19 Christensen, M.; Yunker, L. P. E.; Shiri, P.; Zepel, T.; Prieto, P. L.; Grunert, S.; Bork, F.; Hein, J. E. Automation Isn't Automatic. Chem Sci 2021, 12 (47), 15473-15490. . In order to support the work ow, we developed a hierarchical schema to organize our data (Figure ). We clustered the data elds around the topics of Chemistry, Analytics and Experiments. Within these, related data is combined in data tables in which every line is guaranteed to be unique with respect to one or a combination of several identi ers. A data structure results from linking individual tables containing the same identi ers. All data is stored in a MS SQL Server database unless needed for constant lookup by our Google Sheet application. The way the data is structured and what data points are captured, determines which questions can be answered. In our case, the data structure allows for example to gure out the amount and type of material in each vial and know which compounds were assigned to which peaks in LCMS afterwards. Data is written and consumed through user interfaces or automatically through a Google Cloud Function parsing Waters rpt-les. The latter extracts data from these and writes them to the three data tables labeled Analytics in the bottom right of Figure . Another great parser for rpt-les was recently published: Mason, J.; Wilders, H.; Fallon, D. J.; Thomas, R. P.; Bush, J. T.; Tomkinson, N. C. O.; Rianjongdee, F. Automated LC-MS Analysis and Data Extraction for High-Throughput Chemistry. Digit. Discov. 2023, 2 (6), 1894-1899. . Shown in Figure , the Google Sheet HTE Platform functions as the primary workflow hub. It tracks available compounds for dosing and keeps an inventory of available solids and liquids.