We introduce DashSpace, a live collaborative immersive and ubiquitous analytics (IA/UA) platform designed for handheld and head-mounted Augmented/Extended Reality (AR/XR) implemented using WebXR and open standards. To bridge the gap between existing web-based visualizations and the immersive analytics setting, DashSpace supports visualizing both legacy D3 and Vega-Lite visualizations on 2D planes, and extruding Vega-Lite specifications into 2.5D. It also supports fully 3D visual representations using the Optomancy grammar. To facilitate authoring new visualizations in immersive XR, the platform provides a visual authoring mechanism where the user groups specification snippets to construct visualizations dynamically. The approach is fully persistent and collaborative, allowing multiple participants---whose presence is shown using 3D avatars and webcam feeds---to interact with the shared space synchronously, both co-located and remotely. We present three examples of DashSpace in action: immersive data analysis in 3D space, synchronous collaboration, and immersive data presentations.

We introduce Datamancer, a wearable device enabling bimanual gesture interaction across multi-display ubiquitous analytics environments. Datamancer addresses the gap in gesture-based interaction within data visualization settings, where current methods are often constrained by limited interaction spaces or the need for installing bulky tracking setups. Datamancer integrates a finger-mounted pinhole camera and a chest-mounted gesture sensor, allowing seamless selection and manipulation of visualizations on distributed displays. By pointing to a display, users can acquire the display and engage in various interactions, such as panning, zooming, and selection, using both hands. Our contributions include (1) an investigation of the design space of gestural interaction for physical ubiquitous analytics environments; (2) a prototype implementation of the Datamancer system that realizes this model; and (3) an evaluation of the prototype through demonstration of application scenarios, an expert review, and a user study.

AI's transformative impact on work, education, and everyday life makes it as much a political artifact as a technological one. Current AI models are opaque, centralized, and overly generic. The algorithmic automation they provide threatens human agency and democratic values in both workplaces and daily life. To confront such challenges, we turn to Scandinavian Participatory Design (PD), which was devised in the 1970s to face a similar threat from mechanical automation. In the PD tradition, technology is seen not just as an artifact, but as a locus of democracy. Drawing from this tradition, we propose Participatory AI as a PD approach to human-centered AI that applies five PD principles to four design challenges for algorithmic automation. We use concrete case studies to illustrate how to treat AI models less as proprietary products and more as shared socio-technical systems that enhance rather than diminish human agency, human dignity, and human values.

Consumer-level XR hardware now enables immersive spatial computing, yet most knowledge work remains confined to traditional 2D desktop environments. These worlds exist in isolation: writing emails or editing presentations favors desktop interfaces, while viewing 3D simulations or architectural models benefits from immersive environments. We address this fragmentation by combining spatial hypermedia, shareable dynamic media, and cross-reality computing to provide (1) composability of heterogeneous content and of nested information spaces through spatial transclusion, (2) pervasive cooperation across heterogeneous devices and platforms, and (3) congruent spatial representations despite underlying environmental differences. Our implementation, the Spatialstrates platform, embodies these principles using standard web technologies to bridge 2D desktop and 3D immersive environments. Through four scenarios---collaborative brainstorming, architectural design, molecular science visualization, and immersive analytics---we demonstrate how Spatialstrates enables collaboration between desktop 2D and immersive 3D contexts, allowing users to select the most appropriate interface for each task while maintaining collaborative capabilities.

Many scientists use programming to analyze their data. In this paper, we explore the computational ecosystem of scientists and their socio-technical system of computing through a human-centered approach. By employing contextual inquiry techniques with nine scientists drawn from fields such as theoretical physics, biomedical science, and entomology, we learned that programming is a tool for scientists, and as such the output is more important than the code itself. We found that during analysis, scientists often write code to create plots, and then compare these plots to assess the match of output to their expectation. Participants used ChatGPT while coding. We also found that scientists' programming tools and practices often limit their analysis. Finally, based on a combined human-computer interaction and programming language analysis, we identify drivers and blockers of scientists' work. Our findings uncover opportunities for the design of programming tools and languages.

Analyzing complex data is a non‐linear process that alternates between identifying discrete facts and developing overall assessments and conclusions. In addition, data analysis rarely occurs in solitude; multiple collaborators can be engaged in the same analysis, or intermediate results can be reported to stakeholders. However, current data‐driven communication tools are detached from the analysis process and promote linear stories that forego the hierarchical and branching nature of data analysis, which leads to either too much or too little detail in the final report. We propose a conceptual design for integrated data‐driven reporting that allows for iterative structuring of insights into hierarchies linked to analytic provenance and chosen analysis views. The hierarchies become dynamic and interactive reports where collaborators can review and modify the analysis at a desired level of detail. Our web‐based InsideInsights system provides interaction techniques to annotate states of analytic components, structure annotations, and link them to appropriate presentation views. We demonstrate the generality and usefulness of our system with two use cases and a qualitative expert review.

We present Vistribute, a framework for the automatic distribution of visualizations and UI components across multiple heterogeneous devices. Our framework consists of three parts: (i) a design space considering properties and relationships of interactive visualizations, devices, and user preferences in multi-display environments; (ii) specific heuristics incorporating these dimensions for guiding the distribution for a given interface and device ensemble; and (iii) a web-based implementation instantiating these heuristics to automatically generate a distribution as well as providing interaction mechanisms for user-defined adaptations. In contrast to existing UI distribution systems, we are able to infer all required information by analyzing the visualizations and devices without relying on additional input provided by users or programmers. In a qualitative study, we let experts create their own distributions and rate both other manual distributions and our automatic ones. We found that all distributions provided comparable quality, hence validating our framework.

Visualization tools are often specialized for specic tasks, which turns the user's analytical workow into a fragmented process performed across many tools. In this paper, we present a component model design for data visualization to promote modular designs of visualization tools that enhance their analytical scope. Rather than fragmenting tasks across tools, the component model supports unification, where components—the building blocks of this model—can be assembled to support a wide range of tasks. Furthermore, the model also provides additional key properties, such as support for collaboration, sharing across multiple devices, and adaptive usage depending on expertise, from creating visualizations using dropdown menus, through instantiating components, to actually modifying components or creating entirely new ones from scratch using JavaScript or Python source code. To realize our model, we introduce Vistrates, a literate computing platform for developing, assembling, and sharing visualization components. From a visualization perspective, Vistrates features cross-cutting components for visual representations, interaction, collaboration, and device responsiveness maintained in a component repository. From a development perspective, Vistrates offers a collaborative programming environment where novices and experts alike can compose component pipelines for specific analytical activities. Finally, we present several Vistrates use cases that span the full range of the classic "anytime" and "anywhere" motto for ubiquitous analysis: from mobile and on-the-go usage, through office settings, to collaborative smart environments covering a variety of tasks and devices.