Table Comparison of execution times for representative queries of materials experiment data (MPS) when it is stored in a graph database (MEKG), SQL database (ESAMP), and file system (MEAD). The graph and SQL queries were performed on a t2.xlarge Ubuntu Amazon Web Services (AWS) machine (see Supporting Information). The number of results is shown for each query. The File System database is not applicable (N/A) for query 4 because it does not contain the required information. † Query times were in excess of 10 As a moderately complex provenance-based query, query 3 was chosen to characterize how query time scales with data size. To achieve representative databases of smaller size, 3 sub-databases were created using the earliest 1/8, 1/4, and 1/2 of the Sample-Processes in the MPS, followed by removal of all orphaned samples, processes, analyses, etc. (see Supporting Information). Running query 3 on these databases informs us of how long the query would have taken if it had been performed at these various points in the lab's sequence of experiments. The results for graph and SQL-based version of query 3 are shown in Figure , which illustrates the excellent relative performance of the graph-based query across all data sizes as well as a favorable power-law scaling relationship for the graph-based query. Extrapolating to a database with a billion Sample-Processes, the scaling law provides a projected query execution time of 65 s, illustrating the promise of graph database for aggregating large swaths of materials chemistry data while maintaining operability for both humans and machines.