Annotating multimedia for community folksonomy and ontology building

This is an audio recording which introduces and summarises this project.

Animal Biosafety Risk Assessment and Management Training Video

This training video is intended to familiarise researchers and technicians working in animal containment facilities with appropriate risk assessment and risk management systems. It is in Windows Media Video format which will require a free media player such as Windows Media Player or VLC Media Player (http://www.videolan.org/vlc/) to watch.

IT and other training in the always connected era

Abstract Google and YouTube are quickly becoming the training resource of choice for the IT literate, especially in relation to computer based applications. Many businesses are addressing this training issue in a number of ways, some more successful than others. Find out what the IT services at the university are doing to adapt to this change and contribute to the discussion on how the approach could be improved. Before the talk you could have a look at the following; * One service that has been licenced is Lynda http://go.soton.ac.uk/lynda or lynda.com (note you have to enter www.southampton.ac.uk as the organisation if you don’t log in through the go.soton link) * The IT training team publish a portfolio of systems and courses at http://www.southampton.ac.uk/isolutions/computing/training/portfolio/index.php. * More and more internal systems are being supported through online guides such as http://go.soton.ac.uk/bgsg

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.