Motion tracklets are the basic fragments of the track followed by a moving object and constitute various everyday motion behavior. An accurate estimation of motion tracklets in 3-D space can enable a wide range of applications, ranging from human computer interaction to medical rehabilitation. This paper presents a novel dataset for accurate 6-DoF motion tracklet estimation with the inertial sensors on commodity smartphones. The dataset consists of around 100 minutes of handheld motion with 3 predominant types of motion track-lets and accurate ground truth using the Vicon systems. With the presented dataset, we further benchmarked the trajectory estimation using a lightweight neural odometry model, showcasing how the dataset can be used while providing quantitative performance for downstream tasks. Our dataset, toolkit and source code available at https://github.com/MAPS-Lab/smartphone-tracking-dataset.