How to convert a Word 2003 document into HTML

How to convert Word 2003 into HTML with associated resources folder. Includes examples showing how to create simple navigation within the document.

Printing only part of an MS Word 2010 document

You won't always want to print the whole of your document; here are some useful ways of printing only part of a MS Word 2010 file. For best viewing Download the video.

Using the Document Map in MS Word 2011

The Document Map makes it very easy get an overview of the heading structure of a long document and to navigate aroiund the file quickly. If you watch this video also watch the ones on Using Heading and Contents Styles as well as on Tables of Content. For best viewing Download the video.

SYS-MDSTU Test document

This is a test document, and should be visible to 6 test students only. If showing correctly, the file will appear as a blank word doc.

Dynamic Document Generation from Semantic Web Data

This talk will present an overview of the ongoing ERCIM project SMARTDOCS (SeMAntically-cReaTed DOCuments) which aims at automatically generating webpages from RDF data. It will particularly focus on the current issues and the investigated solutions in the different modules of the project, which are related to document planning, natural language generation and multimedia perspectives. The second part of the talk will be dedicated to the KODA annotation system, which is a knowledge-base-agnostic annotator designed to provide the RDF annotations required in the document generation process.

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.

PowerPoint document for GHCM Website (2016)

Resources created at University of Southampton for the module GIS for Health Care Management (GHCM)

Online document repository for GAH Website (2016)

Resources created at University of Southampton for the module GIS for the Analysis of Health (GAH)