This dataset is a collaborative project by Natural Resources Canada and the federal, provincial and territorial members of the Geographical Names Board of Canada, illustrating a curated selection of official geographical names in Canada that have changed over time. The selection comes from the Canadian Geographical Names Database, displaying additional information including the previous name(s) of each feature, the year and the reason why the names changed along with a short history of each name change.

Data include a collection of annual land cover maps derived from MODIS 250 m spatial resolution remotely sensed imagery for the period 2000 to 2011. Processing of the time series was designed to reduce the occurrence of false change between maps. The method was based on change updating as described in Pouliot et al. (2011, 2013). Change detection accounted for both abrupt changes such as forest harvesting and more gradual changes such as recurrent insect defoliation. To determine the new label for a pixel identified as change, an evidential reasoning approach was used to combine spectral and contextual information. The 2005 MODIS land cover of Canada at 250 m spatial resolution described in Latifovic et al. (2012) was used as the base map. It contains 39 land cover classes, which for time series development was considered too detailed and was reduced to 25 and 19 class versions. The 19 class version corresponds to the North America Land Change Monitoring System (NALCMS) Level 2 legend as described in Latifovic et al. (2012). Accuracy assessment of time series is difficult due to the need to assess many maps. For areas of change in the time series accuracy was found to be 70% based on the 19 class thematic legend.This time series captures the spatial distribution of dominant land cover transitions. It is intended for use in modeling, development of remote sensing products such as leaf area index or land cover based albedo retrievals, and other exploratory analysis. It is not appropriate for use in any rigorous reporting or inventory assessments due to the accuracy of the land cover classification and uncertainty as to the capture of all relevant changes for an application.

Border crossings of electric transmission lines. Mapping Resources implemented as part of the North American Cooperation on Energy Information (NACEI) between the Department of Energy of the United States of America, the Department of Natural Resources of Canada, and the Ministry of Energy of the United Mexican States.The participating Agencies and Institutions shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics, if available, are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time and may differ from other official information. The Agencies and Institutions participants give no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data.Parent Collection:[North American Cooperation on Energy Information, Mapping Data](https://open.canada.ca/data/en/dataset/aae6619f-f9f3-435d-bc32-42decd58b674)

The map displays bedrock formations at or near the surface of the land, on the sea floor above the continental crust that forms the Canadian landmass, and oceanic crust surrounding the landmass. The bedrock units are grouped and coloured according to geological age and composition. The colours of offshore units and oceanic crust are paler and more generalized than those on land, although the constituent units offshore are still easily discernible from their dashed boundaries. This colour design, coupled with the use of a white buffer zone at the coast allows the coastline of Canada to be readily distinguished and still show the grand geological architecture of the Canadian landmass.The map also shows major faults that have disrupted the Earth's crust, onshore and offshore, and a variety of special geological features such as kimberlite pipes, which locally contain diamonds, impact structures suspected to have been caused by meteorites, and extinct and active spreading centres in the surrounding oceans.

The Village dataset is comprised of all the polygons that represent Villages in Alberta. Village is a municipality type defined under the authority of the Municipal Government Act. The formation of a Village can occur when a majority of the buildings are on parcels of land smaller than 1850 square meters and there is a population of 300 or more.

Managed areas (parks, green spaces, etc.) for recreation and relaxation, including municipal equipment and vegetations.attributes:ID - Unique identifierMunicipality - Municipal codeName - Name of the managed areaType - Type of managed areaType - Type of managed area**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Argo is a key component of the Global Ocean Observing System (GOOS) with an array of about 4,000 autonomous instruments reporting on ocean conditions. These floats collect data on ocean temperature and salinity, and in some cases, additional properties that characterize the ocean’s biological and chemical processes. Established in 1999, Argo represents an international collaboration involving contribution from more than 30 nations. Data from Argo floats are made publicly available within 24 hours of collection time, for free. The data provide valuable information on changes to the Earth's climate and hydrological cycle. They are used for a variety of purposes, such as assessing climate change, improving weather forecasts and developing ocean models.Argo Canada, led by Fisheries and Oceans Canada, has been a key contributor to the International Argo Program since its inception in 2001 . The program has been supported by contributions from Department of Environment and Climate Change Canada, Department of National Defense, Dalhousie University, University of Victoria and Ocean Networks Canada.

The transport features of the CanVec series are derived from the National Road Network (NRN) and the National Railway Network (NRWN). The dataset describes roads, trails, bridges, railroads and their related information. The transport features include nautical facilities, track segments, track junctions, railway stations, track crossings, track marker posts, track structures, rail ferry segments, road segments, road ferry segments, road junctions, blocked passages, toll points, aerial cableway features, footbridges, trails, navigational aids, marinas and runways.The CanVec multiscale series is available as prepackaged downloadable files and by user-defined extent via a Geospatial data extraction tool.Related Products (Open Maps Links):[Topographic Data of Canada - CanVec Series](https://open.canada.ca/data/en/dataset/8ba2aa2a-7bb9-4448-b4d7-f164409fe056)

The United States population of California Sea Lions (Zalophus californianus) range from southeast Alaska to the Pacific coast of central Mexico. While this population does not breed in Canada, some sub-adult and adult males migrate northwards to British Columbia (BC) during the non-breeding season with an arrival in August–October and a departure in April–May. The population in coastal BC was assessed in 1985 and again in 2023 from dedicated over-winter aerial surveys. The most recent assessment used counts from 2020–2021surveys that were restricted to southern BC as few animals have been documented further north. Additional observations of California Sea Lions were collected while surveying sites for Steller Sea Lions. These opportunistic counts increased from approximately 1,000 animals in the mid-2000s to several thousand individuals in more recent years. Historically occupied sea lion rookeries and haul-out sites were surveyed with nearby areas monitored for potential shifts in distribution. This dataset contains counts that have been collected from sightings of individuals from 1971 through 2021and replaces a previous dataset limited to the 2016/2017 survey season.

Description:Seasonal temperature climatology of the Northeast Pacific Ocean was computed from historical observations including all available conductivity-temperature-depth (CTD), bottle, expendable bathy-thermograph (XBT), and Argo data in NOAA (http://www.argo.ucsd.edu/), Marine Environmental Data Service (MEDS), and Institute of Ocean Sciences archives over 1980 to 2010 period. Methods:Calculations, including smooth and interpolation, were carried out in sixty-five subregions and up to fifty-two vertical levels from surface to 5000m. Seasonal averages were computed as the median of yearly seasonal values. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain raster layers of seasonal temperature climatology for the Canadian Pacific Exclusive Economic Zone (EEZ), a subset of seasonal climatology of the Northeast Pacific Ocean, in high spatial resolution of 1/300 degree.References:Foreman, M. G. G., W. R. Crawford, J. Y. Cherniawsky, and J. Galbraith (2008). Dynamic ocean topography for the northeast Pacific and its continental margins, Geophys. Res. Lett., 35, L22606, doi: 10.1029/2008GL035152.Data Sources:NOAA, MEDS and IOS observational dataUncertainties:Uncertainties are introduced when quality controlled observational data are spatially interpolated to varying distances from the observation point. Climatological averages are calculated from these interpolated values.