Good use of different color palettes can enhance the storytelling, clarity, and attention aspects of different datasets. I think the blog series introduced the ideas of applying color scales to data nicely. It discussed about three types of datasets: sequential, divergent, and qualitative. These theories are interesting to me. I do know color is a significant variable in datasets, but never thought too much about how different color schemes are suitable for different datasets.
Sequential datasets are suitable for colors scales that are smooth and even. The lightness and saturation of the palette should be uniform. Since this type of datasets includes continuously varying data (ex: from low to high), the color scheme should also reflect the continuously changing quality.
Divergent datasets focus on measuring differences. The concept of divergent palettes is introduced, which is composed of two sequential palettes merging with a neutral central color. This color is usually white and gray.
Qualitative datasets categorize things. As data is separated into different categories and classes, the colors for qualitative datasets should be as diverse and distinct from each other as possible. Because people all perceive things differently, and also because of the simultaneous contrast effect, the maximum number of categories that can be displayed and recognized is probably no more than 12.
I also think it is a big challenge to design a graph with color that can be perceived by normal people and colorblind people at the same time. What will a qualitative dataset look like under this idea?