
Essentially, colour maps with a lot of hues can make images into something like a contour diagram. This can help, because some fields are so ragged that contours are difficult to understand.
Sometimes, a linear mapping between data values and the perceived colour is the end goal. Other times, not.
http://soliton.vm.bytemark.co.uk/pub/cpt-city/imagej/index.h...
I've seen it with a single hue as well.
This kind of colourmap makes it really easy to see contours. The tradeoff is that it's hard to make comparisons between distant areas of the image.
(1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad.
(2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a colormap which has a strong luminance variation across the data range.
(3) For interval and ratio data, both luminance- and saturation-varying colormaps should produce the effect of having equal steps in data value correspond to equal perceptual steps, but the first will be most effective for high spatial frequency data variations and the second will be most effective for low spatial frequency variations.
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[1] https://www.research.ibm.com/people/l/lloydt/color/color.HTM
or as pdf:
https://github.com/frankMilde/interesting-reads/blob/master/...
I disagree with blanket statements like this. Rainbow color maps aren't ideal for showing relative difference in values. But they are great if you want to be able to tell what the value (range) in a given location is. This is almost impossible with a 10-shade grey-scale legend or a height map.
Disclaimer: I use rainbow color maps all the time because I'm often interested in critical value areas of my plots.
So for me reading a Jet plot is basically impossible, they just doesn't make sense. Like, if a value is weak it is the brightest white, then if it gets even lower it gets blue. It peaks at green in the middle and then goes darker and darker as it gets stronger. Totally unintuitive.
Edit: Even worse is that how I see it depends strongly on what medium you use for the red and green channels. On some screens/paints red is basically black while others are almost fully green, depending on exactly where in the spectrum it is.
I think anyone that presents data should read this article:
Because using the emotional response to colors is not scientific. It just makes science look like propaganda.
The issue though is where to draw the center between blue and red? Like, when do we transition from light blue to light red? The average temperature? the freezing point? And even then, what color goes in the middle? White, grey, and black are common, but not necessarily natural or perfect options.
One solution is to use a sequential, non-diverging color map that starts at white/light red, and gets more red with higher temperature (or blue, or green). But then we miss out on that intuitive blue/red relationship, and people may have a hard time interpreting "light red" as a low, cold temperature.
I don't think scientists should condemn one another for their choice of colors in graphs. Make your argument for one set of colors vs another and scientists can decide for themselves if they agree.