The Oil and Gas Rights dataset contains the digital boundaries for existing exploration licences, significant discovery licences, production licences, former permits, former leases and the Norman Wells Proven Area. These boundaries are available for download on the Northern petroleum pesources Website at https://www.rcaanc-cirnac.gc.ca/eng/1100100036087/1538585604719. The Oil and Gas Rights dataset is Crown-Indigenous Relations and Northern Affairs Canada (CIRNAC) and Indigenous Services Canada (ISC) primary source for northern petroleum titles geographic location on maps.

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).

Wells and reservoirs in the City of Trois-Rivières**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Mapping of watersheds in the territory of Quebec City.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Each pixel value corresponds to the difference (anomaly) between the mean “Best-Quality” Max-NDVI of the week specified (e.g. Week 18, 2000-2014) and the “Best-Quality” Max-NDVI of the same week in a specific year (e.g. Week 18, 2015). Max-NDVI anomalies < 0 indicate where weekly Max-NDVI is lower than normal. Anomalies > 0 indicate where weekly Max-NDVI is higher than normal. Anomalies close to 0 indicate where weekly Max-NDVI is similar to normal.

This point-layer shows the locations of components that make up facilities (Facilities are stored in another layer). Examples of facility components are barbeques, picnic tables, benches, or kiosks. See the Comp domain for a complete list. NOTE: Although some of the items in the domain appear to be activities, they are actually physical entities that appear within a facility. A facility component point would be stored in this layer to show a more precise location of the kayak rental place of business. Data is not necessarily complete - updates will occur weekly.

This point layer shows the locations of named places that fall within Parks Canada areas of interest. Data is not necessarily complete - updates will occur weekly.

Point layer showing the locations of facilities. A "facility" is an area that contains one or more components. For example, a visitor centre facility might include two parking lots a playground, a separate building for washrooms, a kiosk for renting canoes, picnic tables and barbeques Data is not necessarily complete - updates will occur weekly.

FluWatch is Canada's national surveillance system that monitors the spread of flu and flu-like illnesses on an on-going basis. Activity Level surveillance is a component of FluWatch that provides an overall assessment of the intensity and geographical spread of laboratory-confirmed influenza cases, influenza-like-illness (ILI) and reported outbreaks for a given surveillance region. Activity Levels are assigned and reported by Provincial and Territorial Ministries of Health. A surveillance region can be classified under one of the four following categories: no activity, sporadic, localized or widespread. For a description of the categories, see the data dictionary resource. For more information on flu activity in Canada, see the FluWatch report. (https://www.canada.ca/en/public-health/services/diseases/flu-influenza/influenza-surveillance/weekly-influenza-reports.html) Note: The reported activity levels are a reflection of the surveillance data available to FluWatch at the time of production. Delays in reporting of data may cause data to change retrospectively.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.