To overcome these challenges, it is becoming increasingly accepted in catalysis research that the application of rigorous protocols for experiments is necessary. We have recently established standardized and detailed procedures for obtaining and reporting heterogeneous-catalysis-research data documented in "experimental handbooks". Crucially, these "clean experiments" are designed to consistently take into account the dynamic nature of the catalyst while generating catalyst samples and measuring their properties and performance. The symbolic-regression sure-independence-screening-and-sparsifying-operator (SISSO) AI analysis of the so-generated data resulted in the identification of correlations as interpretable, typically nonlinear analytical expressions of the most relevant physicochemical parameters -or "materials genes" -reflecting the processes triggering, favoring or hindering the reactivity towards propane oxidation as a selected example of a complex catalytic reaction at the gas-solid interface. To demonstrate the universality of this strategy, we apply here the clean-data-centric approach to identify property-function relationships simultaneously describing the performance in ethane (C2), propane (C3), and n-butane (C4) selective oxidation reactions (Fig. ). Our analysis is based on 12 vanadium-or manganese-based catalysts, which were extensively characterized and present diverse physical properties and catalytic behaviors. We show that the combination of clean data and AI is a prerequisite for the identification of propertyfunction relationships, since these relationships involve multiple parameters related in a nonlinear fashion. The key physicochemical parameters identified not only characterize the catalyst under thermodynamic standard conditions where phases are defined, but they also reflect the properties of the materials under the conditions applied at each reaction, as captured by in situ spectroscopy characterization data. This result highlights that the crystal structure and translational repetitive arrangement of atoms in the surface, used in conventional catalyst design approaches and theoretical models is insufficient to describe selective oxidation catalysis. The data-centric approach accelerates catalyst design while highlighting the underlying processe.