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Geodisy

Geodisy is a research data discovery tool that harvests information from existing research data repositories and displays applicable records onto a map-based search interface.

1
mention
1
contributor

What Geodisy can do for you

What is Geodisy?

Find research data visually, spatially and quickly
Research data can be hard to find, and even harder if you're researching a specific place. Geodisy changes that, giving you a window into the world of research data with map-based tools familiar to everyone. Search by place name, or by drawing a box. The world of research data is yours to discover. Geodisy is the software that will let you do just that.
Who will use Geodisy?

People
Anyone looking for research data who wants to use a map or a box to find data. Researchers, students, journalists, and anyone else with an interest in data from university research will benefit from Geodisy's search tools.

Institutions
The first users of Geodisy will be FRDR, Canada's Federated Research Data Repository, for the quick and easy discovery of Canadian Research Data. When released, anyone with the available infrastructure will be able to plug in Geodisy and make a compatible repository more discoverable.
Why use Geodisy?

Data, and in particular research data, has always been difficult to find. Keywords can be hit and miss, and text based descriptions don't show you where your place of interest lies. Geodisy changes that, showing you where, not just what.

If you're a running a research data repository based on Dataverse, Geodisy will take your repository's data, search for geospatial metadata and files, and copy them to a new system which allows for visual searching. Your original data and search methods are untouched; you have the benefit of both.
How does Geodisy work?

Geodisy is a separate server software component that examines the metadata, such as the study records for research data and any associated data. If Geodisy finds spatial data, metadata and data are harvested, then normalized to have the same geospatial metadata standard. Afterwards, both data and metadata are injected into a geospatial data server and a viewer/search component.

Keywords
Programming languages
  • Java 85%
  • Ruby 9%
  • XSLT 3%
  • HTML 2%
  • JavaScript 1%
License
</>Source code

Participating organisations

University of British Columbia
Canarie
Digital Research Alliance of Canada

Mentions

Contributors

EB
Eugene Barsky
Principal Investigator
University of British Columbia