Mass spectrometry fragmentation spectra, often referred to as MS/MS spectra or tandem MS spectra, play a pivotal role in molecular structure identification across many fields . However, it remains challenging to link such mass spectra to their corresponding molecular structure . A common approach is to use library matching to identify probable candidates in existing, annotated mass spectral libraries . In addition, several machine-learning approaches have been developed to improve mass spectra annotation . Both for conventional library matching and the training of machine learning models, it is crucial that the annotated library data is correct .