The federal government has encouraged open access to publicly funded federal science research results, but it is unclear what knowledge can be gleaned from them and how the knowledge can be used to improve scientific research and shape federal research policies. In this article, we present the results of a preliminary study of cyberlearning projects funded by the National Science Foundation (NSF) that address these issues. Our work demonstrates that text‐mining tools can be used to partially automate the process of finding NSF's cyberlearning awards and characterizing the fine‐grained topics implicit in award abstracts. The methodology we have established to assess NSF's cyberlearning investments should generalize to other areas of research and other repositories of public‐access documents.

PARAMICS is a PARAllel MICroscopic Traffic Simulator which is, to our knowledge, the most powerful of its type in the world. The simulator can model around 200,000 vehicles on around 7,000 roads (taken from real road traffic network data) at faster than 'real-time' rates, making use of 16 K processor TMC Connection Machine CM-200 for the simulation aspect. The project aims to make available to road network planners a new range of tools, and demonstrates that use of high performance computing in real applications is possible and worthwhile, while yielding important and interesting research results.>