This dataset uses RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) satellite images to identify open water regions within ice-covered rivers during winter, with the aim to assess hydrokinetic resources near remote communities reliant on diesel fuel for electricity generation. The data is processed with the HyRASS, a machine learning-based SAR image processing and classification algorithm.Disclaimer:This dataset was designed to identify open water regions within ice-covered rivers for assessing hydrokinetic resources near remote communities reliant on diesel fuel for electricity generation and is subject to the following limitations: • This dataset was derived from RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) satellite images. While these images are generally reliable, they are subject to inherent limitations, including resolution constraints, potential distortion, and occasional inaccuracies in real-time conditions capture. • The HyRASS algorithm is designed to pinpoint open water areas using satellite images, with a particular emphasis on RCM quad polarization (QP) imagery. This specialization means that its effectiveness depends on the accessibility of this specific type of imagery. Consequently, the data it produces might not cover a broad spectrum of time periods. For more reliable results, it's essential to classify areas more regularly, ensuring that detected open water regions are consistent over time.This dataset is intended for preliminary assessment and should not be the sole basis for making critical decisions or investments related to hydrokinetic energy projects. Further validation and in-depth analysis are strongly recommended, and users should conduct their own due diligence and additional research to verify the data accuracy and relevance for specific applications. By accessing and using this dataset, users acknowledge and accept these disclaimers. The providers of this dataset explicitly absolve themselves of any responsibility or liability for any consequences arising from the use, reliance upon, or interpretation of this dataset. Users are advised that their use of the dataset is at their own risk, and they assume full responsibility for any actions or decisions made based on the information contained therein. This disclaimer is in accordance with applicable laws and regulations, and by accessing or utilizing the dataset, users agree to release the providers of this dataset from any legal claims, damages, or liabilities that may arise from such use.

Community Energy and Emissions Inventory (CEEI) Primary Indicators Total 2007 Reporting Year by Community

Community Energy and Emissions Inventory (CEEI) Primary Indicators Per Capita 2007 Reporting Year by Community

During a declared emergency event, this dataset will include the facilities that provide Emergency Social Service assistance.

This data set shows utility points that provide services for: * power * water * communications * heating fuel They include: * fibre optic stations * hydro stations * lock pumping stations This product requires the use of geographic information system (GIS) software.

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, 2020 and March 31, 2021.

Municipal public parking.attributes:ID - Unique IDName - Parking nameNumber of parking spaces - Number of parking spaces**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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.

Community Areas are a standard analytical and service delivery geography for the city of Winnipeg and the Winnipeg Regional Health Authority.Community Areas are a standard analytical and service delivery geography for the city of Winnipeg and the Winnipeg Regional Health Authority.  The Community Areas geographic areas were developed by the Community Data Network (now more commonly referred to as the Winnipeg Community Data Consortium), with input from the WRHA, Manitoba Health, the City of Winnipeg, and other stakeholders. Community areas can be defined to either include or exclude the municipalities of East and West St. Paul. Because the Winnipeg RHA is defined to include East and West St. Paul, use of the geographies in a health services or health status context includes East and West St. Paul. Conversely, because the City of Winnipeg excludes East and West St. Paul, use of the geographies in a municipal administrative context excludes East and West St. Paul. This shapefile reflects the use of Community Areas in a health services or health status context, and includes East and West St. Paul.

## Purpose The Ministry of Agriculture, Food and Agribusiness (OMAFA) is responsible for Ontario’s provincial soil maps and maintains and update them as necessary. Digital Soil Mapping (DSM) is a modern methodology using spatially explicit soils and environmental data to predict soil variation throughout a landscape at a high, consistent resolution. Digital soil maps are being rolled out throughout Ontario’s agricultural land base to update provincial soil maps. ## Reach Provincial soil maps are used in many decision-making processes including: * land use planning * land evaluation * farming practices * best management practices * ecological monitoring * land resource mapping Potential users of this data include: * farmers * certified crop advisors * conservation authorities * academic researchers * land use planners ## Potential impacts Digital soil maps provide more accurate and precise soils data and enables improved management of soil resources across multiple stakeholders. This allows for better decision making to maximize land use efficiency, improve economic efficiency of soil resources and promote soil health and soil conservation. ## Technical description Digital soil mapping combines geo-referenced soil observations with geo-referenced environmental layers to mathematically model soil variation as a function of environment variation. These models are based on well established, but often complex relationships, between soil properties and topography, biology, geology and hydrology.