PURPOSE:To provide demographic parameters and relative indices of abundance for Atlantic herring in NAFO Division 4T.DESCRIPTION:The experimental gillnet survey is conducted in collaboration with Atlantic herring harvesters and uses standardized multi-mesh gillnets. Gillnets are set on spawning grounds in the spring and fall. Data (fish and sample) are collected to provide: (1) relative age-disaggregated indices of abundance, (2) an indicator of relative changes in fishery selectivity over time, and (3) information on the demographic composition of herring on the spawning grounds. PARAMETERS COLLECTED:Sample: sample location; sample weight; mesh size; length frequencyBiological: length; weight; gonad weight; maturity stage (biological); sex (biological); ring count (biological)USE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

The Indigenous Populations of Canada map is derived from the CanEcumene 2.0 Geodatabase using custom tabulations of census-based population data. Indigenous communities within the level of the census sub-division (CSD) were identified using a combination of sources from census field data (see Eddy et. al. 2020 for more details). This map shows the percent of Indigenous population in CanEcumene 2.0 communities using graduated symbols, overlaid upon a population density raster. The larger the symbol, the higher the percentage of Indigenous population in that area. The darker the colour in the underlying raster, the denser is the general population. This map illustrates how the majority of Indigenous populations reside in locations outside of the denser populated areas of Canada.

The RVI/CVI database is derived from the CanEcumene 3.0 GDB (Eddy, et. al. 2023) using a selection of socio-economic variables identified in Eddy and Dort (2011) that aim to capture the overall state of socio-economic conditions of communities as ‘human habitats’. This dataset was developed primarily for application in mapping socio-economic conditions of communities and regions for environmental and natural resource management, climate change adaptation, Impact Assessments (IAs) and Regional Assessments (RAs), and Cumulative Effects Assessment (CEA).The RVI/CVI is comprised of five sub-indicators: 1) population change, 2) age structure, 3) education levels, 4) employment levels, and 5) real estate values. Index values are based on percentile ranks of each sub-indicator, and averaged for each community, and for three ranked groups: 1) all of Canada, 2) by province, and 3) by population size. The data covers the Census periods of 2001, 2006, 2011 (NHS), 2016, and 2021.The index is mapped in two ways: 1) as ‘points’ for individual communities (CVI), and 2) as ‘rasters’ for spatial interpolation of point data (RVI). These formats provide an alternative spatial framework to conventional StatsCan CSD framework. (For more information on this approach see Eddy, et. al. 2020).============================================================================================Eddy, B.G., Muggridge, M., LeBlanc, R., Osmond, J., Kean, C., and Boyd, E. 2023. The CanEcumene 3.0 GIS Database. Federal Geospatial Platform (FGP), Natural Resources Canada. https://gcgeo.gc.ca/viz/index-en.html?keys=draft-3f599fcb-8d77-4dbb-8b1e-d3f27f932a4bEddy B.G., Muggridge M, LeBlanc R, Osmond J, Kean C, Boyd E. 2020. An Ecological Approach for Mapping Socio-Economic Data in Support of Ecosystems Analysis: Examples in Mapping Canada’s Forest Ecumene. One Ecosystem 5: e55881. https://doi.org/10.3897/oneeco.5.e55881Eddy, B.G.; Dort, A. 2011. Integrating Socio-Economic Data for Integrated Land Management (ILM): Examples from the Humber River Basin, western Newfoundland. Geomatica, Vol. 65, No. 3, p. 283-291. doi:10.5623/cig2011-044.

Data presented here comes from the 2016 Census of Canada, Statistics Canada. These datasets are part of the Official-Language Minority Communities Dashboard, an initiative of the Research Team of the Official Languages Branch at Canadian Heritage.Four datasets have been organized based on different population groups and universes and explore selected variables such as age, language, socio-economic and socio-demographic characteristics.

Statistics Canada, in collaboration with the Public Health Agency of Canada and Natural Resources Canada, is presenting selected Census data to help inform Canadians on the public health risk of the COVID-19 pandemic and to be used for modelling analysis.The data provided here show the population counts and percentage distribution for various geographic levels by broad age groups, males, females and both sexes, from the 2016 Census.

CanVec contains more than 60 topographic features classes organized into 8 themes: Transport Features, Administrative Features, Hydro Features, Land Features, Manmade Features, Elevation Features, Resource Management Features and Toponymic Features.This multiscale product originates from the best available geospatial data sources covering Canadian territory. It offers quality topographic information in vector format complying with international geomatics standards.CanVec can be used in Web Map Services (WMS) and geographic information systems (GIS) applications and used to produce thematic maps. Because of its many attributes, CanVec allows for extensive spatial analysis.Related Products (Open Maps Links):**[Constructions and Land Use in Canada - CanVec Series - Manmade Features](https://open.canada.ca/data/en/dataset/fd4369a4-21fe-4070-914a-067474da0fd6)****[Lakes, Rivers and Glaciers in Canada - CanVec Series - Hydrographic Features](https://open.canada.ca/data/en/dataset/9d96e8c9-22fe-4ad2-b5e8-94a6991b744b)****[Administrative Boundaries in Canada - CanVec Series - Administrative Features](https://open.canada.ca/data/en/dataset/306e5004-534b-4110-9feb-58e3a5c3fd97)****[Mines, Energy and Communication Networks in Canada - CanVec Series - Resources Management Features](https://open.canada.ca/data/en/dataset/92dbea79-f644-4a62-b25e-8eb993ca0264)****[Wooded Areas, Saturated Soils and Landscape in Canada - CanVec Series - Land Features](https://open.canada.ca/data/en/dataset/80aa8ec6-4947-48de-bc9c-7d09d48b4cad)****[Transport Networks in Canada - CanVec Series - Transport Features](https://open.canada.ca/data/en/dataset/2dac78ba-8543-48a6-8f07-faeef56f9895)****[Elevation in Canada - CanVec Series - Elevation Features](https://open.canada.ca/data/en/dataset/64aad38d-f692-4ab6-bf2c-f938586c1249)****[Map Labels - CanVec Series - Toponymic Features](https://open.canada.ca/data/en/dataset/b3fdcd34-4533-415f-8f83-68f17f9d5d68)**

GeoAI are buildings, hydrography, forests, and roads automatically extracted using Deep Learning models applied to a source dataset, typically aerial or satellite images. The primary aim of GeoAI is to increase Canada's availability of high-resolution foundational geospatial data for both spatial and temporal coverage.‌The infrastructure and expertise put in place by NRCan enables a rapid, efficient, and scalable data creation process through the use of leading-edge technology and Artificial Intelligence models. Published datasets for a given source can be revisited at a later date as more accurate models are developed and put into production. For now, only static files are available, but as the series develops, new products and services will be added.Several use cases of GeoAI data for change detection have been created. To view them, please follow the links below:- Iqaluit: https://open.canada.ca/data/en/dataset/ef29513d-a007-408c-beba-65f86e7b5b01- Calgary: https://open.canada.ca/data/en/dataset/bd07b597-5166-4b62-ae9c-245b3c62ea03- Québec City: https://open.canada.ca/data/en/dataset/4026720e-f7fb-478e-a58f-c62ebe20005d- Winnipeg: https://open.canada.ca/data/en/dataset/a6874aac-cfcd-4b92-bb3f-6031eedb5b57- Victoria: https://open.canada.ca/data/en/dataset/f011c7b9-f79e-42f8-9481-9bf9788bb189- Trois-Rivières: https://open.canada.ca/data/en/dataset/2b11a323-52c2-43d5-9c09-bc4b15b1bcc8

This service shows the percentage of population aged 25 to 64 years in private households with a postsecondary certificate, diploma or degree by census subdivision, 2016. The data is from the Census Profile, Statistics Canada Catalogue no. 98-316-X2016001.This data pertains to the population aged 25 to 64 years in private households by the highest level of education that a person has successfully completed. Persons with a post-secondary certificate, diploma or degree includes those with an apprenticeship or trades certificate or diploma; a college, CEGEP or other non-university certificate or diploma; a university certificate or diploma below bachelor level or a university certificate, diploma or degree at bachelor level or above. For additional information refer to the 2016 Census Dictionary for ' Highest certificate, diploma or degree'.For additional information refer to the 2016 Census Dictionary for ' Highest certificate, diploma or degree'. To have a cartographic representation of the ecumene with this socio-economic indicator, it is recommended to add as the first layer, the “NRCan - 2016 population ecumene by census subdivision” web service, accessible in the data resources section below.

This series of historic flood susceptibility maps comes from an XGBboost machine learning model trained on major floods from 2005 to 2023. The trained model is then run for each year from 2000 to 2023, including unique temporal characteristics of temperature, precipitation, land use land cover and Normalized Difference Vegetation Index (NDVI), to predict the flood susceptibility of any given year.This dataset forms part of a broader collection of flood susceptibility datasets, offering related information and analyses. The collection includes an overview page with associated publications, historic susceptibility values, temporal trends, and future projections.- [Collection – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc)- [Trends and Extremes – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/3202e0a0-0afb-4120-b102-b0c41f0fb9eb)- [Future - Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/c00f95a3-7bab-4d28-b9cc-b30f06b5afd2)

Compiled by Alberta Data Partnerships Ltd. (ADP), on behalf of the Government of Alberta, the ATS v4.1 Polygons - Section with Road Allowance layer contains polygons that represent the location of Sections and adjacent Road Allowance Segment polygons, derived from the Master Alberta Township System points file published as ATS Version 4.1, dated March 31, 2005, and clipped to an updated Alberta Data Partnerships Ltd. (ADP) created version of the Alberta provincial boundary.