The research data supports Kontogiannis, A., Elgersma, S. V., Sederman, A. J. & Juniper, M. P. Bayesian inverse Navier–Stokes problems: joint flow field reconstruction and parameter learning, https://doi.org/10.48550/arXiv.2406.18464, 2024. The flow MRI experiment is described in section 5.1 of that paper and in citated papers.

The data is contained in two folders: 'highRe' corresponds to data at Reynolds number = 1526; 'lowRe' corresponds to data at Reynolds number = 554.

Each folder contains three npy files (standard binary file format for a NumPy array): flow_data.npy contains the velocity components [u,v,w] on a [115,54,77] uniform mesh; mang.npy contains the mean magnitude image (nuclear spin density) on this mesh; mask_data.npy contains a mask that identifies the fluid regions on this mesh.

The code RUNME.py is a Python file that converts these npy files to centimetres-grammes-seconds (cgs) dimensions and plots slice number 'y_slice_idx' of the data to screen.