I never knew that such relevant papers ever existed.
Published in nature, this is the key takeaway.
A quick digest of important points:
- However, colour maps that visually distort data through uneven colour gradients or are unreadable to those with colour-vision deficiency remain prevalent in science.
- These include, but are not limited to, rainbow-like and red–green colour maps.
- We show how scientifically derived colour maps report true data variations, reduce complexity, and are accessible for people with colour-vision deficiencies.
- The visual evaluation of a colour gradient is important to a variety of different fields such as the first direct impression of a black hole1, the mapping of votes cast in political elections2,3, the planning of an expensive rover route on Martian topography4, the essential communication of climate change5,6, or the critical diagnosis of heart disease7.
- However, when colours are used incorrectly, this can lead to the effective manipulation of data (e.g., by highlighting some data over others), the oversight of the needs of those with colour vision deficiencies, and the removal of meaning when printed in black and white (Supplementary Note 1).
- Colour maps, therefore, are a crucial intersection between science and society.
- For instance, weather forecasts and hazard maps are two examples of immediately societal-relevant data sets that are also repeat offenders for use of the rainbow-like colour maps.
- Although some scientific communities have largely moved away from using distorting colour maps, such as rainbow, there are numerous signs of bad habits returning en masse8.
- For most scientists, the choice of colour maps has become almost passive, with the unscientific rainbow-like colour palette being commonplace.
- Similarly, colour maps that pair red–green are also problematic, but remain widely used.
- At one point, most of the common software programmes applied rainbow as their default palette (e.g., MatLab, Paraview, VisAd, IrisExplorer) despite issues surrounding colour maps like rainbow, and variations thereof, being known for some time9,10,11,12,13,14,15,16,17,18,19,20.
- So, what’s the problem with these colour maps?
- Even though rainbow colour maps might reflect aesthetic attractiveness, the extreme values in the standard Red-Green-Blue (RGB) are very dominant and can, therefore, distract from the underlying visual message21.
- In rainbow colour maps, the yellow is the brightest colour and attracts the eye the most22,23 (see Box 1), but it is neither at the end nor the centre of the colour map, while its greenish shades form a wide band with low perceived colour contrast (Supplementary Fig. 1).
- Building a colour map on a purely physical rather than a perceptual basis significantly alters how we perceive data; it adds artificial boundaries to some parts of the data range, hiding small-scale variations elsewhere, it prevents any visual intuitive order occurring in the data set, and renders the data unreadable for readers with common colour-vision deficiencies (Fig. 2).
- Fig. 1: The superiority of scientifically derived colour maps.
- By knowing what something looks like in advance, the distortion by unscientific colour maps, like jet or rainbow, becomes instantly obvious.
- The look of scientific data is, however, usually unknown a priori, which makes the distortion of an unscientific colour map, and the true data representation of a scientifically derived colour map, like batlow41, less apparent.
- Inferring the true picture from an unscientifically (e.g., jet) coloured data set is incomparably harder than from a data set represented in a perceptually uniform and ordered colour map, like batlow41.
- Fig. 2: Colour vision tests.
- Available perceptually uniform colour maps versus the non-uniform rainbow (i.e., jet; bottom row) as seen with either of the three common forms of human colour-vision deficiency (deuteranopia, protanopia, and tritanopia), and for grey-scale (representing total colour-blindness or simple black-and-white prints).
- Rainbow, the most-widely used colour map, fails to reproduce a meaningful smooth gradient, yet the other colour maps (see Box 2) are all universally readable.
- Colour maps that include both red and green colours with similar lightness cannot be read by a large fraction of the readership (Fig. 2).
- The general estimate is that worldwide 0.5% of women and 8% of men are subject to a colour-vision deficiency.