There are numerous books and articles online about color palette design, usually from a web-design / aesthetic standpoint. But there is more to the use of color than mere aesthetics; color can be used as an effective tool in scientific data visualization. One of the few books on the topic describes its contents as a guide to “how scientists and engineers can use color to help gain insight into their data sets through true color, false color, and pseudocolor imaging.” While the content of the book is a bit beyond the scope of this post, it’s clear that color gets a lot of use in charts and graphs, and being able to better pick colors is beneficial.
ColorBrewer is designed to help users pick a set of colors that is best used to show data on a map. Unlike most color scheme choosers where you pick whether you want muted colors, bright colors, pastel colors, etc., ColorBrewer starts by asking whether the data you are visualizing is sequential, diverging, or qualitative. Sequential and diverging both have to do with quantitative data, e.g. average salaries. Sequential is the more familiar for data; darker colors usually indicate a higher value on whatever metric is measured. Diverging, on the other hand, treats the average as a neutral color and then uses colors with sharply contrasting hues for the high and low ends. Qualitative could also be labeled as ‘categorical’; it means about the same thing.
Among its other features, ColorBrewer can exclude colors that would not come out well when photocopied, as well as those that would be confused by people with color blindness. It also has mechanisms for exporting the RGB/Hex color codes of the generated color palettes for use in other applications.