The back-end server is a Python application that uses Flask [37] to serve the API. For molecule computations and rendering, we used the RDKit [38] python library. We added a back-end component to allow the handling of large datasets (C1) that comes hand-in-hand with RO tasks. To handle these large datasets (C1) and avoid memory issues, the data has to be either (i ) processed in chunks of smaller pieces of data, (ii ) filtered to a subset of the whole dataset, or (iii ) aggregated to reduce the overall size of the dataset. All of these optimizations are done in the back-end after the dataset is uploaded. Figure gives an overview of which CIME4R components use the whole dataset, aggregations of it, or a filtered subset.