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As their clinical symptoms and image appearances on conventional magnetic resonance imaging (MRI) can be astonishingly similar, their accurate differentiation based solely on clinical and radiological information can be very challenging, in particular for “cancer of unknown primary”, where no systemic malignancy is known or found. Nonetheless, these entities possess unique biological properties, necessitating different treatment strategies tailored to the tumor entity. Non-invasive multiparametric MRI offers the potential to identify these distinct biological properties, aiding in the characterization and differentiation of HGGs and BMs. With the rapid advancement in radiomics, researchers are now able to extract quantitative features from images, which can be pivotal for the differential diagnosis and disease characterization of HGG and BM. However, there is a scarcity of publicly available multi-origin brain tumor imaging data for tumor characterization. In this paper, we introduce a multi-center, multi-origin brain tumor MRI (MOTUM) imaging dataset obtained from 67 patients: 29 with high-grade gliomas, 20 with lung metastases, 10 with breast metastases, 2 with gastric metastasis, 4 with ovarian metastasis, and 2 with melanoma metastasis. This dataset includes FLAIR, T1-weighted, contrast enhanced T1-weighted, T2-weighted sequences, segmentation masks of two tumor regions, and clinical data. Our data-sharing initiative is to support the benchmarking of automated tumor segmentation, multi-modal machine learning, and disease differentiation of multi-origin brain tumors in a multi-center setting. 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