CREATTIVE3D multimodal dataset of user behavior in virtual reality

In the context of the ANR CREATTIVE3D project, we join the expertise of computer science, neuroscience, and clinical practitioners, with the aim to analyze the impact that a simulated low-vision condition has on user navigation behavior in complex road crossing scenes: a common daily situation where the difficulty to access and process visual information (e.g., traffic lights, approaching cars) in a timely fashion can lead to serious consequences on a person's safety and well-being. As a secondary objective, we also aim to investigate the potential role virtual reality could play in rehabilitation and training protocols for low-vision patients. This dataset contains the data as part of the study described in An Integrated Framework for Understanding Multimodal Embodied Experiences in Interactive Virtual Reality. The dataset is metadata for the pre-print Exploring, walking, and interacting in virtual reality with simulated low vision: a living contextual dataset To use this dataset, please cite: @article{wu2025exploring, title={Exploring, walking, and interacting in virtual reality with simulated low vision: a living contextual dataset}, author={Wu, Hui-Yin and Robert, Florent and Gallo, Franz Franco and Pirkovets, Kateryna and Qu{\'e}r{\'e}, Cl{\'e}ment and Delachambre, Johanna and Ramano{\"e}l, Stephen and Gros, Auriane and Winckler, Marco and Sassatelli, Lucile and others}, journal={Scientific Data}, volume={12}, number={1}, pages={330}, year={2025}, publisher={Nature Publishing Group UK London} }@inproceedings{robert2023integrated, title={An integrated framework for understanding multimodal embodied experiences in interactive virtual reality}, author={Robert, Florent and Wu, Hui-Yin and Sassatelli, Lucile and Ramanoel, Stephen and Gros, Auriane and Winckler, Marco}, booktitle={Proceedings of the 2023 ACM International Conference on Interactive Media Experiences}, pages={14--26}, year={2023} } Versions 2024-12-18: Updated readme with description of labels, columns, and suggestions on how to start exploring the dataset. We also provide the questionnaire responses and observation notes in English (questionnaire_translation_EN.csv).