Consequently, it is challenging to efficiently access, compare, integrate, and analyze this data because different studies and databases often present their findings in different formats . Web applications for surface chemistry such as CatApp and Catalysis-Hub aim to provide systematic data for activation energies of elementary surface reactions and binding energies from a variety of studies. Yet, the broader challenge of standardizing data across various platforms and integrating existing gas-phase databases remains. The rise of machine learning (ML) in heterogeneous catalysis research has been facilitated by the availability of these databases and the development of cutting-edge approaches to handle the data, leading to the discovery of optimized catalysts . However, the inconsistencies between datasets impedes the seamless data integration and (re)use, which hinders the development of better ML models . These discrepancies underscore the need for standardized practices and benchmarks in DFT calculations to ensure consistency and reproducibility across studies . Due to all of the reasons above, it is common for every investigator of a catalytic reaction to perform electronic structure calculations for all species with a consistent set of DFT settings to obtain meaningful results. Given the millions of calculations present in emerging DFT energy databases , it is imperative that researchers find ways to leverage and reuse existing DFT data whenever possible.