This dataset contains 101 labeled young adult C57BL6J mouse brain MRI scans acquired with a 11.7T MR scanner. As part of the source domain data of Brain Extraction Net (BEN), specific analysis and usage are provided at github and in the manuscript. Since T2WI is the most commonly used modality for rodent brain imaging and we have accumulated extensive brain scans with high-quality annotations in our previous research studies, we first trained the model on the Mouse-T2WI-11.7T dataset, which served as the source-domain dataset, following the conventional fully supervised training strategy. In particular, we focus on the following features: Transferability and flexibility: BEN can adapt to different species, modalities and platforms through its adaptive batch normalization module and semi-supervised learning module. Automatic quality assessment: Unlike traditional toolboxes, which rely on manual inspection to assess the brain extraction quality, BEN incorporates a quality assessment module to automatically evaluate its brain extraction performance. Speed: As a DL-based method, BEN can process an MRI volume faster (<1 second) than traditional toolboxes (several minutes or longer).