We release these four diffusion MRI data sets as part of our recent journal publication; Fick, Rutger H.J., et al. "Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time." Medical Image Analysis (2017). More detailed information about the use of these data sets can also be found in the publication. We acquired test-retest diffusion MRI spin echo sequences from two C57Bl6 wild-type mice on an 11.7 Tesla Bruker scanner. The test and retest acquisition were taken 48 hours from each other. The data consists of 80x160x5 voxels of size 110x110x500\(\mu\)m. Each data set consists of 515 Diffusion-Weighted Images (DWIs) spread over 35 acquisition shells. The shells are spread over 7 gradient strength shells with a maximum gradient strength of 491 mT/m, 5 pulse separation shells between [10.8 - 20.0]ms, and a pulse length of 5ms. We manually created a brain mask and corrected the data from eddy currents and motion artifacts using FSL's eddy. We then drew a region of interest in the middle slice in the corpus callosum, where the tissue is reasonably coherent. - The diffusion MRI data are contained in the files with 'dwis' in the name. - The corpus callosum masks are contained in the files with 'mask' in the name. - The acquisition parameters are contained in the .txt files.

This work was partly supported by ANR ``MOSIFAH" under ANR-13-MONU-0009-01, the ERC under the European Union's Horizon 2020 research and innovation program (ERC Advanced Grant agreement No 694665:CoBCoM), MAXIMS grant funded by ICM's The Big Brain Theory Program and ANR-10-IAIHU-06.

{"references": ["Fick, R.H., Petiet, A., Santin, M., Philippe, A.C., Lehericy, S., Deriche, R. and Wassermann, D., 2017. Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time. Medical Image Analysis."]}