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This dataset comprises two paired sets of computer-aided and manually-corrected segmentation labels for the pre-operative multi-institutional scans of the the Ivy Gliblastom Glioblastom Atlas Project (IvyGAP) collection  collection of The Cancer Imaging Archive (TCIA). The paired sets of manually-corrected labels have been approved by expert board-certified neuroradiologists at the Hospital of the University of Pennsylvania and at Case Western Reserve University. Furthermore, for each of the paired sets of approved labels, a diverse comprehensive panel of radiomic features is provided, along with their corresponding skull-stripped and co-registered multi-parametric (i.e. T1, T1-Gd, T2, T2-FLAIR) magnetic resonance imaging (mpMRI) volumes in NIfTI (.nii.gz) format. Pre-operative mpMRI scans were identified in the IvyGAP collection via radiological assessment. These scans were initially skull-stripped and co-registered, before their tumor segmentation labels were produced by different approaches across the two institutions, but consistent within each one. The segmentations were then revised and any label misclassifications were manually corrected by an expert board-certified neuroradiologist. The final labels were used to extract a rich panel of imaging features, including intensity, volumetric, morphologic, histogram-based and textural parameters (including the novel COLLAGE features), as well as spatial information and diffusion properties extracted from glioma growth models. The generated computer-aided and manually-revised labels enable quantitative computational and clinical studies without the need to repeat manual annotations whilst allowing for comparison across studies. They can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational competitions, such as the International Brain Tumor Segmentation (BraTS) challenge. The provided panel of radiomic features may facilitate research integrative of the molecular characterization offered by the Allen Institute, and hence allow associations with molecular markers (radiogenomics), clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features.