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The SAGE Suite–SAGE1, SAGE2, and SAGE3–translates advances in visualization, cyberinfrastructure, and human-computer interaction into an open, scalable platform that aligns with embodied cognition to support collaborative, spatial reasoning on large displays and personal devices. Over two decades and hundreds of deployed walls worldwide, SAGE has enabled scientists, educators, and students to juxtapose heterogeneous media, sustain shared context, and accelerate sensemaking across the research lifecycle. This paper contributes: (1) a synthesis of the Suite’s translational impact across domains–from biology and atmospheric science to disaster management, health care, public outreach and workforce development; (2) a comparative framing of SAGE3 (the Smart Amplified Group Environment) among Computer Supported Cooperative Work and infinite-canvas tools; (3) the design rationale and user experience foundations of SAGE3’s “spatial thinking operating system,” including boards, rooms, wall viewports, and multi-user attention/flow mechanisms; (4) a modular architecture that delivers low-latency synchronization, extensibility via plugins, and privacy-aware deployment; and (5) a paradigm for human–Artificial Intelligence (AI) collaboration that spatializes notebooks and conversational workflows, enabling multi-user, multi-AI interaction grounded in shared visual context. We also surface systemic challenges in recognizing software-as-instrument within academic incentives and document emergent usage patterns spanning synchronous/asynchronous, co-located/distributed work. SAGE3 demonstrates how open, research-driven cyberinfrastructure can couple spatial cognition with collective intelligence to advance scientific collaboration and decision-making. 

Recent advances in Natural Language Interfaces (NLIs) and Large Language Models (LLMs) have transformed the way we tackle NLP tasks, shifting the focus towards a more Pragmatics-based perspective. This shift enables more natural interactions between humans and voice assistants, which have historically been difficult to achieve. Pragmatics involves understanding how users often speak out of turn, interrupt one another, or provide relevant information without being explicitly asked (maxim of quantity). To explore this, we developed a digital assistant that continuously listens to conversations and proactively generates relevant visualizations during data exploration tasks. In a within-subject study, participants interacted with both proactive and non-proactive versions of a voice assistant while exploring the Hawaii Climate Data Portal (HCDP). Results suggest that interaction with the proactive assistant increased the total number of utterances and discoveries, facilitated quicker and more reliable insights, and led to greater usage of the system’s chart capabilities. Our study highlights the potential of proactive AI in NLIs and identifies key challenges in its implementation, offering insights for future research. 

September 2023 marked the 50th anniversary of the Electronic Visualization Laboratory (EVL). This paper summarizes EVL’s efforts in Visual Data Science, with a focus on the many networked, immersive, collaborative visualization and virtual-reality (VR) systems and applications the Lab has developed and deployed, as well as lessons learned and future plans. 

With the emergence of Artificial Intelligence, it’s becoming essential for everyone—not just scientists and students—to harness its potential to stay competitive, think more critically, and drive innovation in a rapidly evolving world. SAGE3 is an open-source platform designed to help individuals and teams collaborate effectively—with each other and with AI—to accelerate the process of understanding, problem-solving, and discovery. It empowers everyday citizens to become smarter and more innovative by making complex information more accessible and actionable. Developed from over 20 years of National Science Foundation–funded research, SAGE3 is grounded in a deep understanding of how people work together across disciplines and interact with diverse streams of data. SAGE3 supports translational and convergent research, making it ideal for integrating insights from science, technology, community knowledge, and policy to tackle real-world challenges. It enables people to work with large and varied information sources—collaborating seamlessly with AI to reach decisions more quickly, clearly, and confidently. Whether working side-by-side on expansive shared display walls or contributing remotely from a laptop—at home, at work, or while traveling—SAGE3 enables flexible, co-located and distributed collaboration. It transforms static data into shared understanding, powering more informed, creative, and collective decision-making for all. 

With the decreasing cost of consumer display technologies making it easier for universities to have larger displays in classrooms, and the ubiquitous use of online tools such as collaborative whiteboards for remote learning during the COVID-19 pandemic, combining the two can be useful in higher education. This is especially true in visually intensive classes, such as data visualization courses, that can benefit from additional "space to teach," coined after the "space to think" sense-making idiom. In this paper, we reflect on our approach to using SAGE3, a collaborative whiteboard with advanced features, in higher education to teach visually intensive classes, provide examples of activities from our own visually-intensive courses, and present student feedback. We gather our observations into usage patterns for using content-rich canvases in education. 

September 2023 marks the 50th anniversary of the Electronic Visualization Laboratory (EVL) at University of Illinois Chicago (UIC). EVL's introduction of the CAVE Automatic Virtual Environment in 1992, the first widely replicated, projection-based, walk-in, virtual-reality (VR) system in the world, put EVL at the forefront of collaborative, immersive data exploration and analytics. However, the journey did not begin then. Since its founding in 1973, EVL has been developing tools and techniques for real-time, interactive visualizations—pillars of VR. But EVL's culture is also relevant to its successes, as it has always been an interdisciplinary lab that fosters teamwork, where each person's expertise contributes to the development of the necessary tools, hardware, system software, applications, and human interface models to solve problems. Over the years, as multidisciplinary collaborations evolved and advanced scientific instruments and data resources were distributed globally, the need to access and share data and visualizations while working with colleagues, local and remote, synchronous and asynchronous, also became important fields of study. This paper is a retrospective of EVL's past 50 years that surveys the many networked, immersive, collaborative visualization and VR systems and applications it developed and deployed, as well as lessons learned and future plans. 

SAGE3, the newest and most advanced generation of the Smart Amplified Group Environment, is an open-source software designed to facilitate collaboration among scientists, researchers, students, and professionals across various fields. This tutorial aims to introduce attendees to the capabilities of SAGE3, demonstrating its ability to enhance collaboration and productivity in diverse settings, from co-located office collaboration to remote collaboration to both at once, with diverse displays, from personal laptops to large-scale display walls. Participants will learn how to effectively use SAGE3 for brainstorming, data analysis, and presentation purposes, as well as installation of private collaboration servers and development of custom applications. 

SAGE3 is software to augment the cyberinfrastructure-enhanced research and education enterprise by supporting data-intensive collaboration across a wide range of display devices from high-resolution display walls to laptops. This paper provides insight into SAGE3’s implementation, which significantly improves on prior generations of SAGE by leveraging emerging advancements in Web technologies and Artificial Intelligence. We also provide an overview of new usage patterns that we observed with SAGE3. 

Current computational notebooks, such as Jupyter, are a popular tool for data science and analysis. However, they use a 1D list structure for cells that introduces and exacerbates user issues, such as messiness, tedious navigation, inefficient use of large screen space, performance of non-linear analyses, and presentation of non-linear narratives. To ameliorate these issues, we designed a prototype extension for Jupyter Notebooks that enables 2D organization of computational notebook cells into multiple columns. In this paper, we present two evaluative studies to determine whether such “2D computational notebooks” provide advantages over the current computational notebook structure. From these studies, we found empirical evidence that our multi-olumn 2D computational notebooks provide enhanced efficiency and usability. We also gathered design feedback which may inform future works. Overall, the prototype was positively received, with some users expressing a clear preference for 2D computational notebooks even at this early stage of development. 

Translational software research bridges the gap between scientific innovations and practical applications, driving impactful societal advancements. However, developing such software is challenging due to interdisciplinary collaboration, technology adoption, and postfunding sustainability. This article presents the experiences and insights of the Scalable Adaptive Graphics Environment (SAGE) team, which has spent two decades developing translational, cross-disciplinary, collaboration tools to benefit computational science research. With a focus on SAGE and its next-generation iterations, we explore the inherent challenges in translational research, such as fostering cross-disciplinary collaboration, motivating technology adoption, and ensuring postfunding product sustainability. We also discuss the roles of funding agencies, policymakers, and academic institutions in promoting translational research. Although the journey is fraught with challenges, the societal impact and satisfaction derived from translational research underscore its significance in the broader scientific landscape. This article aims to encourage further conversation and the development of effective models for translational software projects.