Data provided shows grants and contributions provided to Canadian firms by National Research Council (NRC) and its Industrial Research Assistance Program (IRAP) between April 1, 2018 and March 31, 2019.

Data provided shows grants and contributions provided to Canadian firms by National Research Council (NRC) and its Industrial Research Assistance Program (IRAP) between April 1, 2021 and March 31, 2022.

This dataset was developed to document the procedures for acquiring underwater images in the Banc-des-Américains Marine Protected Area (MPA). Its primary objective is to provide a comprehensive inventory of metadata linked to imagery campaigns that allowed us to conduct a basic characterization of the area, and now that are used for ecological monitoring of the benthic habitats within the MPA.Metadata were collected during imagery surveys conducted since 2012, using three main methods: drop cameras on the crest, baited drop stereoscopic cameras on both the crest and plains, and towed cameras on the plains. For each operation are shared the type of gear used, sampling method, date, geographic coordinates, and acquisition depth.The released dataset contains only descriptive information related to the acquisition of images : file identifier, location, sampling methodology, depth, and technical imaging parameters. It does not include image files themselves, visual content, or the results of image analyses.A systematic quality control is applied to ensure completeness and consistency of the metadata: field format standardization, validation of GPS coordinates, verification of dates and recorded depths. Clear instructions guide the recording of metadata to guarantee their reliability for management and scientific monitoring purposes.

“Automatically Extracted Buildings” is a raw digital product in vector format created by NRCan. The feature classes of this product delineate polygonal building footprints automatically extracted from airborne Lidar data, high-resolution optical imagery or other sources.The first feature class, Automatically Extracted Buildings by acquisition source, contains building footprints delivered according to the spatial extent of each source dataset used for extraction. When the spatial extents of acquisition sources overlap, footprints for the same building may therefore be duplicated in this class.The second feature class, Optimized Buildings Layer, is an assembled and harmonized layer derived from the buildings by acquisition source. Its objective is to provide a unique representation of each building footprint by removing duplicates and resolving overlaps between sources.

Business / Person that acts as an agent for a lender in arranging consumer loans

The “Businesses by Census Subdivision” is derived from the Statistics Canada’s Business Register. At the request of Agriculture and Agri-Food Canada, Statistics Canada aggregated the number of businesses per NAICS classification and employment class for each Census Subdivision. The data includes the individual occurrences of a business in each census subdivision by indicating its NAICS classification and employment class.The name, location, and any other identifying information about the businesses has been suppressed by Statistics Canada.

This dataset shows the location of Lithoprobe Trans Hudson Orogen seismic data acquisition points for the Province of Saskatchewan.This dataset shows the locations of LITHOPROBE Trans Hudson Orogen seismic data acquisition points for the Province of Saskatchewan. The data was created as a file geodatabase feature class and output for public distribution.  **Please Note – All published Saskatchewan Geological Survey datasets, including those available through the Saskatchewan Mining and Petroleum GeoAtlas, are sourced from the Enterprise GIS Data Warehouse. They are therefore identical and share the same refresh schedule.

In 2010 the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) continued the process of generating annual crop inventory digital maps using satellite imagery. Focusing on the Prairie Provinces, a Decision Tree (DT) based methodology was applied using both optical (AWiFS, Landsat-5, DMC) and radar (RADARSAT-2) based satellite imagery, and having a final spatial resolution of 56m. Methods were also developed to enhance the optical classification with RADARSAT-2 imagery, addressing issues associated with cloud cover. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues. The overall process for Crop Inventory Map includes: satellite data acquisition; field data acquisition for classification training and accuracy assessment; and, operational implementation of the classification methodology.

The RADARSAT Constellation is the evolution of the RADARSAT Program with the objective of ensuring data continuity, improved operational use of Synthetic Aperture Radar (SAR) and improved system reliability. The three-satellite configuration provides daily revisits of Canada's vast territory and maritime approaches, as well as daily access to 90% of the world's surface.RCM is tasked solely by the Government of Canada, to acquire data, first and foremost in support of Government of Canada services and needs. RCM data and services contributes to ensuring the safety and security of Canadians; monitoring and protecting the environment; monitoring of climate change; managing Canada’s natural resources; and stimulating innovation, research and economic development. In addition to these core user areas, there are expected to be a wide range of ad hoc uses of RADARSAT Constellation data in many different applications within the public and private sectors, both in Canada and internationally. The current data set reflects the acquisition plans that are designed to meet the RCM SAR imaging demands of the Government of Canada. These are being made available publicly in advance of the acquisitions. To meet the data needs of the Government of Canada, acquisitions may be changed without notice. After their acquisition and processing, the RCM image products listed in the current data set, will be delivered to the Earth Observation Data Management System - EODMS (https://www.eodms-sgdot.nrcan-rncan.gc.ca/index-en.html) portal of Natural Resources Canada. Users can register to the EODMS portal as public users to retrieve the RCM image products. For those requiring a greater access to RCM imagery consisting of product types or spatial resolutions not available to public users: you may apply to upgrade your public account to an ‘RCM external vetted entity’ EODMS user type account. For more information on this process, please contact the Canadian Space Agency using the information available at the following link : https://www.asc-csa.gc.ca/eng/satellites/radarsat/access-to-data/how-to-become-a-user.aspPublication frequency :I. Future acquisition plans are published every two weeks for a two-week window that starts two weeks from the publication date. As an example, acquisition plan published on April 1st covers acquisitions from April 14 to 27. The next plan is published on April 14th and covers from April 28 to May 11.II. Past acquisitions plans are published monthly and covers a period of one month from the first to the last day As an example, acquisition plan published on April 1st covers acquisition made between the March 1 and March 31. The next plan covers the month of April.

In 2009 the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process of generating annual crop inventory digital maps using satellite imagery. Focusing on the Prairie Provinces, a Decision Tree (DT) based methodology was applied using both optical (AWiFS, Landsat-5) and radar (RADARSAT-2) based satellite imagery, and having a final spatial resolution of 56m. Methods were also developed to enhance the optical classification with RADARSAT-2 imagery, addressing issues associated with cloud cover. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues. The overall process for Crop Inventory Map includes: satellite data acquisition; field data acquisition for classification training and accuracy assessment; and, operational implementation of the classification methodology.The initial methodology was developed in partnership with AAFC Research Branch, and supported in part by the Canadian Space Agency. The long-term objective of this endeavour is to expand from the Prairies and produce an annual crop inventory of the entire agricultural extent of Canada.