Zugehörige Institution(en) am KIT | Institut für Mikrostrukturtechnik (IMT) | ||||||||||||||||||||||||||||||||||||
Publikationstyp | Forschungsdaten | ||||||||||||||||||||||||||||||||||||
Publikationsdatum | 09.11.2022 | ||||||||||||||||||||||||||||||||||||
Erstellungsdatum | 19.04.2022 | ||||||||||||||||||||||||||||||||||||
Identifikator | DOI: 10.5445/IR/1000152278 KITopen-ID: 1000152278 |
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HGF-Programm | 43.35.04 (POF IV, LK 01) Correlative Data Science | ||||||||||||||||||||||||||||||||||||
Lizenz | Creative Commons Namensnennung – Weitergabe unter gleichen Bedingungen 4.0 International | ||||||||||||||||||||||||||||||||||||
Liesmich | RandomShimDB: A subset of the NMR magnet shimming database ShimDBRandomShimDB 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.
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 formatEach folder in RandomShimDB contains the following files:
The RandomShimDB root folder also contains the reference starting shims (ReferenceShims.par). Data loadingWe deliver a python script 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. |
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Art der Forschungsdaten | Dataset |