New political and administrative boundaries Shapefile of Nepal. Downloaded and republished from the Survey Department website.

The attached zip file contains the shapefile for Oakland's city council districts. You need all the files included in the zip file to open the .shp file, so please download the whole zip archive.

Building footprint polygons are updated weekly by ECGIS. They provide a general reference of where buildings in Eaton County are located. These are not survey-grade.

These data provide geologic information, including generalized lithology, geologic age, and paleo-latitude and -longitude of geologic units, for the United States, Canada, and Australia, in an H3 Discrete Global Grid System (DGGS) hexagonal format (Uber Technologies Inc., 2020) with an average hexagon area of 5.16 square kilometers. The data are presented as the shapefile version of ASCII data developed by Lawley and others (2021) for prospectivity modeling of basin-hosted Pb-Zn mineralization in the United States, Canada, and Australia (Lawley and others, 2022). References Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Gadd, M.G., Huston, D.L., Kelley, K.D., Paradis, S., Peter, J.M., and Czarnota, K., 2021, Datasets to support prospectivity modelling for sediment-hosted Zn-Pb mineral systems: Natural Resources Canada Open File 8836, https://doi.org/10.4095/329203. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635. Uber Technologies Inc., 2020, H3: A hexagonal hierarchical geospatial indexing system: GitHub Repository, https://github.com/uber/h3.

This dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.

This file contains European countries in a shapefile format that can be used in python, R or matlab. The file has been created by Drin Marmullaku based on GADM version 4.1 (https://gadm.org/) and distributed according to their license (https://gadm.org/license.html).

Please cite as: Sevdari, Kristian; Marmullaku, Drin (2023). Shapefile of European countries. Technical University of Denmark. Dataset. https://doi.org/10.11583/DTU.23686383 This dataset is distributed under a CCBY-NC-SA 4.0 license

Using the data to create maps for publishing of academic research articles is allowed. Thus you can use the maps you made with GADM data for figures in articles published by PLoS, Springer Nature, Elsevier, MDPI, etc. You are allowed (but not required) to publish these articles (and the maps they contain) under an open license such as CC-BY as is the case with PLoS journals and may be the case with other open access articles. Data for the following countries is covered by a a different license Austria: Creative Commons Attribution-ShareAlike 2.0 (source: Government of Austria)

This shapefile contains drains that are maintained by Eaton County, Michigan Drains Office. This dataset has been in a state of non-maintenance for many years and has known issues with completeness and accuracy. The dataset was created by the Eaton County Drains Office circa 2008. The features in this shapefile estimate the rough location of the centerline of the drain right-of-way as legally described.

The Digital City Map (DCM) data represents street lines and other features shown on the City Map, which is the official street map of the City of New York. The City Map consists of 5 different sets of maps, one for each borough, totaling over 8000 individual paper maps. The DCM datasets were created in an ongoing effort to digitize official street records and bring them together with other street information to make them easily accessible to the public. The Digital City Map (DCM) is comprised of seven datasets; Digital City Map, Street Center Line, City Map Alterations, Arterial Highways and Major Streets, Street Name Changes (areas), Street Name Changes (lines), and Street Name Changes (points).

All of the Digital City Map (DCM) datasets are featured on the Streets App

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

Updates for this dataset, along with other multilayered maps on NYC Open Data, are temporarily paused while they are moved to a new mapping format. Please visit https://www.nyc.gov/site/planning/data-maps/open-data/dwn-digital-city-map.page to utilize this data in the meantime.

ArcGIS and QGIS map packages, with ESRI shapefiles for the DSM2 Model Grid. These are not finalized products. Locations in these shapefiles are approximate.

Monitoring Stations - shapefile with approximate locations of monitoring stations.

DSM2 Grid 2025-05-28 Historical

FC_2023.01

DSM2 v8.2.0, calibrated version:

• dsm2_8_2_grid_map_calibrated.mpkx - ArcGIS Pro map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_grid_map_calibrated.mpk - ArcGIS Desktop map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_grid_map_qgis.zip - QGIS map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_gridmap_shapefiles.zip - A zip file containing all the shapefiles used in the above map packages:
• dsm2_8_2_0_calibrated_channels_centerlines - channel centerlines, follwing the path of CSDP centerlines
• dsm2_8_2_0_calibrated_network_channels - channels represented by straight line segments which are connected the upstream and downstream nodes
• dsm2_8_2_0_calibrated_nodes - DSM2 nodes
• dsm2_8_2_0_calibrated_dcd_only_nodes - Nodes that are only used by DCD
• dsm2_8_2_0_calibrated_and_dcd_nodes - Nodes that are shared by DSM2 and DCD
• dsm2_8_2_0_calibrated_and_smcd_nodes - Nodes that are shared by DSM2 and SMCD
• dsm2_8_2_0_calibrated_gates_actual_loc - The approximate actual locations of each gate in DSM2
• dsm2_8_2_0_calibrated_gates_grid_loc - The locations of each gate in the DSM2 model grid
• dsm2_8_2_0_calibrated_reservoirs - The approximate locations of the reservoirs in DSM2
• dsm2_8_2_0_calibrated_reservoir_connections - Lines showing connections from reservoirs to nodes in DSM2

DSM2 v8.2.1, historical version:

• DSM2 v8.2.1, historical version grid map release notes (PDF), updated 7/12/2022
• DSM2 v8.2.1, historical version grid map, single zoom level (PDF)
• DSM2 v8.2.1, historical version grid map, multiple zoom levels (PDF) - PDF grid map designed to be printed on 3 foot wide plotter paper.
• DSM2 v8.2.1, historical version map package for ArcGIS Desktop: A map package for ArcGIS Desktop containing the grid map layers with symbology.
• DSM2 v8.2.1, historical version grid map shapefiles (zip): A zip file containing the shapefiles used in the grid map.

Change Log

7/12/2022: The document "DSM2 v8.2.1, historical version grid map release notes (PDF)" was corrected by removing section 4.4, which incorrectly stated that the grid included channels 710-714, representing the Toe Drain, and that the Yolo Flyway restoration area was included.

Data presented here include a shapefile that combines fault data for the United States and Canada (Chorlton, 2007; Reed and others, 2005; Styron and Pagani, 2020) and a shapefile of faults for Australia (Chorlton, 2007; Raymond and others, 2012; Styron and Pagani, 2020). These two shapefiles were used as an evidential layer to evaluate the mineral prospectivity for sediment-hosted Pb-Zn deposits (Lawley and others, 2022). References Chorlton, L.B., 2007, Generalized geology of the world: Bedrock domains and major faults in GIS format: a small-scale world geology map with an extended geological attribute database: Geological Survey of Canada Open File 5529, https://doi.org/10.4095/223767. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635. Raymond, O.L., Liu, S., Gallagher, R., Zhang, W., and Highet, L.M., 2012, Surface Geology of Australia 1:1 million scale dataset 2012 edition: Geoscience Australia, http://pid.geoscience.gov.au/dataset/ga/74619. Reed, J.C., Jr., Wheeler, J.O., Tucholke, B.E., Stettner, W.R., and Soller, D.R., 2005, Decade of North American Geology Geologic Map of North America - Perspectives and explanation: Geological Society of America, v. 1, https://doi.org/10.1130/DNAG-CSMS-v1. Styron, R., and Pagani, M., 2020, The GEM global active faults database: Earthquake Spectra, v. 36, p. 160-180, https://doi.org/10.1177/8755293020944182.

Please note this dataset is the most recent version of the Administrative Boundaries (AB). For previous versions of the AB please go to this url: https://data.gov.au/dataset/ds-dga-b4ad5702-ea2b-4f04-833c-d0229bfd689e/details?q=previous

Geoscape Administrative Boundaries is Australia’s most comprehensive national collection of boundaries, including government, statistical and electoral boundaries. It is built and maintained by Geoscape Australia using authoritative government data. Further information about contributors to Administrative Boundaries is available here.

This dataset comprises seven Geoscape products:

• Localities
• Local Government Areas (LGAs)
• Wards
• Australian Bureau of Statistics (ABS) Boundaries
• Electoral Boundaries
• State Boundaries and
• Town Points

Updated versions of Administrative Boundaries are published on a quarterly basis.

Users have the option to download datasets with feature coordinates referencing either GDA94 or GDA2020 datums.

Notable changes in the May 2025 release

• Victorian Wards have seen almost half of the dataset change now reflecting the boundaries from the 2024 subdivision review. https://www.vec.vic.gov.au/electoral-boundaries/council-reviews/ subdivision-reviews.

  • There have been spatial changes (area) greater than 1 km2 to 66 wards in Victoria.

• One new locality ‘Kenwick Island’ has been added to the local Government area ‘Mackay Regional’ in Queensland.

  • There have been spatial changes(area) greater than 1 km2 to the local government areas 'Burke Shire' and 'Mount Isa City' in Queensland.

• There have been spatial changes(area) greater than 1 km2 to the localities ‘Nicholson’, ‘Lawn Hill’ and ‘Coral Sea’ in Queensland and ‘Calguna’, ‘Israelite Bay’ and ‘Balladonia’ in Western Australia.

• An update to the NT Commonwealth Electoral Boundaries has been applied to reflect the redistribution of the boundaries gazetted on 4 March 2025.

• Geoscape has become aware that the DATE_CREATED and DATE_RETIRED attributes in the commonwealth_electoral_polygon MapInfo TAB tables were incorrectly ordered and did not match the product data model. These attributes have been re-ordered to match the data model for the May 2025 release.

IMPORTANT NOTE: correction of issues with the 22 November 2022 release

• On 28 November 2022, the Administrative Boundaries dataset originally released on 22 November 2022 was amended and re-uploaded after Geoscape identified some issues with the original data for 'Electoral Boundaries'.
• As a result of the error, some shapefiles were published in 3D rather than 2D, which may affect some users when importing data into GIS applications.
• The error affected the Electoral Boundaries dataset, specifically the Commonwealth boundary data for Victoria and Western Australia, including 'All States'.
• Only the ESRI Shapefile formats were affected (both GDA94 and GDA2020). The MapInfo TAB format was not affected.
• Because the datasets are zipped into a single file, once the error was fixed by Geoscape all of Administrative Boundaries shapefiles had to be re-uploaded, rather than only the affected files.
• If you downloaded either of the two Administrative Boundary ESRI Shapefiles between 22 November and 28 November 2022 and plan to use the Electoral Boundary component, you are advised to download the revised version dated 28 November 2022. Apologies for any inconvenience.

Further information on Administrative Boundaries, including FAQs on the data, is available here or through Geoscape Australia’s network of partners. They provide a range of commercial products based on Administrative Boundaries, including software solutions, consultancy and support.

Note: On 1 October 2020, PSMA Australia Limited began trading as Geoscape Australia.

The Australian Government has negotiated the release of Administrative Boundaries to the whole economy under an open CCBY 4.0 licence.

Users must only use the data in ways that are consistent with the Australian Privacy Principles issued under the Privacy Act 1988 (Cth).

Users must also note the following attribution requirements:

Preferred attribution for the Licensed Material:

Administrative Boundaries © Geoscape Australia licensed by the Commonwealth of Australia under Creative Commons Attribution 4.0 International license (CC BY 4.0).

Preferred attribution for Adapted Material:

Incorporates or developed using Administrative Boundaries © Geoscape Australia licensed by the Commonwealth of Australia under Creative Commons Attribution 4.0 International licence (CC BY 4.0).

What to Expect When You Download Administrative Boundaries

Administrative Boundaries is large dataset (around 1.5GB unpacked), made up of seven themes each containing multiple layers.

Users are advised to read the technical documentation including the product change notices and the individual product descriptions before downloading and using the product.

Please note this dataset is the most recent version of the Administrative Boundaries (AB). For previous versions of the AB please go to this url: https://data.gov.au/dataset/ds-dga-b4ad5702-ea2b-4f04-833c-d0229bfd689e/details?q=previous

License Information

Last update: August 20, 2024OverviewThis point data was generated and filtered from OpenStreetMap and is intended to represent places of interest in the state of Utah. These may include businesses, restaurants, places of worship, airports, parks, schools, event centers, apartment complexes, hotels, car dealerships…almost anything that you can find in OpenStreetMap (OSM). There are over 23,000 features in the original dataset (March 2022) and users can directly contribute to it through openstreetmap.org. This data is updated approximately once every month and will likely continue to grow over time with user activity.Data SourcesThe original bulk set of OSM data for the state of Utah is downloaded from Geofabrik: https://download.geofabrik.de/north-america/us/utah-latest-free.shp.zipAdditional attributes for the Utah features are gathered via the Overpass API using the following query: https://overpass-turbo.eu/s/1geRData Creation ProcessThe Open Source Places layer is created by a Python script that pulls statewide OSM data from a nightly archive provided by Geofabrik (https://www.geofabrik.de/data/download.html). The archive data contains nearly 20 shapefiles, some that are relevant to this dataset and some that aren't. The Open Source Places layer is built by filtering the polygon and point data in those shapefiles down to a single point feature class with specific categories and attributes that UGRC determines would be of widest interest. The polygon features (buildings, areas, complexes, etc.) are converted to points using an internal centroid. Spatial filtering is done as the data from multiple shapefiles is combined into a single layer to minimize the occurrence of duplicate features. (For example, a restaurant can be represented in OSM as both a point of interest and as a building polygon. The spatial filtering helps reduce the chances that both of these features are present in the final dataset.) Additional de-duplication is performed by using the 'block_id' field as a spatial index, to ensure that no two features of the same name exist within a census block. Then, additional fields are created and assigned from UGRC's SGID data (county, city, zip, nearby address, etc.) via point-in-polygon and near analyses. A numeric check is done on the 'name' field to remove features where the name is less than 3 characters long or more than 50% numeric characters. This eliminates several features derived from the buildings layer where the 'name' is simply an apartment complex building number (ex: 3A) or house number (ex: 1612). Finally, additional attributes (osm_addr, opening_hours, phone, website, cuisine, etc.) are pulled from the Overpass API (https://wiki.openstreetmap.org/wiki/Overpass_API) and joined to the filtered data using the 'osm_id' field as the join key.Field Descriptionsaddr_dist - the distance (m) to the nearest UGRC address point within 25 mosm_id - the feature ID in the OSM databasecategory - the feature's data class based on the 4-digit code and tags in the OSM databasename - the name of the feature in the OSM databasecounty - the county the feature is located in (assigned from UGRC's county boundaries)city - the city the feature is located in (assigned from UGRC's municipal boundaries)zip - the zip code of the feature (assigned from UGRC's approximation of zip code boundaries)block_id - the census block the feature is located in (assigned from UGRC's census block boundaries)ugrc_addr - the nearest address (within 25 m) from the UGRC address point databasedisclaimer - a note from UGRC about the ugrc_near_addr fieldlon - the approximate longitude of the feature, calculated in WGS84 EPSG:4326lat - the approximate latitude of the feature, calculated in WGS84 EPSG:4326amenity - the amenity available at the feature (if applicable), often similar to the categorycuisine - the type of food available (if applicable), multiple types are separated by semicolons (;)tourism - the type of tourist location, if applicable (zoo, viewpoint, hotel, attraction, etc.)shop - the type of shop, if applicablewebsite - the feature's website in the OSM database, if availablephone - the feature's phone number(s) in the OSM database, if availableopen_hours - the feature's operating hours in the OSM database, if availableosm_addr - the feature's address in the OSM database, if availableMore information can be found on the UGRC data page for this layer:https://gis.utah.gov/data/society/open-source-places/

The Northern Canada geodatabase contains a selection of the data from the Atlas of Canada Reference Map - Northern Canada / Nord du Canada (MCR 36). The geodatabase is comprised of two feature data sets (annotation and geometry), and the shaded relief. The annotation feature dataset comprises the annotation feature classes. All annotation feature classes were derived for MCR 36 and all text placements are based on the font type and size used for the reference map. The geometry feature dataset is comprised of data for: boundaries, roads, railways, airports, seaplane bases, ports, populated places, rivers, lakes, mines, oil/natural gas fields, hydroelectric generating stations, federal protected areas, ice shelves, permanent polar sea ice limit and the treeline. The geodatabase can be downloaded as feature data sets or as shapefiles.

Shapefile for 492 Coastal Zone Management Program (CZMP) counties and county equivalents, 2009, extracted from the U.S. Census Bureau's MAF/TIGER database of U.S. counties and cross-referenced to a list of CZMP counties published by the NOAA/NOS Office of Ocean and Coastal Resource Management (OCRM). Data extent to the nearest quarter degree is 141.00 E to 64.50 W longitude and 14.75 S to 71.50 N latitude. TL2009 in this document refers to metadata content inherited from the original U.S. Census Bureau (2009) TIGER/Line shapefile. TL2009: The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent dataset or the shapefiles can be combined to cover the whole nation.

Facilities and features in Chicago parks. For more information, visit http://www.chicagoparkdistrict.com/facilities/search/. To view or use these shapefiles, compression software and special GIS software, such as ESRI ArcGIS or QGIS, is required. To download this file, right-click the "Download" link above and choose "Save link as."

Routes, Stops, Park & Rides, ADA Polygons, and Service Area for the January 2025 Service Period.

GIS data: This data set consists of 6 classes of zoning features: zoning districts, special purpose districts, special purpose district subdistricts, limited height districts, commercial overlay districts, and zoning map amendments.

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

Updates for this dataset, along with other multilayered maps on NYC Open Data, are temporarily paused while they are moved to a new mapping format. Please visit https://www.nyc.gov/site/planning/data-maps/open-data/dwn-gis-zoning.page to utilize this data in the meantime

TRCA GIS Open data on ArcGIS online. This link will take you to an external site URL: https://trca-camaps.opendata.arcgis.com/

These data present geologic map units for the United States (Horton and others, 2017; Wilson and others, 2015) and Australia (Raymond and others, 2012) reclassified to 31 generalized sub-type lithologic groups of igneous, metamorphic, and sedimentary rocks (Lawley and others, 2022). These generalized classifications are based on interpretation of map unit descriptions in the different map compilations. Given that map unit descriptions often contain multiple rock types, there were subjective calls necessary when assigning generalized lithologic classification. The data were developed as part of the tri-national Critical Minerals Mapping Initiative (Kelley, 2020) between the United States, Canada, and Australia, an effort to model and map prospectivity for basin-hosted Pb-Zn mineralization. A national-scale geologic map compilation for Canada is not publicly available. Therefore, Lawley and others (2021) compiled geologic source maps to produce a gridded model layer that is provided in this data release in the Child Items section “Gridded geology shapefiles for the United States, Canada, and Australia.” References Horton, J.D., San Juan, C.A., and Stoeser, D.B., 2017, The State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States (ver. 1.1, August 2017): U.S. Geological Survey Data Series 1052, 46 p., https://doi.org/10.3133/ds1052. Kelley, K.D., 2020, International geoscience collaboration to support critical mineral discovery: U.S. Geological Survey Fact Sheet 2020-3035, 2 p., https://doi.org/10.3133/fs20203035. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Gadd, M.G., Huston, D.L., Kelley, K.D., Paradis, S., Peter, J.M., and Czarnota, K., 2021, Datasets to support prospectivity modelling for sediment-hosted Zn-Pb mineral systems: Natural Resources Canada Open File 8836, https://doi.org/10.4095/329203. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635. Raymond, O.L., Liu, S., Gallagher, R., Zhang, W., and Highet, L.M., 2012, Surface Geology of Australia 1:1 million scale dataset 2012 edition: Geoscience Australia, http://pid.geoscience.gov.au/dataset/ga/74619. Wilson, F.H., Hults, C.P., Mull, C.G., and Karl, S.M., comps., 2015, Geologic map of Alaska: U.S. Geological Survey Scientific Investigations Map 3340, 2 sheets, scale 1:1,584,000, 196-p. pamphlet, https://doi.org/10.3133/sim3340.

The Ontario government, generates and maintains thousands of datasets. Since 2012, we have shared data with Ontarians via a data catalogue. Open data is data that is shared with the public. Click here to learn more about open data and why Ontario releases it. Ontario’s Open Data Directive states that all data must be open, unless there is good reason for it to remain confidential. Ontario’s Chief Digital and Data Officer also has the authority to make certain datasets available publicly. Datasets listed in the catalogue that are not open will have one of the following labels: If you want to use data you find in the catalogue, that data must have a licence – a set of rules that describes how you can use it. A licence: Most of the data available in the catalogue is released under Ontario’s Open Government Licence. However, each dataset may be shared with the public under other kinds of licences or no licence at all. If a dataset doesn’t have a licence, you don’t have the right to use the data. If you have questions about how you can use a specific dataset, please contact us. The Ontario Data Catalogue endeavors to publish open data in a machine readable format. For machine readable datasets, you can simply retrieve the file you need using the file URL. The Ontario Data Catalogue is built on CKAN, which means the catalogue has the following features you can use when building applications. APIs (Application programming interfaces) let software applications communicate directly with each other. If you are using the catalogue in a software application, you might want to extract data from the catalogue through the catalogue API. Note: All Datastore API requests to the Ontario Data Catalogue must be made server-side. The catalogue's collection of dataset metadata (and dataset files) is searchable through the CKAN API. The Ontario Data Catalogue has more than just CKAN's documented search fields. You can also search these custom fields. You can also use the CKAN API to retrieve metadata about a particular dataset and check for updated files. Read the complete documentation for CKAN's API. Some of the open data in the Ontario Data Catalogue is available through the Datastore API. You can also search and access the machine-readable open data that is available in the catalogue. How to use the API feature: Read the complete documentation for CKAN's Datastore API. The Ontario Data Catalogue contains a record for each dataset that the Government of Ontario possesses. Some of these datasets will be available to you as open data. Others will not be available to you. This is because the Government of Ontario is unable to share data that would break the law or put someone's safety at risk. You can search for a dataset with a word that might describe a dataset or topic. Use words like “taxes” or “hospital locations” to discover what datasets the catalogue contains. You can search for a dataset from 3 spots on the catalogue: the homepage, the dataset search page, or the menu bar available across the catalogue. On the dataset search page, you can also filter your search results. You can select filters on the left hand side of the page to limit your search for datasets with your favourite file format, datasets that are updated weekly, datasets released by a particular organization, or datasets that are released under a specific licence. Go to the dataset search page to see the filters that are available to make your search easier. You can also do a quick search by selecting one of the catalogue’s categories on the homepage. These categories can help you see the types of data we have on key topic areas. When you find the dataset you are looking for, click on it to go to the dataset record. Each dataset record will tell you whether the data is available, and, if so, tell you about the data available. An open dataset might contain several data files. These files might represent different periods of time, different sub-sets of the dataset, different regions, language translations, or other breakdowns. You can select a file and either download it or preview it. Make sure to read the licence agreement to make sure you have permission to use it the way you want. Read more about previewing data. A non-open dataset may be not available for many reasons. Read more about non-open data. Read more about restricted data. Data that is non-open may still be subject to freedom of information requests. The catalogue has tools that enable all users to visualize the data in the catalogue without leaving the catalogue – no additional software needed. Have a look at our walk-through of how to make a chart in the catalogue. Get automatic notifications when datasets are updated. You can choose to get notifications for individual datasets, an organization’s datasets or the full catalogue. You don’t have to provide and personal information – just subscribe to our feeds using any feed reader you like using the corresponding notification web addresses. Copy those addresses and paste them into your reader. Your feed reader will let you know when the catalogue has been updated. The catalogue provides open data in several file formats (e.g., spreadsheets, geospatial data, etc). Learn about each format and how you can access and use the data each file contains. A file that has a list of items and values separated by commas without formatting (e.g. colours, italics, etc.) or extra visual features. This format provides just the data that you would display in a table. XLSX (Excel) files may be converted to CSV so they can be opened in a text editor. How to access the data: Open with any spreadsheet software application (e.g., Open Office Calc, Microsoft Excel) or text editor. Note: This format is considered machine-readable, it can be easily processed and used by a computer. Files that have visual formatting (e.g. bolded headers and colour-coded rows) can be hard for machines to understand, these elements make a file more human-readable and less machine-readable. A file that provides information without formatted text or extra visual features that may not follow a pattern of separated values like a CSV. How to access the data: Open with any word processor or text editor available on your device (e.g., Microsoft Word, Notepad). A spreadsheet file that may also include charts, graphs, and formatting. How to access the data: Open with a spreadsheet software application that supports this format (e.g., Open Office Calc, Microsoft Excel). Data can be converted to a CSV for a non-proprietary format of the same data without formatted text or extra visual features. A shapefile provides geographic information that can be used to create a map or perform geospatial analysis based on location, points/lines and other data about the shape and features of the area. It includes required files (.shp, .shx, .dbt) and might include corresponding files (e.g., .prj). How to access the data: Open with a geographic information system (GIS) software program (e.g., QGIS). A package of files and folders. The package can contain any number of different file types. How to access the data: Open with an unzipping software application (e.g., WinZIP, 7Zip). Note: If a ZIP file contains .shp, .shx, and .dbt file types, it is an ArcGIS ZIP: a package of shapefiles which provide information to create maps or perform geospatial analysis that can be opened with ArcGIS (a geographic information system software program). A file that provides information related to a geographic area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open using a GIS software application to create a map or do geospatial analysis. It can also be opened with a text editor to view raw information. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format for sharing data in a machine-readable way that can store data with more unconventional structures such as complex lists. How to access the data: Open with any text editor (e.g., Notepad) or access through a browser. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format to store and organize data in a machine-readable way that can store data with more unconventional structures (not just data organized in tables). How to access the data: Open with any text editor (e.g., Notepad). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A file that provides information related to an area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open with a geospatial software application that supports the KML format (e.g., Google Earth). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. This format contains files with data from tables used for statistical analysis and data visualization of Statistics Canada census data. How to access the data: Open with the Beyond 20/20 application. A database which links and combines data from different files or applications (including HTML, XML, Excel, etc.). The database file can be converted to a CSV/TXT to make the data machine-readable, but human-readable formatting will be lost. How to access the data: Open with Microsoft Office Access (a database management system used to develop application software). A file that keeps the original layout and