Dataset from People's Association. For more information, visit https://data.gov.sg/datasets/d_ddae2233aec5ca47e1d485b54b37fd34/view

This is an auto-generated index table corresponding to a folder of files in this dataset with the same name. This table can be used to extract a subset of files based on their metadata, which can then be used for further analysis. You can view the contents of specific files by navigating to the "cells" tab and clicking on an individual file_kd.

Dataset from National Environment Agency. For more information, visit https://data.gov.sg/datasets/d_5ff6c8be30ee24f83975ffae670c6246/view

Downtown Austin Plan Districts boundaries used in the Austin Core Transportation (ACT) Plan

geodata data package providing geojson polygons for all the world's countries

The UNI-CEN Digital Boundary File Series facilitates the mapping of UNI-CEN census data tables. Boundaries are provided in multiple formats for different use cases: Esri Shapefile (SHP), geoJson, and File Geodatabase (FGDB). SHP and FGDB files are provided in two projections: NAD83 CSRS for print cartography and WGS84 for web applications. The geoJson version is provided in WGS84 only. The UNI-CEN Standardized Census Data Tables are readily merged to these boundary files. For more information about file sources, the methods used to create them, and how to use them, consult the documentation at https://borealisdata.ca/dataverse/unicen_docs. For more information about the project, visit https://observatory.uwo.ca/unicen.

Dataset from National Heritage Board. For more information, visit https://data.gov.sg/datasets/d_800f80592dc0b8eb0c784b1122dad45e/view

#Données of the 2022 Hydroclimatic Atlas ## #Description The Hydroclimatic Atlas describes the current and future water regime of southern Quebec in order to support the implementation of water management practices that are resilient to climate change. These data are from the most recent version of the Hydroclimatic Atlas. ## #Nouveautés * Improvement of the spatial resolution of the hydrographic network; * Greater spatial coverage; * Addition of the CliMEX and CORDEX-NA sets, in addition to the scenarios in the CMIP5 set; * Use of six hydrological platforms; * * Addition of indicators, especially annual ones. * Etc. ## #Liste data available * Link to the new Hydroclimatic Atlas website. * Map of the 24,604 river sections of the Hydroclimatic Atlas with their attributes, available in GeoJSON and shapefile format. To facilitate download and display, the map is divided into 11 GeoJSON files: ABIT (Abitibi and Lac Abitibi region), CND west (North Shore A and B regions), CND east (North Shore regions C, D and E), GASP (North Shore regions C, D and E), GASP (Gaspésie), MONT (Gaspesie), MONT (Montégérie), OUTM (Outaouais Upstream), OUTV (Outaouais Downstream), OUTV (Outaouais Downstream), SAGU (Saguenay), SLNO (St-Laurent Nord-Ouest), SLSO (St-Laurent Sud-Ouest), and VAUD (Vaudreuil). * The CSV tables (“Magnitude...”) for each of the 76 hydrological indicators describing the amplement, the direction and the dispersion for RCP 4.5 and RCP8.5, for the three future horizons (see the documentation for details). * The CSV tables (“Projected indicator...”) for each of the 76 hydrological indicators detailing the flow values with their uncertainty for the historical period and the three future horizons (RCP4.5 and 8.5). See the documentation for more details. * A PDF with the metadata and a more detailed description of the data. ## #Note The 2018 version data is archived on Data Quebec for reference, for example for old reports or analyses referring to this version of the data. Any new study or analysis should use the most recent data available below or on the Atlas website.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

asdsad. Visit https://dataone.org/datasets/sha256%3A61656d7542d69471b0afc990fbd01e9888c89728be44bf2eea6e997e36677a92 for complete metadata about this dataset.

Hand drawn Alabama state border in geojson polygon format. This resource was created to test NWM viewer app.

The UNI-CEN Digital Boundary File Series facilitates the mapping of UNI-CEN census data tables. Boundaries are provided in multiple formats for different use cases: Esri Shapefile (SHP), geoJson, and File Geodatabase (FGDB). SHP and FGDB files are provided in two projections: NAD83 CSRS for print cartography and WGS84 for web applications. The geoJson version is provided in WGS84 only. The UNI-CEN Standardized Census Data Tables are readily merged to these boundary files. For more information about file sources, the methods used to create them, and how to use them, consult the documentation at https://borealisdata.ca/dataverse/unicen_docs. For more information about the project, visit https://observatory.uwo.ca/unicen.

This dataset is of simplified geometries from COD live services deployed June 2019. Simplification methods applied from ESRI libraries using Python, Node.js and Mapshaper.js and based on adapted procedures for best outcomes preserving shape, topology and attributes. These data are not a substitute for the original COD data sets used in GIS applications. No warranties of any kind are made for any purpose and this dataset is offered as-is. Versions of topojson, kml and csv are also available. For a list of other simplified CODs see the address list: https://github.com/UGA-ITOSHumanitarianGIS/mapservicedoc/raw/master/Data/AWSDeploymentURLlist.xlsx

This is a GeoJSON version of this dataset- https://zenodo.org/deposit/4593518

This GeoJSON file is derived from the State Game Land (SGL) vector files provided by the state of Pennsylvania (http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=86). A 1 km buffer was added in QGIS.

For additional information, please see https://zenodo.org/deposit/4593788 .

Demo to save data from a Space to a Dataset. Goal is to provide reusable snippets of code.

Documentation: https://huggingface.co/docs/huggingface_hub/main/en/guides/upload#scheduled-uploads Space: https://huggingface.co/spaces/Wauplin/space_to_dataset_saver/ JSON dataset: https://huggingface.co/datasets/Wauplin/example-space-to-dataset-json Image dataset: https://huggingface.co/datasets/Wauplin/example-space-to-dataset-image Image (zipped) dataset:… See the full description on the dataset page: https://huggingface.co/datasets/Wauplin/example-space-to-dataset-json.

This dataset is of simplified geometries from COD live services deployed June 2019. Simplification methods applied from ESRI libraries using Python, Node.js and Mapshaper.js and based on adapted procedures for best outcomes preserving shape, topology and attributes. These data are not a substitute for the original COD data sets used in GIS applications. No warranties of any kind are made for any purpose and this dataset is offered as-is. Versions of topojson, kml and csv are also available. For a list of other simplified CODs see the address list: https://github.com/UGA-ITOSHumanitarianGIS/mapservicedoc/raw/master/Data/AWSDeploymentURLlist.xlsx

Dataset from Info-communications Media Development Authority. For more information, visit https://data.gov.sg/datasets/d_d8644084f8b54f851a1acbb2f04d5089/view

The AbrirCon extension for CKAN enhances data accessibility by enabling users to seamlessly open various resource types with external online applications like Plotly, Carto, and Geojson.io. This extension adds "Abrir con" links to resource pages, providing users with a direct way to visualize and interact with data using their preferred tools. By supporting a range of file formats, AbrirCon extends CKAN's utility for data exploration and analysis. Key Features: Plotly Integration: Allows users to open CSV, TSV, XLS, and XLSX files directly in Plotly for interactive data visualization. Carto Integration: Enables opening CSV, XLS, XLSX, KML, KMZ, GeoJSON, and SHP files in Carto for geospatial analysis and mapping. Geojson.io Integration: Facilitates opening GeoJSON files in Geojson.io for quick viewing and editing of geospatial data. Easy Installation: Simple installation process involving cloning the repository, installing the extension, and adding abrircon to the ckan.plugins configuration. Configuration Parameters: Requires configuration of specific parameters (not detailed in the Readme), likely to configure the integration with Plotly, Carto and Geojson.io (e.g. API keys or URLs). Technical Integration: The AbrirCon extension integrates with CKAN by adding itself to the ckan.plugins configuration, as described in the readme. This suggests that it likely modifies the resource view templates— specifically the resourceitemexplore block of the resource_item.html file — to insert the "Abrir con" links. When installing, the readme explicitly mentions the order of plugins in ckan.plugins being important, specifically that abrircon should precede any plugins which modify the resourceitemexplore block of resource_item.html. Benefits & Impact: The AbrirCon extension simplifies the process of visualizing and working with data stored in CKAN. By allowing users to quickly open resources in external applications, it reduces the need for manual downloading and uploading of files. This streamlined workflow enhances data exploration and analysis capabilities, making CKAN a more valuable tool for data users. The fact that several city councils contributed to the extension points to its value in the open data ecosystem.

Dataset includes various regional-scale spatial data layers in geojson format.

Nanoplankton from flow cytometry from R/V Endeavor cruise EN321 to the Gulf of Maine and Georges Bank in 1999 as part of the U.S. GLOBEC program. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=Nanoplankton from Flow Cytometry awards_0_award_nid=54610 awards_0_award_number=unknown GB NSF awards_0_funder_name=National Science Foundation awards_0_funding_acronym=NSF awards_0_funding_source_nid=350 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 awards_1_award_nid=54626 awards_1_award_number=unknown GB NOAA awards_1_funder_name=National Oceanic and Atmospheric Administration awards_1_funding_acronym=NOAA awards_1_funding_source_nid=352 cdm_data_type=Other comment=EN321fcmnano object upgraded w/ lat lon date and time Aug 31, 2005
parameter, lt2um, twoto10um and gt10um changed to cell_lt2um, cell_2to10um, cell_gt10um
Sept 13, 2006, gfh Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.2423.1 Easternmost_Easting=-66.5947 geospatial_lat_max=42.1853 geospatial_lat_min=42.0958 geospatial_lat_units=degrees_north geospatial_lon_max=-66.5947 geospatial_lon_min=-66.6032 geospatial_lon_units=degrees_east geospatial_vertical_max=351.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=down geospatial_vertical_units=m infoUrl=https://www.bco-dmo.org/dataset/2423 institution=BCO-DMO instruments_0_acronym=CTD Sea-Bird instruments_0_dataset_instrument_description=Sea Bird CTD, no specific unit identified. See also other SeaBird instruments listed under CTD. instruments_0_dataset_instrument_nid=4193 instruments_0_description=Conductivity, Temperature, Depth (CTD) sensor package from SeaBird Electronics, no specific unit identified. This instrument designation is used when specific make and model are not known. See also other SeaBird instruments listed under CTD. More information from Sea-Bird Electronics. instruments_0_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L05/current/130/ instruments_0_instrument_name=CTD Sea-Bird instruments_0_instrument_nid=447 instruments_0_supplied_name=SeabirdCTD metadata_source=https://www.bco-dmo.org/api/dataset/2423 Northernmost_Northing=42.1853 param_mapping={'2423': {'lat': 'master - latitude', 'depth': 'flag - depth', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/2423/parameters people_0_affiliation=Bigelow Laboratory for Ocean Sciences people_0_person_name=Michael E. Sieracki people_0_person_nid=50446 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Woods Hole Oceanographic Institution people_1_affiliation_acronym=WHOI BCO-DMO people_1_person_name=Ms Dicky Allison people_1_person_nid=50382 people_1_role=BCO-DMO Data Manager people_1_role_type=related project=GB projects_0_acronym=GB projects_0_description=The U.S. GLOBEC Georges Bank Program is a large multi- disciplinary multi-year oceanographic effort. The proximate goal is to understand the population dynamics of key species on the Bank - Cod, Haddock, and two species of zooplankton (Calanus finmarchicus and Pseudocalanus) - in terms of their coupling to the physical environment and in terms of their predators and prey. The ultimate goal is to be able to predict changes in the distribution and abundance of these species as a result of changes in their physical and biotic environment as well as to anticipate how their populations might respond to climate change. The effort is substantial, requiring broad-scale surveys of the entire Bank, and process studies which focus both on the links between the target species and their physical environment, and the determination of fundamental aspects of these species' life history (birth rates, growth rates, death rates, etc). Equally important are the modelling efforts that are ongoing which seek to provide realistic predictions of the flow field and which utilize the life history information to produce an integrated view of the dynamics of the populations. The U.S. GLOBEC Georges Bank Executive Committee (EXCO) provides program leadership and effective communication with the funding agencies. projects_0_geolocation=Georges Bank, Gulf of Maine, Northwest Atlantic Ocean projects_0_name=U.S. GLOBEC Georges Bank projects_0_project_nid=2037 projects_0_project_website=http://globec.whoi.edu/globec_program.html projects_0_start_date=1991-01 sourceUrl=(local files) Southernmost_Northing=42.0958 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-66.6032 xml_source=osprey2erddap.update_xml() v1.3

This layer shows youth (age 16-19) school enrollment and employment status. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Estimates here for 'disconnected youth' differ from estimates of 'idle youth' on Census Bureau's website because idle youth includes those unemployed (actively looking for work). This layer is symbolized by the count of total youth and the percentage of youth who were disconnected. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2012-2016ACS Table(s): B14005 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: October 16, 2018National Figures: American Fact FinderThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This dataset is updated automatically when the most current vintage of ACS data is released each year. The service contains the ACS data as of the current vintage listed. Tabular data is updated annually with the Census Bureau's release schedule. This may alter data values, fields, and boundaries. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.