Humans are really bad at understanding color. And yet, scientific papers are so often covered in bright, eye-catching rainbow diagrams, as researchers try to communicate their findings in exciting, intuitive ways. But why – when humanity can't even agree on the color of a dress – should we think that our old friend ROY G BIV will help someone understand complex scientific ideas?
For decades, there has been a sector of the scientific community arguing that the rainbow needs to be retired. Not only is it entirely useless for colorblind people, it's not even that helpful for the rest of us – we just can't see color in a way that would let us properly interpret data.
"People like to use rainbow because it catches the eye," chemical and biological data analyst Jamie Nuñez explained to Scientific American. "But once the eye actually gets there and people are trying to figure out what’s actually going on inside of the image, that’s kind of where it falls apart."
The problems are manifold: rainbow colors are disorganized (how can you order red, blue, and yellow?), they are nonlinear (how can we tell which color is "halfway" between two others?), and, essentially, our brains just don't work that well with them – we naturally interpret bright colors as more important, regardless of where they occur on the "scale".
And as if the headache wasn't enough to convince us to change our ways, rainbow data visualizations can literally put lives at risk: a Harvard study found that doctors presented with a 3D rainbow model took longer, and were far less accurate, to diagnose heart disease than when shown a 2D model using a red-to-black scale.