Structural elucidation and compound annotation based on untargeted mass spectrometry (MS) data relies on analyzing fragmentation spectra (MS ) by isolating precursor ions before applying collision-induced dissociation (CID) or other fragmentation methods. In this context, spectral libraries take on a crucial role as a community resource for the initial exploration of the chemical space in untargeted MS studies, for example in the fields of biomedicine, environmental protection, and metabolomics. Still, spectral library matching usually results in low annotation rates, leaving most biomarkers or compounds of interest unidentified, even when searching for analogs with modification-aware algorithms. This is mostly because of the low availability of high-quality open mass spectral libraries and of reference standards, especially when it comes to purified natural products. Large open MS libraries, such as GNPS , MoNA, and MassBank EU , currently contain 500,000, 148,000, and 97,000 experimental spectra of 27,000, 14,500, and 17,400 unique compounds, respectively, while compound databases like COCONUT , ChEMBL , or PubChem contain hundreds of thousands to millions of curated structures. There are commercial spectral libraries, such as Wiley, NIST/EPA (48,000 structures), and METLIN , that have restrictions imposed on their use; for example, spectra contained within them cannot be used outside of the tools provided with them, which precludes their use in data science applications. This highlights the importance of creating open spectral libraries. Multi-stage fragmentation (MS n ) data are especially lacking in open resources, as the MassBank EU and MoNA databases contain fewer than 2,000 MS n>2 spectra. The mzCloud spectral database contains the biggest MS n library with more than 16,000,000 spectra for 30,200 unique compounds (as of April 2024), but access to it is restricted, limiting reuse, especially in the context of machine learning. In MS n analysis, precursor ions are selected and fragmented in multiple iterations, producing spectral trees with deeper structural insights. These MS n trees enable the elucidation of complex structures by comparing similar patterns in different fragmentation stages, reducing pattern complexity at every step. Several studies have shown that MS n experiments offer better structural insights than MS 2 fragmentation, especially when glycosidic compounds are concerned. For example, for structures bound to a glycoside, MS typically cleaves only glycosidic bonds, providing limited information, especially regarding aglycones. MS can additionally reveal the exact position of the sugar by presenting unique fragment signals. Accordingly, multi-stage fragmentation can be used to distinguish structural isomers, which has also been demonstrated for isomeric prostaglandins. One challenge in acquiring MS n data is the extended time required to capture a full spectral tree, which is often done using flow injection analysis or direct injection.