The Global Ice-Ocean Prediction System (GIOPS) produces global sea ice and ocean analyses and 10 day forecasts daily. This product contains time-mean sea ice and ocean forecast fields interpolated to two grids. One of the grids is a 0.2° resolution regular latitude-longitude grid covering the global ocean (north of 80° S). The other grid is in north-polar stereographic projection with a 5-km spacing at the standard parallel 60° N and covers the Arctic Ocean and the neighbouring sub-polar seas. Data is available for 50 depths. The data files are in netCDF format and comply with the Climate and Forecast Conventions.

Sports and recreational facilities including: arenas, tube slides, water games, water games, iron and pebble games, game modules, skateboard modules, skating rinks, pools, multi-functional tracks, beaches, courts, dance floors, beaches, courtyards and dance floors, the archery site and the archery site and the fields for baseball, basketball, soccer, petanque, multi-functional tracks, beaches, courts and dance floors, beaches, courtyards and dance floors, the archery site and the fields for baseball, basketball, soccer, petanque, pickleball, soccer, tennis, ultimate frisbee, and volleyball. Attributs:type - Type of installationDetail - Details about the installation (e.g. number of tennis courts, configuration of soccer fields, etc.) NAME - Name of the installationSurface - Type of surfaceLighting - Installation lighting**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Historical Oil Fields are subsurface accumulations or 'pools' of crude oil designated as such for the purposes of the Oil, Gas and Salt Resources Act. This product requires the use of geographic information system (GIS) software.

Fishing event data (e.g. year, date, time, location, catch and effort) and associated biological data from the Offshore Stratified Random Survey component of the annual Sablefish Research and Assessment Survey on the British Columbia coast.IntroductionDFO and the Canadian Sablefish Association (CSA) collaborate to undertake an annual fishery-independent research survey under a joint agreement. The survey employs longline trap gear to obtain catch rate data, gather biological samples, capture oceanographic measurements, and collect tag release and recapture data.Data summaries provided here are for the offshore stratified random sampling design (StRS) component of the survey, which has been conducted annually since 2003. The design of the sablefish survey has developed over time by incorporating and discontinuing components, including individual experimental studies (not available on OpenData). This StRS Survey component differs in methodology from the other two survey components: (1) Standardized trap survey – mainland inlets (1994-present; available on OpenData using link below), and (2) Standardized trap survey – offshore indexing and offshore tagging (1990 – 2010; not yet available on OpenData). The Sablefish offshore stratified random trap survey (StRS) follows a depth and area stratified random sampling design. The survey area is partitioned into five spatial strata (S1 to S5) and three depth strata (RD1 to RD3) for a total of 15 strata. The five spatial strata are S1 (South West Coast Vancouver Island or SWCVI), S2 (North West Coast Vancouver Island or NWCVI), S3 (Queen Charlotte Sound or QCS), S4 (South West Coast of Haida Gwaii or SWCHG), and S5 (North West Coast of Haida Gwaii or NWCHG). The three depth strata are 100-250 fathoms (RD1), 250-450 fathoms (RD2), and 450-750 fathoms (RD3). The area within each of the 15 strata are sectioned into 2 km x 2 km grid cells or ‘fishing blocks’ from which set locations are randomly chosen each year. Survey procedures are standardized and documented in Canadian Technical Reports of Fisheries and Aquatic sciences.Data tables provided for the offshore stratified random survey include (i) effort, (ii) catch, (iii) biological information, (iv) the sampling frame from which blocks are selected for sampling each year, and (v) the calculated coastwide Sablefish biomass index. StRS EffortThis table contains information about the annual survey trips and fishing events (sets). Trip-level information includes the year the survey took place, a unique trip identifier, the vessel that conducted the survey and the trip start and end dates (the dates the vessel was away from the dock conducting the survey). Set-level information includes the date, time, location and depth that fishing took place, the survey spatial and depth strata for the set, reason for the set, soak time, number of traps deployed and number of traps fished. All successful fishing events are included, i.e., those sets that conformed to specified survey standards.StRS CatchThis table contains the catch information from successful fishing events. Catches are identified to species or to the lowest taxonomic level possible. Catches are recorded as fish counts and / or weight. The unique trip identifier and set number are included so that catches can be related to the fishing event information (including capture location).StRS Biological InformationThis table contains the biological data for sampled catches. Data may include any or all of length, weight, sex, maturity, and age. Most of the sampled catch is Sablefish; however, some biological information has been collected on Rockfish, Flatfish and other Roundfish species in some years. Age structures are collected and are archived until required for analyses; therefore, all existing structures have not been aged at this time. Tissue samples (usually a fin clip) may be collected for genetic (DNA) analysis for specific species. Genetic samples may be archived until required for analyses; for more information, please see the data contacts. The unique trip identifier and set number are included so that samples can be related to the fishing event and catch information.Sample FrameThis table contains a list of all of the 2km x 2km grid cells or ‘fishing blocks’ that are part of the stratified random sampling frame. A subset of blocks are randomly selected for sampling each year from this list. For each grid cell, the corresponding depth and spatial strata ID is included. This sample frame can be used to calculate design-based abundance indices for the survey.StRS Biomass IndexThis table contains a coastwide relative biomass index for Sablefish based on the annual StRS survey. Stratified random sampling mean index values and 95% confidence intervals are calculated by year using the classical survey stratified random sampling estimator (Cochran 1977) and the number of possible sampling units per stratum provided by Wyeth et al. (2007). The relative biomass index has been input to the operating model and management procedure used to provide management advice for BC Sablefish since 2011 (Cox et al. 2011).

This dataset contains two geotiff layers. The first layer (1) represents the coastwide distribution of Dungeness crab as predicted from a geostatistical model. The model predicts the mean coastwide probability of Dungeness crab detection using trap sampling gear. The second layer (2) represent the uncertainty in those predictions. Detailed descriptions of these data products can be found in Nephin et al. (2023) and the code used to produce them can be found at https://gitlab.com/dfo-msea/dungeness-sdm/.The objectives of this work was to model the habitat of Dungeness crab (_Metacarcinus magister_), a data-limited coastal marine species, to evaluate the efficacy of data integration when making predictions to geographic areas larger than the area covered by any one data source. In British Columbia, Dungeness crab are sampled regionally and sporadically with a variety of sampling gears and survey protocols, making them an ideal case study to investigate whether the integration of disparate surveys can improve habitat predictions. To that aim, we assemble data from dive, trawl, and baited-trap surveys to generate six candidate generalized linear mixed-effect models with spatial random fields. This dataset contains the mean (1) and difference (2) between the Survey-effect and Gear-effect model predictions.

This dataset provides 1/36-degree monthly mean ocean current climatology (October - March) in the Northeast Pacific. The climatological fields are derived from hourly ocean currents for the perid from 1993 to 2020, simulated using a high-resolution Northeast Pacific Ocean Model (NEPOM).

This dataset provides 1/36-degree monthly-mean ocean current climatology (April - September) in the Northeast Pacific. The climatological fields are derived from hourly ocean currents for the period from 1993 to 2020, simulated using a high-resolution Northeast Pacific Ocean Model (NEPOM).

This deep water substrate bottom type model was created to aid in habitat modeling, and to complement the nearshore bottom patches. It was created from a combination of bathymetrically-derived layers in addition to bottom type observations. Using random forest classification, the relationship between observed substrates and bathymetric derivatives was estimated across the entire area of interest. The raster is categorized into: 1) Rock, 2) Mixed, 3) Sand, 4) Mud

This dataset corresponds to daily snow cover percentage at 1km resolution grid over land areas of Canada from 2006-2010. The data are subsampled by 4km to reduce data volumes and considering the geolocation uncertainty of the input satellite imagery. The daily maps are generated by assimilation of daily cloud screened NOAA AVHRR satellite imagery and Canadian Meteorological Centre (CMC) snow depth analysis snow depth and density fields within an off-line version of the CMC daily snow depth model. The snow depth model is modified to include snowpack reflectance model and a surface radiative transfer scheme that relates vegetation and snowpack reflectance to top-of-canopy bi-directional reflectance. A logistic vegetation phenology model is used to parameterize temporal dynamics of canopy leaf area index. A per-pixel particle filter with a 30 day moving window is applied to assimilation observations corresponding to 1km resolution visible band directional reflectance and normalized difference vegetation index and 24km CMC daily snow depth and monthly snow density fields. The assimilation is forced using daily air temperature and precipitation fields. Validation of the datasets has been performed by comparison to MODIS snow cover maps and in-situ snow depth stations across Canada. Validation suggests similar accuracy to MODIS snow cover products over relatively flat terrain. Validation over mountainous regions is ongoing.

Polygon feature layer of Manitoba's economic regionsThis feature layer depicts the boundaries of economic regions in Manitoba. There are eight regions in Manitoba, defined by Statistics Canada. Fields included [Alias (Field Name): Field description] Economic Region (Economic_Region): The name of the economic region For more information, see Regional Economic Profiles on the Manitoba  government  website.FF