The existing datasets for evaluating Visual Inertial Odometry (VIO) have boosted the research of autonomous agents, but they don¡¯t meet the prosperous research of Augmented Reality (AR) or Mixed Reality (MR) given that they are not collected at real AR scenes and do not account for affecting factors of mobile devices. This paper presents the NEAR dataset, an AR oriented visual-inertial dataset collected with commodity handheld phones with ground truthrts. The dataset has a total of 113 sequences in 49 elaborately designed collection cases at two typical indoor scenes, i.e. the living area and the table area. It also covers plenty of setting adjustments for comparison, including the comparisons of different level textures, illuminations, motion patterns, camera settings and the difference between the rolling shutter and the global shutter.