ShimDB is a collection of 1H NMR signals recorded under the application of shim coil fields and allows inference of changes to the NMR spectrum or FID depending on shim offsets.
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.
RandomShimDB is a subset of the NMR shimming database ShimDB and contains over 15000 instances under application of random shim offsets to the X,Y,Z and Z2 shims. Data is acquired on a Spinsolve 80 Carbon spectrometer (Magritek GmbH, Aachen, Germany, www.magritek.com) using 5%vv H2O in D2O and a water solution with CuSO4 (5mmol/L).
RandomShimDB is part of "Acquisitions with random shim values enhance AI-driven NMR shimming" by M. Becker et al. [1].
For more information about the subset, see https://doi.org/10.5445/IR/1000152278.
[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.
LinearShimDB is a subset of the NMR shimming database ShimDB and contains over 9000 instances under application of linear shim offsets. Data is acquired on a Spinsolve 80 Carbon spectrometer (Magritek GmbH, Aachen, Germany, www.magritek.com) using a water solution with CuSO4 (5mmol/L).
LinearShimDB is part of "Deep Regression with Ensembles enables Fast, First-Order Shimming in low-field NMR" by M. Becker et al. [2].
For more information about the subset, see https://doi.org/10.5445/IR/1000139569.
[2] M.Becker, M.Jouda, A.Kolchinskaya, J.G.Korvink, Deep regression with ensembles enables fast, first-order shimming in low-field NMR, Journal of Magnetic Resonance 2022, 107151, ISSN 1090-7807, https://doi.org/10.1016/j.jmr.2022.107151. (https://www.sciencedirect.com/science/article/pii/S109078072200009X)
Copyright (c) 2022 Becker Moritz
The database is published under CC BY-SA.
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