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Annotations are an essential part of data analysis and communication in visualizations, which focus a readers attention on critical visual elements (e.g. an arrow that emphasizes a downward trend in a bar chart). Annotations enhance comprehension, mental organization, memorability, user engagement, and interaction and are crucial for data externalization and exploration, collaborative data analysis, and narrative storytelling in visualizations. However, we have identified a general lack of understanding of how people annotate visualizations to support effective communication. In this study, we evaluate how visualization students annotate grouped bar charts when answering high-level questions about the data. The resulting annotations were qualitatively coded to generate a taxonomy of how they leverage different visual elements to communicate critical information. We found that the annotations used significantly varied by the task they were supporting and that whereas several annotation types supported many tasks, others were usable only in special cases. We also found that some tasks were so challenging that ensembles of annotations were necessary to support the tasks sufficiently. The resulting taxonomy of approaches provides a foundation for understanding the usage of annotations in broader contexts to help visualizations achieve their desired message. 

Existing guidelines for categorical color selection are heuristic, often grounded in intuition rather than empirical studies of readers' abilities. While design conventions recommend palettes maximize hue differences, more recent exploratory findings indicate other factors, such as lightness, may play a role in effective categorical palette design. We conducted a crowdsourced experiment on mean value judgments in multi-class scatterplots using five color palette families-single-hue sequential, multihue sequential, perceptually-uniform multi-hue sequential, diverging, and multi-hue categorical-that differ in how they manipulate hue and lightness. Participants estimated relative mean positions in scatterplots containing 2 to 10 categories using 20 colormaps. Our results confirm heuristic guidance that hue-based categorical palettes are most effective. However, they also provide additional evidence that scalable categorical encoding relies on more than hue variance.