The applications of AI in materials science are diverse. 1-2 For example, the optimization of synthesis and functional properties in high-throughput experiments requires mathematical models which are iteratively trained in order to ensure an efficient experimental design. The elucidation of materials structures can be facilitated by AI. Besides, new materials can be predicted via the identification of correlations and patterns in experimental and computational data sets. This leads to a variety of data set structures. The interdisciplinary nature of materials science and the multitude of experimental techniques applied produce a broad spectrum of data formats, all of which can ultimately be traced back to spectroscopic, thermodynamic or kinetic relationships and are already standardised to some extent. Experimental data in materials research are usually not "big data", which places additional demands on the methods of data analysis.