Although these databases provide a vast amount of computed information, the absence of standardized data formats and naming conventions poses a challenge for researchers. The databases typically do not contain the raw DFT energies of the structures but rather the derived quantities such as thermophysical properties and activation barriers. In principle, these derived quantities are more general and transferable than raw DFT energies. However, the processes used to derive them and the terminology used to describe them can lead to inconsistencies between different studies . This challenge is further compounded by the variety of DFT codes and the different levels of theory used to generate the data . Due to computational cost, most largescale databases are based on DFT calculations with the generalized gradient (GGA) approximation. Such DFT codes include The Vienna Ab initio Simulation Package (VASP) , Quantum ESPRESSO , NWChem , GPAW , Jaguar , and SPARC , each of which has its own features, functionalities, and specializations. The Atomic Simulation Environment (ASE) or Python Materials Genomics (pymatgen) have helped standardize the interfaces to these codes, but numerical differences persist due to differences in exchange-correlation (xc) functionals, settings (e.g. plane-wave cutoff energy, k-point sampling) and pseudopotentials . For metal oxides and magnetic systems, this is further complicated by the spin state optimization and Hubbard correction (DFT+U) , while electrochemical systems are affected by the potential and solvent model . Advanced techniques such as quantum Monte Carlo, the random phase approximation, or time-dependent DFT can improve physical accuracy but also introduce additional sources of numerical error and ambiguity .