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We have found 1,701 datasets for the keyword "observations océanographiques en profondeur". You can continue exploring the search results in the list below.
Datasets: 104,589
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1,701 Datasets, Page 1 of 171
Cobb Seamount Visual Survey 2012 (AUV)
This dataset contains observations of species occurrences from seafloor imagery collected by the autonomous underwater vehicle (AUV) during the 2012 Expedition to Cobb Seamount. The National Oceanographic and Atmospheric Administration-operated SeaBED-class AUV which collected photographic images from 4 transects ranging from 436 m to 1154 m in depth.
Cobb Seamount Visual Survey 2012 (ROV)
This dataset contains observations of species occurrences from seafloor imagery collected by the remotely operated underwater vehicle (ROV) during the 2012 Expedition to Cobb Seamount. The ROV operated by Fisheries and Oceans Canada was a customized Deep Ocean Engineering Phantom HD2+2 which collected photographic images from 12 transects ranging from 35 m to 211 m in depth.
Baseline oceanographic records for the Eastern Shore Islands Area of Interest
The Eastern Shore Islands was announced as an "Area of Interest" (AOI) in 2018 by the DFO Maritimes region to potentially be considered for a Marine Protected Area under the federal Oceans Act. As part of its mandate for monitoring established and potential conservation areas, DFO Science regularly deploys instruments including conductivity/temperature/depth (CTD) loggers, and other instruments for measuring dissolved oxygen, nutrients, and other chemical ocean properties. This data collection includes temperature and other oceanographic records for the ESI AOI from June 2024 onward. The data are derived from temperature loggers (Hobo Tidbit loggers or similar) and Sea-Bird MicroCATs, but may in future years include current profiles or additional oceanographic data. These data will be used to monitor temperatures in this coastal region to detect any biological shifts associated with temperature and climate fluctuations, and be used to groundtruth oceanographic models.Cite this data as: Jeffery, N., Stanley, R., Pettitt- Wade, H. (2025): Data of: Baseline oceanographic records for the Eastern Shore Islands Area of Interest.Published: September 2025. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/f0020cec-5671-4908-8fdd-11fc097de99d
Coastal thermograph network
This dataset contains the surface temperature and salinity data of the enlarged coastal thermograph network of the St. Lawrence river, estuary and gulf system. It includes data from the Canadian Hydrographic Service water level network (SINECO), the Department of Fisheries and Oceans (DFO)-Quebec long-termed thermograph monitoring program network and the oceanographic buoy network.Each station is linked with a .png file showing the temperature and salinity time series and with a .csv file containing the surface temperature and salinity data themselves (columns : Station,Latitude,Longitude,Date(UTC),Depth/Profondeur(m),Temperature/Température(ºC),Salinity/Salinité(psu)).Supplemental InformationA detailed description of the networks (SINECO, oceanographic buoys and the DFO-Quebec thermograph monitoring program) is available at the St. Lawrence Global Observatory (SLGO) portal :SINECO : https://ogsl.ca/en/tide-gauges-dfo-chs/Oceanographic buoys : https://ogsl.ca/en/marine-conditions-buoys-dfo/Thermographs: https://ogsl.ca/en/marine-conditions-thermographs-dfo/Technical Reports related to the Thermograph Network (the last one is also available at the same hypertext link mentionned above) :Pettigrew, B., Gilbert, D. and Desmarais R. 2016. Thermograph network in the Gulf of St. Lawrence. Can. Tech. Rep. Hydrogr. Ocean Sci. 311: vi + 77 p.Pettigrew, B., Gilbert, D. and Desmarais R. 2017. Thermograph network in the Gulf of St. Lawrence: 2014-2016 update. Can. Tech. Rep. Hydrogr. Ocean Sci. 317: vii + 54 p.
Shallow substrate model (20m) of the Pacific Canadian coast
The shallow substrate bottom type model was created to support near shore habitat modelling. Data sources include both available observations of bottom type and environmental predictor layers including oceanographic layers, fetch, and bathymetry and its derivatives. Using weighted random forest classification from the ranger R package, the relationship between observed bottom type and predictor layers can be determined, allowing bottom type to be classified across the study areas. The predicted raster files are classified as follows: 1) Rock, 2) Mixed, 3) Sand, 4) MudThe categorical substrate model domains are restricted to the extent of the input bathymetry layers (see data sources) which is 5 km from the 50 m depth contour.
Summer Model Outputs and Observations in Discovery Islands, British Columbia
This dataset contains the modelled and observed data used in the publication "Fjord circulation permits persistent subsurface water mass in a long, deep mid-latitude inlet" by Laura Bianucci et al., DFO Ocean Sciences Division, Pacific Region (published in the journal Ocean Science in 2024). An application of the Finite Volume Community Ocean Model (FVCOM v4.1) was run from May 24 to June 27, 2019 in the Discovery Islands region of British Columbia, Canada. Observed temperature and salinity profiles available in this area during this time period are included in the dataset, along with the modelled values at the same times and locations.
Bathymetric Gridded Data Overview
CHS offers 500-metre bathymetric gridded data for users interested in the topography of the seafloor. This data provides seafloor depth in metres and is accessible for download as predefined areas.
Seasonal primary production climatology of the Canadian Pacific Exclusive Economic Zone from BCCM model (1993-2020)
Description:Seasonal mean primary production from the British Columbia continental margin model (BCCM) were averaged over the 1993 to 2020 period and depth-integrated to create seasonal mean climatology of the Canadian Pacific Exclusive Economic Zone. Methods:Total primary production is the sum of diatoms and flagellates production. 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 a raster layer of seasonal depth-integrated primary production climatology for the Canadian Pacific Exclusive Economic Zone at 3 km spatial resolution.Uncertainties:Model results have been extensively evaluated against observations (e.g. altimetry, CTD and nutrient profiles, observed geostrophic currents), which showed the model can reproduce with reasonable accuracy the main oceanographic features of the region including salient features of the seasonal cycle and the vertical and cross-shore gradient of water properties. However, the model resolution is too coarse to allow for an adequate representation of inlets, nearshore areas, and the Strait of Georgia.
Seasonal primary production climatology of the Canadian Pacific Exclusive Economic Zone from BCCM model (1981-2010)
Description:Seasonal mean primary production from the British Columbia continental margin model (BCCM) were averaged over the 1981 to 2010 period and depth-integrated to create seasonal mean climatology of the Canadian Pacific Exclusive Economic Zone. Methods:Total primary production is the sum of diatoms and flagellates production. 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 a raster layer of seasonal depth-integrated primary production climatology for the Canadian Pacific Exclusive Economic Zone at 3 km spatial resolution.Uncertainties:Model results have been extensively evaluated against observations (e.g. altimetry, CTD and nutrient profiles, observed geostrophic currents), which showed the model can reproduce with reasonable accuracy the main oceanographic features of the region including salient features of the seasonal cycle and the vertical and cross-shore gradient of water properties. However, the model resolution is too coarse to allow for an adequate representation of inlets, nearshore areas, and the Strait of Georgia.
CHS_LSSL_Galway2015 North_Atlantic_HFX_Tromso
Geographic bathymetric grid data at 100 m x 100 m pixel resolution.Datum: WGS84Collaboration of Canada, the United States of America and the European Union as part of the Atlantic Ocean Research Alliance's second project under the Galway Statement. Project mapped the North Atlantic seafloor along a transect from Halifax, Canada to Tromsø, Norway to further the understanding of marine habitats, conservation and navigation. Chief Scientist / Primary Investigator name: Paola Travaglini Platform: CCGS Louis S. St- Laurent (Canadian heavy icebreaker)Device 1 type: Multibeam echo-sounder (sonar)Device 1 manufacturer: Kongsberg Device 1 model: EM122, hull installed behind ice protection window Data and Data format:100 m resolution grid of bathymetryBAG format: Bathymetric Attributed Grid ObjectNavigation and positioning: Trimble GNSS receiver + antennas Applanix POS/MV v5 inertial measuring system Horizontal Datum: WGS84 (G1762) Tidal correction:Zero tide applied: tides are not well known for the major part of the data and tides over very deep water are generally negligible. Sound Velocity Profile measurements:In-situ sound velocity profiles were applied.Note on accuracy/S-44 survey standards:Considering the intended output from this survey (IHO Order 1a - Areas shallower than 100 metres where under-keel clearance is less critical but features of concern to surface shipping may exist.) and using an average depth of 2000m as ‘d’ in the IHO Standard Equation - the allowable Total Vertical Uncertainty (TVU) must be < 26m which indeed the data has achieved (by comparison with overlapping datasets from other surveys/agency data).IHO Order 1aHorizontal positioning accuracy: 5.0 m + 5% of depth (95% Confidence level)(~105 m at a mean depth of 2000 m)Vertical positioning accuracy: 2.5 m < 26 m = Sqrt((0.5 m)^2+(0.013 x 2000 m)^2)
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