Parameter Space: Case study 2: Direct Arylation reaction -Learning from and understanding an RO campaign in retrospect In this case study, we asked the chemists to use CIME4R to explore and retrospectively understand a chemical RO campaign. We used the direct arylation optimization that was performed by Shields et al. , as shown in Figure with their proposed EDBO model . The chosen reaction involves three categories of compounds (12 ligands, four bases, and four solvents) and two numeric variables (three temperatures and three concentrations) providing a possible 1728 experiments. The reaction was optimized with EDBO throughout eight iterations with five experiments per iteration. To prepare the data for CIME4R, we created a dataset containing all possible experiments and measurements for the executed experiments. We then added the mean and standard deviation of the yield predictions by EDBO, and the corresponding values from the acquisition function for each experiment cycle. Finally, we also calculated the SHAP values using the Kernel SHAP method of each EDBO input feature for each cycle. The code to create this dataset is publicly available . To visualize the experiments in CIME4R, we performed a weighted t-SNE projection using the experiment parameters and SHAP values (see Supplementary Material, Additional File 1 for details). During the investigation of this dataset in CIME4R, chemists made several discoveries that helped them understand the EDBO campaign (T4). After performing five initial random experiments (cycle 0), the chemists identified that EDBO prioritized experiments with the current best-performing ligand (tBPh-CPhos) for the next three cycles. This development can be seen in the LineUp overview (see Figure ) and in the projection and summary view of the corresponding cycles (Figure ). The chemists found one cluster of potential experiments containing the best conditions up to this cycle with the combination of a tBPh-CPhos ligand, DMAc solvent, and KOAc base, resulting in the experimental yields from 67 -89%. The importance of the ligand, solvent, and base to the current EDBO prediction was also indicated by the positive (pink) SHAP values seen in the first four cycles of the reaction (see Figure ). These were in contrast to the SHAP values of the temperature and concentration, which showed minimal contribution to the prediction (Figure ). From cycle 4 onwards, the algorithm selected only one more experiment using the current best ligand (tBPh-CPhos). Evaluation of this experiment found a measured yield close to the predicted yield (cycle 3 prediction: 67%, actual yield found in cycle 4: 77%). This improved prediction accuracy, as well as a general reduction in the uncertainty of the model (see Figure Cycle 4), suggests this area has been well enough exploited for EDBO to move on to more exploration of the parameter space. The remaining experiments in cycle 4 explored new ligands whilst keeping the choice of base (KOAc) and solvent (DMAc) consistent with the until now best-performing base and solvent combination (see Figure ).