The Canadian Weathership Program collected meteorological data at Station Papa (50N, 145W) in the North Pacific Ocean between 1949 and 1981. In 2014, researchers at the University of Washington (UW) Applied Physics Laboratory (APL) and the National Oceanic and Atmospheric Administration (NOAA) Pacific Marine Environmental Laboratory (PMEL) analyzed this historic data to determine its efficacy as a scientific tool. The data available here are the Government of Canada data files that were utilized for this analysis. The "OWSP Full Data (1949-1981)" file contains the entire Canadian Weathership Program record of data collected from Station Papa and the "OWSP Daily Averaged Wind Speed and Wave Height Data (1949-1981)" file contains daily averaged values of wind speed and wave height generated by the UW APL and NOAA PMEL researchers. The Data Dictionary for each data file contains notes on any quality controls that were applied to the data by the UW APL and NOAA PMEL researchers. The UW documents titled, "Data Documentation for Dataset 1170 (DSI-1170), Surface Marine Data, National Climatic Data Center" (https://digital.lib.washington.edu/researchworks/bitstream/handle/1773/25570/td1170.pdf?sequence=6&isAllowed=y) and "Table detailing units of data values in each file" (https://digital.lib.washington.edu/researchworks/handle/1773/25570), provide further information on the key values, point scales, and other units that were used in these datasets.

Description: Night-time sea surface temperature (SST) was retrieved from the MODIS instrument on the Aqua satellite, with data distributed by the NASA Ocean Biology Processing Group, and averaged into monthly climatological composites. The data span the years 2003-2020; records were created at 1 km pixel resolution to be consistent with other satellite products. Methods: MODIS-Aqua night long-wave Sea Surface Temperature (SST) images were acquired from the NASA Ocean Biology Processing Group at processing Level-2 (version 2018), 1-km resolution, spanning the period 2003-01-01 to 2020-12-31. Image pixels were aligned and mapped to a regular grid using the SeaDAS program, retaining all pixels with a quality level of ‘1’ or lower, which is recommended for scientific analysis. The monthly mean value at all pixels was calculated for individual years, and used to produce maps of the monthly climatological mean and standard deviation of SST. Additionally, the number of occurrences of valid data at each pixel over the period of observation were calculated. Pixels with fewer than two occurrences over the entire period of observation were removed from these maps, and set to a NaN value in the tif files. A few small gaps between pixels (near the edges of individual images) were filled using the median value of surrounding pixels, provided there were greater than 4 values. Finally, all rasters were cropped to the Canadian Exclusive Economic Zone and assigned to the NAD83 geographic coordinate reference system (EPSG:4269), and have a final pixel resolution of approximately 0.01 degrees. The monthly mean, monthly standard deviation, and number of occurrences for all pixels are provided. Data Sources: NASA Ocean Biology Processing Group. (2017). MODIS-Aqua Level 2 Ocean Color Data Version R2018.0. NASA Ocean Biology Distributed Active Archive Center. https://doi.org/10.5067/AQUA/MODIS/L2/OC/2018 Uncertainties: Satellite values have been evaluated against global datasets, and datasets of samples in the Pacific region (see references). However, uncertainties are introduced when averaging together images over time as each pixel has a differing number of observations. Short-lived or spatially limited events may be missed.

McElhanney Consulting Services Ltd (MCSL) has performed a LiDAR and Imagery survey in southern Saskatchewan. The purpose was to generate DEMs for hydraulic modeling of floodplain, digital terrain maps, and other products for portions of the Swift Current Creek valley and other miscellaneous tributaries and related water course valleys in and around the City of Swift Current. The acquisition was completed between the 16th and 25th of October, 2009. The survey consisted of approximately 790 square kilometers of coverage. While collecting the LiDAR data, we also acquired aerial photo in RGB and NIR modes consisting of 1649 frames each. In addition to the main area of interest, McElhanney has acquired some LiDAR and photo of low lying areas adjacent to the project area. This additional area was acquired on speculation that the data may be required in the future. The 3Dimensional laser returns (point cloud) were classified using Microstation (v8), Terrascan and TerraModeler. A series of algorithms based on topography were created to separate laser returns that hit the ground from the ones that hit objects above the ground. Steps taken are: Classified LiDAR surface as Bare earth, Classified other features as non-bare earth or default, Formatted to ASPRS .LAS V1.1 (Class 1 - Default (non-bare earth), Class 2 – Ground points (bare earth)), 239 tiles each 2km x2km generated for LiDAR data, File prefix FF – Classified (Non-Bare Earth and Bare Earth), File Prefix BE – Bare Earth only, Bare Earth Model Key Point (MKPts) surface files are thinned Bare earth LiDAR points. MKPts files generate a virtually identical surface without the large file size, MKPts file format is ASCII (Easting Northing Z-elevation) xyz and LAS format.

The AAFC Infrastructure Flood Mapping in Saskatchewan 1 meter Bare Earth DEM are the bare earth DEMs created at a 1 m interval for the capture area of Saskatchewan. The bare earth DEM elevations were derived from a TIN surface model of the combined DTM Key Point and Ground classes in the LiDAR point cloud tiles. It should be noted that the grid point elevations have been interpolated from the LiDAR points and may contain greater uncertainty depending on the amount of interpolation performed.

The data layer (.shp) presented is the result of an unsupervised classification method for classifying seafloor habitat on German Bank (off South West Nova Scotia, Canada). This method involves separating environmental variables derived from multibeam bathymetry (Slope, Curvature) and backscatter (principal components: Q1, Q2, and Q3) into spatial units (i.e. pixels) and classifying the acoustically separated units into 5 habitat classes (Reef, Glacial Till, Silt, Silt with Bedforms, and Sand with Bedforms) using in situ data (imagery). Benthoscape classes (synonymous to landscape classifications in terrestrial ecology) describe the geomorphology and biology of the seafloor and are derived from elements of the seafloor that were acoustically distinguishable. Unsupervised classifications (acoustic classifications) optimized at 15 classes using Idrisi CLUSTER method (pixel based) Number representing the benthoscape classes (CLASS) derived from in situ imagery and video (See Brown et al., 2012, Figure 3, Table 1). Benthoscape classes (See Brown et al., 2012, Figure 3). Reference: Brown, C. J., Sameoto, J. A., & Smith, S. J. (2012). Multiple methods, maps, and management applications: Purpose made seafloor maps in support of ocean management. Journal of Sea Research, 72, 1–13. https://doi.org/10.1016/j.seares.2012.04.009 Cite this data as: Brown, C. J., Sameoto, J. A., & Smith, S. J. Data of: Benthoscape Map of German Bank. Published: February 2021. Population Ecology Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/b7f81d4a-2cb6-4393-b35b-e536ec63e834

Effective conservation planning relies on understanding population connectivity which can be informed by genomic data. This is particularly important for sessile species like the horse mussel (Modiolus modiolus), a key habitat-forming species and conservation priority in Atlantic Canada), yet little genomic information is available to describe horse mussel connectivity patterns. We used more than 8000 restriction-site associated DNA sequencing-derived single nucleotide polymorphisms and a panel of 8 microsatellites to examine genomic connectivity among horse mussel populations in the Bay of Fundy, along the Scotian Shelf, and in the broader northwestern Atlantic extending to Newfoundland. Despite phenotypic differences between sampling locations, we found an overall lack of genetic diversity and population structure in horse mussels in the Northwest Atlantic Ocean. All sampled locations had low heterozygosity, very low FST, elevated inbreeding coefficients, and deviated from Hardy-Weinberg Equilibrium, highlighting generally low genetic diversity across all metrics. Principal components analysis, Admixture analysis, pairwise FST calculations, and analysis of outlier loci (potentially under selection) all showed no independent genomic clusters within the data, and an analysis of molecular variance showed that less than 1% of the variation within the SNP dataset was found between sampling locations. Our results suggest that connectivity is high among horse mussel populations in the Northwest Atlantic, and coupled with large effective population sizes, this has resulted in minimal genomic divergence across the region. These results can inform conservation design considerations in the Bay of Fundy and support further integration into the broader regional conservation network. Cite this data as: Van Wyngaarden, Mallory et al. (2024). Widespread genetic similarity between Northwest Atlantic populations of the horse mussel, Modiolus modiolus. Published: May 2025. Coastal Ecosystem Science Division, Maritimes Region, Fisheries and Oceans Canada, Dartmouth, NS.

The National Ecological Framework for Canada's "Surficial Geology by Ecozone” dataset contains tables that provide surficial geology information with the ecozone framework polygons. It provides codes that characterize surficial geology (unconsolidated geologic materials) and their English and French-language descriptions as well as information about the area and percentage of the polygon that the material occupies.

Flight lines for the Pasqua, Crooked, Echo, and Round Lakes area within the Qu'Appelle Valley River system in Saskatchewan

These data were created under DFO’s Strategic Program for Ecosystem-based Research and Advice - Aquatic Invasive Species Program: “Evaluation of the movement of marine infrastructure as a pathway for aquatic invasive species spread”. This geodatabase contains floating dock locations in coastal waters of the Pacific Northwest, from Puget Sound, Washington to Southeast Alaska. These data were assembled by Josephine Iacarella and used in an analysis to understand the role of floating infrastructure as a vector in the spread of marine nonindigenous species (Iacarella et al., 2019). The data are represented as point vectors, though docks have associated size estimates. Data were collected with the aim to have the most accurate representation of coastal coverage of structures in 2017. The most recent images from Google Earth were used, though in some areas these date back a few years. Floating docks included those that extended into the subtidal and were not fixed on pilings. Dock locations were binned into size categories, with small docks and associated marina structures grouped together as ‘marina areas’ based on spatial clustering and a visual estimate of size (haphazard measurement selection, n=35 per category; small: 57.2 m2 ± 6.7, medium: 379.1 m2 ± 42.8, marina area: 4,453.5 m2 ± 744.4). A total of 7,809 floating dock sites were recorded, covering an estimated area of 2.3 km2.

In 2019, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, and Guelph.