An exemplar of generating large parallelized simulation datasets is the Folding@home platform . Started in 2000, Folding@home is a distributed computing network that runs MD simulations on donated computing power thanks to thousands of citizen-scientists who download the app onto their hardware. Folding@home runs trajectories as small "work units" that are smaller simulations on the order of nanoseconds. The starting file for a work-unit is generated server-side, which is sent to a client computer somewhere in the world ). The client then runs the work unit and returns it. This is used to generate the subsequent work unit (representing the next chunk of time in a simulation). Afterwards, work units are stitched together to generate a single trajectory. Folding@home allows for the generation of a datasets containing hundreds of trajectories that, in aggregate, capture a protein's energy landscape. These datasets are so large that interpreting them presents a unique "big data" challenge, and requires the development of new methods and software .