This dataset is produced and published annually by Natural Resources Canada. It contains a variety of statistics on Canada’s mineral production, and provides the geographic locations of significant metallic, nonmetallic and coal mines, oil sands mines, selected metallurgical works and gas fields for the provinces and territories of Canada.Related product:- **[Top 100 Exploration Projects](https://open.canada.ca/data/en/dataset/b64179f3-ea0f-4abb-9cc5-85432fc958a0)**

Indices, deposits, mines, and quarries include information relating to architectural crushed or industrial stone, non-metallic substances, and metallic substances.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

A predictive model for Canadian carbonatite-hosted REE ± Nb deposits is presented herein. This model was developed by integrating diverse data layers derived from geophysical, geochronological, and geological sources. These layers represent the key components of carbonatite-hosted REE ± Nb mineral systems, including the source, transport mechanisms, geological traps, and preservation processes. Deep learning algorithms were employed to integrate these layers into a comprehensive predictive framework. Here is a link to the publication that describes this product: https://link.springer.com/article/10.1007/s11053-024-10369-7

This dataset contains Manitoba Agriculture soil survey point data at various scales ranging from highly detailed to broader reconnaissance level information. The intent of this file is to display addtional labels for some attributes of large map polygons.Soil is essential to human survival. We rely on it for the production of food, fibre, timber and energy crops. Together with climate, the soil determines which crops can be grown, where, and how much they will yield. In addition to supporting our agricultural needs, we rely on the soil to regulate the flow of rainwater and to act as a filter for drinking water. With such a tremendously important role, it is imperative that we manage our soils for their long-term productivity, sustainability and health. The first step in sustainable soil management is ensuring that the soil will support the land use activity. For example, only the better agricultural soils in Manitoba will support grain and vegetable production, while more marginal agricultural soils will support forage and pasture-based production. For this reason, agricultural development should only occur in areas where the soil resource will support the agricultural activity. The only way to do this is to understand the soil resource that is available. Soil survey information is the key to understanding the soil resource. Soil survey is an inventory of the properties of the soil (such as texture, internal drainage, parent material, depth to groundwater, topography, degree of erosion, stoniness, pH, and salinity) and their spatial distribution over a landscape. Soils are grouped into similar types and their boundaries are delineated on a map. Each soil type has a unique set of physical, chemical and mineralogical characteristics and has similar reactions to use and management. The information assembled in a soil survey can be used to predict or estimate the potentials and limitations of the soils’ behaviour under different uses. As such, soil surveys can be used to plan the development of new lands or to evaluate the conversion of land to new uses. Soil surveys also provide insight into the kind and intensity of land management that will be needed. The survey scale of soils data for Manitoba ranges from 1:5,000 to 1:126,720, as identified in the 'SCALE' column.This file contains soils data that has been collected at a survey intensity level of the first order. This includes data collected at a scale of 1:5,000. The survey objective at this scale is to collect high precision field scale data and it is mostly used in research plots and other highly intensive areas. It is also applicable to agricultural production and planning such as precision farming, agriculture capability, engineering, recreation, potato/irrigation suitability, and productivity indices. Profile descriptions and samples are collected for all soils. At least one soil inspection exists per delineation and the minimum size delineation is 0.25 acres. The soil taxonomy is generally Phases of Soil Series. The mapping scale is 1:5,000 or 12.7 in/ mile.This file also contains soils data that has been collected in Manitoba at a survey intensity level of the second order. This includes data collected at a scale of 1:20,000. The survey objective at this scale is to collect field scale data and it is mostly used in agricultural production and planning such as precision farming, agriculture capability, engineering, recreation, potato/irrigation suitability, and productivity indices. Soil pits are generally about 200 metres apart and are dug along transects which are about 500 metres apart. This translates to about 32 inspections sites per section(640 acres). The soils in each delineation are identified by field observations and remotely sensed data. Boundaries are verified at closely spaced intervals. Profile descriptions are collected for all major named soils and 10 inspection sites/section and 2 to 3 horizons per site require lab analyses. At least one soil inspection exists in over 90% of delineations and the minimum size delineation is generally about 4 acres at 1:20,000. The soil taxonomy is generally Phases of Soil Series. The mapping scale is 1:20,000 or 3.2 inch/ mile.This file also contains data that has been collected at the third order. This includes scales of 1:40,000 and 1:50,000.The survey objective at this scale is to collect field scale or regional data. If the topography is relatively uniform, appropriate interpretations include agriculture capability, engineering, recreation, potato/irrigation suitability, and productivity indices. Soil pits are generally dug adjacent to section perimeters. This translates to about 16 inspection sites per section(640 acres). Soil boundaries are plotted by observation and remote sensed data. Profile descriptions exist for all major named soils and 2 inspection sites/section and 2 to 3 horizons per site require lab analyses. At least one soil inspection exists in 60-80% of delineations and the minimum size delineation is generally in the 10 to 20acre range. The soil taxonomy is generally Series or Phases of Soil Series. The mapping scale is 1:40,000 or 2 inch/ mile; 1:50,000 or 1.5 inch/mile.This file also contains soils data that has been collected at a survey intensity level of the fourth order. This includes scales of 1:63,360, 1:100,000, 1:125,000, and 1:126,720.The survey objective is to collect provincial data and to provide general soil information about land management and land use. The number of soil pits dug averaged to about 6 inspections per section (640 acres). Soil boundaries are plotted by interpretation of remotely sensed data and few inspections exist. Profile descriptions are collected for all major named soils. At least one soil inspection exists in 30-60% of delineations and the minimum size delineation is 40 acres (1:63,360), 100 acres (1:100,000), 156 acres (126,700) and 623 acres (250,000). The soil taxonomy is generally phases of Subgroup or Association. As of 2022, soil survey field work and reports are still currently being collected in certain areas where detailed information does not exist. This file will be updated as more information becomes available. Typically, this is conducted on an rural municipality basis.In some areas of Manitoba, more detailed and historical information exists than what is contained in this file. However, at this time, some of this information is only available in a hard copy format. This file will be updated as more of this information is transferred into a GIS format.This file has an organizational framework similar to the original SoilAID digital files and a portion of this geographic extent was originally available on the Manitoba Land Initiative (MLI) website.Domains and coded values have also been integrated into the geodatabase files. This allows the user to view attribute information in either an abbreviated or a more descriptive manner. Choosing to display the description of the coded values allows the user to view the expanded information associated with the attribute value (reducing the need to constantly refer to the descriptions within the metadata). To change these settings in ArcCatalog, go to Customize --> ArcCatalog Options --> Tables tab --> check or uncheck 'Display coded value domain and subtype descriptions'.To change these settings in ArcMap, go to Customize --> ArcMapOptions --> Tables tab --> check or uncheck 'Display coded value domain and subtype descriptions'. This setting can also be changed by opening the attribute table, then Table Options (top left) --> Appearance --> check or uncheck 'Display coded value domain and subtype descriptions'. The file also contains field aliases, which can also be turned on or off under Table Options.This same capability is available in ArcGIS Pro.For more info:https://www.gov.mb.ca/agriculture/soil/soil-survey/importance-of-soil-survey-mb.html#

Tow, catch, and length frequency for fish caught during the August sentinel surveys in the southern Gulf of St. Lawrence (NAFO Division 4T). Abundance indices and spatial distribution patterns of commercial groundfish.Note: Due to delays caused by logistic complexities and Covid the project did not take place in 2020

The Canadian indexes of social resilience and vulnerability were created to provide area-based information on resilience and vulnerability to natural hazards and disasters across Canada. Specifically, the Canadian Index of Social Resilience (CISR) aims to reflect a community’s ability to respond to and recover from natural hazards. In contrast, the Canadian Index of Social Vulnerability (CISV) aims to reflect the social vulnerability of an area based on factors that have the potential to amplify the impact of disasters on populations.Before the CISR and CISV were built, indicator frameworks were developed for social resilience and social vulnerability, respectively. Indicators were selected because of their demonstrated association with social resilience or social vulnerability. The selection was informed by the theoretical and research literature, existing indexes, availability of relevant data and engagement with subject-matter experts.The CISR and the CISV were created using data from Dissemination areas (DAs) across the country. The selected indicators were included in a principal component analysis, which is a statistical technique that allows a large number of indicators to be collapsed into a smaller number of interpretable components. Based on the results of the principal component analysis, DA-level scores were calculated for each index. Higher CISR scores correspond to DAs that are more resilient and higher CISV scores correspond to DAs that are more vulnerable.These indexes can be used to better understand areas which may experience the largest disproportional social impacts from natural hazards.

Exposure to wind-driven waves forms a key physical gradient in nearshore environments influencing both ecological communities and human activities. We calculated a relative exposure index (REI) for wind-driven waves covering the coastal zone of the Scotian Shelf-Bay of Fundy bioregion. We derived REI and two other fetch-based indices (sum fetch, minimum fetch) from two formulations of wind fetch (unweighted and effective fetch) for input points in an evenly spaced fishnet grid (50-m resolution) covering a buffered area within 5 km from the coastline and shallower than 50 m depth. We calculated unweighted fetch lengths (m) for 32 compass headings per input point (11.25° intervals), and effective fetch lengths for 8 headings per point (45° intervals). Unweighted fetch is the distance along a given heading from a point in coastal waters to land. Effective fetch is a directionally weighted average of multiple fetch measures around a given heading that reduces the influence of irregular coastline shape on exposure estimates. For fetch calculations, we used land features at a 1:50,000 scale for Canadian administrative boundaries (NrCan 2017), and unknown resolution for St. Pierre and Miquelon, and US states bordering the Gulf of Maine (GADM 2012). The summed and minimum unweighted fetch lengths for each point provide coarse summaries of wave exposure and distance to land, respectively. The relative exposure index (REI) gives a more accurate metric of exposure by combining effective fetch with modelled wind speeds (m s-1) and frequency data. We provide the original calculations of unweighted fetch, effective fetch, and other fetch-based indices (i.e., sum, minimum) in csv format along with the REI layer (GeoTIFF format) resampled to 35-m resolution. With broad spatial coverage and high resolution, these indices can support regional-scale distribution modelling of species and biological assemblages in the coastal zone as well as marine spatial planning activities.When using data please cite following:O'Brien JM, Wong MC, Stanley RRE (2022) A relative wave exposure index for the coastal zone of the Scotian Shelf-Bay of Fundy Bioregion. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.5433567ReferencesGADM database of Global Administrative Areas (2012). Global Administrative Areas, version 2.0. (accessed 2 December 2020). www.gadm.orgNatural Resources Canada (2017) Administrative Boundaries in Canada - CanVec Series - Administrative Features - Open Government Portal. (accessed 2 December 2020). https://open.canada.ca/data/en/dataset/306e5004-534b-4110-9feb-58e3a5c3fd97.

This dataset includes five depth-attenuated relative wave exposure index layers in raster format. Relative Exposure Index (REI) values are calculated based on effective fetch (derived from fetch values) combined with modelled wind data. The output REI layers are attenuated by depth, resulting in greater values in shallow, nearshore areas (Bekkby et al. 2008). The cell values represent an estimate of wave exposure at bottom depth normalized between regions from 0 (protected) to 1 (exposed).The objective of this dataset is to provide an estimate of wave exposure at bottom depth, primarily for use in species distribution modelling. Each single-band raster corresponds to a marine region, which generally coincide with the following layers from the Species Distribution Modelling Boundaries (https://www.gis-hub.ca/dataset/sdm-boundaries) dataset: Nearshore_HG, Nearshore_NCC, Nearshore_QCS, Nearshore_QCS, and Shelf_SalishSea. These layers extend to 50 m depth and up to 5 km from shore.Tabular data (csv files) are also included as part of the data package. These data are the calculated Relative Exposure Index (REI) values with fields for position information. The fetch values from gridded nearshore fetch (https://gis-hub.ca/dataset/gridded-nearshore-fetch) are used as a source dataset and the locations in the REI are the same as the gridded fetch.

Environment and Climate Change Canada’s (ECCC) CMIP6 statistically downscaled agroclimatic indices are an updated version of the CMIP5 agroclimatic indices dataset making use of two different sets of downscaled scenarios created by the Pacific Climate Impacts Consortium (PCIC): 1. Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6), and 2. Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6 (CanDCS-M6). To address the needs of different user groups in Canada, 49 indices, including agroclimatic indices, were proposed by the Canadian adaptation community through a series of consultations. Please see the definition list for the equations of each index. In 2025, PCIC expanded the CMIP6 agroclimatic indices, by adding CanDCS-M6, which includes SSP3-7.0 for most GCMs. Additionally, PCIC introduced 18 new indices to the previous 49. The 67 indices are available for both the CanDCS-U6 and CanDCS-M6.The range of impact-relevant climate indices available for download includes, indices representing counts of the number of days when temperature or precipitation exceeds (or is below) a threshold value; the episode length when a particular weather/climate condition occurs; and indices that accumulate temperature departures above or below a fixed threshold. The statistically downscaled climate indices are available for individual models and ensembles, historical simulations (1951-2014) and three emissions scenarios called “Shared Socioeconomic Pathways” (SSPs), SSP1-2.6, SSP2-4.5, and SSP5-8.5 (2015-2100), at a 10 x 10 km degree grid resolution. The CanDCS-M6 agroclimatic indices dataset also includes SSP3-7.0 results.Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impact assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with a finer spatial scale.Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.

Environment and Climate Change Canada’s (ECCC) Climate Research Division (CRD) and the Pacific Climate Impacts Consortium (PCIC) previously produced statistically downscaled climate scenarios based on simulations from climate models that participated in the Coupled Model Intercomparison Project phase 5 (CMIP5) in 2015. ECCC and PCIC have now updated the CMIP5-based downscaled scenarios with two new sets of downscaled scenarios based on the next generation of climate projections from the Coupled Model Intercomparison Project phase 6 (CMIP6). The scenarios are named Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6) and Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6 (CanDCS-M6).CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 global climate models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5), with PCIC later adding SSP3-7.0 to the CanDCS-M6 dataset. The CanDCS-U6 was downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2) procedure, and CanDCS-M6 was downscaled using the N-dimensional Multivariate Bias Correction (MBCn) method. The CanDCS-U6 dataset was produced using the same downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios, while the CanDCS-M6 dataset implements a new target dataset (ANUSPLIN and PNWNAmet blended dataset).Statistically downscaled individual model output and ensembles are available for download. Downscaled climate indices are available across Canada at 10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impacts assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with finer spatial scale.Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.