Technological developments enable science to gather or produce massively complex data sets which cannot be analyzed or understood via traditional methods. Instead, computer-intensive modeling techniques are used to gain control of the data by producing models of data. Data are reduced, refined, and manipulated by further modeling. The end-products of scientific observation and experiment increasingly are in the form of models not theories. Models are essential to experimental design, underlying the design and control of detectors and experimental conditions. Models developed in one area are used as probes to investigate other phenomena. simulation models provide an alternative source of experimental data. Philosophy of science has paid relatively little attention to such a contemporary modeling practices. Dr. Suppe's first objective in this research is to provide a detailed understanding of contemporary scientific modeling practices. Second, he is exploring the implications of contemporary modeling practices for understanding scientific knowledge. Third he is providing a formal structural analysis of scientific modeling in terms of mapping and embedding relations between set-theoretic structures, extend that analysis to a general analysis that encompasses scientific models, scientific theories, and metamathematical models, and detail relations between scientific models and theories. Fourth, he is going to provide the field of Science and Technology Studies with suitable descriptions and illustrations of contemporary modeling techniques--which scientists typically do not publish.