Since a general trend was obvious (Figure ) with clear outliers (C), the patent dataset was analysed in more detail (the literature dataset is much smaller and currently less suitable for these more detailed investigations). Time series clustering was performed to systematically identify outliers, inspired by examples such as the abrupt decrease in patents for gallopamil (Figure ). Region-based analysis was performed by inventor region to identify chemicals exclusive to certain regions, or regions with similar patenting activities, while specific trends related to chemical classes such as pharmaceuticals, pesticides or persistent compounds like per-and polyfluorinated substances (PFAS) were also explored. Additionally, network analysis was employed to uncover relationships and connectivity patterns among chemicals in the patent dataset. Specifically, several centrality measures were utilized to assess the importance or influence of chemicals within the constructed network, despite their dependency on specific context and sensitivity to small changes in network structure. Further details are available as open source code in the ULPatentTrends 15 repository.