Modern NPs metabolomics workflows produce large amounts of data and information. Each natural extract is characterized by its metadata -taxonomy of the organism, organ or part studied, type of extract, bioactivity, etc. -and hundreds to thousands of metabolites characterized by their LC-MS features, chemical classes, or structural annotations. Molecular networking has appeared in recent years as a revolutionary solution to organize and explore large spectral datasets . While MN offers an efficient way to display spectral similarities, large datasets are still challenging to explore using visualization software only (e.g., Cytoscape) . Limitations in data exploration are quickly reached when trying to map multiple layers of additional information (e.g., bioactivity results, taxonomical origins, chemical classes, etc.). In addition, there are currently no efficient solutions for the sharing, parallel exploration, and reuse of such large heterogeneous datasets.