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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. 

With the commercialization of virtual/augmented reality (VR/AR) devices, there is an increasing interest in combining immersive and non-immersive devices (e.g., desktop computers) for asymmetric collaborations. While such asymmetric settings have been examined in social platforms, significant questions around layout dimensionality in data-driven decision-making remain underexplored. A crucial inquiry arises: although presenting a consistent 3D virtual world on both immersive and non-immersive platforms has been a common practice in social applications, does the same guideline apply to lay out data? Or should data placement be optimized locally according to each device's display capacity? This study aims to provide empirical insights into the user experience of asymmetric collaboration in data-driven decision-making. We tested practical dimensionality combinations between PC and VR, resulting in three conditions: PC2D+VR2D, PC2D+VR3D, and PC3D+VR3D. The results revealed a preference for PC2D+VR3D, and PC2D+VR2D led to the quickest task completion. Our investigation facilitates an in-depth discussion of the trade-offs associated with different layout dimensionalities in asymmetric collaborations. 

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. 

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. 

Narratives are fundamental to our understanding of the world, providing us with a natural structure for knowledge representation over time. Computational narrative extraction is a subfield of artificial intelligence that makes heavy use of information retrieval and natural language processing techniques. Despite the importance of computational narrative extraction, relatively little scholarly work exists on synthesizing previous research and strategizing future research in the area. In particular, this article focuses on extracting news narratives from an event-centric perspective. Extracting narratives from news data has multiple applications in understanding the evolving information landscape. This survey presents an extensive study of research in the area of event-based news narrative extraction. In particular, we screened more than 900 articles, which yielded 54 relevant articles. These articles are synthesized and organized by representation model, extraction criteria, and evaluation approaches. Based on the reviewed studies, we identify recent trends, open challenges, and potential research lines. 

The explosive growth in supercomputers capacity has changed simulation paradigms. Simulations have shifted from a few lengthy ones to an ensemble of multiple simulations with varying initial conditions or input parameters. Thus, an ensemble consists of large volumes of multi-dimensional data that could go beyond the exascale boundaries. However, the disparity in growth rates between storage capabilities and computing resources results in I/O bottlenecks. This makes it impractical to utilize conventional postprocessing and visualization tools for analyzing such massive simulation ensembles. In situ visualization approaches alleviate I/O constraints by saving predetermined visualizations in image databases during simulation. Nevertheless, the unavailability of output raw data restricts the flexibility of post hoc exploration of in situ approaches. Much research has been conducted to mitigate this limitation, but it falls short when it comes to simultaneously exploring and analyzing parameter and ensemble spaces. In this paper, we propose an expert-in-the-loop visual exploration analytic approach. The proposed approach leverages: feature extraction, deep learning, and human expert–AI collaboration techniques to explore and analyze image-based ensembles. Our approach utilizes local features and deep learning techniques to learn the image features of ensemble members. The extracted features are then combined with simulation input parameters and fed to the visualization pipeline for in-depth exploration and analysis using human expert + AI interaction techniques. We show the effectiveness of our approach using several scientific simulation ensembles. 

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.