Mapping Color to Meaning in Colormap Data Visualizations.

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

An Analysis of Automated Visual Analysis Classification: Interactive Visualization Task Inference of Cancer Genomics Domain Experts.

We show how mouse interaction log classification can help visualization toolsmiths understand how their tools are used "in the wild" through an evaluation of MAGI - a cancer genomics visualization tool. Our primary contribution is an evaluation of twelve visual analysis task classifiers, which compares predictions to task inferences made by pairs of genomics and visualization experts. Our evaluation uses common classifiers that are accessible to most visualization evaluators: -nearest neighbors, linear support vector machines, and random forests.

Colorgorical: Creating discriminable and preferable color palettes for information visualization.

We present an evaluation of Colorgorical, a web-based tool for creating discriminable and aesthetically preferable categorical color palettes. Colorgorical uses iterative semi-random sampling to pick colors from CIELAB space based on user-defined discriminability and preference importances. Colors are selected by assigning each a weighted sum score that applies the user-defined importances to Perceptual Distance, Name Difference, Name Uniqueness, and Pair Preference scoring functions, which compare a potential sample to already-picked palette colors.

The relation between visualization size, grouping, and user performance.

In this paper we make the following contributions: (1) we describe how the grouping, quantity, and size of visual marks affects search time based on the results from two experiments; (2) we report how search performance relates to self-reported difficulty in finding the target for different display types; and (3) we present design guidelines based on our findings to facilitate the design of effective visualizations. Both Experiment 1 and 2 asked participants to search for a unique target in colored visualizations to test how the grouping, quantity, and size of marks affects user performance. In Experiment 1, the target square was embedded in a grid of squares and in Experiment 2 the target was a point in a scatterplot.

Molli: interactive visualization for exploratory protein analysis.

Many programs have been designed to view the 3D structures of protein molecules in 2D. However, three types of linked information haven't been previously defined in a systematic way that highlights the interface design challenge. Specifically, a scientist must have sequence, structure, and homology information in working memory to manipulate and understand a protein structure or related protein structures.