To interpret the meanings of colors in visualizations of categorical information, people must
determine how distinct colors correspond to different concepts. This process is easier when …

When data categories have strong color associations, it is useful to use these semantically
meaningful concept-color associations in data visualizations. In this paper, we explore how …

We present an evaluation of Colorgorical, a web-based tool for creating discriminable and
aesthetically preferable categorical color palettes. Colorgorical uses iterative semi-random …

To interpret data visualizations, people must determine how visual features map onto
concepts. For example, to interpret colormaps, people must determine how dimensions of …

We introduce an algorithm for automatic selection of semantically‐resonant colors to
represent data (eg, using blue for data about “oceans”, or pink for “love”). Given a set of …

The interpretation of colors in visualizations is facilitated when the assignments between
colors and concepts in the visualizations match human's expectations, implying that the …

Color is frequently used to encode values in visualizations. For color encodings to be
effective, the mapping between colors and values must preserve important differences in the …

Mapping a set of categorical values to different colors is an elementary technique in data
visualization. Users of visualization software routinely rely on the default colormaps provided …

Visualization designers regularly use color to encode quantitative or categorical data.
However, visualizations “in the wild” often violate perceptual color design principles and …

Our ability to reliably name colors provides a link between visual perception and symbolic
cognition. In this paper, we investigate how a statistical model of color naming can enable …