The library generation pipeline is divided into three tasks, which are detailed in Fig. . It starts with metadata cleanup, experimental planning, and sequence creation (Fig. ), which guides the mixing of standards and subsequent data acquisition (Fig. ), and finishes with automatic MS n spectral library generation in mzmine (Fig. ). Fig. | Mass spectral library building workflow. a, The metadata cleanup process consists of structure cleanup, database queries (optional), and a final step in which data acquisition sequences are generated for both positive and negative ionization modes. b, The highthroughput data acquisition employs robotic and Echo liquid handling for mixing and diluting up to ten compounds and subsequent analysis with a flow-injection-data-dependent acquisition MS n method with a 50% split between sampling and washout times. Acquisition for one tree required a maximum of 13 s. c, The automatic library generation workflow is implemented in mzmine and incorporates data import, mass detection, tree building, and annotation based on the cleaned metadata. Several quality checks are included, and only matching spectra are exported together with their quality scores.