We characterize 16 challenges faced by those investigating and developing remote and synchronous collaborative experiences around visualization. Our work reflects the perspectives and prior research efforts of an international group of 29 experts from across human-computer interaction and visualization sub-communities. The challenges are anchored around five collaborative activities that exhibit a centrality of visualization and multimodal communication. These activities include exploratory data analysis, creative ideation, visualization-rich presentations, joint decision making grounded in data, and real-time data monitoring. The challenges also reflect the changing dynamics of these activities in the face of recent advances in extended reality (XR) and artificial intelligence (AI). As an organizing scheme for future research at the intersection of visualization and computer-supported cooperative work, we align the challenges with a sequence of four sets of research and development activities: technological choices, social factors, AI assistance, and evaluation.

We present Anu.js, a toolkit for web-based immersive analytics (IA). The IA design space is vast, multi-faceted, and everchanging, challenging development in the absence of robust authoring support. The web is a popular platform for visualization applications, research, and teaching, and by leveraging the benefits of web technologies and adopting imperative authoring paradigms we can achieve the necessary expressiveness, compatibility, and ergonomics to support IA research and development. Anu.js adapts D3’s data-binding model to 3D contexts, granting fine-grained control over the creation, representation, animation, performance, and interaction of 3D scene-graphs. Additionally, Anu.js offers declarative prefabs to support common visualization elements and interactions, and synergizes with popular visualization libraries which allows developers to leverage these proven utilities. We demonstrate Anu.js’s potential through our diverse example gallery, expert evaluation, and potential future applications. Through this, Anu.js empowers developers in accelerating the creation of novel and bespoke visualizations for immersive web-based applications.

Head-worn augmented reality (AR) allows audiences to be immersed and engaged in stories told by live presenters. While presenters may also be in AR to have the same level of immersion and awareness as their audience, this symmetric presentation style may diminish important social cues such as eye contact. In this work, we examine the effects this (a)symmetry has on engagement, group awareness, and social interaction in co-located one-on-one augmented presentations. We developed a presentation system incorporating 2D/3D content that audiences can view and interact with in AR, with presenters controlling and delivering the presentation in either a symmetric style in AR, or an asymmetric style with a handheld tablet. We conducted a within- and between-subjects evaluation with 12 participant pairs to examine the differences between these symmetric and asymmetric presentation modalities. From our findings, we extracted four themes and derived strategies and guidelines for designers interested in augmented presentations.

Hybrid options at conferences, which support in-person and remote attendance, have increasingly become the norm in order to broaden participation and promote sustainability. However, hybrid conferences are challenging, where in-person and remote attendees often have disjoint, parallel experiences with limited opportunity to interact with each other. To explore the potential for facilitating social interaction between in-person and remote conference attendees, we designed and built SocialMiXR, a research prototype that uses WebXR technologies to align the physical and virtual worlds into one hybrid space for socialization. We deployed SocialMiXR in a three-day field study with 14 in-person and remote attendees of an engineering conference. Our qualitative results demonstrate that participants felt they were together in the same conference experience, and formed meaningful connections with each other. At the same time, they faced difficulties balancing different realities and capabilities given their separate contexts. We discuss implications for the design of hybrid social experiences at conferences.

Abstract Evaluations—encompassing computational evaluations, benchmarks and user studies—are essential tools for validating the performance and applicability of graph and network layout algorithms (also known as graph drawing). These evaluations not only offer significant insights into an algorithm's performance and capabilities, but also assist the reader in determining if the algorithm is suitable for a specific purpose, such as handling graphs with a high volume of nodes or dense graphs. Unfortunately, there is no standard approach for evaluating layout algorithms. Prior work holds a ‘Wild West’ of diverse benchmark datasets and data characteristics, as well as varied evaluation metrics and ways to report results. It is often difficult to compare layout algorithms without first implementing them and then running your own evaluation. In this systematic review, we delve into the myriad of methodologies employed to conduct evaluations—the utilized techniques, reported outcomes and the pros and cons of choosing one approach over another. Our examination extends beyond computational evaluations, encompassing user‐centric evaluations, thus presenting a comprehensive understanding of algorithm validation. This systematic review—and its accompanying website—guides readers through evaluation types, the types of results reported, and the available benchmark datasets and their data characteristics. Our objective is to provide a valuable resource for readers to understand and effectively apply various evaluation methods for graph layout algorithms. A free copy of this paper and all supplemental material is available at osf.io , and the categorized papers are accessible on our website at https://visdunneright.github.io/gd‐comp‐eval/ .

Immersive analytics has emerged as a promising research area, leveraging advances in immersive display technologies and techniques, such as virtual and augmented reality, to facilitate data exploration and decision-making. This paper presents a systematic literature review of 73 studies published between 2013-2022 on immersive analytics systems and visualizations, aiming to identify and categorize the primary dimensions influencing their design. We identified five key dimensions: Academic Theory and Contribution, Immersive Technology, Data, Spatial Presentation, and Visual Presentation. Academic Theory and Contribution assess the motivations behind the works and their theoretical frameworks. Immersive Technology examines the display and input modalities, while Data dimension focuses on dataset types and generation. Spatial Presentation discusses the environment, space, embodiment, and collaboration aspects in IA, and Visual Presentation explores the visual elements, facet and position, and manipulation of views. By examining each dimension individually and cross-referencing them, this review uncovers trends and relationships that help inform the design of immersive systems visualizations. This analysis provides valuable insights for researchers and practitioners, offering guidance in designing future immersive analytics systems and shaping the trajectory of this rapidly evolving field.

Many collaborative data analysis situations benefit from collaborators utilizing different platforms. However, maintaining group awareness between team members using diverging devices is difficult, not least because common ground diminishes. A person using head-mounted VR cannot physically see a user on a desktop computer even while co-located, and the desktop user cannot easily relate to the VR user’s 3D workspace. To address this, we propose the “eyes-and-shoes” principles for group awareness and abstract them into four levels of techniques. Furthermore, we evaluate these principles with a qualitative user study of 6 participant pairs synchronously collaborating across distributed desktop and VR head-mounted devices. In this study, we vary the group awareness techniques between participants and explore two visualization contexts within participants. The results of this study indicate that the more visual metaphors and views of participants diverge, the greater the level of group awareness is needed. A copy of this paper, the study preregistration, and all supplemental materials required to reproduce the study are available on OSF (link).

Internet routing is largely dependent on Border Gateway Protocol (BGP). However, BGP does not have any inherent authentication or integrity mechanisms that help make it secure. Effective security is challenging or infeasible to implement due to high costs, policy employment in these distributed systems, and unique routing behavior. Visualization tools provide an attractive alternative in lieu of traditional security approaches. Several BGP security visualization tools have been developed as a stop-gap in the face of ever-present BGP attacks. Even though the target users, tasks, and domain remain largely consistent across such tools, many diverse visualization designs have been proposed. The purpose of this study is to provide an initial formalization of methods and visualization techniques for BGP cybersecurity analysis. Using PRISMA guidelines, we provide a systematic review and survey of 29 BGP visualization tools with their tasks, implementation techniques, and attacks and anomalies that they were intended for. We focused on BGP visualization tools as the main inclusion criteria to best capture the visualization techniques used in this domain while excluding solely algorithmic solutions and other detection tools that do not involve user interaction or interpretation. We take the unique approach of connecting (1) the actual BGP attacks and anomalies used to validate existing tools with (2) the techniques employed to detect them. In this way, we contribute an analysis of which techniques can be used for each attack type. Furthermore, we can see the evolution of visualization solutions in this domain as new attack types are discovered. This systematic review provides the groundwork for future designers and researchers building visualization tools for providing BGP cybersecurity, including an understanding of the state-of-the-art in this space and an analysis of what techniques are appropriate for each attack type. Our novel security visualization survey methodology-connecting visualization techniques with appropriate attack types-may also assist future researchers conducting systematic reviews of security visualizations. All supplemental materials are available at https://osf.io/tupz6/.

Abstract Likert scales are often used in visualization evaluations to produce quantitative estimates of subjective attributes, such as ease of use or aesthetic appeal. However, the methods used to collect, analyze, and visualize data collected with Likert scales are inconsistent among evaluations in visualization papers. In this paper, we examine the use of Likert scales as a tool for measuring subjective response in a systematic review of 134 visualization evaluations published between 2009 and 2019. We find that papers with both objective and subjective measures do not hold the same reporting and analysis standards for both aspects of their evaluation, producing less rigorous work for the subjective qualities measured by Likert scales. Additionally, we demonstrate that many papers are inconsistent in their interpretations of Likert data as discrete or continuous and may even sacrifice statistical power by applying nonparametric tests unnecessarily. Finally, we identify instances where key details about Likert item construction with the potential to bias participant responses are omitted from evaluation methodology reporting, inhibiting the feasibility and reliability of future replication studies. We summarize recommendations from other fields for best practices with Likert data in visualization evaluations, based on the results of our survey. A full copy of this paper and all supplementary material are available at https://osf.io/exbz8/ .

The dominant markup language for Web visualizations-Scalable Vector Graphics (SVG)-is comparatively easy to learn, and is open, accessible, customizable via CSS, and searchable via the DOM, with easy interaction handling and debugging. Because these attributes allow visualization creators to focus on design on implementation details, tools built on top of SVG, such as D3.js, are essential to the visualization community. However, slow SVG rendering can limit designs by effectively capping the number of on-screen data points, and this can force visualization creators to switch to Canvas or WebGL. These are less flexible (e.g., no search or styling via CSS), and harder to learn. We introduce Scalable Scalable Vector Graphics (SSVG) to reduce these limitations and allow complex and smooth visualizations to be created with SVG. SSVG automatically translates interactive SVG visualizations into a dynamic virtual DOM (VDOM) to bypass the browser's slow 'to specification' rendering by intercepting JavaScript function calls. De-coupling the SVG visualization specification from SVG rendering, and obtaining a dynamic VDOM, creates flexibility and opportunity for visualization system research. SSVG uses this flexibility to free up the main thread for more interactivity and renders the visualization with Canvas or WebGL on a web worker. Together, these concepts create a drop-in JavaScript library which can improve rendering performance by 3-9× with only one line of code added. To demonstrate applicability, we describe the use of SSVG on multiple example visualizations including published visualization research. A free copy of this article, collected data, and source code are available as open science at osf.io/ge8wp.

Immersive Analytics is a quickly evolving field that unites several areas such as visualisation, immersive environments, and human-computer interaction to support human data analysis with emerging technologies. This research has thrived over the past years with multiple workshops, seminars, and a growing body of publications, spanning several conferences. Given the rapid advancement of interaction technologies and novel application domains, this paper aims toward a broader research agenda to enable widespread adoption. We present 17 key research challenges developed over multiple sessions by a diverse group of 24 international experts, initiated from a virtual scientific workshop at ACM CHI 2020. These challenges aim to coordinate future work by providing a systematic roadmap of current directions and impending hurdles to facilitate productive and effective applications for Immersive Analytics.

Virtual reality (VR) researchers struggle to conduct remote studies. Previous work has focused on working around limitations imposed by traditional crowdsourcing methods. However, the potential for leveraging social VR platforms for HCI evaluations is largely unexplored. These platforms have large VR-ready user populations, distributed synchronous virtual environments, and support for user-generated content. We demonstrate how social VR platforms can be used to practically and ethically produce valid research results by replicating two studies using one such platform (VRChat): a quantitative study on Fitts’ Law and a qualitative study on tabletop collaboration. Our replication studies exhibited analogous results to the originals, indicating the research validity of this approach. Moreover, we easily recruited experienced VR users with their own hardware for synchronous, remote, and collaborative participation. We further provide lessons learned for future researchers experimenting using social VR platforms. This paper and all supplemental materials are available at osf.io/c2amz.

Despite recent improvements in online accessibility, the Internet remains an inhospitable place for users with photosensitive epilepsy, a chronic condition in which certain light stimuli can trigger seizures and even lead to death in extreme cases. In this paper, we explore how current risk detection systems have allowed attackers to take advantage of design oversights and target vulnerable users with photosensitivity on popular social media platforms. Through interviews with photosensitive individuals and a critical review of existing systems, we constructed design requirements for consumer-driven protective systems and developed a prototype browser extension for actively detecting and disarming potentially seizure-inducing GIFs and videos. We validate our system with a comprehensive dataset of simulated GIFs and GIFs collected from social media. Finally, we conduct a novel quantitative analysis of the prevalence of seizure-inducing GIFs across popular social media platforms and contribute recommendations for improving online accessibility for individuals with photosensitivity. All study materials are available at https://osf.io/5a3dy/.

Tools and interfaces are increasingly expected to be synchronous and distributed to accommodate remote collaboration. Yet, adoption of these techniques for data visualization is low partly because development is difficult: existing collaboration software systems either do not support simultaneous interaction or require expensive redevelopment of existing visualizations. We contribute VisConnect: a web-based synchronous distributed collaborative visualization system that supports most web-based SVG data visualizations, balances system safety with responsiveness, and supports simultaneous interaction from many collaborators. VisConnect works with existing visualization implementations with little-to-no code changes by synchronizing low-level JavaScript events across clients such that visualization updates proceed transparently across clients. This is accomplished via a peer-to-peer system that establishes consensus among clients on the per-element sequence of events, and uses a lock service to grant access over elements to clients. We contribute collaborative extensions of traditional visualization interaction techniques, such as drag, brush, and lasso, and discuss different strategies for collaborative visualization interactions. To demonstrate the utility of VisConnect, we present novel examples of collaborative visualizations in the healthcare domain, remote collaboration with annotation, and show in an education case study for e-learning with 22 participants that students found the ability to remotely collaborate on class activities helpful and enjoyable for understanding concepts. A free copy of this paper and source code are available on OSF at osf.io/ut7e6 and at visconnect.us.

Timelines are commonly represented on a horizontal line, which is not necessarily the most effective way to visualize temporal event sequences. However, few experiments have evaluated how timeline shape influences task performance. We present the design and results of a controlled experiment run on Amazon Mechanical Turk (n=192) in which we evaluate how timeline shape affects task completion time, correctness, and user preference. We tested 12 combinations of 4 shapes --- horizontal line, vertical line, circle, and spiral — and 3 data types — recurrent, non-recurrent, and mixed event sequences. We found good evidence that timeline shape meaningfully affects user task completion time but not correctness and that users have a strong shape preference. Building on our results, we present design guidelines for creating effective timeline visualizations based on user task and data types. A free copy of this paper, the evaluation stimuli and data, and code are available https://osf.io/qr5yu/

Abstract Autonomous unmanned aerial vehicles are complex systems of hardware, software, and human input. Understanding this complexity is key to their development and operation. Information visualizations already exist for exploring flight logs but comprehensive analyses currently require several disparate and custom tools. This design study helps address the pain points faced by autonomous unmanned aerial vehicle developers and operators. We contribute: a spiral development process model for grounded evaluation visualization development focused on progressively broadening target user involvement and refining user goals; a demonstration of the model as part of developing a deployed and adopted visualization system; a data and task abstraction for developers and operators performing post‐flight analysis of autonomous unmanned aerial vehicle logs; the design and implementation of D ata C omets , an open‐source and web‐based interactive visualization tool for post‐flight log analysis incorporating temporal, geospatial, and multivariate data; and the results of a summative evaluation of the visualization system and our abstractions based on in‐the‐wild usage. A free copy of this paper and source code are available at osf.io/h4p7g

A brain-computer interface (BCI) platform can be utilized by a user to control an external device without making any overt movements. An EEG-based computer cursor control task is commonly used as a testbed for BCI applications. While traditional computer cursor control schemes are based on sensorimotor rhythm, a new scheme has recently been developed using imagined body kinematics (IBK) to achieve natural cursor movement in a shorter time of training. This article attempts to explore optimal decoding algorithms for an IBK paradigm using EEG signals with application to neural cursor control. The study is based on an offline analysis of 32 healthy subjects' training data. Various machine learning techniques were implemented to predict the kinematics of the computer cursor using EEG signals during the training tasks. Our results showed that a linear regression least squares model yielded the highest goodness-of-fit scores in the cursor kinematics model (70% in horizontal prediction and 40% in vertical prediction using a Theil-Sen regressor). Additionally, the contribution of each EEG channel on the predictability of cursor kinematics was examined for horizontal and vertical directions, separately. A directional classifier was also proposed to classify horizontal versus vertical cursor kinematics using EEG signals. By incorporating features extracted from specific frequency bands, we achieved 80% classification accuracy in differentiating horizontal and vertical cursor movements. The findings of the current study could facilitate a pathway to designing an optimized online neural cursor control.