**Developer**: Rudy Rizzo, MSc and Roland Kreis, PhD
**Name of dataset**: Synthetic brain MR spectroscopy data - sLASER - TE: 35ms - 3T
**Abstract**: 
Synthetic MR spectroscopy data for quantification purposes (included ground-truth concnetration levels) of use for benchmarking (model fitting, machine learning or neural networks algorithms) or of use in deep learning contexts for pre-training, fine-tuning or ablation study. 

Various dataset size and distribution of concentrations for *brain* spectra are included (for full description, see README.md file available at the URL reported below or at https://github.com/bellarude/MRS_detasets). 
Variation of shim quality, SNR levels and MMBG contribution are included. 
Spectra are free of 0th- and 1st-order phase offset as well as freqeuncy drifts.
(optional) Additional downscaled water signal is added (for some datasets) for quantification referencing.
(otpional) Spectral preprocessing to spectrogram is included.

For further details see: [https://doi.org/10.1002/mrm.29561](https://doi.org/10.1002/mrm.29561)
**Type**: svs
**Format**: Matlab .dat
**Sequence**: sLASER, TE: 35ms, 3T
**Credit**: Please cite the publication mentioned below 
**Contact**: roland.kreis@insel.ch
**Publication**: Rizzo R. et al, Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias, Magn. Reson. Med. 89(5):1707-1727 (2023). [https://doi.org/10.1002/mrm.29561](https://doi.org/10.1002/mrm.29561)
**URL**: [https://doi.org/10.48620/229](https://doi.org/10.48620/229)