Zooplankton and ichthyoplankton data are archived in the Institute of Ocean Sciences (IOS) Zooplankton Database. The data available spans from 1980 to 2018 and is an extraction of vertical net hauls as biomass by major taxa collected during surveys conducted in the oceanic and coastal waters of the Northeast Pacific Ocean. The majority of vertical net hauls in this data set were collected from 10 metres above the sea floor or an approximate maximum depth of 250 metres. For further data requests, please use the contact information provided.

Dry spell periods are defined as the number of days (April 1 – October 31) where daily precipitation is less than 0.5 mm. This is not an accumulation of precipitation, simply a count of days. Dry spell products are only generated during the Growing Season, April 1 through October 31.

Each pixel value corresponds to the best quality maximum NDVI recorded within that pixel over the week specified. Poor quality pixel observations are removed from this product. Observations whose quality is degraded by snow cover, shadow, cloud, aerosols, and/or low sensor zenith angles are removed (and are assigned a value of “missing data”). In addition, negative Max-NDVI values, occurring where R reflectance > NIR reflectance, are considered non-vegetated and assigned a value of 0. This results in a Max-NDVI product that should (mostly) contain vegetation-covered pixels. Max-NDVI values are considered high quality and span a biomass gradient ranging from 0 (no/low biomass) to 1 (high biomass).

Each pixel value corresponds to the actual number (count) of valid Best-quality Max-NDVI values used to calculate the mean weekly values for that pixel. Since 2020, the maximum number of possible observations used to create the Mean Best-Quality Max-NDVI for the 2000-2014 period is n=20. However, because data quality varies both temporally and geographically (e.g. cloud cover and snow cover in spring; cloud near large water bodies all year), the actual number (count) of observations used to create baselines can vary significantly for any given week and year.

The “Soil Landscapes of Canada (SLC) Version 2.2” dataset series provides a set of geo-referenced soil areas (polygons) that are linked to attribute data found in the associated Component Table (CMP), Landscape Table (LAT), Carbon Layer Table (CLYR), and Dom/Sub File (DOM_SUB). Together, these datasets describe the spatial distribution of soils and associated landscapes for Canada.

Growing degree days (GDDs) are used to estimate the growth and development of plants and insects during the growing season. Growing Degree Day are computed by subtracting a base value temperature from the mean daily temperature and are assigned a value of zero if negative. Base temperatures are a point below which development does not occur for the organism in question. Growing Degree Day products are created for base 0, 5, 10 and 15 degrees Celsius. GDD values are only accumulated during the Growing Season, April 1 through October 31.

The Agri-Environmental Indicator of Risk of Water Contamination by Phosphorus dataset estimates the relative risk of phosphorus loss from Soil Landscapes of Canada agricultural areas to surface water. The data series for this indicator consists of four (4) datasets: Annual P-Balance, Soil-P-Source, Edge of Field and IROWC-P. Products in this data series present results for predefined areas as defined by the Soil Landscapes of Canada (SLC v.3.2) data series, uniquely identified by SOIL_LANDSCAPE_ID values.

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.

The “AAFC Annual Unit Runoff in Canada" data series illustrates runoff trends across the country by isolines of annual unit runoff for a variety of probabilities of exceedence commonly used by decision makers Annual unit runoff is a measure of runoff volume per square kilometre. This series uses units of cubic decametres (1000 m3) per square kilometre (dam3/km2), which is equivalent to millimetres depth on the landscape. It includes a point data set for the hydrologic stations that were analyzed and seven sets of line work to show the adjusted isolines for 10%, 25%, 50%, 70%, 75%, 80%, and 90% probability of exceedence.

First Fall Frost (0 °C) is defined as the average day, during the second half of the year, of the first occurrence of a minimum temperature at or below 0 °C. These values are calculated across Canada in 10x10 km cells.