Fig.1 A guide for the relation between the characteristics of the data, its task, and the artist’s palettes. The palettes shown above as well as the download page are arranged in a hierarchical pattern.
Jan van Eyck
Vincent van Gogh
1) Choose the palette that aligns with the characteristics of your data distribution. As Figure 1 demonstrates, if a scientist were working with a hierarchical data set, for example, she might choose the colour palette extracted from Picasso’s Seated Woman, a painting that employs varying degrees of saturation and thus sequential attention from areas of highest saturation to low saturation.
The discrete color scale (1 through 8) are arranged in order of importance. 1 (red) is of the highest importance, and should be used to draw the attention to specific areas of the visualization.
The nuetral colors (3 and 4) should be used to color contextual areas of little interest.
The scalar colormaps (A and B) can be used along side the discrete colors to show gradients in the data.
Fig.3 Asteroid impact simulations, Galen Gisler, LANL, shown in six pallets. The top row are palette standards in visualization: traditional rainbow, Moreland’s cool warm and Samsel’s blue orange divergent. The bottom row is the same visualization in three palettes from paintings in which the color scheme aligns with color contrast theory and affect theory: Manet’s calm, analogous palette, Goya’s negative palette, and Van Eyck’s serious, complimentary, cool-warm palette