The current and most detailed version of the approved corporate provincial digital Biogeoclimatic Ecosystem Classification (BEC) Zone/Subzone/Variant/Phase map (version 13.1, July 8 2026). Use this version when performing GIS analysis regardless of scale. This mapping is deliberately extended across the ocean, lakes, glaciers, etc to facilitate intersection with a terrestrial landcover layer of your choice

To identify the overall reach and configuration of the Capital Pathway, show the location of existing pathways (as of 2020) and proposed pathway segments, and distinguish between discovery routes and feeder links.Capital Pathway Strategic Plan, October 2020 https://ncc-website-2.s3.amazonaws.com/documents/Pathways-BOOK-10-15-2020-EN_Final_Compressed.pdf

The Canadian Hydrospatial Network (CHN) is an analysis-ready geospatial network of features that help enable the modelling of surface water flow in Canada. The six main layers and feature types are: flowlines, waterbodies, catchments, catchment aggregates, work units, and hydro nodes. Where possible the CHN is derived from high resolution source data such as Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) and aerial imagery, to name a few. If existing provincial or territorial hydrographic networks meet the standards, they are incorporated into the CHN, otherwise automatic extraction methods are used on the high-resolution source data. To provide full network connectivity, if neither of these methods is possible in a region, the NHN is converted into the CHN until higher-resolution source data is available.Additional value-added attributes are included in the CHN to aid modelling, such as stream order and reach slope. The CHN physical model and features are also closely aligned and harmonized with the USGS 3DHP hydrographic network, which aids trans-border modelling. Where possible geonames (i.e. toponyms) are also added.The CHN is produced and disseminated by hydrologically connected geographic areas called work units. Work units can contain just one watershed, several small adjacent watersheds outletting into a large body of water, or be one of many parts of a larger watershed. In all cases, the features of a work unit are hydrologically connected. This is a more natural approach to data delivery, in comparison to data that is split into tiles. A generalized work unit index file is provided in the downloads to help users decide which files to download.For more information on the CHN please visit the project webpage: https://natural-resources.canada.ca/canadian-hydrospatial-network

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)**

The Canadian Geographical Names Data Base (CGNDB) is the authoritative national database of Canada's geographical names. The purpose of the CGNDB is to store place names and their attributes that have been approved by the Geographical Names Board of Canada (GNBC), the national coordinating body responsible for standards and policies on place names. The CGNDB is maintained by Natural Resources Canada, through the Canada Centre for Mapping and Earth Observation. The geographic extent of the CGNDB is the Canadian landmass and water bodies; the temporal extent is from 1897 to present. This dataset is extracted from the CGNDB on a weekly basis, and consists of current officially approved names, feature type, coordinates of the feature, decision date, source, and other attributes. The output file formats for this product are: text (CSV), Shape (SHP), and Keyhole Markup Language (KML).Content advisory: The Canadian Geographical Names Database contains historical terminology that is considered racist, offensive and derogatory. Geographical naming authorities are in the process of addressing many offensive place names, but the work is still ongoing. For more information, please contact the GNBC Secretariat.

Contact and location information for StrongStart BC Centres. For dynamic contact information for schools, see http://www.bced.gov.bc.ca/apps/imcl/imclWeb/Home.do or view a map of BC districts and schools at http://www.bced.gov.bc.ca/reporting/maps/welcome.php

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, 2024 and March 31, 2025.

To assess the current distribution of kelp beds and other macroalgae in Nova Scotia and Southwest New Brunswick, subtidal drop camera surveys were performed from 2022-2023 at 140 sites. For each site, a GoPro HERO 10 camera was towed along a deep (7-12m) and shallow (3-5m) depth contour until 20 images were acquired per depth. Where possible, species were identified from photos, with particular care given to kelps (defined here as orders Laminariales and Tilopteridales) and fucoids (order Fucales). Crust-forming algae was not counted. Percent cover was calculated using a 10x10 point grid overlaid on each image and recording the dominant cover type at each point. The depth (in meters) of each photo after correcting for tide height ranged from ~0.5m to ~12m. Depths were corrected to chart datum (lowest astronomical tide) using tide predictions from the nearest tide station, taken from tides.gc.ca. Sampling was performed between July and October. Cite this data as: Krumhansl K, Brooks C, Lowen B, DiBacco C, (2025). Camera Surveys of the Subtidal Flora of Nova Scotia and Southwest New Brunswick 2022-2023. Version 1.7. Fisheries and Oceans Canada. Samplingevent dataset. https://ipt.iobis.org/obiscanada/resource?r=camera_surveys_of_the_subtidal_flora_of_nova_scotia_2022-2023&v=1.7For additional information please see:Krumhansl K.A., Brooks C.M., Lowen B., O’Brien J., Wong M., DiBacco C. Loss, resilience and recovery of kelp forests in a region of rapid ocean warming. Annals of Botany 2024 Mar 8; 133(1):73-92. Brooks C.M., Krumhansl K.A. 2023. First record of the Asian Antithamnion sparsum Tokida, 1932 (Ceramiales, Rhodophyta) in Nova Scotia, Canada. BioInvasions Records 12(3):745-725.

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)**