4. Up scale
When the pilot data is sound and the experimental parameters have been tweaked, it is time to up-scale the experiment. This is when larger amounts of time and money can be invested in the project, as the failures and bugs have been ironed out, thus avoiding costly large-scale failures.
There is also an alternative route to the upscale node that bypasses the MVE (the vertical drop down). This path is for hypotheses or research ideas that cannot be tested with a small scale MVE. These questions require large-scale costly data sets: big data. The discovery science framework deals with big data experiments in a slightly different way. Here, the scientist is encouraged to “peek” at the data or run pilot-tests multiple times along the way to the full dataset.