This table contains information about the number of cases reported, inspections conducted, and non-compliances to The Animal Care Act from 2016 to present.This table contains information about the number of cases reported, inspections conducted, and non-compliances to The Animal Care Act for each year, starting in 2016, to the most recent quarter. These data are populated by the Provincial Animal Welfare Database for the Manitoba Animal Welfare Program and are displayed in the Manitoba Animal Welfare Program – Trends chart. The table will be updated on a quarterly basis. Fields included [Alias (Field Name): Field description] Category (Category): Includes the year, beginning in 2016, to the current year (e.g., 2016, 2017, 2018) # of cases reported (F__of_cases_reported): Includes the total number of cases reported for each year # of inspections conducted (F__of_inspections_conducted): Includes the total number of inspections conducted for each year # of non-compliances found* (F__of _non_compliances_found_): Includes the total number of non-compliances found following an inspection for each year * The number of non-compliances found as a result of an inspection by an Animal Protection Officer (APO) include animals deemed abandoned, issued notice of seizure, custody and distress, Director’s Order issued, surrendered ownership and recommendations for improvements.

This table contains information about the status, actions and outcomes from inspections conducted by Animal Protection Officers (APO) and prosecutions in the Manitoba Animal Welfare Program.This table contains information about the status, actions and outcomes from inspections conducted by Animal Protection Officers (APO) and prosecutions in the Manitoba Animal Welfare Program for each year, starting in 2016, to the most recent quarter. This data is populated by the Provincial Animal Welfare Database for the Manitoba Animal Welfare Program and is displayed in the Manitoba Animal Welfare Program – Case Outcomes dashboard. The table will be updated on a quarterly basis. Fields included [Alias (Field Name): Field description] StatusGroups2 (StatusGroups2): Includes the status, actions or outcomes that have occurred throughout each assigned case DashboardGrouping2 (DashboardGrouping2): Includes the dashboard element under which the statuses need to be grouped for each assigned case Year (Year): Includes the year, beginning in 2016 to the current year (e.g., 2016, 2017, 2018) Month (Month): Includes the numeric value of all months within a calendar year (e.g., 1, 2, 3) Quarter (Quarter): Includes the numeric values of all quarters in a calendar year (e.g., 1, 2, 3, 4), where quarter 1 corresponds with January, February and March, quarter 2 corresponds with April, May and June, quarter 3 corresponds with July, August and September and quarter 4 corresponds with October, November and December YQ (YQ): Includes the year and quarter of the most recent 12 quarters (e.g., 2021 Q1, 2021 Q2 )

This data set contains information on inspections carried out by the Health Division of the Real Estate Strategy and Housing Department (formerly the Housing Department).The City of Montreal, through its [Regulation Respecting Health, Maintenance and Security of Housing (R.V.M. 03-096)] (https://montreal.ca/reglements-municipaux/recherche/60d7656afd6531443f578d96), ensures the health, good maintenance and security of housing and residential buildings on its territory. Health refers to all the conditions that make a home suitable for housing.To learn more about the efforts made by the City of Montreal in terms of housing health, consult the [Action Plan for decent housing conditions 2018-2021] (https://montreal.ca/sujets/salubrite-entretien-et-securite-des-logements).**Note: ** This package only concerns inspections carried out by the Health Division of the Real Estate Strategy and Housing Department, and not those carried out by borough staff. The inspection data does not present a portrait of unsanitary conditions or a diagnosis of the condition of the rental housing stock, but rather the work done by the team.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The data represents the result of preventive inspections carried out by the Real Estate and Housing Strategy Department (formerly the Housing Department). Data includes: the physical condition of the inspected parts of the building on a scale of 1 (excellent) to 6 (very poor), whether or not the building was transferred for a complete inspection, and whether or not a statement of violation was served without notice. The period from January 1 to June 1, 2024 served as a pilot project during which several processes were tested and optimized.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This web experience includes four dashboards and graphs that show inspections, the most common food safety violations, and the levels of progressive compliance measures taken by health officers to enforce the law.1. Inspections: This dashboard includes tables showing inspection data collected by the Food Safety and Inspection Directorate over the past five years. <o:p></o:p>Inspection Violations — Overview (arcgis.com) 2. Food safety violations: This dashboard shows the number of violations observed during the years indicated. The number of violations observed is then classified into the category of critical or non-critical offenses. Critical violations are violations that present an immediate risk to food safety and must be corrected within a specified period of time. Non-critical violations do not present an immediate food safety risk but need to be addressed before they become one. <o:p></o:p>Inspection Violations — Overview (arcgis.com) 3. Top five food safety violations: This dashboard shows charts and tables showing the five most common food safety violations observed during inspections. Each type of violation refers to the Manitoba Food Regulations. The five most common violations are expressed as a percentage of all observed food safety violations. <o:p></o:p>Main offences<o:p></o:p> 4. Progressive compliance measures: This dashboard shows how often health officers applied progressive compliance measures to food processing establishments that did not correct food safety violations within the time frame specified in the inspection. The dashboard explains that food processing establishments have a certain amount of time to correct food safety violations based on the risk associated with them. Progressive compliance measures are only applied if the violation is not corrected within the prescribed timeframe. Progressive compliance measures<o:p></o:p>**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Programs that perform a laboratory analysis of body fluids and tissue to determine the presence of irregularities or to identify unknown substances submitted for analysis. Definition is protected by Copyright by Information and Referral Federal of Los Angeles County, Inc (https://211taxonomy.org/subscriptions/#agreement)

Standards Units for an opening represents desired soil conservation limits and stocking outcomes for the defined net area to be reforested. An opening may have one or more standard units. Standards Units may not overlap. Older records may not have standard unit maps available. Stocking standards represent legal silviculture obligations for harvested areas for the defined standard units. This is part of the Silviculture and Land status Tracking dataset, which includes tracking achievement of silviculture obligations on Crown Land

Facilities designed to recover natural gas liquids from a stream of natural gas. These facilities control the quality of the natural gas to be marketed.Mapping Resources implemented as part of the North American Cooperation on Energy Information (NACEI) between the Department of Energy of the United States of America, the Department of Natural Resources of Canada, and the Ministry of Energy of the United Mexican States.The participating Agencies and Institutions shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics, if available, are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time and may differ from other official information. The Agencies and Institutions participants give no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data.Parent Collection:[North American Cooperation on Energy Information, Mapping Data](https://open.canada.ca/data/en/dataset/aae6619f-f9f3-435d-bc32-42decd58b674)

Planting activities reported on openings into RESULTS. More than one planting activitiy may occur within an opening. Planting activiites may also overlap. Planting attribute reporting is mandatory requirement while planting maps are optional. Planting reports includes species and seedlot information. This is a part of Silviculture and Land Status dataset, which includes tracking achievement of silviculture obligations on Crown land

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)Collection:- **[Canada's National Forest Inventory (NFI) 2006](https://open.canada.ca/data/en/dataset/e2fadaeb-3106-4111-9d1c-f9791d83fbf4)**