In many fields complex dynamical stochastic models are needed to describe processes that develop in time and/or space in a random way, usually with temporal or spatial interactions that are important for a proper understanding of the phenomenon under study and for making predictions about the system. A few concrete examples of such stochastic processes are: Interest rates, turbulent flows, communication in networks of neurons, and protein folding. The high speed of present day computers has made use of complex stochastic models feasible, and at the same time, the important developments that have taken place in probability theory, in particular in the area of stochastic calculus, have only to a limited extent been used by statisticians to develop statistical methods for stochastic processes. The principal aim of the DYNSTOCH network is to make a major contribution to the statistical theory and methodology for stochastic processes by taking advantage of the tools of modern probability theory including stochastic calculus and by using highly computer-intensive methods. This is partly done by modelling and statistical data analysis in a number of subject areas including neuro science, physiology, biology, turbulence (wind energy) and finance.Fine modulo