Federating hydrogeological data to visualise Victoria's groundwater Peter Dahlhaus Andrew Macleod Helen Thompson Office of Deputy Vice Chancellor (Researrch) University of Ballarat, Australia Email p.dahlhaus@ballarat.edu.au The Visualising Victoria's Groundwater (VVG) research project brings together a unique collaboration of regional, state, national and international organisations with the common aim of federating groundwater databases in Victoria, Australia. The $2.23M project led by the University of Ballarat uses similar methods to the Groundwater Information Network developed by Natural Resources Canada. The project addresses many of the issues associated with data capture, aggregation, transmission, storage, access, re-use and curation that are identified in the national eResearch infrastructure priorities as essential components in Australia's ongoing research success. In particular, the project provides access to research significant data held outside research institutions by developing the research tools for data linking, analysis and visualisation. The project builds on UB Spatial (www.ubspatial.com.au), an interoperable web-GIS portal and groundwater database that has been developed with the collaborative support of catchment management authorities, government departments and municipalities, all of whom are end-users of the service. The groundwater data includes time-series watertable levels, aquifer characteristics, physical and chemical parameters, lithology and stratigraphy, photographs and documents. A key feature of the system is that data managers maintain custodianship of their data and databases, but allow interoperable exchange by adopting the Open Geospatial Consortium (OGC) standards for groundwater data exchange (GroundwaterML). More efficient research practices and significant productivity gains are anticipated as a result of deploying the federated data. There is also potential for broader benefit through the application of the developed technology in virtually federating other environmental, social and economic datasets.