Roboviz: A Game-Centered Project for Information Visualization Education.

Due to their pedagogical advantages, large final projects in information visualization courses have become standard practice. Students take on a client-real or simulated-a dataset, and a vague set of goals to create a complete visualization or visual analytics product. Unfortunately, many projects suffer from ambiguous goals, over or under-constrained client expectations, and data constraints that have students spending their time on non-visualization problems (e.g., data cleaning). These are important skills, but are often secondary course objectives, and unforeseen problems can majorly hinder students. We created an alternative for our information visualization course: Roboviz, a real-time game for students to play by building a visualization-focused interface. By designing the game mechanics around four different data types, the project allows students to create a wide array of interactive visualizations. Student teams play against their classmates with the objective to collect the most (good) robots. The flexibility of the strategies encourages variability, a range of approaches, and solving wicked design constraints. We describe the construction of this game and report on student projects over two years. We further show how the game mechanics can be extended or adapted to other game-based projects.

Affective Learning Objectives for Communicative Visualizations.

When designing communicative visualizations, we often focus on goals that seek to convey patterns, relations, or comparisons (cognitive learning objectives). We pay less attention to affective intents-those that seek to influence or leverage the audience's opinions, attitudes, or values in some way. Affective objectives may range in outcomes from making the viewer care about the subject, strengthening a stance on an opinion, or leading them to take further action. Because such goals are often considered a violation of perceived 'neutrality' or are 'political,' designers may resist or be unable to describe these intents, let alone formalize them as learning objectives. While there are notable exceptions-such as advocacy visualizations or persuasive cartography-we find that visualization designers rarely acknowledge or formalize affective objectives. Through interviews with visualization designers, we expand on prior work on using learning objectives as a framework for describing and assessing communicative intent. Specifically, we extend and revise the framework to include a set of affective learning objectives. This structured taxonomy can help designers identify and declare their goals and compare and assess designs in a more principled way. Additionally, the taxonomy can enable external critique and analysis of visualizations. We illustrate the use of the taxonomy with a critical analysis of an affective visualization.

Visualizing Uncertainty in Probabilistic Graphs with Network Hypothetical Outcome Plots (NetHOPs).

Probabilistic graphs are challenging to visualize using the traditional node-link diagram. Encoding edge probability using visual variables like width or fuzziness makes it difficult for users of static network visualizations to estimate network statistics like densities, isolates, path lengths, or clustering under uncertainty. We introduce Network Hypothetical Outcome Plots (NetHOPs), a visualization technique that animates a sequence of network realizations sampled from a network distribution defined by probabilistic edges. NetHOPs employ an aggregation and anchoring algorithm used in dynamic and longitudinal graph drawing to parameterize layout stability for uncertainty estimation. We present a community matching algorithm to enable visualizing the uncertainty of cluster membership and community occurrence. We describe the results of a study in which 51 network experts used NetHOPs to complete a set of common visual analysis tasks and reported how they perceived network structures and properties subject to uncertainty. Participants' estimates fell, on average, within 11% of the ground truth statistics, suggesting NetHOPs can be a reasonable approach for enabling network analysts to reason about multiple properties under uncertainty. Participants appeared to articulate the distribution of network statistics slightly more accurately when they could manipulate the layout anchoring and the animation speed. Based on these findings, we synthesize design recommendations for developing and using animated visualizations for probabilistic networks.

Learning Objectives, Insights, and Assessments: How Specification Formats Impact Design.

Despite the ubiquity of communicative visualizations, specifying communicative intent during design is ad hoc. Whether we are selecting from a set of visualizations, commissioning someone to produce them, or creating them ourselves, an effective way of specifying intent can help guide this process. Ideally, we would have a concise and shared specification language. In previous work, we have argued that communicative intents can be viewed as a learning/assessment problem (i.e., what should the reader learn and what test should they do well on). Learning-based specification formats are linked (e.g., assessments are derived from objectives) but some may more effectively specify communicative intent. Through a large-scale experiment, we studied three specification types: learning objectives, insights, and assessments. Participants, guided by one of these specifications, rated their preferences for a set of visualization designs. Then, we evaluated the set of visualization designs to assess which specification led participants to prefer the most effective visualizations. We find that while all specification types have benefits over no-specification, each format has its own advantages. Our results show that learning objective-based specifications helped participants the most in visualization selection. We also identify situations in which specifications may be insufficient and assessments are vital.

Communicative Visualizations as a Learning Problem.

Significant research has provided robust task and evaluation languages for the analysis of exploratory visualizations. Unfortunately, these taxonomies fail when applied to communicative visualizations. Instead, designers often resort to evaluating communicative visualizations from the cognitive efficiency perspective: "can the recipient accurately decode my message/insight?" However, designers are unlikely to be satisfied if the message went 'in one ear and out the other.' The consequence of this inconsistency is that it is difficult to design or select between competing options in a principled way. The problem we address is the fundamental mismatch between how designers want to describe their intent, and the language they have. We argue that visualization designers can address this limitation through a learning lens: that the recipient is a student and the designer a teacher. By using learning objectives, designers can better define, assess, and compare communicative visualizations. We illustrate how the learning-based approach provides a framework for understanding a wide array of communicative goals. To understand how the framework can be applied (and its limitations), we surveyed and interviewed members of the Data Visualization Society using their own visualizations as a probe. Through this study we identified the broad range of objectives in communicative visualizations and the prevalence of certain objective types.

SmartCues: A Multitouch Query Approach for Details-on-Demand through Dynamically Computed Overlays.

Details-on-demand is a crucial feature in the visual information-seeking process but is often only implemented in highly constrained settings. The most common solution, hover queries (i.e., tooltips), are fast and expressive but are usually limited to single mark (e.g., a bar in a bar chart). 'Queries' to retrieve details for more complex sets of objects (e.g., comparisons between pairs of elements, averages across multiple items, trend lines, etc.) are difficult for end-users to invoke explicitly. Further, the output of these queries require complex annotations and overlays which need to be displayed and dismissed on demand to avoid clutter. In this work we introduce SmartCues, a library to support details-on-demand through dynamically computed overlays. For end-users, SmartCues provides multitouch interactions to construct complex queries for a variety of details. For designers, SmartCues offers an interaction library that can be used out-of-the-box, and can be extended for new charts and detail types. We demonstrate how SmartCues can be implemented across a wide array of visualization types and, through a lab study, show that end users can effectively use SmartCues.

A generative model for scientific concept hierarchies.

In many scientific disciplines, each new 'product' of research (method, finding, artifact, etc.) is often built upon previous findings-leading to extension and branching of scientific concepts over time. We aim to understand the evolution of scientific concepts by placing them in phylogenetic hierarchies where scientific keyphrases from a large, longitudinal academic corpora are used as a proxy of scientific concepts. These hierarchies exhibit various important properties, including power-law degree distribution, power-law component size distribution, existence of a giant component and less probability of extending an older concept. We present a generative model based on preferential attachment to simulate the graphical and temporal properties of these hierarchies which helps us understand the underlying process behind scientific concept evolution and may be useful in simulating and predicting scientific evolution.

VizItCards: A Card-Based Toolkit for Infovis Design Education.

Shifts in information visualization practice are forcing a reconsideration of how infovis is taught. Traditional curricula that focused on conveying research-derived knowledge are slowly integrating design thinking as a key learning objective. In part, this is motivated by the realization that infovis is a wicked design problem, requiring a different kind of design work. In this paper we describe, VizItCards, a card-driven workshop developed for our graduate infovis class. The workshop is intended to provide practice with good design techniques and to simultaneously reinforce key concepts. VizItCards relies on principles of collaborative-learning and research on parallel design to generate positive collaborations and high-quality designs. From our experience of simulating a realistic design scenario in a classroom setting, we find that our students were able to meet key learning objectives and their design performance improved during the class. We describe variants of the workshop, discussing which techniques we think match to which learning goals.

Link-Prediction Enhanced Consensus Clustering for Complex Networks.

Many real networks that are collected or inferred from data are incomplete due to missing edges. Missing edges can be inherent to the dataset (Facebook friend links will never be complete) or the result of sampling (one may only have access to a portion of the data). The consequence is that downstream analyses that "consume" the network will often yield less accurate results than if the edges were complete. Community detection algorithms, in particular, often suffer when critical intra-community edges are missing. We propose a novel consensus clustering algorithm to enhance community detection on incomplete networks. Our framework utilizes existing community detection algorithms that process networks imputed by our link prediction based sampling algorithm and merges their multiple partitions into a final consensus output. On average our method boosts performance of existing algorithms by 7% on artificial data and 17% on ego networks collected from Facebook.

Hypothetical Outcome Plots Outperform Error Bars and Violin Plots for Inferences about Reliability of Variable Ordering.

Many visual depictions of probability distributions, such as error bars, are difficult for users to accurately interpret. We present and study an alternative representation, Hypothetical Outcome Plots (HOPs), that animates a finite set of individual draws. In contrast to the statistical background required to interpret many static representations of distributions, HOPs require relatively little background knowledge to interpret. Instead, HOPs enables viewers to infer properties of the distribution using mental processes like counting and integration. We conducted an experiment comparing HOPs to error bars and violin plots. With HOPs, people made much more accurate judgments about plots of two and three quantities. Accuracy was similar with all three representations for most questions about distributions of a single quantity.

A deeper understanding of sequence in narrative visualization.

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.

Benefitting InfoVis with visual difficulties.

Many well-cited theories for visualization design state that a visual representation should be optimized for quick and immediate interpretation by a user. Distracting elements like decorative "chartjunk" or extraneous information are avoided so as not to slow comprehension. Yet several recent studies in visualization research provide evidence that non-efficient visual elements may benefit comprehension and recall on the part of users. Similarly, findings from studies related to learning from visual displays in various subfields of psychology suggest that introducing cognitive difficulties to visualization interaction can improve a user's understanding of important information. In this paper, we synthesize empirical results from cross-disciplinary research on visual information representations, providing a counterpoint to efficiency-based design theory with guidelines that describe how visual difficulties can be introduced to benefit comprehension and recall. We identify conditions under which the application of visual difficulties is appropriate based on underlying factors in visualization interaction like active processing and engagement. We characterize effective graph design as a trade-off between efficiency and learning difficulties in order to provide Information Visualization (InfoVis) researchers and practitioners with a framework for organizing explorations of graphs for which comprehension and recall are crucial. We identify implications of this view for the design and evaluation of information visualizations.

Biomedical term mapping databases.

Longer words and phrases are frequently mapped onto a shorter form such as abbreviations or acronyms for efficiency of communication. These abbreviations are pervasive in all aspects of biology and medicine and as the amount of biomedical literature grows, so does the number of abbreviations and the average number of definitions per abbreviation. Even more confusing, different authors will often abbreviate the same word/phrase differently. This ambiguity impedes our ability to retrieve information, integrate databases and mine textual databases for content. Efforts to standardize nomenclature, especially those doing so retrospectively, need to be aware of different abbreviatory mappings and spelling variations. To address this problem, there have been several efforts to develop computer algorithms to identify the mapping of terms between short and long form within a large body of literature. To date, four such algorithms have been applied to create online databases that comprehensively map biomedical terms and abbreviations within MEDLINE: ARGH (http://lethargy.swmed.edu/ARGH/argh.asp), the Stanford Biomedical Abbreviation Server (http://bionlp.stanford.edu/abbreviation/), AcroMed (http://medstract.med.tufts.edu/acro1.1/index.htm) and SaRAD (http://www.hpl.hp.com/research/idl/projects/abbrev.html). In addition to serving as useful computational tools, these databases serve as valuable references that help biologists keep up with an ever-expanding vocabulary of terms.

SaRAD: a Simple and Robust Abbreviation Dictionary.

MOTIVATION: Due to recent interest in the use of textual material to augment traditional experiments it has become necessary to automatically cluster, classify and filter natural language information. RESULTS: The Simple and Robust Abbreviation Dictionary (SaRAD) provides an easy to implement, high performance tool for the construction of a biomedical symbol dictionary. The algorithms, applied to the MEDLINE document set, result in a high quality dictionary and toolset to disambiguate abbreviation symbols automatically.

A literature based method for identifying gene-disease connections.

We present a statistical method that can swiftly identify, from the literature, sets of genes known to be associated with given diseases. It offers a comprehensive way to treat alias symbols, a statistical method for computing the relevance of the gene to the query, and a novel way to disambiguate gene symbols from other abbreviations. The method is illustrated by finding genes related to breast cancer.