ResXR: validated infrastructure for reproducible studies of human behavior in Extended Reality

Extended Reality (XR) combines experimental control and ecological validity, yet behavioral XR research lacks shared infrastructure: building immersive experiments demands specialized engineering, and custom tools yield data in custom formats that other laboratories cannot readily reanalyze. We present ResXR (Research with XR), an open-source toolkit providing a path from immersive experiment to standardized dataset and quality report, running on standalone headsets. A Unity template records synchronized head, hand, eye, and face tracking with per-sample hardware timestamps; an independent Python pipeline validates quality, masks flagged intervals, and exports raw and derivative datasets in Motion-BIDS format with self-contained quality reports. Three ready-to-run paradigms span common behavioral designs. ResXR is an idea new to XR research: sensor data from consumer headsets must be empirically validated rather than taken from vendor documentation, grounding its schema and quality flags in stress-tested sensor behavior. Its aim is a transparent, community-extensible foundation for reproducible XR experimentation.