 Hello, it's my pleasure to be speaking with you today from the traditional and unseated territory of the Moscow people. My name is Eugene Barosky and I'm the head of UBC Libraries Research Commons. Today I want to be speaking with you about Geodacy, which is the platform we developed to create geospatial discovery for Canadian research data. Geodacy is an open source discovery tool that allows our users to find open data from multiple Canadian repositories in a visual way. The results are driven by a map and we work with data that is geospatial in nature or simply associated with the location. Yeah, support us and provide complimentary search to the Canadian federated research data repository or further. All our work has been open source and all our code is available for free for others to adopt. As we all know working in a profession for a long time, data can be difficult to find. When we search for data about a specific place like a park in Northern British Columbia, using keywords is actually not easy. Here's a text below that will allow us to search for a forest in Northern British Columbia. So here we use tons of synonyms and proximity operators, which is not ideal when you look for something very specific. Wouldn't be great, we said that you could search for a specific area just by driving a map around it. And this is what exactly what Geodacy does. It allows us to find Canadian research data sets about a specific place by driving a map. I will just have one slide about Geodacy infrastructure. At this moment in phase one, we are working with scholars portal data versus, which have more than 60 various institutional data versus for places like University of British Columbia, McGill University, University of Toronto, Waterloo and others. We're extracting the data out of data versus and taking it into our Geodacy and middleware, the pipeline, where in the backhand, we are adding value by crosswalking the metadata into standardized format, but also adding bounding boxes, which really drive Geodacy. At the end, we ship the data into our Geo Blacklight instance, where it's available for researchers to use. Geodacy is based on bounding boxes. Everything that we take in goes through our pipeline to generate those. We either take the bounding boxes that were entered by the depositors, but these are really few. Most of the bounding boxes we use are programmatically generated from either the geospatial files directly or from the place named metadata that is available in the data versus files. Each geospatial file, even if it comes in a bunch of places, a bunch of files will have its own landing page and we will connect those together in a linked record. The datasets that do not have coordinates, geospatial coordinates, or geospatial file types, will be not eligible for the inclusion because they are not relevant to the geospatial discovery. For instance, genomics datasets that do not have a specific metadata about location will not be included. First, you'll try to analyze the geospatial files using GDAL. If we aren't unsuccessful, if we cannot extract the geospatial metadata out, you'll look at the bounding boxes that are provided in the native interfaces like DataRus. If we can extract the geospatial coordinates out, we will do so. And if not, you'll seek place names out of the country and states and cities that we discover in the metadata records. This is a quick overview of our metadata processes. The data we take from DataRus goes into geodesys metadata schema, where we cross-walk it into a standardized ISO metadata, ISO19115, and those records we ship to open geo metadata in GitHub for other institutions like Stanford or University of Michigan to use in their geoblack-like applications. But we also cross-walk it to geoblack-like schema metadata, which we use for discovery. In phase two, on which we are working right now, and it should be ready in the early spring, we are adding multiple other repositories into a geodesy pipeline. But we're doing so by harvesting the metadata into a furthest harvester and doing a cross-walk from right there. This way we do not have to develop cross-walks from each native repository, such as CCAN or DataRus. We just use furthest harvester as our metadata source. I have mentioned furthest before. Furthest is a federated research data repository for Canada, which is our discovery interface for Canadian research data. This is a place we expose more than 60 various data sources for Canadian institutions. Geodesy lives right within further and at this moment provides a complimentary map-based search for geospatial data. I will be sharing a few slides just to give you a feel how it looks like and then we will go and drive further in person. We allow to search geodesy in two ways, either by a keyword or most importantly by a location where there's a map you can drive around and focus on a specific location to find the research data associated with that location. For instance, when I drive my map over Northern British Columbia, I find multiple datasets that focus on that geographical area. Moreover, each dataset will have its own landing page where we explain where we took the data, be allowed to download it in a standardized ISO metadata format, be allowed access to the original files but also link it to various other files that are connected to this study. If the data is geospatial in nature, you'll also display it on the map. Let's take a deep dive. As you listen to my presentation, feel free to go to or just Google Geodesy or go to geo.further.ca, you can also access it from the furthers homepage. As I mentioned, DataVersion is our source of data in this moment. So we are just a discovery interface. Everything we have in Geodesy platform is coming from a Canadian DataVersion at this point. And in early spring, you'll have the entire further index which includes more than 17 various capabilities included in our platform. Let's drive to Northern BC, draw a geographical area and I want to find what kind of research data can be found about this specific area. Interestingly, as I move my mouse over our data sets, the locations will change. For instance, when I go to Great Bear Rainforest, the data that we are taking it from will be displayed in the map. Each data file is a landing page which we allow to see from the Geodesy interface, but we also link it to connected data sets that have the same connections to that area. Also, we allow to download a clean ISO metadata record for each data set. It looks like this. Those XML files allow other institutions to pick up and search our data right away from their Geo Blacklight interfaces. If the data itself is geospatial in nature, we will allow to focus on the shape files, for instance, and display it right away in the landing page. For instance, I will grab one example where we can show how Canadian Boreal Forest is being displayed right into the landing page of the Geodesy platform. At this point, our users can take that data and export into their GIS applications or just use our platform to view the data. Of course, people can analyze the data by various place, granularity options, or by an institution, by a source where we take the data from. I'll go back to my presentation. Our next steps for the Geodesy are to complete the integration with the entire further index to allow more than 70 various platforms, including Haika Institute, different CCANs from the provincial and federal government to be inserted into the Geodesy pipeline. We also plan to complete the French translation of the user interface and contribute it back to the Geo Blacklight community. I would like to acknowledge and thank our great partners at the Portage Network. When we receive all our funding from the new Digital Research Infrastructure Organizations in Canada or in short, in the Rio. We have been fortunate to work with multiple institutions and multiple teams during the last two years. And I would like to acknowledge that we couldn't have done it without them. Thank you very much. I'm glad to... I will be happy to see your questions as they come, but also feel free to see our code and explanations and guides through our GitHub page. Thank you very much.