An Empirical Evaluation of the GPT-4 Multimodal Language Model on Visualization Literacy Tasks.

Large Language Models (LLMs) like GPT-4 which support multimodal input (i.e., prompts containing images in addition to text) have immense potential to advance visualization research.

IntiVisor: A Visual Analytics System for Interaction Log Analysis.

Application developers frequently augment their code to produce event logs of specific operations performed by their users. Subsequent analysis of these event logs can help provide insight about the users' behavior relative to its intended use. The analysis process typically includes both event organization and pattern discovery activities.

HoopInSight: Analyzing and Comparing Basketball Shooting Performance Through Visualization.

Data visualization has the power to revolutionize sports. For example, the rise of shot maps has changed basketball strategy by visually illustrating where "good/bad" shots are taken from. As a result, professional basketball teams today take shots from very different positions on the court than they did 20 years ago.

More Than Data Stories: Broadening the Role of Visualization in Contemporary Journalism.

Data visualization and journalism are deeply connected. From early infographics to recent data-driven storytelling, visualization has become an integrated part of contemporary journalism, primarily as a communication artifact to inform the general public. Data journalism, harnessing the power of data visualization, has emerged as a bridge between the growing volume of data and our society.

IMRT QA result prediction via MLC transmission decomposition.

Background: Quality assurance measurement of IMRT/VMAT treatment plans is resource intensive, and other more efficient methods to achieve the same confidence are desirable.

Purpose: We aimed to analyze treatment plans in the context of the treatment planning systems that created them, in order to predict which ones will fail a standard quality assurance measurement. To do so, we sought to create a tool external to the treatment planning system that could analyze a set of MLC positions and provide information that could be used to calculate various evaluation metrics.

The Effect of Mouse Size on Dose from an X-Rad320 Irradiator.

Irradiation protocols for murine experiments often use standardized dose rate estimates for calculating dose delivered, regardless of physical variations between mouse subjects. This work sought to determine the significance of mouse size on absorbed dose. Five mouse-like phantoms of various sizes based on the mouse whole-body (MOBY) model were 3D printed.

A Critical Reflection on Visualization Research: Where Do Decision Making Tasks Hide?

It has been widely suggested that a key goal of visualization systems is to assist decision making, but is this true? We conduct a critical investigation on whether the activity of decision making is indeed central to the visualization domain. By approaching decision making as a user task, we explore the degree to which decision tasks are evident in visualization research and user studies. Our analysis suggests that decision tasks are not commonly found in current visualization task taxonomies and that the visualization field has yet to leverage guidance from decision theory domains on how to study such tasks.

NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries.

Natural language interfaces (NLls) have shown great promise for visual data analysis, allowing people to flexibly specify and interact with visualizations. However, developing visualization NLIs remains a challenging task, requiring low-level implementation of natural language processing (NLP) techniques as well as knowledge of visual analytic tasks and visualization design. We present NL4DV, a toolkit for natural language-driven data visualization.

How to Ask What to Say?: Strategies for Evaluating Natural Language Interfaces for Data Visualization.

In this article, we discuss challenges and strategies for evaluating natural language interfaces (NLIs) for data visualization. Through an examination of prior studies and reflecting on own experiences in evaluating visualization NLIs, we highlight benefits and considerations of three task framing strategies: Jeopardy-style facts, open-ended tasks, and target replication tasks. We hope the discussions in this article can guide future researchers working on visualization NLIs and help them avoid common challenges and pitfalls when evaluating these systems.

Interweaving Multimodal Interaction With Flexible Unit Visualizations for Data Exploration.

Multimodal interfaces that combine direct manipulation and natural language have shown great promise for data visualization. Such multimodal interfaces allow people to stay in the flow of their visual exploration by leveraging the strengths of one modality to complement the weaknesses of others. In this article, we introduce an approach that interweaves multimodal interaction combining direct manipulation and natural language with flexible unit visualizations.

Reaching Broader Audiences With Data Visualization.

The visualization research community can and should reach broader audiences beyond data-savvy groups of people, because these audiences could also greatly benefit from visual access to data. In this article, we discuss four research topics-personal data visualization, data visualization on mobile devices, inclusive data visualization, and multimodal interaction for data visualization-that, individually and collaboratively, would help us reach broader audiences with data visualization, making data more accessible.

Touch? Speech? or Touch and Speech? Investigating Multimodal Interaction for Visual Network Exploration and Analysis.

Interaction plays a vital role during visual network exploration as users need to engage with both elements in the view (e.g., nodes, links) and interface controls (e.

Critical Reflections on Visualization Authoring Systems.

An emerging generation of visualization authoring systems support expressive information visualization without textual programming. As they vary in their visualization models, system architectures, and user interfaces, it is challenging to directly compare these systems using traditional evaluative methods. Recognizing the value of contextualizing our decisions in the broader design space, we present critical reflections on three systems we developed -Lyra, Data Illustrator, and Charticulator.

A Heuristic Approach to Value-Driven Evaluation of Visualizations.

To interpret data visualizations, people must determine how visual features map onto concepts. For example, to interpret colormaps, people must determine how dimensions of color (e.g.

Augmenting Visualizations with Interactive Data Facts to Facilitate Interpretation and Communication.

Recently, an increasing number of visualization systems have begun to incorporate natural language generation (NLG) capabilities into their interfaces. NLG-based visualization systems typically leverage a suite of statistical functions to automatically extract key facts about the underlying data and surface them as natural language sentences alongside visualizations. With current systems, users are typically required to read the system-generated sentences and mentally map them back to the accompanying visualization.

Orko: Facilitating Multimodal Interaction for Visual Exploration and Analysis of Networks.

Data visualization systems have predominantly been developed for WIMP-based direct manipulation interfaces. Only recently have other forms of interaction begun to appear, such as natural language or touch-based interaction, though usually operating only independently. Prior evaluations of natural language interfaces for visualization have indicated potential value in combining direct manipulation and natural language as complementary interaction techniques.

Vispubdata.org: A Metadata Collection About IEEE Visualization (VIS) Publications.

We have created and made available to all a dataset with information about every paper that has appeared at the IEEE Visualization (VIS) set of conferences: InfoVis, SciVis, VAST, and Vis. The information about each paper includes its title, abstract, authors, and citations to other papers in the conference series, among many other attributes. This article describes the motivation for creating the dataset, as well as our process of coalescing and cleaning the data, and a set of three visualizations we created to facilitate exploration of the data.

Visualizing Social Media Content with SentenTree.

We introduce SentenTree, a novel technique for visualizing the content of unstructured social media text. SentenTree displays frequent sentence patterns abstracted from a corpus of social media posts. The technique employs design ideas from word clouds and the Word Tree, but overcomes a number of limitations of both those visualizations.

GLO-STIX: Graph-Level Operations for Specifying Techniques and Interactive eXploration.

The field of graph visualization has produced a wealth of visualization techniques for accomplishing a variety of analysis tasks. Therefore analysts often rely on a suite of different techniques, and visual graph analysis application builders strive to provide this breadth of techniques. To provide a holistic model for specifying network visualization techniques (as opposed to considering each technique in isolation) we present the Graph-Level Operations (GLO) model.

OnSet: A Visualization Technique for Large-scale Binary Set Data.

Visualizing sets to reveal relationships between constituent elements is a complex representational problem. Recent research presents several automated placement and grouping techniques to highlight connections between set elements. However, these techniques do not scale well for sets with cardinality greater than one hundred elements.

GLO-STIX: Graph-Level Operations for Specifying Techniques and Interactive eXploration.

The field of graph visualization has produced a wealth of visualization techniques for accomplishing a variety of analysis tasks. Therefore analysts often rely on a suite of different techniques, and visual graph analysis application builders strive to provide this breadth of techniques. To provide a holistic model for specifying network visualization techniques (as opposed to considering each technique in isolation) we present the () model.

Understanding interfirm relationships in business ecosystems with interactive visualization.

Business ecosystems are characterized by large, complex, and global networks of firms, often from many different market segments, all collaborating, partnering, and competing to create and deliver new products and services. Given the rapidly increasing scale, complexity, and rate of change of business ecosystems, as well as economic and competitive pressures, analysts are faced with the formidable task of quickly understanding the fundamental characteristics of these interfirm networks. Existing tools, however, are predominantly query- or list-centric with limited interactive, exploratory capabilities.

Combining computational analyses and interactive visualization for document exploration and sensemaking in jigsaw.

Investigators across many disciplines and organizations must sift through large collections of text documents to understand and piece together information. Whether they are fighting crime, curing diseases, deciding what car to buy, or researching a new field, inevitably investigators will encounter text documents. Taking a visual analytics approach, we integrate multiple text analysis algorithms with a suite of interactive visualizations to provide a flexible and powerful environment that allows analysts to explore collections of documents while sensemaking.

Mental models, visual reasoning and interaction in information visualization: a top-down perspective.

Although previous research has suggested that examining the interplay between internal and external representations can benefit our understanding of the role of information visualization (InfoVis) in human cognitive activities, there has been little work detailing the nature of internal representations, the relationship between internal and external representations and how interaction is related to these representations. In this paper, we identify and illustrate a specific kind of internal representation, mental models, and outline the high-level relationships between mental models and external visualizations. We present a top-down perspective of reasoning as model construction and simulation, and discuss the role of visualization in model based reasoning.

How Can Visual Analytics Assist Investigative Analysis? Design Implications from an Evaluation.

Despite the growing number of systems providing visual analytic support for investigative analysis, few empirical studies of the potential benefits of such systems have been conducted, particularly controlled, comparative evaluations. Determining how such systems foster insight and sensemaking is important for their continued growth and study, however. Furthermore, studies that identify how people use such systems and why they benefit (or not) can help inform the design of new systems in this area.