In this paper, we have investigated the aggregation of different datasets in the field of materials informatics and its impact on the performance of machine learning (ML) models for property predictions. In our evaluation, we showed that classical ML models experienced a reduction in performance under all considered aggregation strategies, indicating that the aggregation of diverse datasets can introduce noise and hinder model learning and generalization. Deep learning models exhibited more robustness, but most changes in accuracy were not statistically significant. This suggests that while deep learning models are less affected by the aggregation of datasets, they may not necessarily benefit significantly from it. Furthermore, we simulated a data acquisition process within a single dataset and compared a random data acquisition approach with one guided by the DiSCoVeR algorithm. Surprisingly, we found that prioritizing chemical diversity through the DiSCoVeR-guided approach did not lead to a faster convergence toward better accuracy but rather degraded performance. Our findings highlight the challenges and limitations of data aggregation in materials informatics and emphasize the need for caution when merging different data sources.