The dataset includes subsurface stratigraphic picks for the top of the Foremost Formation (Belly River Group) in the Alberta Plains (Townships 1 to 52, Ranges 1W4 to 5W5) made from downhole wireline geophysical well logs. The top of the Foremost was picked at the base of a continuous sandstone or siltstone bed (low-gamma-ray) of variable thickness overlying the Taber coal zone. Well data were screened to detect errors resulting from deviated wells, as well as incorrect ground and kelly bushing elevation data. We used statistical methods to identify local and regional statistical outliers, which were examined individually.

The Common Attribute Schema for Forest Resource Inventories (CASFRI) is a Canadian forest resource inventory data repository. Forest resource inventory datasets in CASFRI are harmonized to a common data model so that data collected by different agencies following different standards can be used together. Participating provincial, territorial and federal government departments and agencies share current and historical map-based forest resource inventory datasets through CASFRI so that their data are available to users who’s areas of interest span multiple jurisdictions. CASFRI was originally developed by academic researchers (Cumming et al., https://doi.org/10.1139/cjfr-2014-0102). This flavour of CASFRI (CASFRIv5) was developed anew in collaboration with academic researchers at the University of Laval to provide a government version of CASFRI that is findable, accessible, interoperable, and reusable. It uses the most up-to-date forest inventory data provided by participating provincial, territorial, and federal government departments and agencies. CASFRIv5 is hosted on the Canadian Council of Forest Ministers’ data portal, the National Forest Information System (http://nfis.org).

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)Collection:- **[Canada's National Forest Inventory (NFI) 2006](https://open.canada.ca/data/en/dataset/e2fadaeb-3106-4111-9d1c-f9791d83fbf4)**

BiologicEcologic ISO Feature Dataset symbolization and publication. September 5, 2017.

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)Collection:- **[Canada's National Forest Inventory (NFI) 2006](https://open.canada.ca/data/en/dataset/e2fadaeb-3106-4111-9d1c-f9791d83fbf4)**

Priority for snow removal on the road network according to three service levels (1, 2 and 3) or under provincial jurisdiction (MTQ) .attributs:ID - Unique IdentifierPriority - Service Level or Provincial Jurisdiction (MTQ)**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

First Responders identifies the location of ambulance, fire, police and coast guard facilities in British Columbia.

The High Arctic dataset comes from the Petroleum and Environmental Management Tool (PEMT). The online tool was decommissioned in 2019 and the data was transferred to Open Data in order to preserve it.The PEMT was originally developed in 2009 to help guide development in the Canadian Arctic by Indian and Northern Affairs Canada (INAC). The online tool mapped the sensitivities of a variety of Arctic features, ranging from whales to traditional harvesting, across the Arctic. The tool was intended to aid government, oil and gas companies, Aboriginal groups, resource managers and public stakeholders in better understanding the geographic distribution of areas which are sensitive for environmental and socio-economic reasons. The study area is located in the High Arctic Archipelago and contains both marine and terrestrial components. The boundaries of the study area are based on the NOGB leasing grids applied in the High Arctic, under which exploration, significant discovery and production licenses may be issued. The Sverdrup Basin (and Lancaster Sound) has the highest known oil and gas potential of the sedimentary basins of the Arctic Islands (Nunavut Planning Commission 2000) and it is expected that there is oil and gas potential on Melville Island and Bathurst Island (Sivummut Economic Development Strategy Group 2003). To date, no gas has been produced, and 321,470 m³ of oil has been produced from the Bent Horn oil field (Morrell et al. 1995). DISCLAIMER: Please refer to the PEMT Disclaimer document or the Resource Constraints - Use Limitation in the Additional Information section below.Note: This is one of the 3 (three) datasets included in the PEMT application which includes the Beaufort Sea and Mackenzie Delta and Eastern Arctic datasets.

The selection of an Area of Interest (AOI) marks the beginning of the Oceans Act Marine Protected Area (MPA) establishment process led by Fisheries and Oceans Canada. The process includes completing detailed ecological and socioeconomic assessments, setting conservation objectives, determining boundaries and management measures, and eventually developing the regulations under section 35 of the Oceans Act. Collaboration, consultation, and engagement occurs with stakeholders and partners throughout each step of this process.

Ecologically and Biologically Significant Areas (EBSAs) are areas within Canada's oceans that have been identified through formal scientific assessments as having special biological or ecological significance when compared with the surrounding marine ecosystem.Failure to define an area as an EBSA does not mean that it is unimportant ecologically. All areas serve ecological functions to some extent and require sustainable management. Rather, areas identified as EBSAs should be viewed as the most important areas where, with existing knowledge, regulators and marine users should be particularly risk averse to ensure ecosystems remain healthy and productive.Why are EBSAs identified?EBSA information is used to inform marine planning, including environmental assessment and the siting of marine-based activities, by:- Informing and guiding project-specific or regional environmental assessments;- Informing and guiding industries and regulators in their planning and operations, for example: EBSAs have been acknowledged and referred to (often as "Special Areas" or "Potentially Sensitive Areas") in oil and gas related assessments;- EBSA information has been provided to proponents of submarine cable projects to be used for route planning purposes;- Informing and guiding Integrated Oceans Management (IOM) process within five Large Ocean Management Areas (LOMAs) and twelve marine bioregions;- Serving as a basis for the identification of Areas of Interest (AOIs) and of Marine Protected Areas (MPAs) (individually and in the context of planning bioregional networks of MPAs).How are EBSAs identified?The process used to identify EBSAs is generally comprised of two phases. The first phase involves compiling scientific data and knowledge of a marine area's ecosystems - notably fish species, marine mammals, sea birds, marine flora, marine productivity, physical and chemical conditions and geology. "Knowledge" includes experiential knowledge of long-time uses of the areas. In some cases (e.g., in the Arctic), substantial efforts are taken to collect traditional knowledge on ecosystems and environmental conditions from community members, fish harvests, hunters and individuals whose knowledge of the study area complement often helps fill scientific data gaps.In the second phase, the available information for a marine area (e.g. a bioregion) is assessed against five nationally-established science-based criteria including:- Uniqueness: How distinct is the ecosystem of an area compared to surrounding ones?- Aggregation: Whether or not species populate or convene to the study area?- Fitness consequence: How critical the area is to the life history of the species that use it (e.g. is it a spawning or feeding ground)?- Naturalness: How pristine or disturbed by human activities is the study area?- Resilience: What is the ability of the ecosystem to bounce back if it is disturbed?Progress to date and next stepsEBSAs have been identified for large portions of Canada's Atlantic and Pacific Oceans as well as most of the Arctic oceans. EBSAs will continue to be identified in priority areas as resources become available to carry out the process. The boundaries or locations of existing EBSAs may be modified to reflect both new knowledge and changing environmental conditions.