Designing introductory materials is extremely important when developing new information visualization techniques. All users, regardless of their domain knowledge, first must learn how to interpret the visually encoded information in order to infer knowledge from visualizations. Yet, despite its significance, there has been little research on how to design effective introductory materials for information visualization. This paper presents a study on the design of online guides that educate new users on how to utilize information visualizations, particularly focusing on the employment of exercise questions in the guides. We use two concepts from educational psychology, learning type (or learning style) and teaching method, to design four unique types of online guides. The effects of the guides are measured by comprehension tests of a large group of crowdsourced participants.The tests covered four visualization types (graph, scatter plot, storyline, and tree map) and a complete range of visual analytics tasks. Our statistical analyses indicate that online guides which employ active learning and the top-down teaching method are the most effective. Our study provides quantitative insight into the use of exercise questions in online guides for information visualizations and will inspire further research on design considerations for other elements in introductory materials.

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R ecent research suggests that personal visualizations serve a variety of purposes, from data analytics to personal data management to promoting self-awareness and facilitating self-expression. 1 These visualizations often employ different design styles, depending on the target audience and the intended application context.Although existing design guidelines can help developers with general visualization design, 2,3 there are several features unique to personal visualizations that these guidelines do not take into consideration. For example, personal visualizations can provide users with visual cues to recall personal memories. 4,5 They can also promote self-awareness by providing real-time visual feedback to users, 6 or they can even be used as a medium for selfexpression. 7 These utilities are unique to personal visualizations intended for personal consumption and are gaining increasing interest from the public.Despite this growing interest, little research has focused on what elements are essential to the design of visualizations with these capabilities (see the sidebar for more details). These personal user experiences depend on the content of the users' personal data and their attitudes toward the data. This means that the effect of personal visualizations varies depending on the user, which makes it difficult to measure in a controlled setting.In this article, we present three distinct visu-alizations of Facebook data and a study of how users react to those designs. The three designs each represent a category of visualization-traditional, illustrative, and artistic-defined in earlier research. 8,9 The visualizations illustrate data such as the user's friends and posts, and messages, as well as their Like interaction with friends. The visualizations focus on three usage contexts:■ exploring the personal visualization of the user's own data, ■ comparing two personal visualizations based on the same design (one displaying the user's data, and another displaying the user's friend's data), and ■ analyzing a series of personal visualizations based on the same design and then inferring patterns and identifying outliers.Our analysis of the study data shows that the visualization design profoundly impacts the user's experience. The results suggest that, although traditional designs can provide detailed information in an easy-to-understand form, they tend to be less effective for triggering personal memories and inspiring self-awareness in the viewer. The more abstract visualization types are better at motivating users to explore the data and help users to form novel insights into their own behavior. We also found that the abstract visualizations impose additional difficulty for determining concrete data, which suggests directions for future study.Through our extensive user study, we derive implications on how visualization design affects the visualization's utility with respect to the user's sub-In an effort to determine what elements need to be considered when designing personal visualizations, this research study ...

Dark matter simulations, performed using N-body methods with a finite set of tracer particles to discretize the initially uniform distribution of mass, are an invaluable method for exploring the formation of the universe. Definining a tetrahedral mesh in phase spacewith the tracer particles at initialization serving as vertices-yields a more accurate density field. At later timesteps, the mesh selfintersects to an enormous degree, making pre-sorting impossible. Kaehler et al [2012] visualize the mesh using cell projection, but their method requires order-independent compositing, which limits its flexibility. Our work renders the mesh using state of the art order-independent transparency (OIT) techniques to composite fragments in correct depth order. This also allows us to render variables other than density, such as velocity. We implement a number of OIT optimizations to handle the high depth complexity (on the order of 10 7 depth layers for 2x10 9 particles) of the data. Our performance measurements show near-interactive framerates for our hybrid renderer despite the large number of depth layers.