The automated construction of datasets has become increasingly relevant for data-driven computational chemistry. Data-driven approaches to computational chemistry essentially include high-throughput screening of molecules and materials by quantum chemical (QC) computations as well as the use of large-scale computed data to train machine learning (ML) models for property prediction. Both tasks rely on extensive datasets curated to cover vast and diverse regions of chemical space. Within this context, crystallographic data repositories constitute an excellent pool of synthesized structures available in large size and chemical diversity. The Cambridge Structural Database (CSD) contains, for instance, over a million of experimental crystal structures collected over several decades. Yet, the proper exploitation of crystallographic information for the computational chemistry is not straightforward. For datasets to be adapted for both the training of ML models and high-throughput QC searches, they must include the essential information needed to run an electronic structure computation, such as the structure (R), the molecular charge (Q) as well as the spin multiplicity. Owing to the lack of information about metal oxidation (OS) and spin states, reliably retrieving the molecular charge and spin multiplicity is especially difficult when transition metals (TM) are involved. To overcome this limitation, we recently developed cell2mol , a software that specifically interprets crystallographic data to retrieve the Cartesian coordinates, total charges, and connectivity of all individual molecules in the unit cell, including the OS of metal ions. While cell2mol provides a thorough unit cell interpretation, the original version was not coded to characterize ground state spins.