Understand how data changes over the optimization cycles. Temporal variables are measurements, or calculations based on such measurements, that are accumulated along the optimization process. For example, after each cycle, we add new values for measured yield to the dataset. We can also add the model predictions that are based on measurement results and are therefore updated after every cycle. (C2) Visualizing temporal data: The introduced time series information adds a new layer of complexity to the dataset. Visualizing a mix of static and temporal data is not straightforward and requires careful consideration in the design process.