DA-07: Run an interactive data science application using a community resource for back-end computation

Executive Summary: 
A researcher runs an interactive data science application (e.g., notebook server, R Studio, MATLAB, Paraview, VSCode) using a community resource for the back-end analysis tasks.
User Importance Summary: 
In the data science discipline, interactive notebooks are a widely-used and useful method for both developing and sharing analysis methods. They are used to teach and learn general techniques. Researchers also use them to share the specific steps that have been used to analyze specific datatsets. Advanced analysis methods often require high-performance and high-throughput computing, so it's important that researchers are able to couple interactive notebooks with powerful computing systems.
Target Communities and Sizes: 
Data science researchers - 1,000 < N < 10,000 Data science students - 10,000 < N < 100,000 Data science educators - 100 < N < 1,000