The acquisition of 37,829 reference compounds was performed within 23 days and in 9,060 injections spanning both polarities. The automatic library generation then took one hour. The addition of spectral quality metrics, e.g., explained intensities and signals, during library building, facilitates the interpretation of the spectral quality without checking each scan manually or filtering for the use of only high-quality data. The new library for 30,008 unique compounds contains a total of 2,350,646 dereplicated (only the highest TIC) and merged MS n spectra extracted for both polarities, with a majority of 803,405 spectra acquired in positive mode. The 30,008 unique detected and extracted compounds constitute the first open library of this kind, considering the limited availability of open MS n data thus far. The library includes individual spectra, together with merged spectra for individual fragmentation energies as well as for different energies. Each MS n tree was further merged into a pseudo-MS 2 spectrum for better compatibility with tools that only function at the MS 2 level (e.g., GNPS or SIRIUS). We expect that the demonstrated high-throughput method will facilitate rapid growth in the public availability of reference spectra and that this unique resource will stimulate the development of new machine learning-based tools leveraging the spectral tree dependency of fragment ions across multi-stage fragmentation. Newer models for MS2DeepScore and MS2Query are already trained with the new MS n Lib (MS 2 only) together with GNPS data. The DreaMS project uses MS n Lib for evaluation and fine-tuning of their model for specialized tasks, such as predicting fluorinated compounds. Looking ahead, we aim to acquire additional compound libraries and expand the covered chemical space by continuously adding to the free and open MS n Lib resource. The primary challenge in constructing large-scale mass spectral libraries is access to compound libraries, which are often costly and required in small quantities. This necessitates collaboration. We are grateful to our collaborators, who have already provided 7 compound libraries, and are actively reaching out to community members to share commercial, purified, or synthesized compounds. MS n Lib data has been acquired on a single Orbitrap ID-X instrument because of its MS n capabilities, however, we plan to build other MS 2 libraries by including data from various instruments and fragmentation techniques, also capturing additional identifiers like retention time and ion mobility. This will enhance the diversity and utility of open mass spectral libraries. Substructure prediction by MassFrontier, CFM-ID, or other tools may add additional value to the MS n Lib resource. However, substructure prediction requires extensive manual validation and we invite the mass spectrometry community to join our effort in developing solutions.