Having explored the integration of rNets with automation frameworks such as AMUSE, we have established a robust environment for the analysis of reaction networks. The importance of automatic data generation cannot be overstated, yet equally crucial is the adherence to the FAIR (findable, accessible, interoperable, and reusable) principles for data reporting . This balance presents a significant challenge in managing data from computationally characterized reaction networks. Computational chemistry packages generate very heterogeneous output files, for which either manual inspection or the development of specific parsers are essential to retrieve the required information. Because of this, is also common for researchers to only share these properties that have been extracted, thus leaving likely relevant data that has already been computed out of the set of information that is eventually published. Moreover, the definition of the relationships between the chemical entities represented by the finished simulations, e.g., the specification of the chemical reactions taking place in a given reaction network, does also lack a general normalization.