Every two years the WECC (Western Electricity Coordinating Council) releases an Anchor Data Set (ADS) to be analyzed with a Production Cost Models (PCM) and which represents the expected loads, resources, and transmission topology 10 years in the future from a given reference year. For hydropower resources, the WECC relies on members to provide data to parameterize the hydropower representation in production cost models. The datasets consist of plant-level hydropower generation, flexibility, ramping, and mode of operations and are tied to the hydropower representation in those production cost models. In 2022, PNNL supported the WECC by developing the WECC ADS 2032 hydropower dataset [1]. The WECC ADS 2032 hydropower dataset (generation and flexibility) included an update of the climate year conditions (2018 calendar year), consistency in representation across the entire US WECC footprint, updated hydropower operations over the core Columbia River, and a higher temporal resolution (weekly instead of monthly)[1] associated with a GridView software update (weekly hydro logic). Proprietary WECC utility hydropower data were used when available to develop the monthly and weekly datasets and were completed with HydroWIRES B1 methods to develop the Hydro 923 plus (now RectifHydPlus weekly hydropower dataset) [2] and the flexibility parameterization [3]. The team worked with Bonneville Power Administration to develop hydropower datasets over the core Columbia River representative of the post-2018 change in environmental regulation (flex spill). Ramping data are considered proprietary, were leveraged from WECC ADS 2030, and were not provided in the release, nor are the WECC-member hydropower data. This release represents the WECC ADS 2034 hydropower dataset. The generator database was first updated by WECC. Based on a review of hourly generation profiles, 16 facilities were transitioned from fixed schedule to dispatchable (380.5MW). The operations of the core Columbia River were updated based on Bonneville Power Administration's long-term hydro-modeling using 2020-level of modified flows and using fiscal year 2031 expected operations. The update was necessary to reflect the new environmental regulation (EIS2023). The team also included a newly developed extension over Canada [4] that improves upon existing data and synchronizes the US and Canadian data to the same 2018 weather year. Canadian facilities over the Peace River were not updated due to a lack of available flow data. The team was able to modernize and improve the overall data processing using modern tools as well as provide thorough documentation and reproducible workflows [5,6]. The datasets have been incorporated into the 2034 ADS and are in active use by WECC and the community. WECC ADS 2034 hydropower datasets contain generation at weekly and monthly timesteps, for US hydropower plants, monthly generation for Canadian hydropower plants, and the two merged together. Separate datasets are included for generation by hydropower plant and generation by individual generator units. Only processed data are provided. Original WECC-utility hourly data are under a non-disclosure agreement and for the sole use of developing this dataset. [1] Voisin, N., Harris, K. M., Oikonomou, K., Turner, S., Johnson, A., Wallace, S., Racht, P., et al. (2022). WECC ADS 2032 Hydropower Dataset (PNNL-SA-172734). See presentation (Voisin N., K.M. Harris, K. Oikonomou, and S. Turner. 04/05/2022. "WECC 2032 Anchor Dataset - Hydropower." Presented by N. Voisin, K. Oikonomou at WECC Production Cost Model Dataset Subcommittee Meeting, Online, Utah. PNNL-SA-171897.). [2] Turner, S. W. D., Voisin, N., Oikonomou, K., & Bracken, C. (2023). Hydro 923: Monthly and Weekly Hydropower Constraints Based on Disaggregated EIA-923 Data (v1.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8212727 [3] Stark, G., Barrows, C., Dalvi, S., Guo, N., Michelettey, P., Trina, E., Watson, A., Voisin, N., Turner, S., Oikonomou, K. and Colotelo, A. 2023 Improving the Representation of Hydropower in Production Cost Models, NREL/TP-5700-86377, United States. https://www.osti.gov/biblio/1993943 [4] Son, Y., Bracken, C., Broman, D., & Voisin, N. (2025). Monthly Hydropower Generation Dataset for Western Canada (1.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14984725 [5] https://github.com/HydroWIRES-PNNL/weccadshydro/ [6] Voisin, N., Broman, D., Abernethy-Cannella, K., Bracken, C., Son, Y., & Harris, K. (2025). WECC ADS 2034 Hydropower Generation Code (weccadshydro). Zenodo. https://doi.org/10.5281/zenodo.15417594 Dataset Files: File Description Timestep Spatial Extent US_Monthly_Plant.csv Generation data for US plants at a monthly timestep Monthly US US_Weekly_Plant.csv Generation data for US plants at a weekly timestep Weekly US US_Monthly_Unit.csv Generation data for US plants by generator units at a monthly timestep Monthly US US_Weekly_Unit.csv Generation data for US plants by gen...

The dataset contains large ensembles of bias adjusted daily climate model outputs of minimum temperature, maximum temperature, precipitation, relative humidity, surface pressure, wind speed, incoming shortwave radiation, and incoming longwave radiation on a 0.5-degree grid over North America. Intended uses include hydrological/land surface impact modelling and related event attribution studies. The CanLEADv1 dataset is based on archived climate model simulations in the Canadian Regional Climate Model Large Ensemble (CanRCM4 LE) https://open.canada.ca/data/en/dataset/83aa1b18-6616-405e-9bce-af7ef8c2031c and Canadian Earth System Model Large Ensembles (CanESM2 LE) https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c datasets. Specifically, CanLEADv1 provides bias adjusted daily climate variables over North America derived from 50 member initial condition ensembles of CanESM2 (ALL and NAT radiative forcings) and CanESM2-driven CanRCM4 (ALL radiative forcings) simulations (Scinocca et al., 2016; Fyfe et al., 2017). Raw CanESM2 LE and CanRCM4 LE outputs are bias adjusted (Cannon, 2018; Cannon et al., 2015) so that they are statistically consistent with two observationally-constrained historical meteorological forcing datasets (S14FD, Iizumi et al., 2017; EWEMBI, Lange, 2018). File names, formats, and metadata headers follow the recommended Data Reference Syntax for bias-adjusted Coordinated Regional Downscaling Experiment (CORDEX) simulations (Nikulin and Legutke, 2016). Multiple initial condition simulations can be used to investigate the externally forced response, internal variability, and the relative role of external forcing and internal variability on the climate system (e.g., Fyfe et al., 2017). Large ensembles of ALL and NAT simulations can be compared in event attribution studies (e.g., Kirchmeier-Young et al., 2017). Availability of bias adjusted outputs from the CanESM2-CanRCM4 modelling system can be used to investigate the added value of dynamical downscaling (Scinocca et al., 2016). Multiple observational datasets are used for bias adjustment to partly account for observational uncertainty (Iizumi et al., 2017). For CanESM2 LE, there are two sets of radiative forcing scenarios (ALL, which consists of historical and RCP8.5 forcings for the periods 1950-2005 and 2006-2100, respectively, and NAT, which consists of historicalNat forcings for the period 1950-2020), two observationally-constrained target datasets for bias adjustment (S14FD and EWEMBI), and 50 ensemble members, which gives a total of 2 × 2 × 50 = 200 sets of outputs. For CanRCM4 LE, historicalNat simulations were not run; hence, there are 2 × 50 = 100 sets of outputs. In both cases, CanLEADv1 provides variables on the CORDEX NAM-44i 0.5-degree grid. CanESM2 outputs (~2.8-degree grid) and CanRCM4 outputs (0.44-degree grid), are bilinearly interpolated onto the NAM-44i grid before bias adjustment. A multivariate version of quantile mapping (Cannon, 2018) is used to adjust the distribution of each simulated variable, as well as the statistical dependence between variables, so that these properties match those of the target observational dataset. Bias adjustment is performed on a grid cell by grid cell basis. Outside of the historical calibration period, the climate change signal simulated by the climate model is preserved (Cannon et al., 2015). References: Cannon, A. J. (2018). Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables. Climate Dynamics, 50(1-2), 31-49. Cannon, A. J., Sobie, S. R., & Murdock, T. Q. (2015). Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? Journal of Climate, 28(17), 6938-6959. Fyfe, J. C., Derksen, C., Mudryk, L., Flato, G. M., Santer, B. D., Swart, N. C., Molotch, N. P., Zhang, X., Wan, H., Arora, V. K., Scinocca, J., & Jiao, Y. (2017). Large near-term projected snowpack loss over the western United States. Nature Communications, 8, 14996. Iizumi, T., Takikawa, H., Hirabayashi, Y., Hanasaki, N., & Nishimori, M. (2017). Contributions of different bias-correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes. Journal of Geophysical Research: Atmospheres, 122(15), 7800-7819. Kirchmeier-Young, M. C., Zwiers, F. W., Gillett, N. P., & Cannon, A. J. (2017). Attributing extreme fire risk in Western Canada to human emissions. Climatic Change, 144(2), 365-379. Lange, S. (2018). Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset. Earth System Dynamics, 9(2), 627-645.

The open data portal catalogue is a downloadable dataset containing some key metadata for the general datasets available on the Government of Canada's Open Data portal. Resource 1 is generated using the ckanapi tool (external link) Resources 2 - 8 are generated using the Flatterer (external link) utility. ###Description of resources: 1. Dataset is a JSON Lines (external link) file where the metadata of each Dataset/Open Information Record is one line of JSON. The file is compressed with GZip. The file is heavily nested and recommended for users familiar with working with nested JSON. 2. Catalogue is a XLSX workbook where the nested metadata of each Dataset/Open Information Record is flattened into worksheets for each type of metadata. 3. datasets metadata contains metadata at the dataset level. This is also referred to as the package in some CKAN documentation. This is the main table/worksheet in the SQLite database and XLSX output. 4. Resources Metadata contains the metadata for the resources contained within each dataset. 5. resource views metadata contains the metadata for the views applied to each resource, if a resource has a view configured. 6. datastore fields metadata contains the DataStore information for CSV datasets that have been loaded into the DataStore. This information is displayed in the Data Dictionary for DataStore enabled CSVs. 7. Data Package Fields contains a description of the fields available in each of the tables within the Catalogue, as well as the count of the number of records each table contains. 8. data package entity relation diagram Displays the title and format for column, in each table in the Data Package in the form of a ERD Diagram. The Data Package resource offers a text based version. 9. SQLite Database is a .db database, similar in structure to Catalogue. This can be queried with database or analytical software tools for doing analysis.

On the continental scale, climate is an important determinant of the distributions of plant taxa and ecoregions. To quantify and depict the relations between specific climate variables and these distributions, we placed modern climate and plant taxa distribution data on an approximately 25-kilometer (km) equal-area grid with 27,984 points that cover Canada and the continental United States (Thompson and others, 2015). The gridded climatic data include annual and monthly temperature and precipitation, as well as bioclimatic variables (growing degree days, mean temperatures of the coldest and warmest months, and a moisture index) based on 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and absolute minimum and maximum temperatures for 1951-1980 interpolated from climate-station data (WeatherDisc Associates, 1989). As described below, these data were used to produce portions of the "Atlas of relations between climatic parameters and distributions of important trees and shrubs in North America" (hereafter referred to as "the Atlas"; Thompson and others, 1999a, 1999b, 2000, 2006, 2007, 2012a, 2015). Evolution of the Atlas Over the 16 Years Between Volumes A & B and G: The Atlas evolved through time as technology improved and our knowledge expanded. The climate data employed in the first five Atlas volumes were replaced by more standard and better documented data in the last two volumes (Volumes F and G; Thompson and others, 2012a, 2015). Similarly, the plant distribution data used in Volumes A through D (Thompson and others, 1999a, 1999b, 2000, 2006) were improved for the latter volumes. However, the digitized ecoregion boundaries used in Volume E (Thompson and others, 2007) remain unchanged. Also, as we and others used the data in Atlas Volumes A through E, we came to realize that the plant distribution and climate data for areas south of the US-Mexico border were not of sufficient quality or resolution for our needs and these data are not included in this data release. The data in this data release are provided in comma-separated values (.csv) files. We also provide netCDF (.nc) files containing the climate and bioclimatic data, grouped taxa and species presence-absence data, and ecoregion assignment data for each grid point (but not the country, state, province, and county assignment data for each grid point, which are available in the .csv files). The netCDF files contain updated Albers conical equal-area projection details and more precise grid-point locations. When the original approximately 25-km equal-area grid was created (ca. 1990), it was designed to be registered with existing data sets, and only 3 decimal places were recorded for the grid-point latitude and longitude values (these original 3-decimal place latitude and longitude values are in the .csv files). In addition, the Albers conical equal-area projection used for the grid was modified to match projection irregularities of the U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977) from which plant taxa distribution data were digitized. For the netCDF files, we have updated the Albers conical equal-area projection parameters and recalculated the grid-point latitudes and longitudes to 6 decimal places. The additional precision in the location data produces maximum differences between the 6-decimal place and the original 3-decimal place values of up to 0.00266 degrees longitude (approximately 143.8 m along the projection x-axis of the grid) and up to 0.00123 degrees latitude (approximately 84.2 m along the projection y-axis of the grid). The maximum straight-line distance between a three-decimal-point and six-decimal-point grid-point location is 144.2 m. Note that we have not regridded the elevation, climate, grouped taxa and species presence-absence data, or ecoregion data to the locations defined by the new 6-decimal place latitude and longitude data. For example, the climate data described in the Atlas publications were interpolated to the grid-point locations defined by the original 3-decimal place latitude and longitude values. Interpolating the data to the 6-decimal place latitude and longitude values would in many cases not result in changes to the reported values and for other grid points the changes would be small and insignificant. Similarly, if the digitized Little (1971, 1976, 1977) taxa distribution maps were regridded using the 6-decimal place latitude and longitude values, the changes to the gridded distributions would be minor, with a small number of grid points along the edge of a taxa's digitized distribution potentially changing value from taxa "present" to taxa "absent" (or vice versa). These changes should be considered within the spatial margin of error for the taxa distributions, which are based on hand-drawn maps with the distributions evidently generalized, or represented by a small, filled circle, and these distributions were subsequently hand digitized. Users wanting to use data that exactly match the data in the Atlas volumes should use the 3-decimal place latitude and longitude data provided in the .csv files in this data release to represent the center point of each grid cell. Users for whom an offset of up to 144.2 m from the original grid-point location is acceptable (e.g., users investigating continental-scale questions) or who want to easily visualize the data may want to use the data associated with the 6-decimal place latitude and longitude values in the netCDF files. The variable names in the netCDF files generally match those in the data release .csv files, except where the .csv file variable name contains a forward slash, colon, period, or comma (i.e., "/", ":", ".", or ","). In the netCDF file variable short names, the forward slashes are replaced with an underscore symbol (i.e., "_") and the colons, periods, and commas are deleted. In the netCDF file variable long names, the punctuation in the name matches that in the .csv file variable names. The "country", "state, province, or territory", and "county" data in the .csv files are not included in the netCDF files. Data included in this release: - Geographic scope. The gridded data cover an area that we labelled as "CANUSA", which includes Canada and the USA (excluding Hawaii, Puerto Rico, and other oceanic islands). Note that the maps displayed in the Atlas volumes are cropped at their northern edge and do not display the full northern extent of the data included in this data release. - Elevation. The elevation data were regridded from the ETOPO5 data set (National Geophysical Data Center, 1993). There were 35 coastal grid points in our CANUSA study area grid for which the regridded elevations were below sea level and these grid points were assigned missing elevation values (i.e., elevation = 9999). The grid points with missing elevation values occur in five coastal areas: (1) near San Diego (California, USA; 1 grid point), (2) Vancouver Island (British Columbia, Canada) and the Olympic Peninsula (Washington, USA; 2 grid points), (3) the Haida Gwaii (formerly Queen Charlotte Islands, British Columbia, Canada) and southeast Alaska (USA, 9 grid points), (4) the Canadian Arctic Archipelago (22 grid points), and (5) Newfoundland (Canada; 1 grid point). - Climate. The gridded climatic data provided here are based on the 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and include annual and monthly temperature and precipitation. The CRU CL 2.0 data were interpolated onto the approximately 25-km grid using geographically-weighted regression, incorporating local lapse-rate estimation and correction. Additional bioclimatic variables (growing degree days on a 5 degrees Celsius base, mean temperatures of the coldest and warmest months, and a moisture index calculated as actual evapotranspiration divided by potential evapotranspiration) were calculated using the interpolated CRU CL 2.0 data. Also included are absolute minimum and maximum temperatures for 1951-1980 interpolated in a similar fashion from climate-station data (WeatherDisc Associates, 1989). These climate and bioclimate data were used in Atlas volumes F and G (see Thompson and others, 2015, for a description of the methods used to create the gridded climate data). Note that for grid points with missing elevation values (i.e., elevation values equal to 9999), climate data were created using an elevation value of -120 meters. Users may want to exclude these climate data from their analyses (see the Usage Notes section in the data release readme file). - Plant distributions. The gridded plant distribution data align with Atlas volume G (Thompson and others, 2015). Plant distribution data on the grid include 690 species, as well as 67 groups of related species and genera, and are based on U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977), regional atlases (e.g., Benson and Darrow, 1981), and new maps based on information available from herbaria and other online and published sources (for a list of sources, see Tables 3 and 4 in Thompson and others, 2015). See the "Notes" column in Table 1 (https://pubs.usgs.gov/pp/p1650-g/table1.html) and Table 2 (https://pubs.usgs.gov/pp/p1650-g/table2.html) in Thompson and others (2015) for important details regarding the species and grouped taxa distributions. - Ecoregions. The ecoregion gridded data are the same as in Atlas volumes D and E (Thompson and others, 2006, 2007), and include three different systems, Bailey's ecoregions (Bailey, 1997, 1998), WWF's ecoregions (Ricketts and others, 1999), and Kuchler's potential natural vegetation regions (Kuchler, 1985), that are each based on distinctive approaches to categorizing ecoregions. For the Bailey and WWF ecoregions for North America and the Kuchler potential natural vegetation regions for the contiguous United States (i.e.,

Distribution of pollution prevention methods by establishment size at the Canada level, every two years. The unit of measure is percent.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage. Sample excludes the Northwest Territories, Nunavut, and Yukon, which represent approximately 0.3% of the population.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in Canada was 1,004 individuals.

Mode of data collection

Other [oth]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

The Social Policy Simulation Database/Model (SPSD/M) is a tool designed to analyze the financial interactions between governments and individuals in Canada. It is used to evaluate the effects of the tax and transfer system on costs and income redistribution. The SPSD/M has four basic components: a database (the SPSD), a model (the SPSM, which includes a set of simulation algorithms), software for data extraction and data reporting, and user documentation. The SPSD/M is designed to be used in the analysis of the financial interactions between governments and individuals in Canada. 1. The SPSD/M is a representative and non-confidential statistical database of individuals in the context of their families, with sufficient information on each individual to enable the calculation of taxes paid to the government as well as amounts remitted by governments.2. SPSM is a static accounting model that processes every individual and family in the SPSD, calculates taxes and transfers using algorithms that simulate adopted or proposed programs, and reports on results. A sophisticated software environment gives the user a great deal of influence over the model's inputs and outputs, enabling him or her to modify existing programs or examine entirely new projects. Inside MSPS, there are two models, configured as two separate computer programs.2a.The central program, SPSM, is a microsimulation model that calculates taxes and transfers for individuals and families. These calculations are performed for everyone in the SPSD, and the results are then aggregated to produce estimates. The SPSM is a static incidence model and is not intended to simulate how an individual's behavior is likely to change in response to various policy options. The MSPS includes software that enables the user to perform summation and extraction operations on the information contained in the database. 2b. The consumption tax model (COMTAX) is a model based on macro-economic input-output data. This model is not part of the current version of SPSD/M, but the results obtained with it are. COMTAX estimates federal and provincial sales taxes and equivalent consumption taxes by province, household, expenditure category and tax type. This model is necessary because many consumption taxes are levied at various stages of production, not at the retail stage. The rates calculated by the COMTAX model can be used as input parameters to the SPSM to produce estimates of consumption taxes paid, directly and indirectly, by any given household. 3. Data extraction and reporting software are configured as functions that are accessed via the model. They enable the user to produce formatted output data and perform specific types of analysis. 4. The user documentation is voluminous and comprehensive. It is divided into three manuals containing a number of guides. There are also two ways to run SPSM: using the Visual SPSM interface or the Classic SPSM mode. 1. SPSM Visual: The SPSM Visual interface allows users to modify model parameters, run SPSM simulations and examine output. 2.SPSM Classic: SPSM can also be run from the command interpreter (cmd). For current data from the Social Policy Simulation Database/Model, see Statistics Canada

Original provider: Caroline Fox, Dalhousie University and Raincoast Conservation Foundation

Dataset credits: Caroline Fox, Dalhousie University and Raincoast Conservation Foundation

Abstract: Associated publication abstract: Increasingly disrupted and altered, the world’s oceans are subject to immense and intensifying anthropogenic pressures. Of the biota inhabiting these ecosystems, marine birds are among the most threatened. For conservation efforts targeting marine birds to be effective, quantitative information relating to their at-sea density and distribution is typically a crucial knowledge component. In this study, we generated predictive machine learning ensemble models for 13 marine bird species and 7 groups (representing 24 additional species) in Canada’s Pacific coast waters, including several species listed under Canada’s Species at Risk Act. Predictive models were based on systematic marine bird line transect survey information collected in spring, summer, and fall on Canada’s Pacific coast (2005−2008). Multiple Covariate Distance Sampling (MCDS) was used to estimate marine bird density along transect segments. Spatial and temporal environmental predictors, including remote sensing information, were used in model ensembles, which were constructed using 4 machine learning algorithms in Salford Systems Predictive Modeler v7.0 (SPM7): Random Forests, TreeNet, Multivariate Adaptive Regression Splines, and Classification and Regression Trees. Predictive models were subsequently combined to generate seasonal and overall predictions of areas important to marine birds based on normalized marine bird species or group richness and densities. Our results employ open access data sharing and are intended to better inform marine bird conservation efforts and management planning on Canada’s Pacific coast and for broader-scale geographic initiatives across North America and elsewhere.

Supplemental information: Marine bird line-transect survey information collected using Distance Sampling in coastal British Columbia, Canada (2005-2008) is provided in three forms: (1) raw, unadjusted marine bird sightings; (2) for a subset of species, marine bird density estimates along 1km transect segments using Multiple Covariates Distance Sampling (MCDS), and; (3) for a subset of species, surface density estimates per ~14km2 hexagon using machine learning ensemble modeling. For data products 2 and 3, the marine bird subsets were restricted to species sighted in sufficient numbers for analysis. Surveys were completed by Raincoast Conservation Foundation.

1. Raw data: raw, unadjusted sighting of marine bird species on water and in flight. Attributes such as column labels are included in the attributes definition section.

Note that several species alpha codes are non-standard, due to grouping of species identifications (e.g., large gulls and dark shearwaters).

ANMU = Ancient Murrelet ANMUf = Ancient Murrelet family (varying #s of parents and chicks, or just chicks) BAEA = Bald Eagle BEKI = Belted Kingfisher BFAL = Black-footed Albatross BLKI = Black-legged Kittiwake BLOY = Black Oystercatcher BLSC = Black Scoter BLTU = Black Turnstone BOGU = Bonaparte's Gull BRAC = Brandt's Cormorant BRAN = Brant Goose BUFF = Bufflehead Duck BULS = Buller's Shearwater CAAU = Cassin's Auklet CAGU = California Gull CANG = Canada Goose COLO = Common Loon COME = Common Merganser COMU = Common Murre COMUf = Common Murre family (parent with chick, or just chicks) CORA = Common Raven DARK = Sooty Shearwater, Short-tailed Shearwater, Flesh-footed Shearwater DCCO = Double-crested Cormorant DEJU = Dark-eyed Junco DUNL = Dunlin FTSP = Fork-tailed Storm Petrel GBHE = Great Blue Heron GWGU = Glaucous-winged Gull HADU = Harlequin Duck HETHGU = Herring Gull/Thayer's Gull HOGR = Horned Grebe HOPU = Horned Puffin LAAL = Laysan Albatross LEFTSP = mixed flock Fork-tailed and Leach's Storm-petrels LESP = Leach's Storm Petrel LTDU = Longtail Duck LTJA = Long-tailed Jaeger MALL = Mallard Duck MAMU = Marbled Murrelet MEGU = Mew Gull NOCR = Northwestern Crow NOFU = Northern Fulmar NSHO = Northern Shoveler OSPR = Osprey PAJA = Parasitic Jaeger PALO = Pacific Loon PECO = Pelagic Cormorant PFSH = Pink-footed Shearwater PIGU = Pigeon Guillemot POJA = Pomarine Jaeger RBME = Red-breasted Merganser RHAU = Rhinoceros Auklet RNGR = Red-necked Grebe RNPH = Red-necked Phalarope RTLO = Red-throated Loon RUHU = Rufous Hummingbird SAGU = Sabine's Gull SNGO = Snow Goose STAL = Short-tailed Albatross SUSC = Surf Scoter THGU = Thayer's Gull TOWA = Townsend's Warbler TRES = Tree Swallow TUPU = Tufted Puffin TUPUf = Tufted Puffin family (parent with chick) WEGR = Western Grebe WEGU = Western Gull WHIM = Whimbrel WWSC = White-winged Scoter YBLO = Yellow-billed Loon

UNAL = Unidentified Alcid
UNCO = Unidentified cormorant
UNDU = Unidentified ducks in the distance
UNGE = Unidentified Geese in the distance
UNGO = Unidentified Goldeneye
UNGR = Unidentified Grebe
ULGU = Unidentified Larus Gull
UNJA = Unidentified Jaeger
UNLO = Unidentified Loon
UNSO = Unidentified Scoter
UNSW = Unidentified Shearwater
UNSH = Unidentified Shorebirds
UNST = Unidentified Storm-petrel
UNTE = Unidentified Tern
UNTU = Unidentified Turnstone

1. Marine bird density estimates along 1km transect segments using Multiple Covariates Distance Sampling (MCDS).

Note that several species alpha codes are non-standard, due to grouping of species identifications (e.g., large gulls and dark shearwaters).

ANMU = Ancient Murrelet BFAL = Black-footed Albatross CAAU = Cassin's Auklet COMU = Common Murre CORM = Cormorants (Brandt's, Double-crested, Pelagic) DARK = Dark shearwaters (Flesh-footed, Short-tailed, Sooty) FTSP = Fork-tailed Storm-petrel GREB = Grebes (Horned, Red-necked, Western) LESP = Leach's Storm-petrel lgGULL = large Larus spp. gulls (California, Glaucous-winged, American, Thayer's) LOON = Loons (Yellow-billed, Common, Red-throated, Pacific) MAMU = Marbled Murrelet NOFU = Northern Fulmar PFSH = Pink-footed Shearwater PIGU = Pigeon Guillemot RHAU = Rhinoceros Auklet RNPH = Red=necked Phalarope SCOT = Scoters (Black, White-winged, Surf) smGULL = small gulls (Black-legged Kittiwake, Bonaparte's, Mew, Sabine's) TUPU = Tufted Puffin

Field names represent, using ANMU and BFAL as the examples:

• first few fields represent summary fields (i.e., FID, Shape)
• SEGID = unique line transect segment ID. Can use this field to join across species files.
• VOYAGE = On planned transect (T) or on passage (P), which are unplanned transects.
• SPEED = vessel speed (knts).
• MO = Month, numeric (1-12).
• SegLength = Segment length. Most should = 1 km, but shorter segments have been retained.
• Season = Spring (april, may, june), Summer (August), Fall (October, November).
• Point_X and Point_Y = x and y coordinates using BC Albers.
• Effort = Same as SegLength.
• DATE = year-month-day.
• YEAR = year.
• DAY_YR = Day of the year, beginning with with January 1 = 1.
• AREA = Segment length (km) X perpendicular distance (km) from boat for that particular species (unit = km2; identified using MCDS Distance Analysis).
• ANMUw_D (all other examples BIRDw_D) = estimated density of Ancient Murrelets along the transect segment (including family groups, see below). Lowercase "w" = birds on water.
• ANMUf_D = exception for ANMU family groups. Lowercase "f" = family groups on water (parent(s) with flightless chicks or flightless chicks alone).
• BFALs_D (all other examples BIRDs_D) = estimated density of Black-footed ALbatrosses along the transect segment. Lowercase "s" = birds in flight. Note that flying bird density estimates should be used and interpreted with caution.

1. Density estimations per hexagon (approx. 14km2):

Shape file name represents the bird species (e.g., ANMU = Ancient Murrelet) plus "w" (w = density estimates of birds on water only) or "sw" (sw = density estimates of combination of birds in flight and on water).

Note that several species alpha codes are non-standard, due to grouping of species identifications (e.g., large gulls and dark shearwaters).

ANMU = Ancient Murrelet BFAL = Black-footed Albatross CAAU = Cassin's Auklet COMU = Common Murre CORM = Cormorants (Brandt's, Double-crested, Pelagic) DARK = Dark shearwaters (Flesh-footed, Short-tailed, Sooty) FTSP = Fork-tailed Storm-petrel GREB = Grebes (Horned, Red-necked, Western) LESP = Leach's Storm-petrel lgGULL = large Larus spp. gulls (California, Glaucous-winged, American, Thayer's) LOON = Loons (Yellow-billed, Common, Red-throated, Pacific) MAMU = Marbled Murrelet NOFU = Northern Fulmar PFSH = Pink-footed Shearwater PIGU = Pigeon Guillemot RHAU = Rhinoceros Auklet RNPH = Red=necked Phalarope SCOT = Scoters (Black, White-winged, Surf) smGULL = small gulls (Black-legged Kittiwake, Bonaparte's, Mew, Sabine's) TUPU = Tufted Puffin

Field names represent, using ANMUw as the example:

• first few ields represent summary fields (i.e., FID, Shape and Id)
• HexagonID = unique hexagon cell ID. Can use this field to join across species files.
• X_coord and Y_Coord = should be self explanatory.
• spr_ANMUw = estimated Ancient Murrelet on water density estimates (birds/km2) in spring (April 2007, May 2007, June 2008)
• sum_ANMUw = same as above, except in summer (August 2005, 2006 and 2008)
• fal_ANMUw = same as above, except in fall (October and November 2007)
• ANMUw_AnAv = average across spring, summer, and fall density estimates

Data on the number of owners of two residential properties, the type and assessment value of the properties they own, for the provinces of Nova Scotia, Ontario and British Columbia, their census metropolitan areas and associated census subdivisions.

Description: This dataset contains layers of predicted occurrence for 65 groundfish species as well as overall species richness (i.e., the total number of species present) in Canadian Pacific waters, and the median standard error per grid cell across all species. They cover all seafloor habitat depths between 10 and 1400 m that have a mean summer salinity above 28 PSU. Two layers are provided for each species: 1) predicted species occurrence (prob_occur) and 2) the probability that a grid cell is an occurrence hotspot for that species (hotspot_prob; defined as being in the lower of: 1) 0.8, or 2) the 80th percentile of the predicted probability of occurrence values across all grid cells that had a probability of occurrence greater than 0.05.). The first measure provides an overall prediction of the distribution of the species while the second metric identifies areas where that species is most likely to be found, accounting for uncertainty within our model. All layers are provided at a 1 km resolution. Methods: These layers were developed using a species distribution model described in Thompson et al. 2023. This model integrates data from three fisheries-independent surveys: the Fisheries and Oceans Canada (DFO) Groundfish Synoptic Bottom Trawl Surveys (Sinclair et al. 2003; Anderson et al. 2019), the DFO Groundfish Hard Bottom Longline Surveys (Lochead and Yamanaka 2006, 2007; Doherty et al. 2019), and the International Pacific Halibut Commission Fisheries Independent Setline Survey (IPHC 2021). Further details on the methods are found in the metadata PDF available with the dataset. Abstract from Thompson et al. 2023: Predictions of the distribution of groundfish species are needed to support ongoing marine spatial planning initiatives in Canadian Pacific waters. Data to inform species distribution models are available from several fisheries-independent surveys. However, no single survey covers the entire region and different gear types are required to survey the range of habitats that are occupied by groundfish. Bottom trawl gear is used to sample soft bottom habitat, predominantly on the continental shelf and slope, whereas longline gear often focuses on nearshore and hardbottom habitats where trawling is not possible. Because data from these two gear types are not directly comparable, previous species distribution models in this region have been limited to using data from one survey at a time, restricting their spatial extent and usefulness at a regional scale. Here we demonstrate a method for integrating presence-absence data across surveys and gear types that allows us to predict the coastwide distributions of 66 groundfish species in British Columbia. Our model leverages the use of available data from multiple surveys to estimate how species respond to environmental gradients while accounting for differences in catchability by the different surveys. Overall, we find that this integrated method has two main benefits: 1) it increases the accuracy of predictions in data-limited surveys and regions while having negligible impacts on the accuracy when data are already sufficient to make predictions, 2) it reduces uncertainty, resulting in tighter confidence intervals on predicted species occurrences. These benefits are particularly relevant in areas of our coast where our understanding of habitat suitability is limited due to a lack of spatially comprehensive long-term groundfish research surveys. Data Sources: Research data was provided by Pacific Science’s Groundfish Data Unit for research surveys from the GFBio database between 2003 and 2020 for all species which had at least 150 observations, across all gear type and survey datasets available. Uncertainties: These are modeled results based on species observations at sea and their related environmental covariate predictions that may not always accurately reflect real-world groundfish distributions though methods that integrate different data types/sources have been demonstrated to improve model inference by increasing the accuracy of the predictions and reducing uncertainty.

Data includes: board and school information, grade 3 and 6 EQAO student achievements for reading, writing and mathematics, and grade 9 mathematics EQAO and OSSLT. Data excludes private schools, Education and Community Partnership Programs (ECPP), summer, night and continuing education schools.

How Are We Protecting Privacy?

Results for OnSIS and Statistics Canada variables are suppressed based on school population size to better protect student privacy. In order to achieve this additional level of protection, the Ministry has used a methodology that randomly rounds a percentage either up or down depending on school enrolment. In order to protect privacy, the ministry does not publicly report on data when there are fewer than 10 individuals represented.

* Percentages depicted as 0 may not always be 0 values as in certain situations the values have been randomly rounded down or there are no reported results at a school for the respective indicator. * Percentages depicted as 100 are not always 100, in certain situations the values have been randomly rounded up.

The information in the School Information Finder is the most current available to the Ministry of Education at this time, as reported by schools, school boards, EQAO and Statistics Canada. The information is updated as frequently as possible.

This information is also available on the Ministry of Education's School Information Finder website by individual school.

Descriptions for some of the data types can be found in our glossary.

School/school board and school authority contact information are updated and maintained by school boards and may not be the most current version. For the most recent information please visit: https://data.ontario.ca/dataset/ontario-public-school-contact-information.

Data on the main mode of commuting by commuting duration, time leaving for work, industry sectors (2-digit code) from the North American Industry Classification System (NAICS) 2017, occupation broad category (1-digit code) from the National Occupational Classification (NOC) 2021 and gender.

This dataset comprises agricultural data from the 2016 and 2021 Agricultural Censuses conducted by Statistics Canada. It includes information on farm types, geographic distribution, farm sizes, and technology adoption for both census years. Additionally, there is demographic data on farm operators' age and gender for the 2021 Census. The dataset provides insights into key agricultural factors for evidence-based policy and innovation design. It covers the 2016 and 2021 censuses, featuring three datasets: one detailing farm operator demographics and two detailing the number of farmers by region, farm type, farm size, and the number of farmers that have adopted technologies. The types of technologies differ between the two census periods. Data suppression is not applied to this dataset. Geographical regions are based on the 10 provinces (excluding the three territories), farm types are categorized by NAICS codes (3 digits), and farm size is measured in acres.

Within this work we address the relevance of integrating molecular techniques; particularly environmental DNA (eDNA) methods into biological monitoring programs. We focused on the Sebastes genus; a group of important fish in the Pacific Northwest of North America. They are particularly interesting from an evolutionary standpoint due to a high degree of interspecific sequence conservation paired with a high degree of intraspecific sequence variability. We used a novel whole mitochondrial genome approach to overcome these challenges and create successful targeted qPCR eDNA assays for species of particular importance. We also used a metabarcoding approach to compare molecular community diversity estimates with those made by conventional diving methods. We found that molecular techniques were highly sensitive and could detect more species overall. However, both molecular and conventional methods have their strengths. In terms of species and habitat protection, using multiple techniques together will be most impactful.

This data was collected in the territory of the Kitasoo/X'ais x'ais First Nation with the logistical support and permission of the Kitasoo/X'ais x'ais Stewardship Authority. If you download or use this data for any purpose please contact Christina Service the Wildlife Biologist and Science Coordinator at research@kxsa.ca.

[This dataset was processed using the GBIF eDNA converter tool.]

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage.

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1003.

Mode of data collection

Landline and Cellular Telephone

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

This table contains 20 series, with data for years 1961 - 2012 (not all combinations necessarily have data for all years), and was last released on 2015-03-26. This table contains data described by the following dimensions (Not all combinations are available): Geography (10 items: Canada; Nova Scotia; New Brunswick; Newfoundland and Labrador ...), Value (2 items: Present value calculation; timber stocks; method I; Present value calculation; timber stocks; method II ...).

Methods used for e-commerce sales for businesses locations that reported e-commerce sales for the performing arts industry, for Canada, for 2 years of data.

Objective: Much of the extensive quantitative research linking socio-economic position (SEP) and health utilizes three common indicators: income, occupation and education. Existing survey data may enable researchers to include indicators of additional forms of capital in their analyses, permitting more nuanced consideration of the relationship between SEP and health. Our objective was to identify the breadth of survey questions related to economic, cultural, and social capital available through Statistics Canada surveys, and the extent to which those surveys also include health measures.Methods: We compiled a list of all population-based Statistics Canada surveys, and developed a broad list of potential indicators of forms of capital. We systematically searched the surveys for those indicators and health measures, analyzing their co-occurrence.Results: Traditional SEP indicators were present in 73% of surveys containing health measures, while additional indicators of social and cultural capital were available in 57%.Conclusion: Existing national survey data represent an under-exploited opportunity for research examining the relationship between various forms of capital and health in Canada. Future empirical explorations of these data could enrich our theoretical understanding of health inequities.

The National Broadband Data represents coverage information across Canada for existing broadband service providers with their associated technology types. The coverage information is aggregated and deployed over a grid of hexagons, which cover areas of roughly 25 square km each. Broadband Internet service availability is provided for download/upload speed markers (5/1, 10/2, 25/5 and 50/10 Mbps) where more than 75% of total dwellings covered within the hexagon have access to broadband service offerings meeting these markers. In order to improve the granularity of the broadband data, ISED and the CRTC are providing aggregated and anonymous broadband services data based on the pseudo-household statistical model, hence achieving higher precision in depicting the broadband Internet service availability. This information is available below under the "NBD PHH Speeds" resource. For more information on the pseudo-household statistical model, refer to the Pseudo-Household Demographic Distribution dataset. A representation of broadband services per 250m road segments is now available for download under the “NBD Roads” resource. To generate this dataset, the NBD PHH Speeds information was projected over the nearest road arc from Statistics Canada’s Road Network File, and those roads were spliced in approximately 250m segments. NEW: The data has been augmented to include new presentation layers as published on the National Broadband Map.

Context

Its been two years since the news that Canada has legalized weed hit us, so I was like why don't we get a dataset from Kaggle to practice a bit of data analysis and to my surprise I cannot find a weed dataset which reflects the economics behind legalized weed and how it has changed over time ,so I just went to the Canadian govt data site , and ola they have CSV files on exactly what I wanted floating around on their website and all I did was to download it straight up, and here I am to share it with the community.

Content

We have a series of CSV files each having data about things like supply, use case, production, etc but before we go into the individual files there are a few data columns which are common to all csv files

• Ref_date: Reference period for the series being released
• Dimension Name: Name of dimension. There can be up to 10 dimensions in a data table.
• DGUID: Dissemination Geography Unique Identifier.
• Unit of measure: The unit of measure applied to a member given in the text. There can be multiple units of measure in a table.
• Unit of measure ID: The unique reference code associated with a particular unit of measure.
• Scalar factor: The scalar factor associated with a data series, displayed as text. There can be multiple scalar factors in a table.
• Scalar_ID: The unique numeric reference code associated with a particular scalar factor.
• Vector: Unique variable length reference code time-series identifier, consisting of the letter 'V', followed by up to 10 digits.
• Coordinate: Concatenation of the member ID values for each dimension.
• Status: Shows various states of a data value using symbols. These symbols are described in the symbol legend and notes contained in the metadata file. Some symbols accompany a data value while others replace a data value. i.e. – A, B, C, D, E, F,.., X, 0s
• Symbol: Describes data points that are preliminary or revised, displayed using the symbols p and r. These symbols accompany a data.

Understanding metadata files:

Cube Title: The title of the table. The output files are unilingual and thus will contain either the English or French title.

Product Id (PID): The unique 8 digit product identifier for the table.

CANSIM Id: The ID number which formally identified the table in CANSIM. (where applicable)

URL: The URL for the representative (default) view of a given data table.

Cube Notes: Each note is assigned a unique number. This field indicates which notes, if any, are applied to the entire table.

Archive Status: Describes the status of a table as either 'Current' or 'Archived'. Archived tables are those that are no longer updated.

Frequency: Frequency of the table. (i.e. annual)

Start Reference Period: The starting reference period for the table.

End Reference Period: The end reference period for the table.

Total Number of Dimensions: The total number of dimensions contained in the table.

Dimension Name: The name of a dimension in a table. There can be up to 10 dimensions in a table. (i.e. – Geography)

Dimension ID: The reference code assigned to a dimension in a table. A unique reference Dimension ID code is assigned to each dimension in a table.

Dimension Notes: Each note is assigned a unique number. This field indicates which notes are applied to a particular dimension.

Dimension Definitions: Reserved for future development.

Member Name: The textual description of the members in a dimension. (i.e. – Nova Scotia, Ontario (members of the Geography dimension))

Member ID: The code assigned to a member of a dimension. There is a unique ID for each member within a dimension. These IDs are used to create the coordinate field in the data file. (see the 'coordinate' field in the data record layout).

Classification (where applicable): Classification code for a member. Definitions, data sources and methods

Parent Member ID: The code used to display the hierarchical relationship between members in a dimension. (i.e. – The member Ontario (5) is a child of the member Canada (1) in the dimension 'Geography')

Terminated: Indicates whether a member has been terminated or not. Terminated members are those that are no longer updated.

Member Notes: Each note is assigned a unique number. This field indicates which notes are applied to each member.

Member definitions: Reserved for future development.

Symbol Legend: The symbol legend provides descriptions of the various symbols which can appear in a table. This field describes a comprehensive list of all possible symbols, regardless of whether a selected symbol appears in a particular table.

Survey Code: The unique code associated with a survey or program from which the data in the table is derived. Data displayed in one table may be derived ...