RandomShimDB: A subset of the NMR magnet shimming database ShimDB

The dataset parameters were obtained by relative offsets from the reference shim values in a range R with weighting W, following Gaussian noise sampling. For each combination, the raw FID, acquisition parameters, and the shim values were stored. | Topic | Parameter | Value | |------------------------|------------------|-----------------------| | Dataset parameters | Shims | X,Y,Z,Z2 | | | Weightings W | [1.2, 1.0, 2.0, 18.0] | | | Shim range R | +/- 50 | | | Sample I | H2O+CuSO4 | | | Sample II | 5vol% H2O in D2O | | | Nr. spectra | {5000,10000} | | Acquisition parameters | Nucleus | 1H | | | Bandwidth | 5 kHz | | | Points | 32768 | | | Repetition time | 2000 ms | | | Phase correction | phi_0 | **We strongly encourage researchers to extend ShimDB with their own subsets to stimulate developments.

We offer to include raw data or links to your publications into ShimDB.** ## Files format Each folder in RandomShimDB contains the following files: - data.1d -> the raw FID. - shims.par -> Shim values, where only linear shims are non-zero. - acqu.par -> Acquisition parameters. - proc.par -> Processing parameters. The RandomShimDB root folder also contains the reference starting shims (ReferenceShims.par). ## Data loading We deliver a python script ```utils_IO.py``` alongside ShimDB to easily load the database into numpy array structure using the nmrglue packages[2].

The following python libraries and packages are required: os, numpy, glob, nmrglue (>= v0.9.dev0) # References [1] M. Becker, S. Lehmkuhl, S. Kesselheim, J. G. Korvink, and M. Jouda, “Acquisitions with random shim values enhance AI-driven NMR shimming,” J. Magn. Reson., p. 107323, 2022, doi: doi.org/10.1016/j.jmr.2022.107323. [2] J. J. Helmus and C. P. Jaroniec, “Nmrglue: An open source Python package for the analysis of multidimensional NMR data,” J. Biomol.

NMR, vol. 55, no. 4, pp. 355–367, 2013, doi: doi.org/10.1007/s10858-013-9718-x.