Data set covers metrics and metadata related to wild collected copepods Calanus spp. (C. hyperboreus, C. glacialis, C. finmarchicus) and Metridia longa: - body size in prosome length [PL]- dry weight [DW]- lipid content (oil sac area [OSA] and oil sac volume [OSV])Spatial coverage: North Atlantic sampling sites- Scotian Shelf (SS)- Gulf of Saint Lawrence (GSL)- Gulf of Maine-Georges Bank-Nantucket Shoals (GoM)- Newfoundland shelf (NFL)Cite this data as: Helenius LK, Head EJH, Jekielek P, Orphanides CD, Pepin P, Plourde S, Ringuette M, Walsh HJ, Runge JA, Johnson CL. Calanus spp. size and lipid content metrics in North Atlantic, 1977-2019. Published September 2022. Ocean Ecosystem Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/72e6d3a1-06e7-4f41-acec-e0f1474b555b

These data consist of the Recreational Shark Fishing Tournament landings database (1993-2022 inclusive) and the Canadian Dart tag database (2006 onwards; updated annually). Both were collected by the Maritimes Science Division of Fisheries and Oceans Canada. The landings records include biological sampling from 4266 animals and the dart tag records include 4138 tagging and 97 recapture events to date. Potential users should consult Bowlby et al. (2022) for the description, management history, and technical details pertaining to these data. Information is focused on Blue Sharks because they were the primary species captured at recreational tournaments.Cite this data as: Bowlby, H., Joyce, W. Recreational Shark Fishing Tournament Landings Data and Canadian Dart Tag Database. Published January 2023 . Population Ecology Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/4309f1f7-6779-416d-9660-c02f0f99b482

These datasets show the general spatial distribution of commercial fishing harvest and landed values by fishery on a 1km x 1km planning grid. They aggregate key statistics around fleet specific fishing activity and catch in British Columbia (BC) within the exclusive economic zone (EEZ). These gridded data describe the annual average landed weight (Rounded KGs), and landed catch values (CAD $2016) of the subject fishery over the period. The data represented were created from logbook records and matched to prices from fish slips submitted to DFO by participants of BC’s commercial fishing fleets. The dataset is comprised of an aggregate of all species over 10, 9, or 5 years of fishing seasons, depending on the fishery. To preserve potentially proprietary information, a privacy filtering Rule of Five has been applied to each planning unit (each 1km x 1km planning unit). If any planning units do not meet this minimum of 5 unique vessels/unique identifiers during the time span then they are flagged as being filtered and an average of all filtered planning units is applied.The accompanying GeoDB contains two data layers, “all_fisheries_filtered_gridded “, which includes all of the commercial fisheries data in 1km x 1km grids, and “DFO_marine_bioregions_NSB_subregions”, which includes polygon feature boundaries for the federal marine bioregions and Northern Shelf bioregion sub-regions.This dataset contains data for the following fisheries:- Bottom trawl (2012-2016)- Midwater trawl (2012-2016)- Shrimp by trawl (2007-2016)- Prawn trap (2007-2016)- Rockfish (2012-2016)- Sablefish (2007-2016)- Halibut (2007-2016)- Combo trips - halibut/sablefish (2007-2016)- Lingcod (2007-2016)- Green sea urchin (2006-2015)- Red sea urchin (2007-2015)- Sea cucumber (2008-2016)- Geoduck (2007-2015)

These datasets show commercial fisheries catch weight landings of directed fisheries and bycatch from the Scotian Shelf, the Bay of Fundy, and Georges Bank from NAFO Divisions 4VWX and the Canadian portions of 5Y and 5Z. Atlantic Canadian inter-regional maps of four species (Atlantic Halibut, Bluefin Tuna, Redfish and Scallop) are also included from NAFO Divisions 4RST, 3KLMNOP, and 2GHJ. Five-year composite maps (2014–2018) that aggregate catches for each map series are publicly available. The maps aggregate catch weight (kg) per 10 km2 hexagon grid cell for selected species, species groupings and gear types to identify important fishing areas. These maps may be used for decision making in coastal and oceans management, including marine spatial planning, environmental emergency response operations and protocols, Marine Stewardship Council certification processes, marine protected area networks, and ecological risk assessment.These datasets have been filtered to comply with the Government of Canada's privacy policy. Privacy assessments were conducted to identify NAFO unit areas containing data with less than five vessel IDs, licence IDs or fisher IDs. If this threshold was not met, catch weight locations were withheld from these unit areas to protect the identity or activity of individual vessels or companies.Maps were created for the following species, species groupings and gear types:1. Groundfish (all species)2. Groundfish Bottom Trawl3. Groundfish Gillnet4. Groundfish Bottom Longline5. Groundfish (quarterly composites Q1, Q2, Q3, Q4)6. Atlantic Cod7. Atlantic Cod, Haddock and Pollock8. Flatfish9. Atlantic Halibut10. Greenland Halibut (Turbot)11. Hagfish12. Cusk13. Dogfish14. Redfish15. Red Hake16. Silver Hake17. White Hake18. Monkfish19. Sculpin20. Skate21. Wolffish22. Squid23. Herring24. Mackerel25. Large Pelagics26. Bluefin Tuna27. Other Tuna28. Swordfish29. Porbeagle, Mako and Blue Shark30. Snow Crab31. Other Crab32. Scallop33. Scallop (quarterly composites Q1, Q2, Q3, Q4)34. Offshore Clam35. Shrimp36. Offshore Lobster37. Disputed Zone Area 38B Lobster38. Whelk

This data set contains Manitoba market cattle prices and volumes sold at local auction marts.Description of Cattle Prices -source

Fisheries and Oceans Canada (DFO) statistical unit areas (also referred to as unit areas) are smaller portions of the Northwest Atlantic Fisheries Organization (NAFO) Subareas, Divisions, and Subdivisions defined in the Convention on Cooperation in the Northwest Atlantic Fisheries (2020). The spatial representation of boundaries defined by NAFO has recently been updated to address inconsistencies and errors in earlier versions, which is available here: https://open.canada.ca/data/dataset/59af1c96-fc8f-4fa0-b398-d65e953eadaa#comment-41855. Unit areas are used by DFO for a variety of purposes including, but not limited to stock assessments, privacy screening of fisheries maps, and statistical analyses.Unit areas were originally defined throughout the 1940s and 1950s. Subsequently, several revisions and alternate versions were created by DFO in various regions leading to inconsistencies in spatial extent and naming conventions. These changes have not been formally documented. The datasets often have topological errors such as gaps and overlaps, especially along diagonals and coastlines. In addition, inconsistencies have arisen from conflicting interpretations of the NAFO divisions, where not all land points are defined by coordinates. Therefore, DFO, with input from the Canadian Hydrographic Service (CHS), has created an updated version of the DFO unit areas file to address inconsistencies and errors among previous versions. The recently updated NAFO divisions were used as the basis for an updated DFO unit areas file.This version of the DFO unit areas must not be used for legal purposes. Legal descriptions of fishing area boundaries can be found in the Atlantic Fisheries Regulations, Maritime Provinces Fishery Regulations, and DFO License Conditions.

The Automatic Identification System (AIS) is a global, satellite-based and terrestrial-based ship tracking system that uses shipborne equipment to remotely track vessel identification and positional information and is typically required on vessels of 300 gross tonnage or more on an international voyage, of 500 gross tonnage or more not on an international voyage, and passenger ships of all sizes. AIS tracking technologies are primarily used in support of real-time maritime domain awareness and for maritime security and safety of life at sea. This report describes a geographic information system (GIS) analysis of 2019 AIS data to produce yearly and monthly vessel density maps of all vessel classes combined and yearly density maps of each vessel class. The year 2019 was selected to portray shipping densities in a pre-COVID 19 pandemic depiction of the maritime transport sector in the Northwest Atlantic. Vessel density map applications include use in spatial analysis and decision support for marine spatial planning.In 2023 the process was applied to the years 2013 through to 2022 and were made available using the same processes that were applied to the original 2019 datasets.

The Canadian National Wetlands Inventory (CNWI) is a comprehensive, publicly available national geodatabase developed by the Canadian Wildlife Service (CWS) of Environment and Climate Change Canada (ECCC), in collaboration with federal, provincial, and territorial governments, academia, Indigenous groups, and Non-Governmental Organizations (NGOs). It consists of the best available wetland mapping data, along with its metadata, published in a standardized manner.The CNWI is continuously updated through the compilation of existing data and the acquisition of new high-resolution datasets to address coverage gaps, with an emphasis on peatlands and coastal wetlands, which are key habitats for greenhouse gas (GHG) sequestration. ECCC plans to use the CNWI to train and validate machine-learning algorithms to delineate and classify wetlands at a national scale and to measure trends over time. This will directly support Canada’s Nature-Based Climate Solutions by informing biodiversity conservation, guiding climate change mitigation and adaptation strategies, and supporting GHG emissions reporting.The CNWI was initially released in February 2024 with 13 source datasets. In June 2025, the Inventory was updated to include 14 additional datasets. Collectively, these 27 source datasets comprise approximately 12.1 million wetland polygon features, covering a total area of roughly 640,000 square kilometers across ten provinces and territories (BC, MB, NB, NL, NS, PE, ON, QC, SK, YT). These source datasets were cross-walked into a standardized CNWI classification schema, which is based on two foundational documents: the Canadian Wetland Classification System (National Wetlands Working Group, 1997) and the Canadian Wetland Inventory Data Model (2016).The CNWI Schema contains five major wetland classes (Bog, Fen, Swamp, Marsh, and Shallow/Open Water) and eight subclasses (Rich Fen, Poor Fen, Organic Swamp, Mineral Swamp, Organic Marsh, Mineral Marsh, Shallow Water, and Open Water). Non-conforming wetlands can be categorized into three groups: Peatland, Mixed, and Unclassified. For more information on the CNWI and the related database, please refer to the CNWI User Manual and other supporting documents that accompany this publication. The User Manual provides detailed information on how data are collected, managed, and distributed to meet CNWI data standards.

## Purpose The Ministry of Agriculture, Food and Agribusiness (OMAFA) is responsible for Ontario’s provincial soil maps and maintains and update them as necessary. Digital Soil Mapping (DSM) is a modern methodology using spatially explicit soils and environmental data to predict soil variation throughout a landscape at a high, consistent resolution. Digital soil maps are being rolled out throughout Ontario’s agricultural land base to update provincial soil maps. ## Reach Provincial soil maps are used in many decision-making processes including: * land use planning * land evaluation * farming practices * best management practices * ecological monitoring * land resource mapping Potential users of this data include: * farmers * certified crop advisors * conservation authorities * academic researchers * land use planners ## Potential impacts Digital soil maps provide more accurate and precise soils data and enables improved management of soil resources across multiple stakeholders. This allows for better decision making to maximize land use efficiency, improve economic efficiency of soil resources and promote soil health and soil conservation. ## Technical description Digital soil mapping combines geo-referenced soil observations with geo-referenced environmental layers to mathematically model soil variation as a function of environment variation. These models are based on well established, but often complex relationships, between soil properties and topography, biology, geology and hydrology.

Updated 2025-02-21. Feature layer of current Boil Water Advisories issued for Public Water Systems (PWS) in Manitoba.Feature layer of current Boil Water Advisories issued for Public Water Systems (PWS) in Manitoba.