Here, we present a new open resource consisting of data acquisition methods, data analysis software, and a freely accessible MS n library (MS n Lib). We designed a novel method for highthroughput analysis and applied it to 34,413 unique compounds. For 30,008 compounds, we obtained fragmentation trees covering positive, negative, or both ion modes, which was achieved in a mere 23 days. We processed the data in an automated, fully open-source workflow that we implemented in the mzmine project. We anticipate that this unique resource of cleaned individual and merged spectra, amounting to 2,350,646 MS n and 357,065 MS 2 entries, will be especially useful for annotating untargeted LC-MS -based metabolomics data. Especially fragmentation patterns for substructures can substantially improve annotation rates because they reveal the origin of the fragment ions. Additionally, untargeted LC-MS data can be annotated with greater confidence in the future when the whole mass spectral tree is matched. Furthermore, this resource will help to improve the adoption and learning rate of machine learning applications for predicting structures and chemical classes based on substructure information in MS n data. Most algorithms to date have been trained on GNPS spectral libraries, as it is the biggest open library resource.