Recently, deep neural networks have shown great potentials for solving dipole inversion of quantitative susceptibility mapping (QSM) with improved results. However, these studies utilized their limited dataset for network training and inference, making the conclusion might be untrustworthy. Thus, a common dataset is needed for a fair comparison between different QSM reconstruction networks. Additionally, finding an in vivo reference susceptibility map that matches acquired single-orientation phase data remains an open problem. Susceptibility tensor imaging (STI) \chi_{33} and Calculation of Susceptibility through Multiple Orientation Sampling (COSMOS) are considered reference susceptibillity candidates. However, a large number of multi-orientation GRE data for STI or COSMOS reconstruction is now unavailable for training supervised neural networks for QSM. In this study, we reported the largest multi-orientation dataset, to the best of our knowledge in the QSM research field, with a total of 144 scans of 8 healthy subjects collected using a 3D GRE sequence from the same MR scanner. In addition, the parcellation of deep gray matter is also provided for extracting susceptibility values automatically.