Artificial intelligence (AI) has been employed to uncover correlations, patterns, and anomalies in materials science and catalysis, accelerating the design of improved or even novel materials. However, AI and machine learning typically need a large amount of data and only a few AI approaches are applicable to number of materials that can be synthesized and characterized in detail by experiments (e.g., < 10 2 ). For such data-efficient approaches, it is then crucial that the data is of high quality. Nevertheless, only the smallest part of the available heterogeneous-catalysis data meets the requirement for data-efficient AI. This is due to several reasons. First, the kinetics of the formation of the active states of a catalyst is often neglected while designing the experiments for measuring the catalyst properties and performance. Thus, the same system may run on different paths depending on the workflow of the experiment, generating different active states and thus leading to inconsistent data and compromising the reproducibility. Secondly, most of the systematic studies focus on specific chemically related materials and reactions, and negative results are often omitted. Such subjective bias and the lack of diversity in terms of materials and process parameters (e.g., good as well as bad catalysts and a wide range of reaction conditions) prevents the derivation of general property-function relationships. Finally, incomplete reporting and lack of metadata also make catalysisresearch data difficult to use.