We utilize dimensionality reduction (DR) to get an overview and be able to comprehend a parameter space (T1). This generates a two-dimensional space that is visualized in the projection view . Apart from choosing UMAP, t-SNE, or PCA as DR techniques, users can specify which features are more or less important than others by applying weights to the variables. By exploring different weight settings, users may analyze the parameter space under different perspectives and pick the one that best facilitates the analysis (see Supplementary Material, Additional File 1 for details). CIME4R includes two views that show this two-dimensional projection of the parameter space (see Figure ): (i ) A scatter plot visualizing the filtered subset, where each experiment setting corresponds to one point; and (ii ) an aggregated view of the entire parameter space (i.e., all possible reaction combinations), which is shown as colored, hexagonal bins in the background of the projection view. When experimenting with different techniques to analyze the aggregated two-dimensional space, we tried to visualize the data as a continuous heatmap and used interpolated values for areas without data points, similar to the landscape shown in other work (see Supplementary Material, Additional File 1 for details). However, the visualization suggested the presence of data (i.e., we do not actually have values in some areas, but fill the space with calculated values) and continuity (i.e., assuming that there is a continuous change between experiments in a parameter space) that do not exist, which might mislead users. After careful consideration, we use discrete bins to show the aggregated values. We decided to use hexagonal bins as a trade-off between squares, which are space-filling, and circles, where borders have a uniform distance to the center . To give the aggregated view more flexibility, we implemented the hex bins to dynamically adapt to the zoom level. This means that users receive an abstract overview at first, but when zooming in, the hex bins become more fine-grained making the view more detailed (see Supplementary Material, Additional File 1 for details). Overall, the projection view gives users an overview of the entire parameter space of the dataset in two dimensions and a more detailed view of the filtered subset, helping them to comprehend a parameter space (T1).