Designing for Visualization in Motion: Embedding Visualizations in Swimming Videos.

We report on challenges and considerations for supporting design processes for visualizations in motion embedded in sports videos. We derive our insights from analyzing swimming race visualizations and motion-related data, building a technology probe, as well as a study with designers. Understanding how to design situated visualizations in motion is important for a variety of contexts. Competitive sports coverage, in particular, increasingly includes information on athlete or team statistics and records. Although moving visual representations attached to athletes or other targets are starting to appear, systematic investigations on how to best support their design process in the context of sports videos are still missing. Our work makes several contributions in identifying opportunities for visualizations to be added to swimming competition coverage but, most importantly, in identifying requirements and challenges for designing situated visualizations in motion. Our investigations include the analysis of a survey with swimming enthusiasts on their motion-related information needs, an ideation workshop to collect designs and elicit design challenges, the design of a technology probe that allows to create embedded visualizations in motion based on real data (Fig. 1), and an evaluation with visualization designers that aimed to understand the benefits of designing directly on videos.

A Generic Interactive Membership Function for Categorization of Quantities.

The membership function is to categorize quantities along with a confidence degree. This article investigates a generic user interaction based on this function for categorizing various types of quantities without modification, which empowers users to articulate uncertainty categorization and enhance their visual data analysis significantly. We present the technique design and an online prototype, supplementing with insights from three case studies that highlight the technique's efficacy among different types of quantities. Furthermore, we conduct a formal user study to scrutinize the process and reasoning users employ while utilizing our technique. The findings indicate that our technique can help users create customized categories. Both our code and the interactive prototype are made available as open-source resources, intended for application across varied domains as a generic tool.

Visualization in Motion: A Research Agenda and Two Evaluations.

We contribute a research agenda for visualization in motion and two experiments to understand how well viewers can read data from moving visualizations. We define visualizations in motion as visual data representations that are used in contexts that exhibit relative motion between a viewer and an entire visualization. Sports analytics, video games, wearable devices, or data physicalizations are example contexts that involve different types of relative motion between a viewer and a visualization. To analyze the opportunities and challenges for designing visualization in motion, we show example scenarios and outline a first research agenda. Motivated primarily by the prevalence of and opportunities for visualizations in sports and video games we started to investigate a small aspect of our research agenda: the impact of two important characteristics of motion-speed and trajectory on a stationary viewer's ability to read data from moving donut and bar charts. We found that increasing speed and trajectory complexity did negatively affect the accuracy of reading values from the charts and that bar charts were more negatively impacted. In practice, however, this impact was small: both charts were still read fairly accurately.

VisQA: X-raying Vision and Language Reasoning in Transformers.

Visual Question Answering systems target answering open-ended textual questions given input images. They are a testbed for learning high-level reasoning with a primary use in HCI, for instance assistance for the visually impaired. Recent research has shown that state-of-the-art models tend to produce answers exploiting biases and shortcuts in the training data, and sometimes do not even look at the input image, instead of performing the required reasoning steps. We present VisQA, a visual analytics tool that explores this question of reasoning vs. bias exploitation. It exposes the key element of state-of-the-art neural models - attention maps in transformers. Our working hypothesis is that reasoning steps leading to model predictions are observable from attention distributions, which are particularly useful for visualization. The design process of VisQA was motivated by well-known bias examples from the fields of deep learning and vision-language reasoning and evaluated in two ways. First, as a result of a collaboration of three fields, machine learning, vision and language reasoning, and data analytics, the work lead to a better understanding of bias exploitation of neural models for VQA, which eventually resulted in an impact on its design and training through the proposition of a method for the transfer of reasoning patterns from an oracle model. Second, we also report on the design of VisQA, and a goal-oriented evaluation of VisQA targeting the analysis of a model decision process from multiple experts, providing evidence that it makes the inner workings of models accessible to users.

Research response to coronavirus disease 2019 needed better coordination and collaboration: a living mapping of registered trials.

OBJECTIVES: Researchers worldwide are actively engaging in research activities to search for preventive and therapeutic interventions against coronavirus disease 2019 (COVID-19). Our aim was to describe the planning of randomized controlled trials (RCTs) in terms of timing related to the course of the COVID-19 epidemic and research question evaluated. STUDY DESIGN AND SETTING: We performed a living mapping of RCTs registered in the WHO International Clinical Trials Registry Platform. We systematically search the platform every week for all RCTs evaluating preventive interventions and treatments for COVID-19 and created a publicly available interactive mapping tool at https://covid-nma.com to visualize all trials registered. RESULTS: By August 12, 2020, 1,568 trials for COVID-19 were registered worldwide. Overall, the median ([Q1-Q3]; range) delay between the first case recorded in each country and the first RCT registered was 47 days ([33-67]; 15-163). For the 9 countries with the highest number of trials registered, most trials were registered after the peak of the epidemic (from 100% trials in Italy to 38% in the United States). Most trials evaluated treatments (1,333 trials; 85%); only 223 (14%) evaluated preventive strategies and 12 postacute period intervention. A total of 254 trials were planned to assess different regimens of hydroxychloroquine with an expected sample size of 110,883 patients. CONCLUSION: This living mapping analysis showed that COVID-19 trials have relatively small sample size with certain redundancy in research questions. Most trials were registered when the first peak of the pandemic has passed.

Structuring Visualization Mock-Ups at the Graphical Level by Dividing the Display Space.

Mock-ups are rapid, low fidelity prototypes, that are used in many design-related fields to generate and share ideas. While their creation is supported by many mature methods and tools, surprisingly few are suited for the needs of information visualization. In this article, we introduce a novel approach to creating visualizations mock-ups, based on a dialogue between graphic design and parametric toolkit explorations. Our approach consists in iteratively subdividing the display space, while progressively informing each division with realistic data. We show that a wealth of mock-ups can easily be created using only temporary data attributes, as we wait for more realistic data to become available. We describe the implementation of this approach in a D3-based toolkit, which we use to highlight its generative power, and we discuss the potential for transitioning towards higher fidelity prototypes.

FiberClay: Sculpting Three Dimensional Trajectories to Reveal Structural Insights.

Visualizing 3D trajectories to extract insights about their similarities and spatial configuration is a critical task in several domains. Air traffic controllers for example deal with large quantities of aircrafts routes to optimize safety in airspace and neuroscientists attempt to understand neuronal pathways in the human brain by visualizing bundles of fibers from DTI images. Extracting insights from masses of 3D trajectories is challenging as the multiple three dimensional lines have complex geometries, may overlap, cross or even merge with each other, making it impossible to follow individual ones in dense areas. As trajectories are inherently spatial and three dimensional, we propose FiberClay: a system to display and interact with 3D trajectories in immersive environments. FiberClay renders a large quantity of trajectories in real time using GP-GPU techniques. FiberClay also introduces a new set of interactive techniques for composing complex queries in 3D space leveraging immersive environment controllers and user position. These techniques enable an analyst to select and compare sets of trajectories with specific geometries and data properties. We conclude by discussing insights found using FiberClay with domain experts in air traffic control and neurology.

Sports Tournament Predictions Using Direct Manipulation.

An advanced interface for sports tournament predictions uses direct manipulation to allow users to make nonlinear predictions. Unlike previous interface designs, the interface helps users focus on their prediction tasks by enabling them to first choose a winner and then fill out the rest of the bracket. In real-world tests of the proposed interface (for the 2014 FIFA World Cup tournament and 2015/2016 UEFA Champions League), the authors validated the use of direct manipulation as an alternative to widgets. Using visitor interaction logs, they were able to determine the strategies people use to perform predictions and identify potential areas of improvement for further prediction interfaces.

UpSet: Visualization of Intersecting Sets.

Understanding relationships between sets is an important analysis task that has received widespread attention in the visualization community. The major challenge in this context is the combinatorial explosion of the number of set intersections if the number of sets exceeds a trivial threshold. In this paper we introduce UpSet, a novel visualization technique for the quantitative analysis of sets, their intersections, and aggregates of intersections. UpSet is focused on creating task-driven aggregates, communicating the size and properties of aggregates and intersections, and a duality between the visualization of the elements in a dataset and their set membership. UpSet visualizes set intersections in a matrix layout and introduces aggregates based on groupings and queries. The matrix layout enables the effective representation of associated data, such as the number of elements in the aggregates and intersections, as well as additional summary statistics derived from subset or element attributes. Sorting according to various measures enables a task-driven analysis of relevant intersections and aggregates. The elements represented in the sets and their associated attributes are visualized in a separate view. Queries based on containment in specific intersections, aggregates or driven by attribute filters are propagated between both views. We also introduce several advanced visual encodings and interaction methods to overcome the problems of varying scales and to address scalability. UpSet is web-based and open source. We demonstrate its general utility in multiple use cases from various domains.

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.

Visual sedimentation.

We introduce Visual Sedimentation, a novel design metaphor for visualizing data streams directly inspired by the physical process of sedimentation. Visualizing data streams (e. g., Tweets, RSS, Emails) is challenging as incoming data arrive at unpredictable rates and have to remain readable. For data streams, clearly expressing chronological order while avoiding clutter, and keeping aging data visible, are important. The metaphor is drawn from the real-world sedimentation processes: objects fall due to gravity, and aggregate into strata over time. Inspired by this metaphor, data is visually depicted as falling objects using a force model to land on a surface, aggregating into strata over time. In this paper, we discuss how this metaphor addresses the specific challenge of smoothing the transition between incoming and aging data. We describe the metaphor's design space, a toolkit developed to facilitate its implementation, and example applications to a range of case studies. We then explore the generative capabilities of the design space through our toolkit. We finally illustrate creative extensions of the metaphor when applied to real streams of data.

SoccerStories: a kick-off for visual soccer analysis.

This article presents SoccerStories, a visualization interface to support analysts in exploring soccer data and communicating interesting insights. Currently, most analyses on such data relate to statistics on individual players or teams. However, soccer analysts we collaborated with consider that quantitative analysis alone does not convey the right picture of the game, as context, player positions and phases of player actions are the most relevant aspects. We designed SoccerStories to support the current practice of soccer analysts and to enrich it, both in the analysis and communication stages. Our system provides an overview+detail interface of game phases, and their aggregation into a series of connected visualizations, each visualization being tailored for actions such as a series of passes or a goal attempt. To evaluate our tool, we ran two qualitative user studies on recent games using SoccerStories with data from one of the world's leading live sports data providers. The first study resulted in a series of four articles on soccer tactics, by a tactics analyst, who said he would not have been able to write these otherwise. The second study consisted in an exploratory follow-up to investigate design alternatives for embedding soccer phases into word-sized graphics. For both experiments, we received a very enthusiastic feedback and participants consider further use of SoccerStories to enhance their current workflow.