The instrument vendor typically flags such artifacts in the spectra, but the ThermoRawFileParser failed to remove those artifacts in MS 3-n during data conversion. Therefore, mzmine's 'Scan signal removal' was applied to remove reoccurring known contaminant signals. Due to their uncommon mass defects, real spectral data should remain unaffected. This step should be used with caution, and only abundant frequently detected signals should be removed that cannot be explained by a subformula from the original structures. The 'MS n tree feature list builder' then extracted flow injection-MS 1 time traces for each MS 2 precursor and built MS n ion trees. The resulting feature list was annotated by mzmine's 'Custom database search' using the cleaned metadata sheet from the first step. Supplementary Fig. visualizes an example feature list with database annotation. Most crucially, the unique_sample_id column was selected to limit annotations to those samples that actually contain the corresponding compounds in the mix of 8 to 10 compounds. This step paired the monoisotopic mass from the metadata with a user-defined list of adducts to define the search space. Our advice is to search for a broad range of ion adducts, in-source fragments, and clusters in a first iteration before limiting the ion selection to those with the highest detection frequencies in the final library building workflow. Too many included adducts will yield error-prone spurious hits. Ion formation depends on the chemical structures analyzed, the solvents used, the sample matrix, and the ionization conditions (see the Online Methods). tree features annotated as specific ion species of the input compounds were exported to various open spectral library formats, e.g., .json (mzmine), .mgf, or .msp. This export took each annotated MS n tree, scored and filtered the spectral quality of all fragmentation spectra (Supplementary Fig. ), and applied spectral merging on each MS level and fragmentation energy separately. The resulting list contains a merged and single best (highest total ion chromatogram (TIC) spectrum for each precursor ion and fragmentation energy, together with a merged spectrum across all fragmentation energies. Finally, a pseudo-MS 2 spectrum was created by merging the whole MS n tree (Supplementary Fig. ). This spectrum can be useful as input to annotation tools that only support MS 2 data, such as SIRIUS and GNPS Ion Identity Molecular Networking (IIMN) . Before writing the new library entries, further quality checks were applied to the MS 1 and MS n levels. First, precursor isolation purity was scored within the corresponding MS 1 scans before and after each MS n tree (Supplementary Fig. ). This percentage score takes a main signal in the center of the isolation window, divided by the intensity of all signals falling into the actual isolation window. Two options are provided to either skip or just flag library entries that exhibit low precursor purity. Our general approach was to flag such spectra and to split the library later to match the study requirements. Running the workflow in the mzmine batch mode applied all steps in a pipeline with reproducible results. This way, mzmine generated feature lists for intermediate steps facilitating the validation of results and backtracking of potential error sources (Supplementary Fig. ). Finally, the workflow reimported the MS n tree library as an automated parsing test. Positive and negative ionization modes were processed with separate mzmine batch configurations, due to the different adduct settings, resulting in individual libraries for each polarity. Each spectral library entry comprises metadata fields including the compound name, structural information, adduct type, collision energy, precursor m/z isolation window, quality check results, polarity, and spectrum type (single best scan or various merging types). Library-specific information can be added in the library export metadata, including the instrument type, contact details, and dataset ID, if data was uploaded to a public repository. The dataset ID also generates universal spectrum identifiers (USI) for each spectral library entry. USI facilitates traceability of FAIR data similar to a file path into the public domain, denoting the dataset, filename, and scan number. Merged scans contain all USI of their source scans. The available metadata is useful to filter the MS n Lib into subsets, e.g., for specific instruments.