As this last point is generally taken for granted, little attention has been dedicated to it in the literature on materials informatics. Given this and the limited availability of experimental data, it is natural for practitioners to consider the aggregation of diverse data sources. However, data aggregation in materials informatics presents unique challenges compared to most ML datasets . Unlike many other domains, chemical datasets can often be unbalanced, small in size, or collected under diverse experimental conditions. Additionally, the ranges of values can be considerably large, and the data space can exhibit pronounced discontinuities. As an example, in the context of thermoelectric materials, the introduction of chemistry defects through doping can lead to substantial alterations in electronic properties . These challenges emphasize the need for careful consideration when aggregating different datasets in materials informatics research.