RandomShimDB: A subset of the NMR magnet shimming database ShimDB

RandomShimDB is a subset of the NMR magnet shimming database ShimDB and contains over 15000 instances. Data is aquired on a Spinsolve 80 Carbon spectrometer (Magritek GmbH, Aachen, Germany, www.magritek.com) on 5%vv H2O in D2O and a water solution with CuSO4 (5mmol/L). RandomShimDB is part of "Acquisitions with random shim values enhances AI-driven NMR shimming" by M. Becker et al. [1]. The acquisition procedure was as follows. The manufacturer's automated shimming technique, based on the downhill simplex method, was used to obtain a reference spectrum. Then, the shims X, Y, Z and Z2 were varied. 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: https://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: https://doi.org/10.1007/s10858-013-9718-x.