DevLan/vispubs huggingface dataset:
Epigraphics: Message-Driven Infographics Authoring
Authors: Tongyu Zhou, Jeff Huang 0002, Gromit Yeuk-Yin Chan
Published at CHI, 2024

The message a designer wants to convey plays a pivotal role in directing the design of an infographic, yet most authoring workflows start with creating the visualizations or graphics first without gauging whether they fit the message. To address this gap, we propose Epigraphics, a web-based authoring system that treats an "epigraph" as the first-class object, and uses it to guide infographic asset creation, editing, and syncing. The system uses the text-based message to recommend visualizations, graphics, data filters, color palettes, and animations. It further supports between-asset interactions and fine-tuning such as recoloring, highlighting, and animation syncing that enhance the aesthetic cohesiveness of the assets. A gallery and case studies show that our system can produce infographics inspired by existing popular ones, and a task-based usability study with 10 designers show that a text-sourced workflow can standardize content, empower users to think more about the big picture, and facilitate rapid prototyping.
Evaluating Navigation and Comparison Performance of Computational Notebooks on Desktop and in Virtual Reality
Authors: Sungwon In, Eric Krokos, Kirsten Whitley, Chris North 0001, Yalong Yang 0001
Published at CHI, 2024

The computational notebook serves as a versatile tool for data analysis. However, its conventional user interface falls short of keeping pace with the ever-growing data-related tasks, signaling the need for novel approaches. With the rapid development of interaction techniques and computing environments, there is a growing interest in integrating emerging technologies in data-driven workflows. Virtual reality, in particular, has demonstrated its potential in interactive data visualizations. In this work, we aimed to experiment with adapting computational notebooks into VR and verify the potential benefits VR can bring. We focus on the navigation and comparison aspects as they are primitive components in analysts' workflow. To further improve comparison, we have designed and implemented a Branching&Merging functionality. We tested computational notebooks on the desktop and in VR, both with and without the added Branching&Merging capability. We found VR significantly facilitated navigation compared to desktop, and the ability to create branches enhanced comparison.
Exploring Visualizations for Precisely Guiding Bare Hand Gestures in Virtual Reality
Authors: Xizi Wang, Ben Lafreniere, Jian Zhao 0010
Published at CHI, 2024

Bare hand interaction in augmented or virtual reality (AR/VR) systems, while intuitive, often results in errors and frustration. However, existing methods, such as a static icon or a dynamic tutorial, can only inform simple and coarse hand gestures and lack corrective feedback. This paper explores various visualizations for enhancing precise hand interaction in VR. Through a comprehensive two-part formative study with 11 participants, we identified four types of essential information for visual guidance and designed different visualizations that manifest these information types. We further distilled four visual designs and conducted a controlled lab study with 15 participants to assess their effectiveness for various single- and double-handed gestures. Our results demonstrate that visual guidance significantly improved users' gesture performance, reducing time and workload while increasing confidence. Moreover, we found that the visualization did not disrupt most users' immersive VR experience or their perceptions of hand tracking and gesture recognition reliability.
Fast-Forward Reality: Authoring Error-Free Context-Aware Policies with Real-Time Unit Tests in Extended Reality
Authors: Xun Qian, Tianyi Wang 0004, Xuhai Xu, Tanya R. Jonker, Kashyap Todi
Published at CHI, 2024

Advances in ubiquitous computing have enabled end-user authoring of context-aware policies (CAPs) that control smart devices based on specific contexts of the user and environment. However, authoring CAPs accurately and avoiding run-time errors is challenging for end-users as it is difficult to foresee CAP behaviors under complex real-world conditions. We propose Fast-Forward Reality, an Extended Reality (XR) based authoring workflow that enables end-users to iteratively author and refine CAPs by validating their behaviors via simulated unit test cases. We develop a computational approach to automatically generate test cases based on the authored CAP and the user's context history. Our system delivers each test case with immersive visualizations in XR, facilitating users to verify the CAP behavior and identify necessary refinements. We evaluated Fast-Forward Reality in a user study (N=12). Our authoring and validation process improved the accuracy of CAPs and the users provided positive feedback on the system usability.
From Exploration to End of Life: Unpacking Sustainability in Physicalization Practices
Authors: Luiz Morais, Georgia Panagiotidou, Sarah Hayes, Tatiana Losev, Rebecca Noonan, Uta Hinrichs
Published at CHI, 2024

Data physicalizations have gained prominence across domains, but their environmental impact has been largely overlooked. This work addresses this gap by investigating the interplay between sustainability and physicalization practices. We conducted interviews with experts from diverse backgrounds, followed by a survey to gather insights into how they approach physicalization projects and reflect on sustainability. Our thematic analysis revealed sustainability considerations throughout the entire physicalization life cycle—a framework that encompasses various stages in a physicalization's existence. Notably, we found no single agreed-upon definition for sustainable physicalizations, highlighting the complexity of integrating sustainability into physicalization practices. We outline sustainability challenges and strategies based on participants' experiences and propose the Sustainable Physicalization Practices (SuPPra) Matrix, providing a structured approach for designers to reflect on and enhance the environmental impact of their future physicalizations.
Functional Design Requirements to Facilitate Menstrual Health Data Exploration
Authors: Georgianna Lin, Pierre-William Lessard, Minh Ngoc Le, Brenna Li, Fanny Chevalier, Khai N. Truong, Alex Mariakakis
Published at CHI, 2024

Menstrual trackers currently lack the affordances required to help individuals achieve their goals beyond menstrual event predictions and symptom logging. Taking an initial step towards this aspiration, we propose, validate, and refine five functional design requirements for future interface designs that facilitate menstrual data exploration. We interviewed 30 individuals who menstruate and collected their feedback on the practical application of these requirements. To elicit ideas and impressions, we designed two proof-of-concept interfaces to use as design probes with similar core functionalities but different presentations of phase timing predictions and signal arrangement. Our analysis revealed participants' feedback regarding the presentation of predictions for menstrual-related events, the visualization of future signal patterns, personalization abilities for viewing signals relevant to their menstrual experience, the availability of resources to understand the underlying biological connections between signals, and the ability to compare multiple cycles side-by-side with context.
Glanceable Data Visualizations for Older Adults: Establishing Thresholds and Examining Disparities Between Age Groups
Authors: Zack While, Tanja Blascheck, Yujie Gong, Petra Isenberg, Ali Sarvghad
Published at CHI, 2024

We present results of a replication study on smartwatch visualizations with adults aged 65 and older. The older adult population is rising globally, coinciding with their increasing interest in using small wearable devices, such as smartwatches, to track and view data. Smartwatches, however, pose challenges to this population: fonts and visualizations are often small and meant to be seen at a glance. How concise design on smartwatches interacts with aging-related changes in perception and cognition, however, is not well understood. We replicate a study that investigated how visualization type and number of data points affect glanceable perception. We observe strong evidence of differences for participants aged 75 and older, sparking interesting questions regarding the study of visualization and older adults. We discuss first steps toward better understanding and supporting an older population of smartwatch wearers and reflect on our experiences working with this population. Supplementary materials are available at https://osf.io/7x4hq/.
Go-Go Biome: Evaluation of a Casual Game for Gut Health Engagement and Reflection
Authors: Nandini Pasumarthy, Shreyas Nisal, Jessica Danaher, Elise van den Hoven, Rohit Ashok Khot
Published at CHI, 2024

Experts emphasise that maintaining a healthy gut microbial balance requires the public to understand factors beyond diet, such as physical activity, lifestyle, and other real-world influences. Games as experiential systems are known to foster playful engagement and reflection. We propose a novel approach to promoting activity engagement for gut health and its reflection through the design of the Go-Go Biome game. The game simulates the interplay between friendly and unfriendly gut microbes, encouraging real-world activity engagement for gut-microbial balance through interactive visuals, unstructured play mechanics, and reflective design principles. A field study with 14 participants revealed that important facets of our game design led to awareness, playful visualisation, and reflection on factors influencing gut health. Our findings suggest four design lenses– bio-temporality, visceral conversations, wellness comparison, and inner discovery, to aid future playful design explorations to foster gut health engagement and reflection.
How Do Analysts Understand and Verify AI-Assisted Data Analyses?
Authors: Ken Gu, Ruoxi Shang, Tim Althoff, Chenglong Wang, Steven Mark Drucker
Published at CHI, 2024

Data analysis is challenging as it requires synthesizing domain knowledge, statistical expertise, and programming skills. Assistants powered by large language models (LLMs), such as ChatGPT, can assist analysts by translating natural language instructions into code. However, AI-assistant responses and analysis code can be misaligned with the analyst's intent or be seemingly correct but lead to incorrect conclusions. Therefore, validating AI assistance is crucial and challenging. Here, we explore how analysts understand and verify the correctness of AI-generated analyses. To observe analysts in diverse verification approaches, we develop a design probe equipped with natural language explanations, code, visualizations, and interactive data tables with common data operations. Through a qualitative user study (n=22) using this probe, we uncover common behaviors within verification workflows and how analysts' programming, analysis, and tool backgrounds reflect these behaviors. Additionally, we provide recommendations for analysts and highlight opportunities for designers to improve future AI-assistant experiences.
How Do Low-Vision Individuals Experience Information Visualization?
Authors: Yanan Wang, Yuhang Zhao 0001, Yea-Seul Kim
Published at CHI, 2024

In recent years, there has been a growing interest in enhancing the accessibility of visualizations for people with visual impairments. While much of the research has focused on improving accessibility for screen reader users, the specific needs of people with remaining vision (i.e., low-vision individuals) have been largely unaddressed. To bridge this gap, we conducted a qualitative study that provides insights into how low-vision individuals experience visualizations. We found that participants utilized various strategies to examine visualizations using the screen magnifiers and also observed that the default zoom level participants use for general purposes may not be optimal for reading visualizations. We identified that participants relied on their prior knowledge and memory to minimize the traversing cost when examining visualization. Based on the findings, we motivate a personalized tool to accommodate varying visual conditions of low-vision individuals and derive the design goals and features of the tool.
Input Visualization: Collecting and Modifying Data with Visual Representations
Authors: Nathalie Bressa, Jordan Louis, Wesley Willett, Samuel Huron
Published at CHI, 2024

We examine input visualizations, visual representations that are designed to collect (and represent) new data rather than encode preexisting datasets. Information visualization is commonly used to reveal insights and stories within existing data. As a result, most contemporary visualization approaches assume existing datasets as the starting point for design, through which that data is mapped to visual encodings. Meanwhile, the implications of visualizations as inputs and as data sources have received little attention—despite the existence of visual and physical examples stretching back centuries. In this paper, we present a design space of 50 input visualizations analyzing their visual representation, data, artifact, context, and input. Based on this, we identify input modalities, purposes of input visualizations, and a set of design considerations. Finally, we discuss the relationship between input visualization and traditional visualization design and suggest opportunities for future research to better understand these visual representations and their potential.
KnitScape: Computational Design and Yarn-Level Simulation of Slip and Tuck Colorwork Knitting Patterns
Authors: Hannah Twigg-Smith, Emily Whiting, Nadya Peek
Published at CHI, 2024

Slipped and tucked stitches introduce small areas of deformation that compound and result in emergent textures on knitted fabrics. When used together with color changes and ladders, these can also produce dramatic colorwork and openwork effects. However, designing slip and tuck colorwork patterns is challenging due to the complex interactions between operations, yarns, and deformations. We present KnitScape, a browser-based tool for design and simulation of stitch patterns for knitting. KnitScape provides a design interface to specify 1) operation repeats, 2) color changes, and 3) needle positions. These inputs are used to build a graph of yarn topology and run a yarn-level spring simulation. This enables visualization of the deformation that arises from slip and tuck operations. Through its design tool and simulation, KnitScape enables rapid exploration of a complex colorwork design space. We demonstrate KnitScape with a series of example swatches.
Looking Together ≠ Seeing the Same Thing: Understanding Surgeons' Visual Needs During Intra-operative Coordination and Instruction
Authors: Vitaliy Popov, Xinyue Chen, Jingying Wang, Michael Kemp, Gurjit Sandhu, Taylor Kantor, Natalie Mateju, Xu Wang 0016
Published at CHI, 2024

Shared gaze visualizations have been found to enhance collaboration and communication outcomes in diverse HCI scenarios including computer supported collaborative work and learning contexts. Given the importance of gaze in surgery operations, especially when a surgeon trainer and trainee need to coordinate their actions, research on the use of gaze to facilitate intra-operative coordination and instruction has been limited and shows mixed implications. We performed a field observation of 8 surgeries and an interview study with 14 surgeons to understand their visual needs during operations, informing ways to leverage and augment gaze to enhance intra-operative coordination and instruction. We found that trainees have varying needs in receiving visual guidance which are often unfulfilled by the trainers' instructions. It is critical for surgeons to control the timing of the gaze-based visualizations and effectively interpret gaze data. We suggest overlay technologies, e.g., gaze-based summaries and depth sensing, to augment raw gaze in support of surgical coordination and instruction.
MAIDR: Making Statistical Visualizations Accessible with Multimodal Data Representation
Authors: Jooyoung Seo, Yilin Xia, Bongshin Lee, Sean McCurry, Yu Jun Yam
Published at CHI, 2024

This paper investigates new data exploration experiences that enable blind users to interact with statistical data visualizations—bar plots, heat maps, box plots, and scatter plots—leveraging multimodal data representations. In addition to sonification and textual descriptions that are commonly employed by existing accessible visualizations, our MAIDR (multimodal access and interactive data representation) system incorporates two additional modalities (braille and review) that offer complementary benefits. It also provides blind users with the autonomy and control to interactively access and understand data visualizations. In a user study involving 11 blind participants, we found the MAIDR system facilitated the accurate interpretation of statistical visualizations. Participants exhibited a range of strategies in combining multiple modalities, influenced by their past interactions and experiences with data visualizations. This work accentuates the overlooked potential of combining refreshable tactile representation with other modalities and elevates the discussion on the importance of user autonomy when designing accessible data visualizations.
Make Interaction Situated: Designing User Acceptable Interaction for Situated Visualization in Public Environments
Authors: Qian Zhu 0010, Zhuo Wang, Wei Zeng 0004, Wai Tong, Weiyue Lin, Xiaojuan Ma
Published at CHI, 2024

Situated visualization blends data into the real world to fulfill individuals' contextual information needs. However, interacting with situated visualization in public environments faces challenges posed by users' acceptance and contextual constraints. To explore appropriate interaction design, we first conduct a formative study to identify users' needs for data and interaction. Informed by the findings, we summarize appropriate interaction modalities with eye-based, hand-based and spatially-aware object interaction for situated visualization in public environments. Then, through an iterative design process with six users, we explore and implement interactive techniques for activating and analyzing with situated visualization. To assess the effectiveness and acceptance of these interactions, we integrate them into an AR prototype and conduct a within-subjects study in public scenarios using conventional hand-only interactions as the baseline. The results show that participants preferred our prototype over the baseline, attributing their preference to the interactions being more acceptable, flexible, and practical in public.
Milliways: Taming Multiverses through Principled Evaluation of Data Analysis Paths
Authors: Abhraneel Sarma, Kyle Hwang, Jessica Hullman, Matthew Kay 0001
Published at CHI, 2024

Multiverse analyses involve conducting all combinations of reasonable choices in a data analysis process. A reader of a study containing a multiverse analysis might question—are all the choices included in the multiverse reasonable and equally justifiable? How much do results vary if we make different choices in the analysis process? In this work, we identify principles for validating the composition of, and interpreting the uncertainty in, the results of a multiverse analysis. We present Milliways, a novel interactive visualisation system to support principled evaluation of multiverse analyses. Milliways provides interlinked panels presenting result distributions, individual analysis composition, multiverse code specification, and data summaries. Milliways supports interactions to sort, filter and aggregate results based on the analysis specification to identify decisions in the analysis process to which the results are sensitive. To represent the two qualitatively different types of uncertainty that arise in multiverse analyses—probabilistic uncertainty from estimating unknown quantities of interest such as regression coefficients, and possibilistic uncertainty from choices in the data analysis—Milliways uses consonance curves and probability boxes. Through an evaluative study with five users familiar with multiverse analysis, we demonstrate how Milliways can support multiverse analysis tasks, including a principled assessment of the results of a multiverse analysis.
Momentary Stressor Logging and Reflective Visualizations: Implications for Stress Management with Wearables
Authors: Sameer Neupane, Mithun Saha, Nasir Ali, Timothy Hnat, Shahin Alan Samiei, Anandatirtha Nandugudi, David M. Almeida, Santosh Kumar 0001
Published at CHI, 2024

Commercial wearables from Fitbit, Garmin, and Whoop have recently introduced real-time notifications based on detecting changes in physiological responses indicating potential stress. In this paper, we investigate how these new capabilities can be leveraged to improve stress management. We developed a smartwatch app, a smartphone app, and a cloud service, and conducted a 100-day field study with 122 participants who received prompts triggered by physiological responses several times a day. They were asked whether they were stressed, and if so, to log the most likely stressor. Each week, participants received new visualizations of their data to self-reflect on patterns and trends. Participants reported better awareness of their stressors, and self-initiating fourteen kinds of behavioral changes to reduce stress in their daily lives. Repeated self-reports over 14 weeks showed reductions in both stress intensity (in 26,521 momentary ratings) and stress frequency (in 1,057 weekly surveys).
Natural Language Dataset Generation Framework for Visualizations Powered by Large Language Models
Authors: Hyung-Kwon Ko, Hyeon Jeon, Gwanmo Park, Dae Hyun Kim 0005, Nam Wook Kim, Juho Kim, Jinwook Seo
Published at CHI, 2024

We introduce VL2NL, a Large Language Model (LLM) framework that generates rich and diverse NL datasets using Vega-Lite specifications as input, thereby streamlining the development of Natural Language Interfaces (NLIs) for data visualization. To synthesize relevant chart semantics accurately and enhance syntactic diversity in each NL dataset, we leverage 1) a guided discovery incorporated into prompting so that LLMs can steer themselves to create faithful NL datasets in a self-directed manner; 2) a score-based paraphrasing to augment NL syntax along with four language axes. We also present a new collection of 1,981 real-world Vega-Lite specifications that have increased diversity and complexity than existing chart collections. When tested on our chart collection, VL2NL extracted chart semantics and generated L1/L2 captions with 89.4% and 76.0% accuracy, respectively. It also demonstrated generating and paraphrasing utterances and questions with greater diversity compared to the benchmarks. Last, we discuss how our NL datasets and framework can be utilized in real-world scenarios. The codes and chart collection are available at https://github.com/hyungkwonko/chart-llm.