This is a collection of references that I have found useful in learning about color in data visualization. It is loosely ordered, starting with explanations of deficiencies of the widely-used "rainbow" color map, and moving on to recommendations for good color maps.
Rogowitz & Treinish, IBM
A thorough, scholarly discussion on why the color maps we choose are important.
Borland & Taylor II, UNC Chapel Hill
Could be considered the seminal work on the rainbow color map. Cited by many of the articles here.
Skau, UNC Charlotte
This author boils down the problems with the “rainbow” map, with examples. A good, quick read.
Kosara, Tableau Software
Similar to the Skau article, above, but with different examples. Also points to some tools for generating better color maps. Another quick read.
van der Walt & Smith, UC Berkeley
A discussion by the team that developed the new default colormaps for matplotlib (a plotting library for SciPy.) This page is extremely thorough in presenting lightness and color-blindness perception for every color map under discussion. I like referencing this page for the color-blindness charts.
Moreland, Sandia National Labs
One of the first resources I found. The author provides examples of sequential and diverging color maps for scientific data. He also provides the code to generate the maps. He recommends using diverging maps for 3-D surfaces, but I find that simple surfaces like antenna patterns (mostly convex) do lend themselves well to sequential maps. (I also prefer to avoid the debate on what value should be the “diverging” value for a purely sequential dataset.)
Niccoli, MyCarta Geoscience
A series of articles that others consistently cite. Includes discussion on lightness (L*) gradient in color maps. It is rather hard to navigate, though.