Projection View Once users have uploaded a file, data points are shown in a twodimensional scatterplot with random initial positions-if x and y coordinates are not explicitly provided-and can be projected using Uniform Manifold Approximation and Projection (UMAP, ) as dimensionality reduction technique. Users can choose the attributes that are to be used for projection and whether they are to be standardized to have a zero mean and unit variance. Fingerprints, latent space representations from neural networks, or molecular descriptors are good initial choices for the projection. An example of a projected dataset is shown in Figure "Projection View". Projections can be stored, and users can switch between stored projections to compare different representations of the data (T1).