The “Annual Unit Runoff (dam3/km2) for a 75% Probability of Exceedence” dataset is a line data set that covers the extent of Canada. It shows the 75% Probability of exceedence annual unit runoff.

Geographic bathymetric grid data at 100 m x 100 m pixel resolution. Datum: WGS84 Collaboration 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 bathymetry BAG format: Bathymetric Attributed Grid Object Navigation 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 1a Horizontal 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)

Bay Scale Assessment of Nearshore Habitat Bras dOr Lake - Malagawash 2007 2008 data is part of the publication Bay Scale Assessment of Nearshore Habitat Bras d'Or Lakes. A history of nearshore benthic surveys of Bras d’Or Lake from 2005 – 2011 is presented. Early work utilized drop camera and fixed mount sidescan. The next phase was one of towfish development, where camera and sidescan were placed on one platform with transponder-based positioning. From 2009 to 2011 the new towfish was used to ground truth an echosounder. The surveys were performed primarily in the northern half of the lake; from 10 m depth right into the shallows at less than 1 m. Different shorelines could be distinguished from others based upon the relative proportions of substrate types and macrophyte canopy. The vast majority of macrophytes occurred within the first 3 m of depth. This zone was dominated by a thin but consistent cover of eelgrass (Zostera marina L.) on almost all shores with a current or wave regime conducive to the growth of this plant. However, the eelgrass beds were frequently in poor shape and the negative impacts of commonly occurring water column turbidity, siltation, or possible localized eutrophication, are suspected. All survey data were placed into a Geographic Information System, and this document is a guide to that package. The Geographic Information System could be used to answer management questions such as the placement and character of habitat compensation projects, the selection of nearshore protected areas or as a baseline to determine long term changes. Vandermeulen, H. 2016. Video-sidescan and echosounder surveys of nearshore Bras d’Or Lake. Can. Tech. Rep. Fish. Aquat. Sci. 3183: viii + 39 p. Cite this data as: Vandermeulen H. Bay Scale Assessment of Nearshore Habitat Bras d'Or Lake - Malagawash 2007 - 2008. Published May 2022. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.

The Prince Edward Island Detailed Soil Survey is a dataset series describing the spatial distribution of soils and associated landscapes in the Canadian province of Prince Edward Island. Soil landscape information compiled and published over the previous several decades provided the basis for the development of this relational database. The graphic soil landscape polygons are intended to be represented at a scale of 1:75,000. The associated soil landscape information and soil characteristics are described in a standard format in the Component (CMP), Soil Name File (SNF) and Soil Layer File (SLF) tables.

The National Ecological Framework for Canada's "Total Land and Water Area by Ecodistrict” dataset provides land and water area values for ecodistrict framework polygons, in hectares. It includes attributes for a polygon’s total area, land-only area and large water body area.

This LiDAR DEM, originated from the Government of Manitoba (2019-05), was hydro-conditioned with a recent culvert inventory including GIS data from the Province of Manitoba for provincial highways as well as hard copy maps from the Rural Municipality of Lorne. It also included GPS and desktop surveys as part of a collaborative effort between Agriculture and Agri-Food Canada, Swan Lake First Nation, and Pembina Valley Watershed District. The hydro-conditioned DEM was used by the International Institute for Sustainable Development (IISD) as input for hydrological modelling of catchments near Swan Lake flowing into the Pembina River (Pembina River Watershed, MB) to spatially target water-related agricultural beneficial management practices (e.g, flood management infrastructure, water retention structures, nutrient and sediment load reduction practices). The DEM spatial extent represents the area of interest referred to as the “Swan Lake Study Area”, as part of the Eastern Prairies Living Lab, AAFC (2019-2023).

The’ Qu'Appelle Valley Lakes system – Topography and Imagery’ series consists of topographic and imagery data for lakes within the Qu'Appelle River Valley in central Saskatchewan. This data was collected in the fall of 2008 and consists of contour lines, shorelines, spot heights, tile index, and imagery

Inventory carried out in order to increase knowledge about built heritage. Starting with the 2015 pre-inventory, 608 heritage properties were selected. The selection of these properties was carried out in order to target properties with exceptional (7), superior (183) and good (418) heritage values. To these were added some of the properties that were already protected by a PIIA regulation and the properties built before 1900, as well as all the wayside crosses and calvaries, representing 181 additional properties, for a total of 789 heritage assets that are the subject of the inventory.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The National Ecological Framework for Canada's "Soil Texture by Ecozone” dataset contains tables that provide soil texture information within the ecozone framework polygon. It provides soil texture codes and their English and French language descriptions as well as the percentage of the polygon that the component occupies. Soil texture indicates the relative proportions of the various soil separates (sand, silt, clay) as described by classes of texture. Soil separates are mineral particles, 2.0 mm in diameter and include: gravel 0.2 -7.5 cm and cobbles 7.5-25.0 cm. There are 12 texture group classes definitions and one class definition for Not Applicable (which indicates, for example, water, ice or urban areas).

This dataset contains results from an eelgrass classification for Bouctouche Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 688 sites. Two-thirds of these sites were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 83.7% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 187 sites that were within the classification area, 131 were correct, 51 were "one-off", and 5 were incorrect [(131 + (51/2))/ 187 = 0.837].