Overall, these different application examples showcase some of the exciting opportunities offered by the ENPKG framework. The possibility of querying linked spectral, chemical, taxonomical, and bioactivity data of thousands of samples using a single technology streamlines the exploration of such multi-dimensional datasets. The workflow's key features include the sample-centric approach and the efficient connection to existing public resources. On the one hand, the sample-centric approach ensures that the data generated over time can be added to existing dataset(s) quickly and efficiently, as demonstrated through the integration of samples profiled several years apart on different analytical platforms. This possibility of incremental addition of samples opens the door to efficient large-scale (re)analysis, known as repository-scale analysis . In addition, the deposition of all LC-HRMS and spectral data on the GNPS MassIVE repository allows their straightforward visualization (LC-MS 2 chromatograms and fragmentation spectra, using the GNPS dashboard) and the rapid launch of in-depth spectral analysis on selected samples or spectra (e.g., using MASST, MassQL, or other GNPS workflows) . On the other hand, connection to existing databases allows a more direct interpretation of metabolomics data by matching the 'known' with the 'unknown'. Using federated queries, it is possible to connect experimental information to existing KG-formated knowledge. This step however also depends on the knowledge available in curated public DBs, highlighting the central role of these infrastructures in advancing scientific knowledge.