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

CanVec contains more than 60 topographic features classes organized into 8 themes: Transport Features, Administrative Features, Hydro Features, Land Features, Manmade Features, Elevation Features, Resource Management Features and Toponymic Features.This multiscale product originates from the best available geospatial data sources covering Canadian territory. It offers quality topographic information in vector format complying with international geomatics standards.CanVec can be used in Web Map Services (WMS) and geographic information systems (GIS) applications and used to produce thematic maps. Because of its many attributes, CanVec allows for extensive spatial analysis.Related Products (Open Maps Links):**[Constructions and Land Use in Canada - CanVec Series - Manmade Features](https://open.canada.ca/data/en/dataset/fd4369a4-21fe-4070-914a-067474da0fd6)****[Lakes, Rivers and Glaciers in Canada - CanVec Series - Hydrographic Features](https://open.canada.ca/data/en/dataset/9d96e8c9-22fe-4ad2-b5e8-94a6991b744b)****[Administrative Boundaries in Canada - CanVec Series - Administrative Features](https://open.canada.ca/data/en/dataset/306e5004-534b-4110-9feb-58e3a5c3fd97)****[Mines, Energy and Communication Networks in Canada - CanVec Series - Resources Management Features](https://open.canada.ca/data/en/dataset/92dbea79-f644-4a62-b25e-8eb993ca0264)****[Wooded Areas, Saturated Soils and Landscape in Canada - CanVec Series - Land Features](https://open.canada.ca/data/en/dataset/80aa8ec6-4947-48de-bc9c-7d09d48b4cad)****[Transport Networks in Canada - CanVec Series - Transport Features](https://open.canada.ca/data/en/dataset/2dac78ba-8543-48a6-8f07-faeef56f9895)****[Elevation in Canada - CanVec Series - Elevation Features](https://open.canada.ca/data/en/dataset/64aad38d-f692-4ab6-bf2c-f938586c1249)****[Map Labels - CanVec Series - Toponymic Features](https://open.canada.ca/data/en/dataset/b3fdcd34-4533-415f-8f83-68f17f9d5d68)**

GeoAI are buildings, hydrography, forests, and roads automatically extracted using Deep Learning models applied to a source dataset, typically aerial or satellite images. The primary aim of GeoAI is to increase Canada's availability of high-resolution foundational geospatial data for both spatial and temporal coverage.‌The infrastructure and expertise put in place by NRCan enables a rapid, efficient, and scalable data creation process through the use of leading-edge technology and Artificial Intelligence models. Published datasets for a given source can be revisited at a later date as more accurate models are developed and put into production. For now, only static files are available, but as the series develops, new products and services will be added.Several use cases of GeoAI data for change detection have been created. To view them, please follow the links below:- Iqaluit: https://open.canada.ca/data/en/dataset/ef29513d-a007-408c-beba-65f86e7b5b01- Calgary: https://open.canada.ca/data/en/dataset/bd07b597-5166-4b62-ae9c-245b3c62ea03- Québec City: https://open.canada.ca/data/en/dataset/4026720e-f7fb-478e-a58f-c62ebe20005d- Winnipeg: https://open.canada.ca/data/en/dataset/a6874aac-cfcd-4b92-bb3f-6031eedb5b57- Victoria: https://open.canada.ca/data/en/dataset/f011c7b9-f79e-42f8-9481-9bf9788bb189- Trois-Rivières: https://open.canada.ca/data/en/dataset/2b11a323-52c2-43d5-9c09-bc4b15b1bcc8

The level curves are derived from a LiDAR survey carried out in May 2014.The equidistance is 25 cm, the absolute planimetric accuracy (XY) isof approximately 40 cm and the absolute altimeter accuracy (Z) is approximately 20 cm.The urban sectors covered are the urban perimeters of Rouyn, Noranda,Granada, Évain (partially), Lac-Dufault and the airport.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Yukon Oil and Gas Dispositions. Created from the disposition abstracts and the Oil and Gas Land Division System. For more information visit [https://yukon.ca/en/doing-business/licensing/apply-oil-and-gas-rights#disposition-overview](https://yukon.ca:443/en/doing-business/licensing/apply-oil-and-gas-rights)Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

PURPOSE:Chlorophyll a is a photosynthetic pigment common to aquatic algae, and its measurement can provide an estimate of free-floating algae abundance in an aquatic system. For more than 40 years, the Fisheries and Oceans Canada - Great Lakes Laboratory for Fisheries and Aquatic Sciences (DFO-GLLFAS) office in Sault Ste. Marie Ontario has collected water samples for chlorophyll a spectrophotometric analysis, producing data in the form of mg/m3. The collection of chlorophyll a data was generally conducted to assist in characterizing the aquatic systems level of primary productivity, since that can influence fish abundance. This was a supplementary measure of the quality of fish habitat in these systems for a variety of fish-related projects of relevance to DFO’s mandate. Consistency of data collection was dependent on the duration of each project and funding available. DESCRIPTION:The initial study collected chlorophyll a data from the five lakes in the Turkey Lakes Watershed (TLW – Upper and Lower Batchawana Lakes, Wishart Lake, Little Turkey Lake and Turkey Lake) as well as Quinn Lake which is located outside of the TLW (see Smokorowski et al. 2006) in partnership with Environment and Climate Change Canada (ECCC) and Natural Resources Canada (NRCAN), as part of Canada’s Acid Rain Initiative. The TLW study was established in 1979 to evaluate the impacts of acid rain on terrestrial and aquatic ecosystems. This dataset encompasses chlorophyll a data collected from five lakes in the TLW and Quinn Lake (data also collected by the Canadian Wildlife Service and Environment and Climate Change Canada) from 1983 - 2010 with some gaps of varying duration (Webster et al., 2021). Note that an experimental habitat removal took place in some TLW lakes and Quinn Lake in 1999 and 2000. Specifically, in autumn of 1999, 50% of the coarse woody material was removed from the shorelines of Little Turkey Lake and Quinn Lake, and in autumn of 2000, 50% of the course woody material was removed from Wishart Lake shoreline (details available in Smokorowski et al., 2006). Methods Summary: Sampling was only conducted during the open water season and the frequency and extent of sampling frequency in each waterbody varied and was project- and funding-dependent. There are no chlorophyll a data for the years 1986, 1989 – 1990, 1996 – 1997, 2006-2007 and 2009.Sampling was accomplished by rinsing on site an appropriately-cleaned 1 L, brown-opaque polyethylene bottles at least three times. For hand draw samples, the bottle was immersed sub-surface (less than 0.5 meters) to collect the sample. Composite tube samples were collected using an integrated water sampling tube that collected epilimnion water from the entire water column to a depth of 5 meters. Up to five - 1 L (1000 mL) samples were collected per station and returned to the lab on ice. Each 1 L sample was filtered using glass fiber filters (Whatman GF/C, 42.5 mm) within one day of sampling, and then frozen prior to standard chlorophyll a analysis (American Public Health Association [APHA] 1985). From 1983-1998, Chlorophyll a concentrations (mg/L) was calculated based on APHA 1985; from 1998 to present the calculation was based on APHA (1998). The sample dataset information includes the georeferenced sampling locations, the raw data for chlorophyll a calculations, and the calculated chlorophyll a concentrations using both the APHA (1985) and APHA (1998) methods. This dataset was published in partnership with the Canadian Institute of Ecology and Evolution - Living Data Project, which was funded by a CREATE grant from the Natural Science and Engineering Research Council of Canada. We would like to recognize Caroline Dallstream for her effort in publishing this dataset. SAMPLING METHODS:Methods Summary: Sampling was only conducted during the open water season and the frequency and extent of sampling frequency in each waterbody varied and was project- and funding-dependent. There are no chlorophyll a data for the years 1986, 1989 – 1990, 1996 – 1997, 2006-2007 and 2009. See Sampling was accomplished by rinsing on site an appropriately-cleaned 1 L, brown-opaque polyethylene bottles at least three times. For hand draw samples, the bottle was immersed sub-surface (less than 0.5 meters) to collect the sample. Composite tube samples were collected using an integrated water sampling tube that collected epilimnion water from the entire water column to a depth of 5 meters. Up to five - 1 L (1000 mL) samples were collected per station and returned to the lab on ice. Each 1 L sample was filtered using glass fiber filters (Whatman GF/C, 42.5 mm) within one day of sampling, and then frozen prior to standard chlorophyll a analysis (American Public Health Association [APHA] 1985). From 1983-1998, Chlorophyll a concentrations (mg/L) was calculated based on APHA 1985; from 1998 to present the calculation was based on APHA (1998). The sample dataset information includes the georeferenced sampling locations, the raw data for chlorophyll a calculations, and the calculated chlorophyll a concentrations using both the APHA (1985) and APHA (1998) methods.USE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

The Regional Deterministic Wave Prediction System (RDWPS) produces wave forecasts out to 48 hours in the future using the third generation spectral wave forecast model WaveWatch III® (WW3). The model is forced by the 10 meters winds from the High Resolution Deterministic Prediction System (HRDPS). Over the Great Lakes, an ice forecast from the Water Cycle Prediction System of the Great Lakes (WCPS) is used by the model to attenuate or suppress wave growth in areas covered by 25% to 75% and more than 75% ice, respectively. Over the ocean, an ice forecast from the Regional Ice Ocean Prediction System (RIOPS) is used: in the Northeast Pacific, waves propagate freely for ice concentrations below 50%, above this threshold there is no propagation; in the Northwest Atlantic the same logic is used as in the Great Lakes. Forecast elements include significant wave height, peak period, partitioned parameters and others. This system includes several domains: Lake Superior, Lake Huron-Michigan, Lake Erie, Lake Ontario, Atlantic North-West and Pacific North-East.

The Regional Deterministic Wave Prediction System (RDWPS) produces wave forecasts out to 48 hours in the future using the third generation spectral wave forecast model WaveWatch III® (WW3). The model is forced by the 10 meters winds from the High Resolution Deterministic Prediction System (HRDPS). Over the Great Lakes, an ice forecast from the Water Cycle Prediction System of the Great Lakes (WCPS) is used by the model to attenuate or suppress wave growth in areas covered by 25% to 75% and more than 75% ice, respectively. Over the ocean, an ice forecast from the Regional Ice Ocean Prediction System (RIOPS) is used: in the Northeast Pacific, waves propagate freely for ice concentrations below 50%, above this threshold there is no propagation; in the Northwest Atlantic the same logic is used as in the Great Lakes. Forecast elements include significant wave height, peak period, partitioned parameters and others. This system includes several domains: Lake Superior, Lake Huron-Michigan, Lake Erie, Lake Ontario, Atlantic North-West and Pacific North-East.

This data shows the spatial extent of surface disturbance mapping projects completed by GIS contractors on behalf of Environment Yukon. Within these polygons, features were digitized using high resolution satellite imagery and orthophotos; outside these polygons, disturbance mapping is either incomplete or non-existent.Distributed from [GeoYukon](https://yukon.ca/en/statistics-and-data/mapping/explore-map-data-using-geoyukon) by the [Government of Yukon](https://yukon.ca/) . Discover more digital map data and interactive maps from Yukon's digital [map](https://yukon.ca/en/maps) data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

The study involved sampling during a winter subsistence fishery at Brock Lake in November 2003, and a physical, chemical and biological assessment of the lake in July 2004 and July 2005. Data including physical, chemical and biological variables were published as Roux, M.-J., Harwood, L. A., Illasiak, J., Babaluk, J.A., and de Graff, N. 2011. Fishery resources and habitats in a headwater lake of the Brock River, NT, 2003-2005. Can. Manuscr. Rep. Fish. Aquat. Sci. 2932: viii + 61 p.