Future research efforts should focus on developing more effective approaches for dataset aggregation in materials informatics. As an example, supervised learning algorithms may be used to recognize and aggregate only chemical families with a higher impact on a validation error. Furthermore, to facilitate the integration of diverse datasets and enhance the reproducibility and comparability of research outcomes, the community should consider revising data saving and storing standards, as well as creating automatic ML-driven detectors for nonsense identification. By addressing these challenges, we can enhance the quality, reliability, and efficiency of data aggregation in materials informatics, leading to improved ML models and accelerated materials discovery.