About us

The Data-Intensive Research Group sits in the School of Informatics of the University of Edinburgh and is part of the UK's National e-Science Centre and the Centre for Intelligent Systems and their Applications. We collaborate closely with the National e-Science Hub of the University of Glasgow and EPCC of the University of Edinburgh.

Our mission

Data-intensive refers to huge volumes of data, complex patterns of data integration and analysis and intricate interactions between combinations of users and systems that deal with these data. Our mission is to advance methods that harness the power of data and computation in collaborative environments. Our goal is to support the life cycle of data to information to knowledge in a multi-disciplinary and multi-organisational context. To achieve this we pursue research in e-Science and Informatics and apply our ideas in several scientific and industrial domains.

Our expertise

On a daily basis, we collaborate with scientists from a range of disciplines where the aim is to progress their research by the application of our expertise and to advance our understanding of the problem areas we study:

  • Effective data analysis, data mining and combinatorial optimisation algorithms.
  • Distributed systems and architectures for efficient orchestrating data and computation.
  • Intuitive collaboration environments to enable domain-specific researchers to make use of the above systems.

Our success

In our annual progress reports we summarise our success in terms scientific output and major achievements. Have a look at our growing list of publications or keep track of our latest successes in our news section (RSS feed).

Our software solutions

  • Rapid allows fast development and low-cost maintenance of custom web portals for scientific computing. (Suitable for portal designers.)
  • OGSA-DAI is middleware to assist with access and integration of data from separate sources via the grid. (Suitable for service providers.)
  • powerNest is a software tool enabling experimenters to explore the effect of sampling on noise propagation throughout qPCR assays.. (Suitable for end-users.)
  • PrivacyGuard is middleware to anonymise DICOM data. (Suitable for service providers.)