Catalysis at the University of Southampton

This is a video resource to support the teaching of catalysis at A-level. It features explanations of the underlying theory, coupled with an outline of cutting research in this area of Chemistry at the University of Southampton, which relates to the A-level topic. The video files are in the .zip folder, and instructions for how to access them can be found in the attached document. You will also find a Word document called 'Skeleton notes', which is designed to be printed out by students and completed as they watch the video. We will be seeking feedback from students who use the resource, to find out their views about its effectiveness in educational terms. If you have any comments, or if you spot any errors, please contact Dr David Read (d.read@soton.ac.uk).

The End of the World Wide Web

Nothing lasts forever. The World Wide Web was an essential part of life for much of humantiy in the early 21st century, but these days few people even remember that it existed. Members of the Web Science research group will present several possible scenarios for how the Web, as we know it, could cease to be. This will be followed by an open discussion about the future we want for the Web and what Web Science should be doing today to help make that future happen, or at least avoid some of the bad ones.

Predicting sense of community and participation by applying machine learning to open government data

Community capacity is used to monitor socio-economic development. It is composed of a number of dimensions, which can be measured to understand the possible issues in the implementation of a policy or the outcome of a project targeting a community. Measuring community capacity dimensions is usually expensive and time consuming, requiring locally organised surveys. Therefore, we investigate a technique to estimate them by applying the Random Forests algorithm on secondary open government data. This research focuses on the prediction of measures for two dimensions: sense of community and participation. The most important variables for this prediction were determined. The variables included in the datasets used to train the predictive models complied with two criteria: nationwide availability; sufficiently fine-grained geographic breakdown, i.e. neighbourhood level. The models explained 77% of the sense of community measures and 63% of participation. Due to the low geographic detail of the outcome measures available, further research is required to apply the predictive models to a neighbourhood level. The variables that were found to be more determinant for prediction were only partially in agreement with the factors that, according to the social science literature consulted, are the most influential for sense of community and participation. This finding should be further investigated from a social science perspective, in order to be understood in depth.