This paper introduces the concept of Virtual Laboratories of Viability as a new generation of immersive scientific environments within the framework of Vitology. Building upon the concepts of the Space of Harmony and immersive visualization, virtual laboratories are proposed as integrated platforms for investigating the viability of systems of diverse nature through computational modeling, interactive experimentation, artificial intelligence, digital twins, and interdisciplinary collaboration. The article discusses the conceptual architecture of such laboratories, their role in exploring dynamic processes of viability, and their potential applications in scientific research, education, engineering, ecology, medicine, organizational management, and intelligent systems. It is argued that virtual laboratories can significantly expand the possibilities of interdisciplinary research by providing safe, scalable, and adaptive environments for studying viability under various conditions and development scenarios. Keywords Vitology; Virtual Laboratories of Viability; immersive technologies; Space of Harmony; viability; computational modeling; artificial intelligence; digital twins; interactive experimentation; interdisciplinary research; scientific visualization; intelligent systems.
Collaborative Immersive Environments for Viability Research represent a new generation of scientific ecosystems designed to support collective exploration of viability within the framework of Vitology. Unlike conventional virtual laboratories intended primarily for individual exp…
Immersive Educational Environments represent a new generation of educational ecosystems designed to integrate immersive technologies with scientific learning within the framework of Vitology. Unlike conventional digital learning platforms, immersive educational environments provi…
This paper introduces the concept of Immersive Digital Twins of Viable Systems as a new stage in the development of intelligent scientific infrastructures within the framework of Vitology. The proposed approach integrates digital twins, immersive technologies, artificial intellig…
This dataset release represents Part 4 of the comprehensive young trefoil crop agricultural analysis project. While Part 1, Part 2 and Part 3 provided the raw image captures and camera parameters and machine-learning-ready image tiles. Part 4 delivers fully processed, georeferenc…
This record contains field-deployment datasets for two seabird species, streaked shearwaters and black-tailed gulls, used in the paper “Automated Ethogram Elaboration: A Cross-species Deep-learning Model Deployed On-board Enables Acceleration-triggered Capture of Diverse Behaviou…
INR-QSM — a subject-specific UNSUPERVISED deep-learning dipole inversion using an implicit neural representation. No pretrained weights: a sine-activated coordinate MLP (SIREN) is OPTIMIZED per-subject so that the susceptibility it represents, pushed through the QSM dipole forwar…