Working Paper: Open Government Data

In diesem Working Paper sollen wesentliche Erkenntnisse und Forderungen aus der - bisher vor allem englischsprachigen - Diskussion über die webgerechte Freigabe öffentlicher Daten zusammengefaßt werden. Das Paper versteht sich als Ausgangspunkt für Diskussion und Strategieentwicklung, ohne letztere selbst leisten zu können. Die Entwicklungspotentiale von Open Government Data (OGD) sollen zunächst aus der Sicht verschiedener Beteiligter dargestellt werden. Mit den in den Sebastopol-Prinzipien formulierten grundlegenden Anforderungen an OGD wird der Begriff schließlich definiert. Anhand von Veröffentlichungen des W3C kann schließlich die Bedeutung der Verwendung und (Weiter-)Entwicklung offener Standards für OGD gezeigt werden, daneben aber auch die Hauptprobleme eines entsprechenden Change Managements im öffentlichen Sektor. Abschließend werden einige modellhafte Beispiele für die praktische Umsetzung von OGD angeführt.

Dynamic graphics of parametrically linked multivariate methods used in compositional data analysis

Many multivariate methods that are apparently distinct can be linked by introducing one or more parameters in their definition. Methods that can be linked in this way are correspondence analysis, unweighted or weighted logratio analysis (the latter also known as "spectral mapping"), nonsymmetric correspondence analysis, principal component analysis (with and without logarithmic transformation of the data) and multidimensional scaling. In this presentation I will show how several of these methods, which are frequently used in compositional data analysis, may be linked through parametrizations such as power transformations, linear transformations and convex linear combinations. Since the methods of interest here all lead to visual maps of data, a "movie" can be made where where the linking parameter is allowed to vary in small steps: the results are recalculated "frame by frame" and one can see the smooth change from one method to another. Several of these "movies" will be shown, giving a deeper insight into the similarities and differences between these methods

Introduction to the Semantic Web and the Web of Linked Data

Presentation given as part of the EPrints/dotAC training event on 26 Mar 2010.

Linked Data Infrastructures and HE Challenges

ECSS Talk

Open Equipment data flow

Short set of slides explaining the workflow from a university website to equipment.data.ac.uk

Stream Reasoning with Linked Data

Slides describing streaming data, data stream processing systems and stream reasoning Also we have some description of CSPARQL

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.

Linked Data

Introduction to Linked Data and Semantic Web for data scientists