Extended reality head-mounted displays are increasingly employed as research instruments for collecting behavioral data, yet logged signals typically traverse multiple processing stages–firmware, proprietary runtimes, APIs, engine plugins, and application logic–before reaching datasets. Each stage can introduce delay, transform signals, or restrict access to raw measurements, meaning identical physical events may appear differently depending on where in this pipeline data are captured. Despite these implications, many studies treat data logging as an implementation detail, often omitting disclosure of access points, software versions, or sampling strategies, thereby compromising reproducibility and cross-study comparability. This paper proposes that extended reality data collection can be treated as a layer-aware design problem. Four contributions are presented: (1) a taxonomy identifying five pipeline layers (hardware/firmware, runtime/middleware, engine/framework, application, and network) with distinct temporal and fidelity characteristics; (2) an analysis of how layer selection affects latency, sampling rates, and signal transformations; (3) practical guidelines for layer-aware data logging; and (4) a concrete instantiation for the Meta Quest Pro, accompanied by an open-source Unity plugin enabling layer-aware logging across modalities. These contributions provide a practical framework for transparent, reproducible, and methodologically rigorous extended reality behavioral data collection.

From education to medicine to entertainment, a wide range of industrial and academic fields now utilize eXtended Reality (XR) technologies. This diversity and growing use are boosting research and leading to an increasing number of XR experiments involving human subjects. The main aim of these studies is to understand the user experience in the broadest sense, such as the user cognitive and emotional states. Behavioral data collected during XR experiments, such as user movements, gaze, actions, and physiological signals constitute precious assets for analyzing and understanding the user experience. While they contribute to overcome the intrinsic flaws of explicit data such as post-experiment questionnaires, the required acquisition and analysis tools are costly and challenging to develop, especially for 6DoF (Degrees of Freedom) XR experiments. Moreover, there is no common format for XR behavioral data, which restrains data-sharing, and thus hinders wide usages across the community, replicability of studies, and the constitution of large datasets or meta-analysis. In this context, we present PLUME, an open-source software toolbox (PLUME Recorder, PLUME Viewer, PLUME Python) that allows for the exhaustive record of XR behavioral data (including synchronous physiological signals), their offline interactive replay and analysis (with a standalone application), and their easy sharing due to our compact and interoperable data format. We believe that PLUME can greatly benefit the scientific community by making the use of behavioral and physiological data available for the greatest, contributing to the reproducibility and replicability of XR user studies, enabling the creation of large datasets, and contributing to a deeper understanding of user experience.

The impact of olfactory cues on user experience in virtual reality is increasingly studied. However, results are still heterogeneous and existing studies difficult to replicate, mainly due to a lack of standardized olfactory displays. In that context, we present Nebula, a low-cost, open-source, olfactory display capable of diffusing scents at different diffusion rates using a nebulization process. Nebula can be used with PC VR or autonomous head-mounted displays, making it easily transportable without the need for an external computer. The device was calibrated to diffuse at three diffusion rates: no diffusion, low and high. For each level, the quantity of delivered odor was precisely characterized using a repeated weighting method. The corresponding perceived olfactory intensities were evaluated by a psychophysical experiment on sixteen participants. Results demonstrated the device capability to successfully create three significantly different perceived odor intensities (Friedman test p < 10− 6, Wilcoxon tests padj < 10− 3), without noticeable smell persistence and with limited noise and discomfort. For reproducibility and to stimulate further research in the area, 3D printing files, electronic hardware schemes, and firmware/software source-code are made publicly available.