The datasets contain land surface evapotranspiration (ET, in mm of H2O) for Canada's landmass at a spatial resolution of 5-km and temporal intervals of a month and a year over a 24-year period of 2000-2023. The ET was produced by the Land Surface Model EALCO (Ecological Assimilation of Land and Climate Observations) developed at Natural Resources Canada. The EALCO model was run at a 30-minute time step. The monthly (or annual) ET in the datasets is the sum of the 30-minute ET values in a month (or a year). Dew and frost formations simulated by EALCO are included in the ET as negative values, so the ET represents the net water flux between land surface and the atmosphere. Details of the datasets and the EALCO ET modelling algorithms can be found in Wang (2007, Simulation of Evapotranspiration and Its Response to Plant Water and CO2 Transfer Dynamics. J. Hydrometeorology, 9, 426-443, doi: 10.1175/2007JHM918.1) and Wang et al. (2013, Spatial and seasonal variations in evapotranspiration over Canada’s landmass. Hydrol. Earth Syst. Sci., 17, 3561–3575, doi:10.5194/hess-17-3561-2013).

The datasets contain water surface evaporation (PET, in mm of H2O) over Canada's landmass at a spatial resolution of 10-km and temporal intervals of a month and a year over a 24-year period of 2000-2023. The PET was produced by the Land Surface Model EALCO (Ecological Assimilation of Land and Climate Observations) developed at Natural Resources Canada. The PET algorithm in EALCO integrates the dynamic surface evolutions of liquid water, ice, and snow-on-ice for a waterbody into the Penman Equation. The PET was simulated at a daily time step. The monthly (or annual) PET in the datasets is the sum of the daily PET values in a month (or a year). Dew and frost formations simulated by EALCO are included in the PET as negative values, so the PET represents the net water flux between water surface and the atmosphere. Details of the dataset and the EALCO PET modelling algorithms can be found in Li, Wang, and Li (2020, Spatial variations and long‑term trends of potential evaporation in Canada. Scientific Reports, 10: 22089, doi.org/10.1038/s41598-020-78994-9).

The Innu Audio Index is an extract from the Canadian Geographical Names Data Base (CGNDB) of geographical names with associated audio. The shared audio with the Geographical Names Board of Canada (GNBC) is the intellectual property of the Innu Nation. The points represent official geographical names in Innu-aimun, the language of the Innu Nation. The CGNDB is the authoritative national database of Canada's geographical names. It contains geographical names and their attributes that have been approved by the GNBC, the national coordinating body responsible for standards and policies on place names.The GNBC is working to increase awareness of existing Indigenous place names and help promote the revitalization of Indigenous cultures and languages. The GNBC does not warrant or guarantee that the information is accurate, complete or current at all times. For more information, to report data errors, or to suggest improvements, please contact the GNBC Secretariat at Natural Resources Canada with questions or for more information.

The ASTER instrument that was launched onboard NASA’s Terra spacecraft in December 1999 has an along-track stereoscopic capability using two telescopes in its near infrared spectral band to acquire data from nadir and backward views. Over 1.2 million scenes (level-1A products) acquired between March 2000 and August 2008 were used to generate the ASTER Global DEM (ASTGTM) collection.  For more information on the ASTER Global DEM, please see the metadata link.

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 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 Ocean Data Inventory database is an inventory of all of the oceanographic time series data held by the Ocean Science Division at the Bedford Institute of Oceanography. The data archive includes about 5800 current meter and acoustic doppler time series, 4500 coastal temperature time series from thermographs, as well as a small number (200) of tide gauges. Many of the current meters also have temperature and salinity sensors. The area for which there are data is roughly defined as the North Atlantic and Arctic from 30° - 82° N, although there are some minor amounts of data from other parts of the world. The time period is from 1960 to present. The database is updated on a regular basis.

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.

Argo is a key component of the Global Ocean Observing System (GOOS) with an array of about 4,000 autonomous instruments reporting on ocean conditions. These floats collect data on ocean temperature and salinity, and in some cases, additional properties that characterize the ocean’s biological and chemical processes. Established in 1999, Argo represents an international collaboration involving contribution from more than 30 nations. Data from Argo floats are made publicly available within 24 hours of collection time, for free. The data provide valuable information on changes to the Earth's climate and hydrological cycle. They are used for a variety of purposes, such as assessing climate change, improving weather forecasts and developing ocean models.Argo Canada, led by Fisheries and Oceans Canada, has been a key contributor to the International Argo Program since its inception in 2001 . The program has been supported by contributions from Department of Environment and Climate Change Canada, Department of National Defense, Dalhousie University, University of Victoria and Ocean Networks Canada.

We evaluated an autonomous environmental DNA sampler produced by Dartmouth Ocean Technologies Inc (Dartmouth, Canada) compared to time-at-sample filtration in the laboratory to determine the performance of moored samplers for monitoring in the marine world. We deployed three autonomous samplers from DOT in the Bedford Basin (Canada) over a nine-week period in summer/fall 2023. The samplers filtered seawater in situ at programmed interviews over this time period, and we collected contemporaneous samples with a standard vacuum pump during each sampling period. Both eDNA sample types captured similar fish diversity, including typical diversity for the Northwest Atlantic. The invertebrate community detected using the COI marker was different between each sample type, likely due to differences in filter pore size. We found biofouling on the moored samplers was minimal over the study period, even in a high-traffic area such as the Bedford Basin, likely due to the relatively short experimental period, and copper screening covering in the inlet and outlet valves of the instruments. Overall, our results show promise to deploy autonomous eDNA samplers in marine conservation areas to contribute to monitoring in the temperate ocean, but further testing over longer periods of time is needed to determine if DNA remains well-preserved in the autonomous samplers at ambient ocean temperatures.Cite this data as: Jeffery, N.W., Van Wyngaarden, M., and Stanley, R.R.E. Evaluating an Autonomous eDNA Sampler for Marine Environmental Monitoring: Short- and Long-Term Applications. Published: December 2024. Coastal Ecosystems Science Division, Maritimes Region, Fisheries and Oceans Canada, Dartmouth NS.