Realistic human grasping data is of vital importance in the research of human grasping behavior analysis and human-like robotic grasping. A grasping dataset should include object shape information， contact points， and hand shapes and poses. However， related works often capture images or videos to estimate the human grasping behavior， which leads to the inaccuracy of joint degrees of freedom. Virtual Reality （VR） technology was used to establish a virtual environment， and digital gloves were used to directly capture 3D objects and hand poses in the virtual environment as capturing data. The proposed dataset contains 91 objects with various shapes （each with 108 poses） from 49 object categories， and 52 173 3D hand grasps， which scale and richness are far more than existing dataset used to study human grasping behavior and human-centered grasp technology. In addition， the collected dataset was used for grasp saliency analysis and human-like grasping calculation， and the experimental results demonstrate the practical value of this dataset.