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1.
Implementation of the Science on a Sphere Visualization System as a Web Application
Jurij Anžič, Ciril Bohak, 2025, independent scientific component part or a chapter in a monograph

Abstract: SOS is a visualization system originally developed by the U.S. National Oceanic and Atmospheric Administration National Oceanic and Atmospheric Administration (NOAA) to project dynamic global datasets onto a spherical display, enabling audiences to explore complex Earth system processes in an intuitive and immersive way. While highly efective in museums, science centers, and classrooms, the traditional Science on a Sphere (SoS) hardware installation requires dedicated infrastructure, limiting its accessibility and scalability. This paper presents the design and implementation of a web-based application that reproduces the core functionality of the Science on a Sphere (SoS) system in a browser environment. The application utilizes modern web technologies to render real-time spherical visualizations of global datasets. Users can load, manipulate, and interact with datasets such as atmospheric phenomena, ocean currents, or planetary imagery without the need for specialized hardware. The system also supports interactive controls for rotating, zooming, and overlaying multiple data layers, extending the pedagogical potential of Science on a Sphere (SoS) by enabling personal exploration on laptops, tablets, and mobile devices. By transitioning Science on a Sphere (SoS) from a physical installation to a lightweight, browser-based platform, the proposed solution broadens access to scientific visualizations, promotes remote and classroom learning, and ensures that Science on a Sphere (SoS) content can be integrated into modern online education ecosystems.
Keywords: Science on a Sphere (SOS), Web-based visualization, WebGL, WebGPU, Geospatial data, Educational technology
Published in RUP: 29.01.2026; Views: 250; Downloads: 10
.pdf Full text (1,24 MB)

2.
Is open source the future of AI? : a data-driven approach
Domen Vake, Bogdan Šinik, Jernej Vičič, Aleksandar Tošić, 2025, original scientific article

Abstract: Large language models (LLMs) have become central to both academic research and industrial applications, fueling debates on their accuracy, usability, privacy, and potential misuse. While proprietary models benefit from substantial investments in data and computing resources, open-sourcing is often suggested as a means to enhance trust and transparency. Yet, open-sourcing comes with its own challenges, such as risks of illicit applications, limited financial incentives, and intellectual property concerns. Positioned between these extremes are hybrid approaches—including partially open models and licensing restrictions—that aim to balance openness with control. In this paper, we adopt a data-driven approach to examine the open-source development of LLMs. By analyzing contributions in model improvements, modifications, and methodologies, we assess how community efforts impact model performance. Our findings indicate that the open-source community can significantly enhance models, demonstrating that community-driven modifications can yield efficiency gains without compromising performance. Moreover, our analysis reveals distinct trends in community growth and highlights which architectures benefit disproportionately from open-source engagement. These insights provide an empirical foundation to inform balanced discussions among industry experts and policymakers on the future direction of AI development.
Keywords: large language models, artificial intelligence, open source, data science, HuggingFace
Published in RUP: 25.09.2025; Views: 714; Downloads: 4
.pdf Full text (606,12 KB)
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