Once we have network data on the UF scientific community, we can use SNA to visualize and analyze specific kinds of collaboration (e.g. publications vs grants); the position and centrality of particular departments, centers or institutes within UF’s scientific network; individual characteristics of UF researchers, be they network properties (e.g. actor centrality) or non-network attributes (e.g. researcher’s number of publications); the evolution of the UF scientific network over the years. SNA methods also allow us to detect cohesive subgroups(“communities”) of researchers who tend to work together in the university. Furthermore, the UF network can be aggregated from the individual level of researchers to the collective level of UF organizations, so as to visualize networks of collaborations among UF departments, institutes or academic units.