B. Chapter "future" utility of the force field choice. Large datasets may provide the foundation for future simulations but to make valid comparisons one would have to use the same force field. Thus it is important to consider using a force field that can be consistently used over the span of generating many datasets . In general, large parallel MD datasets are sampling from equilibrium fluctuations. While many appropriate integrators for sampling thermal motions exist such as velocity Verlet, it is important to consider the types of questions and predictions you are looking to answer with your dataset, and in turn choosing an appropriate integrator. For example, many larger parallel dataests collected in NPT have are able to sufficiently predict thermodynamic and kinetic biomolecular proprties using the Stochastic or Leapfrog Langevin integrators . Other simulation parameters, such as pressure regulation (via a barostat), boundary conditions, and time-step size, should be tailored to the specific assumptions about your system. Likewise, one should ensure that these parameters are chosen such that the dataset will have high future utility to ensure valid comparison against future datasets; we recommend avoiding older methods to regulate simulations, such as the Berendsen Barostat, which is shown to sample inaccurate underlying distributions . Before launching any large data collection efforts, it is critical to have some hypothesis regarding the desired conformational events, and the type of conformational transitions to be observed. Are you looking to sample large conformational changes, such as domain movements, or more subtle, local fluctuations? What are the timescales of the motions you would be considering? The type of sampling will impact how many simulations you need to run, how often to save frames, and how long trajectories need to be run. If the desired conformational motion occurs over microseconds, you'll need to run simulations for sufficient lengths of time to ensure you can capture it, which can be on the order of milliseconds . Keep in mind that longer timescales typically require more aggregate simulation time or enhanced sampling methods to efficiently explore the conformational landscape.