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Geodisy: Geospatial Discovery for Canadian Research Data Dante, Paul; Goodwin, Mark; Barsky, Eugene; Lee, Tang; Thornberry, Evan; Lesack, Paul; Domingue, Edith 2019-06-01

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Geodisy: Geospatial Discovery for Canadian Research DataFUNDED BY CANARIE (RDM-059)Paul Dante, Mark Goodwin, Eugene Barsky, Tang Lee, Evan Thornberry, Paul Lesack, & Edith DomingueUniversity of British Columbia LibraryMay 20192GEODISY: GEOSPATIAL DISCOVERYWE AIM FOR CANADIAN RESEARCH DATA TO BE SEARCHED, FILTERED, AND BROWSED USING GEOGRAPHIC LOCATIONS AS WELL AS WITH TEXT. ● Search results are driven by an interactive map● Location is the primary search facet, linking resources from a similar area● Relies less on textual searching, which is not ideal for spatial data34GEODISY: GEOSPATIAL DISCOVERYWhy use it?● Data can be difficult to find! When searching for data about a particular place, keywords can be hit or miss. Geodisy will show you where, in addition to what.● Geodisy will benefit any research area that has use for location-based discovery, including climate change, community development, public health, conservation, and many more.5GEODISY: GEOSPATIAL DISCOVERYWho will use it?People InstitutionsResearchers, students, journalists, etcAny entity with the available infrastructureCanada’s Federated Research Data Repository (FRDR)6GEODISY: GEOSPATIAL DISCOVERYGeospatial discovery is possible using location descriptions and metadata● Geospatial data = machine readable using a GIS● Non-geospatial data = discovery comes from descriptive metadata*● Bounding boxes = rectangles representing the spatial extent of a data set*to generate bounding boxes from non-geospatial data we are using 7GEODISY: GEOSPATIAL DISCOVERYGeodisy (re-)uses 3 open-source software components● Dataverse: Research data repository +● GeoServer: Server for publishing and distributing geospatial data+● GeoBlacklight: Geospatial discovery layer*Geodisy source code and documentation is available in github - GEOSPATIAL DISCOVERY* Icons by Mani Cheng from the Noun Project9GEODISY: GEOSPATIAL DISCOVERYProject pipeline (in steps):1. Software will query datasets from the Scholars Portal Dataverses (and later fromUAlberta, Dal, UNB, UManitoba, etc.) to determine which have geospatial information2. Software will harvest metadata from relevant non-geospatial datasets3. Software will harvest metadata and data files from geospatial datasets4. Software will transform metadata to more universal standard (ISO 19115) and add bounding boxes if needed (GDAL or Geonames)5. Software will deposit geospatial data and “geo”metadata into Geoserver6. Metadata will be harvested by GeoBlacklight for discovery10GEODISY: GEOSPATIAL DISCOVERYReminder:● For the initial step, for March 2020, Geodisy is funded to work with Canadian Dataverses only...11GEODISY: GEOSPATIAL DISCOVERY (EXPECTED UI – example from NYU)12GEODISY: GEOSPATIAL DISCOVERY (EXPECTED UI – NYU)13GEODISY: GEOSPATIAL DISCOVERYCORE PROJECT TEAM  (UBC) • Eugene Barsky – Principal Investigator• Paul Dante – Software Developer • Edith Domingue – ARC Client Services Manager• Mark Goodwin – Geospatial Metadata Coordinator• Tang Lee – Project Manager• Paul Lesack – Co-Principal Investigator• Evan Thornberry – Co-Principal InvestigatorPROJECT PARTNERS• Jason Brodeur – McMaster University• Marcel Fortin – University of Toronto• Alex Garnett – SFU • Amber Leahey – Scholars Portal• Jason Hlady – University of Saskatchewan• Venkat Mahadevan – UBC ARC• Todd Trann – University of Saskatchewan• Lee Wilson – Portage NetworkLaunching in spring 202014GEODISY: GEOSPATIAL DISCOVERYKeep up to date:#Geodisy on social media @MarkJWGoodwin, @thornberry_evanThank you!


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