  This article presents a new dataset containing motion and physiological data
of users playing the game "Half-Life: Alyx". The dataset specifically targets
behavioral and biometric identification of XR users. It includes motion and
eye-tracking data captured by a HTC Vive Pro of 71 users playing the game on
two separate days for 45 minutes. Additionally, we collected physiological data
from 31 of these users. We provide benchmark performances for the task of
motion-based identification of XR users with two prominent state-of-the-art
deep learning architectures (GRU and CNN). After training on the first session
of each user, the best model can identify the 71 users in the second session
with a mean accuracy of 95% within 2 minutes. The dataset is freely available
under https://github.com/cschell/who-is-alyx
