Dashboards are one of the most common use cases for data visualization, and their design and contexts of use are considerably different from exploratory visualization tools. In this paper, we look at the broad scope of how dashboards are used in practice through an analysis of dashboard examples and documentation about their use. We systematically review the literature surrounding dashboard use, construct a design space for dashboards, and identify major dashboard types. We characterize dashboards by their design goals, levels of interaction, and the practices around them. Our framework and literature review suggest a number of fruitful research directions to better support dashboard design, implementation, and use.

Abstract-Animation has been used to show trends in multi-dimensional data. This technique has recently gained new prominence for presentations, most notably with Gapminder Trendalyzer. In Trendalyzer, animation together with interesting data and an engaging presenter helps the audience understand the results of an analysis of the data. It is less clear whether trend animation is effective for analysis. This paper proposes two alternative trend visualizations that use static depictions of trends: one which shows traces of all trends overlaid simultaneously in one display and a second that uses a small multiples display to show the trend traces side-by-side. The paper evaluates the three visualizations for both analysis and presentation. Results indicate that trend animation can be challenging to use even for presentations; while it is the fastest technique for presentation and participants find it enjoyable and exciting, it does lead to many participant errors. Animation is the least effective form for analysis; both static depictions of trends are significantly faster than animation, and the small multiples display is more accurate.Index Terms-Information visualization, animation, trends, design, experiment. INTRODUCTION: TREND VISUALIZATIONInformally, the term trend means to have a general tendency (Webster's Dictionary). A trend in data is an observed general tendency. The most common way to see a trend in data is to plot a variable's change over time on a line chart or bar chart. If there is a general increase or decrease over time, this is perceived as a trend up or down. If there is a general increase/decrease that reverses direction, it is perceived as a reversing trend (for up to a few reversals). If there are more than a few reversals, it appears to be cyclic or noisy data, and no trend is perceived. Plotting multiple variables on a timeline (as in a multiple line chart) sometimes allows the user to see counter-trends. For example, if most of the variables are generally increasing and a few are decreasing, the decreasing variables can pop out and be perceived as counter-trends. If there is not much variation for any variable, it is possible to fit a regression line or curve and plot it as a trend line or trend curve. More formally, trend estimation is a statistical technique for identifying these trend lines or trend curves [5]. For purposes of discussion in this paper, we will focus only on informal trends that can be perceived visually without statistical trend estimation.The simple approach described above only works for a number of variables along one dimension plotted against another dimension (usually time). What is the best way to see trends in two or three dimensions simultaneously?Gapminder Trendalyzer [8] is an animated bubble chart designed to show trends over time in three dimensions. Both the size and locations of bubbles smoothly animate as time passes. This technique appears to be very effective in presentations, where a presenter tells the observer where to focus attention. It makes the ...

Conveying a narrative with visualizations often requires choosing an order in which to present visualizations. While evidence exists that narrative sequencing in traditional stories can affect comprehension and memory, little is known about how sequencing choices affect narrative visualization. We consider the forms and reactions to sequencing in narrative visualization presentations to provide a deeper understanding with a focus on linear, 'slideshow-style' presentations. We conduct a qualitative analysis of 42 professional narrative visualizations to gain empirical knowledge on the forms that structure and sequence take. Based on the results of this study we propose a graph-driven approach for automatically identifying effective sequences in a set of visualizations to be presented linearly. Our approach identifies possible transitions in a visualization set and prioritizes local (visualization-to-visualization) transitions based on an objective function that minimizes the cost of transitions from the audience perspective. We conduct two studies to validate this function. We also expand the approach with additional knowledge of user preferences for different types of local transitions and the effects of global sequencing strategies on memory, preference, and comprehension. Our results include a relative ranking of types of visualization transitions by the audience perspective and support for memory and subjective rating benefits of visualization sequences that use parallelism as a structural device. We discuss how these insights can guide the design of narrative visualization and systems that support optimization of visualization sequence.

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Benzene oxide, the initial metabolite of the human carcinogen benzene, reacts with DNA producing 7-phenylguanine (7-PhG) and other products. We developed a highly sensitive liquid chromatography-nanoelectrospray ionization-high resolution tandem mass spectrometry-parallel reaction monitoring method for the analysis of 7-PhG in DNA. Accuracy and precision of the method were established and the detection limit was about 8amol of 7-PhG injected on the column and less than 1 adduct per 10(9) nucleotides in DNA. 7-PhG was detected in calf thymus DNA reacted with 1μM to 10mM benzene oxide. The method was applied for the analysis of DNA isolated from bone marrow, lung, and liver of B6C3F1 mice treated by gavage with 50mg/kg benzene in corn oil 5 times weekly for 4weeks. 7-PhG was not detected in any of these DNA samples. The method was applied to DNA from mouse hepatocytes exposed to 100μM benzene oxide and human TK-6 lymphoblasts exposed to 100μM, 1, and 10mM benzene oxide. 7-PhG was only detected in TK-6 cell DNA from the 10mM exposure. The method was also applied to leukocyte DNA from 10 smokers and 10 nonsmokers. 7-PhG was detected in only one DNA sample, from a nonsmoker. The results of this study do not support the hypothesis that the benzene oxide-DNA adduct 7-PhG is involved in carcinogenesis by benzene.

Abstract-Co-located collaboration can be extremely valuable during complex visual analytics tasks. We present an exploratory study of a system designed to support collaborative visual analysis tasks on a digital tabletop display. Fifteen participant pairs employed Cambiera, a visual analytics system, to solve a problem involving 240 digital documents. Our analysis, supported by observations, system logs, questionnaires, and interview data, explores how pairs approached the problem around the table. We contribute a unique, rich understanding of how users worked together around the table and identify eight types of collaboration styles that can be used to identify how closely people work together while problem solving. We show how the closeness of teams' collaboration and communication influenced how they performed on the task overall. We further discuss the role of the tabletop for visual analytics tasks and derive design implications for future co-located collaborative tabletop problem solving systems.

Usenet is a complex socio‐technical phenomenon, containing vast quantities of information. The sheer scope and complexity make it a challenge to understand the many dimensions across which people and communication are interlinked. In this work, we present visualizations of several aspects and scales of Usenet that combine to highlight the range of variation found in newsgroups. We examine variations within hierarchies, newsgroups, authors, and social networks. We find a remarkable diversity, with clear variations that mark starting points for mapping the broad sweep of behavior found in this and other social cyberspaces. Our findings provide the basis for initial recommendations for those cultivating, managing, contributing, or consuming collectively constructed conversational content.