Animating Hypothetical Trips to Communicate Space-Based Temporal Uncertainty on Digital Maps.

This paper explores a novel approach to communicating plausible space-based temporal variability of travel durations. Digital maps most often only convey single numerical values as the estimated duration for a path and this piece of information does not account for the multiple scenarios hidden behind this point estimate, nor for the temporal uncertainty along the route (e.g., the likelihood of being slowed down at an intersection). We explore conveying this uncertainty by animating hypothetical trips onto maps in the form of moving dots along one or more paths. We conducted a study with 16 participants and observed that they were able to correctly extract and infer simple information from our uncertainty visualizations but that identifying moving dots' changes in speed is a more complex task. We discuss design challenges and implications for future visualizations of space-based temporal uncertainty.

TimeSplines: Sketch-Based Authoring of Flexible and Idiosyncratic Timelines.

Timelines are essential for visually communicating chronological narratives and reflecting on the personal and cultural significance of historical events. Existing visualization tools tend to support conventional linear representations, but fail to capture personal idiosyncratic conceptualizations of time. In response, we built TimeSplines, a visualization authoring tool that allows people to sketch multiple free-form temporal axes and populate them with heterogeneous, time-oriented data via incremental and lazy data binding. Authors can bend, compress, and expand temporal axes to emphasize or de-emphasize intervals based on their personal importance; they can also annotate the axes with text and figurative elements to convey contextual information. The results of two user studies show how people appropriate the concepts in TimeSplines to express their own conceptualization of time, while our curated gallery of images demonstrates the expressive potential of our approach.

ChartWalk: Navigating large collections of text notes in electronic health records for clinical chart review.

Before seeing a patient for the first time, healthcare workers will typically conduct a comprehensive clinical chart review of the patient's electronic health record (EHR). Within the diverse documentation pieces included there, text notes are among the most important and thoroughly perused segments for this task; and yet they are among the least supported medium in terms of content navigation and overview. In this work, we delve deeper into the task of clinical chart review from a data visualization perspective and propose a hybrid graphics+text approach via ChartWalk, an interactive tool to support the review of text notes in EHRs. We report on our iterative design process grounded in input provided by a diverse range of healthcare professionals, with steps including: (a) initial requirements distilled from interviews and the literature, (b) an interim evaluation to validate design decisions, and (c) a task-based qualitative evaluation of our final design. We contribute lessons learned to better support the design of tools not only for clinical chart reviews but also other healthcare-related tasks around medical text analysis.

Interactive Data Comics.

This paper investigates how to make data comics interactive. Data comics are an effective and versatile means for visual communication, leveraging the power of sequential narration and combined textual and visual content, while providing an overview of the storyline through panels assembled in expressive layouts. While a powerful static storytelling medium that works well on paper support, adding interactivity to data comics can enable non-linear storytelling, personalization, levels of details, explanations, and potentially enriched user experiences. This paper introduces a set of operations tailored to support data comics narrative goals that go beyond the traditional linear, immutable storyline curated by a story author. The goals and operations include adding and removing panels into pre-defined layouts to support branching, change of perspective, or access to detail-on-demand, as well as providing and modifying data, and interacting with data representation, to support personalization and reader-defined data focus. We propose a lightweight specification language, COMICSCRIPT, for designers to add such interactivity to static comics. To assess the viability of our authoring process, we recruited six professional illustrators, designers and data comics enthusiasts and asked them to craft an interactive comic, allowing us to understand authoring workflow and potential of our approach. We present examples of interactive comics in a gallery. This initial step towards understanding the design space of interactive comics can inform the design of creation tools and experiences for interactive storytelling.

Data Comics for Reporting Controlled User Studies in Human-Computer Interaction.

Inspired by data comics, this paper introduces a novel format for reporting controlled studies in the domain of human-computer interaction (HCI). While many studies in HCI follow similar steps in explaining hypotheses, laying out a study design, and reporting results, many of these decisions are buried in blocks of dense scientific text. We propose leveraging data comics as study reports to provide an open and glanceable view of studies by tightly integrating text and images, illustrating design decisions and key insights visually, resulting in visual narratives that can be compelling to non-scientists and researchers alike. Use cases of data comics study reports range from illustrations for non-scientific audiences to graphical abstracts, study summaries, technical talks, textbooks, teaching, blogs, supplementary submission material, and inclusion in scientific articles. This paper provides examples of data comics study reports alongside a graphical repertoire of examples, embedded in a framework of guidelines for creating comics reports which was iterated upon and evaluated through a series of collaborative design sessions.

Designing Narrative-Focused Role-Playing Games for Visualization Literacy in Young Children.

Building on game design and education research, this paper introduces narrative-focused role-playing games as a way to promote visualization literacy in young children. Visualization literacy skills are vital in understanding the world around us and constructing meaningful visualizations, yet, how to better develop these skills at an early age remains largely overlooked and understudied. Only recently has the visualization community started to fill this gap, resulting in preliminary studies and development of educational tools for use in early education. We add to these efforts through the exploration of gamification to support learning, and identify an opportunity to apply role-playing game-based designs by leveraging the presence of narratives in data-related problems involving visualizations. We study the effects of including narrative elements on learning through a technology probe, grounded in a set of design considerations stemming from visualization, game design and education science. We create two versions of a game - one with narrative elements and one without - and evaluate our instances on 33 child participants between 11- to 13-years old using a between-subjects study design. Despite participants requiring double the amount of time to complete their game due to additional narrative elements, the inclusion of such elements were found to improve engagement without sacrificing learning; our results indicate no significant differences in development of graph-reading skills, but significant differences in engagement and overall enjoyment of the game. We report observations and qualitative feedback collected, and note areas for improvement and room for future work.

Quality of life was similar in children with congenital diaphragmatic hernia and oesophageal atresia and related to respiratory morbidity.

AIM: To assess quality of life (QoL) in children with congenital diaphragmatic hernia (CDH) and to compare it with oesophageal atresia (OA). METHODS: A cross-sectional study in CDH children (≥7 years) was conducted in Lille University Hospital, France, from January 2013 to April 2014. History, lung function (rest, exercise) and Pediatric Quality of Life Inventory questionnaires (PedsQoL 4.0) were collected. Data of OA children were previously published. RESULTS: Fifty-four CDH patients (male: 53%, median age: 11 years, IQR 9-14) were compared to 54 OA patients (male: 61%, median age: 13 years, IQR: 11-15). CDH children had significantly more frequent history of pneumonia (30% vs 13%), exercise limitation (54% vs 35%) and chest deformity (39% vs 11%); 46% had an obstructive pattern and 66% an abnormal cardiopulmonary exercise test. The median PedsQoL total score in children was 81 (IQR 73-90) in CDH and 81 (IQR 72-91) in OA (P = .8). In CDH, duration of neonatal oxygen therapy, hospitalisation for respiratory disease, exercise limitation, inhaled corticosteroids treatment, chest deformity, abnormal cardiopulmonary exercise test and lower forced expiratory volume in one second were significantly associated with lower QoL scores. CONCLUSION: PedsQoL scores remained satisfactory in CDH children with CDH, with no difference compared to OA. Patients with respiratory morbidity and lung function impairment, who displayed lower scores, should be identified in order to optimise their management in reference centres.

Common Fate for Animated Transitions in Visualization.

The Law of Common Fate from Gestalt psychology states that visual objects moving with the same velocity along parallel trajectories will be perceived by a human observer as grouped. However, the concept of common fate is much broader than mere velocity; in this paper we explore how common fate results from coordinated changes in luminance and size. We present results from a crowdsourced graphical perception study where we asked workers to make perceptual judgments on a series of trials involving four graphical objects under the influence of conflicting static and dynamic visual factors (position, size and luminance) used in conjunction. Our results yield the following rankings for visual grouping: motion > (dynamic luminance, size, luminance); dynamic size > (dynamic luminance, position); and dynamic luminance > size. We also conducted a follow-up experiment to evaluate the three dynamic visual factors in a more ecologically valid setting, using both a Gapminder-like animated scatterplot and a thematic map of election data. The results indicate that in practice the relative grouping strengths of these factors may depend on various parameters including the visualization characteristics and the underlying data. We discuss design implications for animated transitions in data visualization.

Doccurate: A Curation-Based Approach for Clinical Text Visualization.

Before seeing a patient, physicians seek to obtain an overview of the patient's medical history. Text plays a major role in this activity since it represents the bulk of the clinical documentation, but reviewing it quickly becomes onerous when patient charts grow too large. Text visualization methods have been widely explored to manage this large scale through visual summaries that rely on information retrieval algorithms to structure text and make it amenable to visualization. However, the integration with such automated approaches comes with a number of limitations, including significant error rates and the need for healthcare providers to fine-tune algorithms without expert knowledge of their inner mechanics. In addition, several of these approaches obscure or substitute the original clinical text and therefore fail to leverage qualitative and rhetorical flavours of the clinical notes. These drawbacks have limited the adoption of text visualization and other summarization technologies in clinical practice. In this work we present Doccurate, a novel system embodying a curation-based approach for the visualization of large clinical text datasets. Our approach offers automation auditing and customizability to physicians while also preserving and extensively linking to the original text. We discuss findings of a formal qualitative evaluation conducted with 6 domain experts, shedding light onto physicians' information needs, perceived strengths and limitations of automated tools, and the importance of customization while balancing efficiency. We also present use case scenarios to showcase Doccurate's envisioned usage in practice.

Observations and Reflections on Visualization Literacy in Elementary School.

In this article, we share our reflections on visualization literacy and how it might be better developed in early education. We base this on lessons we learned while studying how teachers instruct, and how students acquire basic visualization principles and skills in elementary school. We use these findings to propose directions for future research on visualization literacy.

PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models.

PhenoLines is a visual analysis tool for the interpretation of disease subtypes, derived from the application of topic models to clinical data. Topic models enable one to mine cross-sectional patient comorbidity data (e.g., electronic health records) and construct disease subtypes-each with its own temporally evolving prevalence and co-occurrence of phenotypes-without requiring aligned longitudinal phenotype data for all patients. However, the dimensionality of topic models makes interpretation challenging, and de facto analyses provide little intuition regarding phenotype relevance or phenotype interrelationships. PhenoLines enables one to compare phenotype prevalence within and across disease subtype topics, thus supporting subtype characterization, a task that involves identifying a proposed subtype's dominant phenotypes, ages of effect, and clinical validity. We contribute a data transformation workflow that employs the Human Phenotype Ontology to hierarchically organize phenotypes and aggregate the evolving probabilities produced by topic models. We introduce a novel measure of phenotype relevance that can be used to simplify the resulting topology. The design of PhenoLines was motivated by formative interviews with machine learning and clinical experts. We describe the collaborative design process, distill high-level tasks, and report on initial evaluations with machine learning experts and a medical domain expert. These results suggest that PhenoLines demonstrates promising approaches to support the characterization and optimization of topic models.

Supporting Handoff in Asynchronous Collaborative Sensemaking Using Knowledge-Transfer Graphs.

During asynchronous collaborative analysis, handoff of partial findings is challenging because externalizations produced by analysts may not adequately communicate their investigative process. To address this challenge, we developed techniques to automatically capture and help encode tacit aspects of the investigative process based on an analyst's interactions, and streamline explicit authoring of handoff annotations. We designed our techniques to mediate awareness of analysis coverage, support explicit communication of progress and uncertainty with annotation, and implicit communication through playback of investigation histories. To evaluate our techniques, we developed an interactive visual analysis system, KTGraph, that supports an asynchronous investigative document analysis task. We conducted a two-phase user study to characterize a set of handoff strategies and to compare investigative performance with and without our techniques. The results suggest that our techniques promote the use of more effective handoff strategies, help increase an awareness of prior investigative process and insights, as well as improve final investigative outcomes.

La femme mal barrée. Croyances en La femme, de l‘hystérie aux théories psychanalytiques / A mulher mal barrada. Crenças na mulher, da histeria às teorias psicanalíticas / The not wholly barred Woman. Beliefs in Woman, from hysteria to psychoanalytic theories / La mujer mal barrada. Creencias en La mujer, de la histeria a las teorías psicoanalíticas / Die schlecht gestrichene Frau. Ansichten zur Frau, von der Hysterie zu den psychoanalytischen Theorien

La question “qu’est-ce qu’une femme?” et la logique de l’exception sur laquelle se règle l’hystérique dans son rapport à la féminité témoignent de sa croyance selon laquelle la barre portée par Lacan sur La femme n’existe pas. La distinction entre cette logique et la logique féminine du pas-tout sera l’occasion de retracer les croyances sur la féminité dans la théorie psychanalytique et d’interroger les usages paradoxaux qui sont faits des positions dites féminine et masculine.

A pergunta “O que é uma mulher?” e a lógica da exceção pela qual a histérica se rege em sua relação com a feminilidade dão testemunho de sua crença de que não existe a barra colocada por Lacan sobre a Mulher. A distinção entre essa lógica e a lógica feminina do “não todo” será ocasião para situar novamente as crenças sobre a feminilidade no marco da teoria psicanalítica e para interrogar os usos paradoxais das posições “feminina” e “masculina”.

The question “What is a woman?” and the logic of exception on which a hysterical woman bases herself in her relationship with femininity reveal one of her beliefs: that the bar placed by Lacan on Woman does not exist. The distinction between this logic and the feminine logic of the “not-whole” is the opportunity to once again place beliefs on femininity within psychoanalytic theory and to question the paradoxical uses of “feminine” and “masculine” positions.

La pregunta “¿Qué es una mujer?” y la lógica de la excepción, que rige a la histérica en su relación con la femineidad, atestiguan sobre su creencia de que no existe la barra colocada por Lacan sobre la mujer. La distinción entre esta lógica y la lógica femenina del “no todo” será la ocasión para volver a situar las creencias sobre la femineidad en la teoría psicoanalítica y para interrogar los usos paradójicos de las posiciones “femenina” y “masculina”.

Die Frage „Was ist eine Frau?“ und die Ausnahmelogik wonach sich die Hysterikerin in ihrem Bezug auf die Weiblichkeit richtet, zeugen von ihrem Glauben daran, dass der Strich, mit dem Lacan Die Frau gestrichen hat, nicht existiert. Die Unterscheidung zwischen dieser Logik und der weiblichen Logik des „nicht-alles“ ist Anlass dazu, die übernommenen Vorstellungen zur Weiblichkeit innerhalb der psychoanalytischen Theorie neu zu positionieren, sowie die paradoxe Benutzung „weiblichen“ und „männlichen“ Positionen zu hinterfragen.

Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data Based on User-Authored Annotations.

User-authored annotations of data can support analysts in the activity of hypothesis generation and sensemaking, where it is not only critical to document key observations, but also to communicate insights between analysts. We present annotation graphs, a dynamic graph visualization that enables meta-analysis of data based on user-authored annotations. The annotation graph topology encodes annotation semantics, which describe the content of and relations between data selections, comments, and tags. We present a mixed-initiative approach to graph layout that integrates an analyst's manual manipulations with an automatic method based on similarity inferred from the annotation semantics. Various visual graph layout styles reveal different perspectives on the annotation semantics. Annotation graphs are implemented within C8, a system that supports authoring annotations during exploratory analysis of a dataset. We apply principles of Exploratory Sequential Data Analysis (ESDA) in designing C8, and further link these to an existing task typology in the visualization literature. We develop and evaluate the system through an iterative user-centered design process with three experts, situated in the domain of analyzing HCI experiment data. The results suggest that annotation graphs are effective as a method of visually extending user-authored annotations to data meta-analysis for discovery and organization of ideas.

PhenoStacks: Cross-Sectional Cohort Phenotype Comparison Visualizations.

Cross-sectional phenotype studies are used by genetics researchers to better understand how phenotypes vary across patients with genetic diseases, both within and between cohorts. Analyses within cohorts identify patterns between phenotypes and patients (e.g., co-occurrence) and isolate special cases (e.g., potential outliers). Comparing the variation of phenotypes between two cohorts can help distinguish how different factors affect disease manifestation (e.g., causal genes, age of onset, etc.). PhenoStacks is a novel visual analytics tool that supports the exploration of phenotype variation within and between cross-sectional patient cohorts. By leveraging the semantic hierarchy of the Human Phenotype Ontology, phenotypes are presented in context, can be grouped and clustered, and are summarized via overviews to support the exploration of phenotype distributions. The design of PhenoStacks was motivated by formative interviews with genetics researchers: we distil high-level tasks, present an algorithm for simplifying ontology topologies for visualization, and report the results of a deployment evaluation with four expert genetics researchers. The results suggest that PhenoStacks can help identify phenotype patterns, investigate data quality issues, and inform data collection design.

PhenoBlocks: Phenotype Comparison Visualizations.

The differential diagnosis of hereditary disorders is a challenging task for clinicians due to the heterogeneity of phenotypes that can be observed in patients. Existing clinical tools are often text-based and do not emphasize consistency, completeness, or granularity of phenotype reporting. This can impede clinical diagnosis and limit their utility to genetics researchers. Herein, we present PhenoBlocks, a novel visual analytics tool that supports the comparison of phenotypes between patients, or between a patient and the hallmark features of a disorder. An informal evaluation of PhenoBlocks with expert clinicians suggested that the visualization effectively guides the process of differential diagnosis and could reinforce the importance of complete, granular phenotypic reporting.

The Not-so-Staggering Effect of Staggered Animated Transitions on Visual Tracking.

Interactive visual applications often rely on animation to transition from one display state to another. There are multiple animation techniques to choose from, and it is not always clear which should produce the best visual correspondences between display elements. One major factor is whether the animation relies on staggering-an incremental delay in start times across the moving elements. It has been suggested that staggering may reduce occlusion, while also reducing display complexity and producing less overwhelming animations, though no empirical evidence has demonstrated these advantages. Work in perceptual psychology does show that reducing occlusion, and reducing inter-object proximity (crowding) more generally, improves performance in multiple object tracking. We ran simulations confirming that staggering can in some cases reduce crowding in animated transitions involving dot clouds (as found in, e.g., animated 2D scatterplots). We empirically evaluated the effect of two staggering techniques on tracking tasks, focusing on cases that should most favour staggering. We found that introducing staggering has a negligible, or even negative, impact on multiple object tracking performance. The potential benefits of staggering may be outweighed by strong costs: a loss of common-motion grouping information about which objects travel in similar paths, and less predictability about when any specific object would begin to move. Staggering may be beneficial in some conditions, but they have yet to be demonstrated. The present results are a significant step toward a better understanding of animation pacing, and provide direction for further research.

Interactive exploration of implicit and explicit relations in faceted datasets.

Many datasets, such as scientific literature collections, contain multiple heterogeneous facets which derive implicit relations, as well as explicit relational references between data items. The exploration of this data is challenging not only because of large data scales but also the complexity of resource structures and semantics. In this paper, we present PivotSlice, an interactive visualization technique which provides efficient faceted browsing as well as flexible capabilities to discover data relationships. With the metaphor of direct manipulation, PivotSlice allows the user to visually and logically construct a series of dynamic queries over the data, based on a multi-focus and multi-scale tabular view that subdivides the entire dataset into several meaningful parts with customized semantics. PivotSlice further facilitates the visual exploration and sensemaking process through features including live search and integration of online data, graphical interaction histories and smoothly animated visual state transitions. We evaluated PivotSlice through a qualitative lab study with university researchers and report the findings from our observations and interviews. We also demonstrate the effectiveness of PivotSlice using a scenario of exploring a repository of information visualization literature.

Using concrete scales: a practical framework for effective visual depiction of complex measures.

From financial statistics to nutritional values, we are frequently exposed to quantitative information expressed in measures of either extreme magnitudes or unfamiliar units, or both. A common practice used to comprehend such complex measures is to relate, re-express, and compare them through visual depictions using magnitudes and units that are easier to grasp. Through this practice, we create a new graphic composition that we refer to as a concrete scale. To the best of our knowledge, there are no design guidelines that exist for concrete scales despite their common use in communication, educational, and decision-making settings. We attempt to fill this void by introducing a novel framework that would serve as a practical guide for their analysis and design. Informed by a thorough analysis of graphic compositions involving complex measures and an extensive literature review of scale cognition mechanisms, our framework outlines the design space of various measure relations--specifically relations involving the re-expression of complex measures to more familiar concepts--and their visual representations as graphic compositions.

Exploratory analysis of time-series with ChronoLenses.

Visual representations of time-series are useful for tasks such as identifying trends, patterns and anomalies in the data. Many techniques have been devised to make these visual representations more scalable, enabling the simultaneous display of multiple variables, as well as the multi-scale display of time-series of very high resolution or that span long time periods. There has been comparatively little research on how to support the more elaborate tasks associated with the exploratory visual analysis of timeseries, e.g., visualizing derived values, identifying correlations, or discovering anomalies beyond obvious outliers. Such tasks typically require deriving new time-series from the original data, trying different functions and parameters in an iterative manner. We introduce a novel visualization technique called ChronoLenses, aimed at supporting users in such exploratory tasks. ChronoLenses perform on-the-fly transformation of the data points in their focus area, tightly integrating visual analysis with user actions, and enabling the progressive construction of advanced visual analysis pipelines.