Important note - July 6, 2022: Update stopped. Considering the changes in the vaccination recommendations issued on June 20, 2022 by the Committee on Immunization of Quebec, an adaptation of the indicators to assess and monitor vaccination coverage during the fall 2022 campaign is in progress. Since the monitoring of the indicators as they were disseminated up to now is no longer possible, the dataset will no longer be updated for the time being. The files from the last update remain available. This game presented the daily portrait of the vaccination status of new cases and new hospitalizations of COVID-19 in Quebec. The most up-to-date data presented is from the day before.

This game presents the daily portrait of the number of confirmed cases of COVID-19 in Quebec. Important note: As of April 12, 2023, the data source for COVID-19 deaths has changed. Data is updated on a weekly basis. Cases and deaths that occurred on the Sunday, Monday and Tuesday before the Wednesday went online are not available. Please refer to the methodology notes for more details.

Ce jeu présente le portrait quotidien du nombre de cas confirmés de COVID-19 au Québec. Note importante : Depuis le 12 avril 2023, la source de données des décès attribuables à la COVID-19 a été modifiée. Les données sont mises à jour hebdomadairement. Les cas et décès ayant eu lieu le dimanche, lundi et mardi précédent la mise en ligne du mercredi ne sont pas disponibles. Veuillez consulter les notes méthodologiques pour plus de détails.

SARS-CoV-2 wastewater surveillance data and metadata in the Open Data Model format. Part 1: Québec City Authors

• Therrien, J-D¹
• Maere, T.¹
• Sanchez-Quete, F.²
• Tsitouras, A.²
• Goitom, E.³
• Cloutier, F.⁴
• Dufour, D.⁴
• Proulx, F. ⁴
• Nicolaï, N.¹
• Philippe, R.¹
• Tohidi, M.¹
• Dorner, S.³
• Frigon, D.²
• Vanrolleghem, P.A.¹

Affiliations

• ¹ modelEAU, Département de génie civil et de génie des eaux, Université Laval
• ² Microbial Community Engineering Lab (MiCEL), Department of Civil Engineering, McGill University
• ³ Polytechnique Montréal
• ⁴ Ville de Québec

General Remarks

Wastewater-based surveillance of SARS-CoV-2 virus can detect between 1 and 30 infected individuals per 100,000 (including asymptomatic ones) by analyzing the population's sewage. As such, this method is very attractive since it costs only a fraction of clinical testing (as low as 1%). Human faeces may contain the virus a few days before a person becomes ill. Thus, this approach allows for detection of outbreaks 2-7 days before the increase in reported cases stemming from clinical screening tests (Bibby et al., 2021). Wastewater-based surveillance complements clinical testing by geolocating outbreaks, which may help targeting intensive screening programs. Moreover, it provides a quick indication of whether new public health measures (e.g., masks, social distancing, confinement, and curfew) are effective.

Sampling

The reported dataset contains open data collected in the province of Québec as part of the SARS-CoV-2 wastewater-based surveillance program CentrEau-COVID. Four of the largest cities in the province (Montréal, Laval, Québec City, and Trois-Rivières), as well as the municipalities of four rural regions (Mauricie, Centre-du-Québec, Bas-St-Laurent, and Gaspésie) participated in the program. The entire dataset includes 31 sampling sites covering approximately half the population of the province of Québec (population size of 8.5 million). The timeframe covered by the dataset varies for each site. The earliest surveillance program was launched in March 2020, others followed soon after. Samples were collected using various methods, such as 24h composite samples, grab samples, and passive sampling using variations on the Moore swab method (Schang et al., 2020)

Analysis

Prior to the analysis of the samples for SARS-CoV-2, physiochemical parameters such as total suspended solids (TSS), turbidity, conductivity, ammonium concentration, and pH were measured. The samples were subsequently concentred by filtration using a MEC filter (0.45 um), followed by total RNA extraction using the Qiagen AllPrep PowerViral DNA/RNA Kit (Qiagen, USA) with some modifications (beta-mercaptoethanol concentration raised to 10% and lysis performed at 55 °C for 30 minutes) (Ahmed et al., 2020). SARS-CoV-2 viral RNA was detected by a one-step RT-qPCR. To assess the RNA recovery rate of the procedure, samples were spiked before extraction with a known concentration of Bovine Respiratory Syncytial Virus (BRSV) using the Zoetis INFORCE 3 vaccine (Zoetis, USA). In addition to SARS-CoV-2, samples were assessed for Pepper Mild Mottle Virus (PMMoV), the daily load of which is hypothesized to represent the fecal load contributions to the samples at a given site and time. PCR conditions and primer used to collect viral data are described in the files primers.md and PCR conditions.md.

Compilation

The measurements on wastewater samples carried out by the participating laboratories of this study are found in the WWMeasure table. The values provided by municipalities come from laboratories accredited by the Centre d'expertise en analyse environnementale du Québec (CEAEQ), in compliance with the latter's quality assurance protocols. The COVID-19-related public health data found in the CPHD table were collected from the Institut National de Santé Publique du Québec (INSPQ)'s public reports. Wastewater data taken in-situ at the sampling sites (e.g., the flow at pumping stations or water resource recovery facilities (WRRFs)) are found in the SiteMeasure table and were taken by the institutions responsible for managing the sites. All of the data, stemming from multiple sources, were combined into the Open Data Model (ODM) standard format using the ODM-Import python package (see also Structure).

Validation

Wastewater and sample data were manually assessed for quality by our research collaborators. Data points for which the quality appeared to be uncertain were tagged with the value True in the qualityFlag column. Conversely, data deemed of good quality have a quality flag of False. Data that were not checked have a quality flag of NA. Textual comments describing the issues with the data points in more detail are also included in the dataset using the notes column of the relevant tables. Note that data validation was carried out by the data custodians responsible for each city in the dataset according to available resources. As the project continues and data validation is undertaken on more sections of the dataset, data may be re-analyzed, flagged, or commented as needed. Revisions to the dataset will be reported to the best of our ability.

Structure

The data contained in this dataset has been structured according to the Open Data Model (ODM) for Wastewater-Based Surveillance. This model provides a standardized dictionary to collect and share data and metadata stemming from wastewater-based surveillance programs. By convention, it splits all data into 10+ thematic tables with each record representing a unique measurement, i.e., long format. For convenience, the wide folder presents the data found in all the other tables in a wide format, i.e., multiple measurements are aligned by timestamp, with each column representing a different parameter.

Acknowledgements

The authors would like to acknowledge that this dataset was collected thanks to the financial support of the Fonds de Recherche du Québec, the Molson Foundation, the Trottier Family Foundation, CentrEau and NSERC. The authors would also like to acknowledge the efforts of Douglas Manuel (Ottawa Hospital) and Howard Swerdfeger (Public Health Agency of Canada) for their original idea for the Open Data Model and continued development.

References

1. Ahmed, W., Bertsch, P.M., Bivins, A., Bibby, K., Farkas, K., Gathercole, A., Haramoto, E., Gyawali, P., Korajkic, A., McMinn, B.R., Mueller, J.F., Simpson, S.L., Smith, W.J.M., Symonds, E.M., Thomas, K. v., Verhagen, R., Kitajima, M., 2020. Comparison of virus concentration methods for the RT-qPCR-based recovery of murine hepatitis virus, a surrogate for SARS-CoV-2 from untreated wastewater. Science of the Total Environment 739. https://doi.org/10.1016/j.scitotenv.2020.139960

2. Bibby, K., Bivins, A., Wu, Z., North, D., 2021. Making waves: Plausible lead time for wastewater based epidemiology as an early warning system for COVID-19. Water Research 202, 117438. https://doi.org/10.1016/j.watres.2021.117438

3. Schang, C., Crosbie, N., Nolan, M., Poon, R., Wang, M., Jex, A., Scales, P., Schmidt, J., Thorley, B.R., Henry, R., Kolotelo, P., Langeveld, J., Schilperoort, R., Shi, B., Einsiedel, S., Thomas, M., Black, J., Wilson, S., McCarthy, D.T., 2020. Passive sampling of viruses for wastewater-based epidemiology: a case-study of SARS-CoV-2 [WWW Document]. URL https://www.researchgate.net/publication/347103410\_Passive\_sampling\_of\_viruses\_for\_wastewater-based\_epidemiology\_a\_case-study\_of\_SARS-CoV-2?channel=doi&linkId=5fd800f392851c13fe892393&showFulltext=true (accessed 1.18.21).

People who have been granted permanent resident status in Canada. Please note that in these datasets, the figures have been suppressed or rounded to prevent the identification of individuals when the datasets are compiled and compared with other publicly available statistics. Values between 0 and 5 are shown as “--“ and all other values are rounded to the nearest multiple of 5. This may result to the sum of the figures not equating to the totals indicated.

Data Québec's municipal partners began work in 2018 in order to determine the datasets most likely to be used by citizens. Building on the work of other Canadian jurisdictions, six criteria have been established to assess the value of data. Based on these criteria, high-value datasets were targeted. It is therefore starting in 2019 that the municipal partners of Data Quebec use this list of high-value datasets to prioritize their open data dissemination work. Also since 2017, partners have been regularly setting standards for data dissemination. Therefore, future work is planned in order to establish new standards for these high-value data. This portrait shows the progress of the dissemination and standardization of these datasets as well as their availability by municipality. High-value criteria: * Target socio-economic and environmental issues * Offer better service delivery * Encourage innovation and sustainable economic growth * Increase government transparency and accountability as well as information flow * Meet strong community demand * Meet strong community demand * Data quality

This dataset includes tweets from provincial and territorial government officials. In cases when a health official does not use Twitter (e.g. Dr. Bonnie Henry), other official accounts for the province or territory have been substituted. The dataset does not include tweets from federal government officials. The tweet IDs were collected using Documenting the Now's Twarc library (https://github.com/DocNow/twarc). The date of the earliest available tweet is different for each handle. The date of the latest available tweet will not be later than the upload date for each file. See the file-level information below. The tweet ids were extracted from the raw JSON files retrieved from Twitter using Twarc. However, Twitter's terms of use do not permit the sharing of the raw JSON files for this dataset. The raw JSON files can be retrieved from Twitter, provided the content is still available, using the 'hydrate' command within Twarc. The researchers retained the source JSON files and may be contacted by other researchers if they wish to access them. The files of tweet ids will be updated over time and this metadata, the files and this readme.txt file will be updated accordingly. Raw JSON files were harvested using Twarc's 'timeline' command. The 'timeline' command retrieves the most recent tweets from the specified handle, to a maximum of approximately 3,300 tweets. The data for each handle was collected approximately weekly, starting in January 2021. In order not to lose earlier tweets, we concatenated the JSON for each new 'timeline' crawl to the earlier crawls and de-duplicated the combined JSON using Twarc's 'deduplicate' command. We then used Twarc's 'dehydrate' command to extract just the tweet ids from the deduplicate JSON file. Finally, we sorted the tweet ids numerically so that they would appear in ascending date order. The basic workflow looks like: twarc timeline --> concatenate JSON files --> deduplicate resulting JSON file --> dehydrate tweet ids --> sort tweet ids. The Twitter handles include: @ArrudaHoracio: Dr. Horacio Arruda, Directeur national de santé publique et SMA en santé publique MSSS Québec. Tweets in this file start on 2013-03-11. @CMOH_Alberta: Dr. Deena Hinshaw, Alberta’s Chief Medical Officer of Health. Tweets in this file start on 2020-03-26. @CMOH_NL: Dr. Janice Fitzgerald, Newfoundland and Labrador's Chief Medical Officer of Health. Tweets in this file start on 2020-04-27. @GOVofNUNAVUT: Government of Nunavut. Tweets in this file start on 2018-08-20. @GlavineLeo: Leo Glavine, MLA Kings West, Nova Scotia, Minister of Health and Wellness, Minister of Seniors. Tweets in this file start on 2017-04-17. @HealthNS: Nova Scotia Health. Tweets in this file start on 2019-05-D8. @Johnrockdoc: John Haggie, Newfoundland and Labrador's Minister, Health & Community Services. Tweets in this file start on 2020-01-19. @IainTRankin: Iain Rankin, Premier of Nova Scotia from 2021-02-23. MLA for Timberlea-Prospect. Tweets in this file start on 2012-08-08. @MLAStefanson: Heather Stefanson, Manitoba's Minister of Health and Seniors Care, MLA for Tuxedo Tweets in this file start on 2012-02-11. @MerrimanPaul: Paul Merriman. Saskatchewan's Minister of Social Services. Tweets in this file start on 2018-11-18. @MinistreMcCann: Danielle McCann. Députée de Sanguinet - Ministre de l'Enseignement supérieur. Tweets in this file start on 2019-03-14. @NBHealth: New Brunswick Department of Health. Tweets in this file start on 2013-02-26. @NWT_CPHO: Kami Kandola, Northwest Territories Chief Public Officer of Health. Tweets in this file start on 2013-10-16. @PFrostOldCrow: Pauline Frost Yukon MLA for Vuntut Gwich'in. Minister of Health and Social Services, Environment and Minister responsible for the Yukon Housing Corporation. Tweets in this file start on 2016-08-05. @SaskHealth: Saskatchewan Health Authority official twitter account. Tweets in this file start on 2020-03-15. @ShephardDorothy: Dorothy Shephard, Minister of Health for New Brunswick. Tweets in this file start on 2016-04-16. @StephenMcNeil: Stephen McNeil, Premier of Nova Scotia from 2013-10-23 to 2021-02-23. MLA for Annapolis. Tweets in this file start on 2017-05-05. @adriandix: Adrian Dix, MLA for Vancouver-Kingsway and BC Minister of Health. Tweets in this file start on 2019-09-20. @celliottability: Christine Elliott, Deputy Premier of Ontario and Minister of Health, MPP for Newmarket-Aurora. Tweets in this file start on 2018-02-24. @epdevilla: Dr. Eileen de Villa, Toronto's Medical Officer of Health. Tweets in this file start on 2013-02-21. @jsjaylward: James Aylward, Minister for Transportation & Infrastructure for PEI. MLA for District 6 Stratford-Keppoch. Tweets in this file start on 2015-03-30. @juliegreenMLA: Julie Green, NWT Minister of Health and Social Services, Minister Responsible for Persons wit... Visit https://dataone.org/datasets/sha256%3A6926de50d5ad28e6d3b4820345ba4dc1b5dd549afa67117940a8819dd53b98cf for complete metadata about this dataset.

The emergency data schedule shows the number of patients on a stretcher, the number of patients on a stretcher more than 24 hours, and the number of patients on a stretcher more than 48 hours from the last hour. The results are presented for each installation in the whole of Quebec in the form of a database. The data comes from the Provincial Emergency Console (CPU), and is updated every hour.

Note that topographic maps at a scale of 1/20,000 are no longer updated. For the latest update date, see the metadata. The reference cartographic data is now constituted according to a continuous information layer approach: * AQRéseau+ * Geobase of the Quebec Hydrographic Network (GRHQ) * Administrative divisions at the scale of 1/20,000 (SDA)] (https://www.donneesquebec.ca/recherche/fr/dataset/decoupages-administratifs) * Geobase of the Quebec Hydrographic Network (GRHQ) * Administrative divisions at the scale of 1/20,000 (SDA) * Administrative divisions at the scale of 1/20,000 (SDA) * Geobase of the Quebec Hydrographic Network (GRHQ) * Administrative divisions at the scale of 1/20,000 (SDA) Topographic maps at a scale of 1/20,000 constitute the official cartographic base of the Government of Quebec. They cover almost all of the territory south of the 52nd parallel. The data is extracted from aerial photographs at a scale of 1:40,000 taken at an altitude of 6,300 meters. They offer an accuracy of approximately four meters in planimetry. In hypsometry, it is about two meters for dimensional points and about five meters for level curves. Each file covers an area of approximately 250 km2. The main components are: * Hydrography (lakes, rivers, streams, streams, swamps, etc.). * Vegetation (forests, peatlands, nurseries, orchards, etc.). * Human constructions: * transport infrastructures (roads, bridges, airports, etc.); * buildings (roads, bridges, airports, etc.); * buildings (mobile homes, silos, greenhouses, etc.); * equipment (docks, electric power transmission lines, surface reservoirs, etc.); * equipment (docks, electrical power lines, surface reservoirs, etc.).); * designated areas (golf courses, loan banks, etc.). * The relief (the contour lines are generally ten meters equidistant and, in in some cases, they may vary between eight and twenty meters).*This third party metadata element was translated using an automated translation tool (Amazon Translate).

Data on locations reserved for planting in areas designated as “public domain” including street borders and off-street areas (parks and public squares). This data is complementary to tree assets. Please note that in many cases, the City's data on the spatial location of trees may be inaccurate or out of date. In addition, in some boroughs, park trees are not indicated.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The dataset contains all the method and data published by Emmanuel Dubois during his PhD project entitled impact of global changes on groundwater recharge in cold and humid climate, case study in southern Quebec (Canada). This research, carried out under the direction of Prof. Marie Larocque (UQAM), was part of a project aiming at developing new knowledge about the groundwater resources to anticipate the impact of climate change in southern Québec (Canada) and funded by the Québec Ministry of Environment and fight against climate change (MELCC). The study area, comprised of eight river watersheds and located between the St. Lawrence River and the USA-Quebec border (35 800 km2), is a strategic agricultural region with a hydrological dynamic led by cold winters and warm summers. The general objective of the research was to quantify the current and future impact of climate change on regional scale the groundwater recharge (GWR) in cold and humid climates, to better anticipate future conditions. Estimates of GWR were simulated with a 500 m x 500 m resolution and a monthly time step using the HydroBudget model (Dubois et al., 2021b), developed during the project. The model was calibrated over the 1961-2017 period using river flows and baseflows (Dubois et al., 2021a). It was used to simulate GWR over the 1961-2017 period (past) and the 1951-2100 period (scenarios). Each chapter of the thesis corresponds to a published (or submitted) article in a peer review journal. The data associated with each article were made public in individual Dataverse datasets. As well, the code of the HydroBudget model was made public on Dataverse (Dubois et al., 2021b), with an application example and a user guide (Dubois et al., 2021d). Each of these datasets contains detailed metadata, licences, and possible usage restrictions. Users are invited to refer to the individual datasets for more information. Chapter 2 of the thesis presents the article “Simulation of long-term spatiotemporal variations in regional-scale groundwater recharge: contributions of a water budget approach in cold and humid climates” published in the journal Hydrology and Earth Science System in 2021 (Dubois et al., 2021a). The associated GWR simulations over the 1961-2017 period are available here (on the Dataverse platform; Dubois et al., 2021c): https://doi.org/10.5683/SP3/TFNPQF. Chapter 3 of the thesis presents the article “Climate Change Impacts on Groundwater Recharge in Cold and Humid Climates: Controlling Processes and Thresholds” published in the special issue “Application of Climatic Data in Hydrologic Models” of the journal Climate in 2022 (Dubois et al., 2022a). The associated GWR simulations over the 1951-2100 period are available here (on the Dataverse platform; Dubois et al., 2022b): https://doi.org/10.5683/SP3/SWH4O1. Chapter 4 of the thesis presents the article “Impact of land cover changes on long-term and regional-scale groundwater recharge simulation in cold and humid climates” that was submitted for publication in June 2022. The associated GWR simulations over the 1951-2100 period will be available in a new Dataverse dataset as soon as the article is accepted for publication.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

Data Sources: Banque informatisée des oiseaux de mer au Québec (BIOMQ: ECCC-CWS Quebec Region) Atlantic Colonial Waterbird Database (ACWD: ECCC-CWS Atlantic Region).. Both the BIOMQ and ACWD contain records of individual colony counts, by species, for known colonies located in Eastern Canada. Although some colonies are censused annually, most are visited much less frequently. Methods used to derive colony population estimates vary markedly among colonies and among species. For example, census methods devised for burrow-nesting alcids typically rely on ground survey techniques. As such, they tend to be restricted to relatively few colonies. In contrast, censuses of large gull or tern colonies, which are geographically widespread, more appropriately rely on a combination of broad-scale aerial surveys, and ground surveys at a subset of these colonies. In some instances, ground surveys of certain species are not available throughout the study area. In such cases, consideration of other sources, including aerial surveys, may be appropriate. For example,data stemming from a 2006 aerial survey of Common Eiders during nesting, conducted by ECCC-CWS in Labrador, though not yet incorporated in the ACWD, were used in this report. It is important to note that colony data for some species, such as herons, are not well represented in these ECCC-CWS databases at present. Analysis of ACWD and BIOMQ data (ECCC-CWS Quebec and Atlantic Regions): Data were merged as temporal coverage, survey methods and geospatial information were comparable. Only in cases where total counts of individuals were not explicitly presented was it necessary to calculate proxies of total counts of breeding individuals (e.g., by doubling numbers of breeding pairs or of active nests). Though these approaches may underestimate the true number of total individuals associated with a given site by failing to include some proportion of the non-breeding population (i.e., visiting adult non-breeders, sub-adults and failed breeders), tracking numbers of breeding individuals (or pairs) is considered to be the primary focus of these colony monitoring programs.In order to represent the potential number of individuals of a given species that realistically could be and may historically have been present at a given colony location (see section 1.1), the maximum total count obtained per species per site since 1960 was used in the analyses. In the case of certain species,especially coastal piscivores (Wires et al. 2001; Cotter et al. 2012), maxima reached in the 1970s or 1980s likely resulted from considerable anthropogenic sources of food, and these levels may never be seen again. The effect may have been more pronounced in certain geographic areas. Certain sites once used as colonies may no longer be suitable for breeding due to natural and/or human causes, but others similarly may become suitable and thus merit consideration in long-term habitat conservation planning. A colony importance index (CII) was derived by dividing the latter maximum total count by the potential total Eastern Canadian breeding population of that species (the sum of maximum total counts within a species, across all known colony sites in Eastern Canada). The CII approximates the proportion of the total potential Eastern Canadian breeding population (sum of maxima) reached at each colony location and allowed for an objective comparison among colonies both within and across species. In some less-frequently visited colonies, birds (cormorants, gulls, murres and terns, in particular) were not identified to species. Due to potential biases and issues pertaining to inclusion of these data, they were not considered when calculating species’ maximum counts by colony for the CII. The IBA approach whereby maximum colony counts are divided by the size of the corresponding actual estimated population for each species (see Table 3.1.2; approximate 1% continental threshold presented) was not used because in some instances individuals were not identified to species at some sites, or population estimates were unavailable.Use of both maxima and proportions of populations (or an index thereof) presents contrasting, but complementary, approaches to identifying important colonial congregations. By examining results derived from both approaches, attention can be directed at areas that not only host large numbers of individuals, but also important proportions of populations. This dual approach avoids attributing disproportionate attention to species that by their very nature occur in very large colonies (e.g., Leach’s Storm Petrel) or conversely to colonies that host important large proportions of less-abundant species (Roseate Tern, Caspian Tern, Black-Headed Gull, etc.), but in smaller overall numbers. Point Density Analysis (ArcGIS Spatial Analyst) with kernel estimation, and a 10-km search radius,was used to generate maps illustrating the density of colony measures (i.e., maximum count by species,CII by species), modelled as a continuous field (Gatrell et al. 1996). Actual colony locations were subsequently overlaid on the resulting cluster map. Sites not identified as important should not be assumed to be unimportant.

Dataset of species/gear type commercial fisheries from 2012 to 2021 in the Eastern Canada Regions. Only fish harvested from the NL, Maritimes, Gulf, Quebec and Eastern Arctic regions are included (Species Sought). The data was obtained from Statistical Services, Fisheries and Oceans Canada (DFO) and consists of commercial species/gear type landings data from 2012 to 2021 taken from Northwest Atlantic Fisheries Organization (NAFO) Subareas 0, 2, 3, 4 and 5 and fished in the NL, Maritimes, Gulf, Quebec and Eastern Arctic regions. The layer was created by overlaying a 2 minute hexagonal grid (approx. 10km2 cell) on species/gear type commercial fisheries point data and summing the total landings by weight reported for each cell over the ten year period. Therefore, the value of each grid cell is equal to the total species/gear type landings in kg from 2012 to 2021 for the area, and may represent many fishing events from several vessels over the ten year period. All landings are from Canadian vessels greater than 35-ft, and does not include information pertaining to international fishing vessels (i.e., St. Pierre). Individuals should exercise caution when interpreting this data. Data has not been altered and is mapped from the original logbook entry for each record prior to amalgamation. Data may contain errors such as inaccurate or nonviable coordinates, landed weights and/or species identification. For example, cases of fishing events reported in a NAFO Division with corresponding coordinates falling outside that particular NAFO Division or fishing events which appear to be located on a land mass due to rounding errors in the original entries. Such cases were excluded from the dataset. Only one location is given for each fishing event; therefore, a fishing activity that would normally cover a large area (i.e., trawling) is only shown in a single location. Some species may not include all records or locations where activity is taking place due to regional differences in permissions for mapping, or because the fishery is only partially georeferenced (e.g. Lobster). The locations/areas shown should only be used as an estimation of fishing intensity and a general guide of where particular species/gear type fishing occurs. This dataset has been privacy screened to comply with the Government of Canada's privacy policy. Privacy assessments were conducted to identify NAFO unit areas containing data with less than five vessel IDs, license IDs and fisher IDs. If this threshold was not met, catch weight locations have been withheld from these statistical areas to protect the identity or activity of individual vessels or companies. In some instances, permissions were obtained to map species or gears with a limited number of vessels, licenses, or fisher ids. The withheld areas are indicated by the unit area that has been removed and given a weight of -9999.

The center point of the inhabited place is located, as the case may be, at the center of the economic and commercial activity of the inhabited place, in the oldest neighborhood, at a road intersection, near an important physical landmark such as a church or a town hall, or at the center of gravity of a concentration of homes. Place names are mostly names of inhabited places. Those written in straight characters correspond to the official names of municipalities and those in italics represent common names (former municipality, village, district, sector, hamlet, locality). In some cases, the common name is shown in brackets along with the official name. Names of uninhabited places are included as geographical reference points.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Note: The data release is complete as of August 14th, 2023. 1. (Added April 4th) Canada and Census Divisions = Early April 2023 2. (Added May 1st) Ontario, British Columbia, and Alberta Census Subdivisions (CSDs) = Late April 2023 3a. (Added June 8th) Manitoba and Saskatchewan CSDs 3b. (Added June 12th) Quebec CSDs = June 12th 2023 4. (Added June 30th) Newfoundland and Labrador, Prince Edward Island, New Brunswick, and Nova Scotia CSDs = Early July 2023 5. (Added August 14th) Yukon, Northwest Territories, and Nunavut CSDs = Early August 2023. For more information, please visit HART.ubc.ca. Housing Assessment Resource Tools (HART) This dataset contains 18 tables which draw upon data from the 2021 Census of Canada. The tables are a custom order and contains data pertaining to core housing need and characteristics of households. 17 of the tables each cover a different geography in Canada: one for Canada as a whole, one for all Canadian census divisions (CD), and 15 for all census subdivisions (CSD) across Canada. The last table contains the median income for all geographies. Statistics Canada used these median incomes as the "area median household income (AMHI)," from which they derived some of the data fields within the Shelter Costs/Household Income dimension. Included alongside the data tables is a guide to HART's housing need assessment methodology. This guide is intended to support independent use of HART's custom data both to allow for transparent verification of our analysis, as well as supporting efforts to utilize the data for analysis beyond what HART did. There are many data fields in the data order that we did not use that may be of value for others. The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and data fields: Geography: - Country of Canada, all CDs & Country as a whole - All 10 Provinces (Newfoundland, Prince Edward Island (PEI), Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia), all CSDs & each Province as a whole - All 3 Territories (Nunavut, Northwest Territories, Yukon), all CSDs & each Territory as a whole Data Quality and Suppression: - The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. - Area suppression is used to replace all income characteristic data with an 'x' for geographic areas with populations and/or number of households below a specific threshold. If a tabulation contains quantitative income data (e.g., total income, wages), qualitative data based on income concepts (e.g., low income before tax status) or derived data based on quantitative income variables (e.g., indexes) for individuals, families or households, then the following rule applies: income characteristic data are replaced with an 'x' for areas where the population is less than 250 or where the number of private households is less than 40. Source: Statistics Canada - When showing count data, Statistics Canada employs random rounding in order to reduce the possibility of identifying individuals within the tabulations. Random rounding transforms all raw counts to random rounded counts. Reducing the possibility of identifying individuals within the tabulations becomes pertinent for very small (sub)populations. All counts are rounded to a base of 5, meaning they will end in either 0 or 5. The random rounding algorithm controls the results and rounds the unit value of the count according to a predetermined frequency. Counts ending in 0 or 5 are not changed. Universe: Full Universe: Private Households in Non-farm Non-band Off-reserve Occupied Private Dwellings with Income Greater than zero. Households examined for Core Housing Need: Private, non-farm, non-reserve, owner- or renter-households with incomes greater than zero and shelter-cost-to-income ratios less than 100% are assessed for 'Core Housing Need.' Non-family Households with at least one household maintainer aged 15 to 29 attending school are considered not to be in Core Housing Need, regardless of their housing circumstances. Data Fields: Note 1: Certain data fields from the original .ivt...

The 2006 Census data showed that Anglophones, that is the population whose mother tongue is English, made up the majority of the population in Canada, about 57.8%. This was the case for all provinces and territories except Quebec, where the majority of the population reported French as mother tongue. In total, 22.1% of the population in Canada were Francophones, which is the population with French as their mother tongue. Allophones, the population who reported a non-official language as mother tongue, made up 20%.

Quarterly data on vehicle registration by fuel type, vehicle type and number of vehicles, Canada, the provinces, census metropolitan areas and census sub-divisions.

Number and rate (per 100,000 population) of homicide victims, Canada and Census Metropolitan Areas, 1981 to 2024.

The number of maternal deaths and maternal mortality rates for selected causes, 2000 to most recent year.