34 datasets found
  1. Latin America and Caribbean - Utility Benchmarking Database

    • data.subak.org
    • datacatalog.worldbank.org
    • +1more
    csv, pdf
    Updated Feb 16, 2023
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    World Bank Group (2023). Latin America and Caribbean - Utility Benchmarking Database [Dataset]. https://data.subak.org/dataset/latin-america-and-caribbean-utility-benchmarking-database-2008
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    pdf, csvAvailable download formats
    Dataset updated
    Feb 16, 2023
    Dataset provided by
    World Bank Grouphttp://www.worldbank.org/
    World Bankhttp://worldbank.org/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Caribbean, Latin America
    Description

    Investments in infrastructure have been on the development agenda of Latin American and Caribbean (LCR) countries as they move towards economic and social progress. Investing in infrastructure is investing in human welfare by providing access to and quality basic infrastructure services. Improving the performance of the electricity sector is one such major infrastructure initiative and the focus of this benchmarking data. A key initiative for both public and private owned distribution utilities has been to upgrade their efficiency as well as to increase the coverage and quality of service. In order to accomplish this goal, this initiative serves as a clearing house for information regarding the country and utility level performance of electricity distribution sector. This initiative allows countries and utilities to benchmark their performance in relation to other comparator utilities and countries. In doing so, this benchmarking data contributes to the improvement of the electricity sector by filling in knowledge gaps for the identification of the best performers (and practices) of the region. This benchmarking database consists of detailed information of 25 countries and 249 utilities in the region. The data collected for this benchmarking project is representative of 88 percent of the electrification in the region. Through in-house and field data collection, consultants compiled data based on accomplishments in output, coverage, input, labor productivity, operating performance, the quality of service, prices, and ownership. By serving as a mirror of good performance, the report allows for a comparative analysis and the ranking of utilities and countries according to the indicators used to measure performance. Although significant efforts have been made to ensure data comparability and consistency across time and utilities, the World Bank and the ESMAP do not guarantee the accuracy of the data included in this work. Acknowledgement: This benchmarking database was prepared by a core team consisting of Luis Alberto Andres (Co-Task Team Leader), Jose Luis Guasch (Co-Task Team Leader), Julio A. Gonzalez, Georgeta Dragoiu, and Natalie Giannelli. The team was benefited by data contributions from Jordan Z. Schwartz (Senior Infrastructure Specialist, LCSTR), Lucio Monari (Lead Energy Economist, LCSEG), Katharina B. Gassner (Senior Economist, FEU), and Martin Rossi (consultant). Funding was provided by the Energy Sector Management Assistance Program (ESMAP) and the World Bank. Comments and suggestion are welcome by contacting Luis Andres (landres@worldbank.org)

  2. N

    Library LinkOut

    • datadiscovery.nlm.nih.gov
    • data.virginia.gov
    • +3more
    application/rdfxml +5
    Updated Feb 8, 2022
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    (2022). Library LinkOut [Dataset]. https://datadiscovery.nlm.nih.gov/d/526z-s65v
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    csv, xml, application/rssxml, json, tsv, application/rdfxmlAvailable download formats
    Dataset updated
    Feb 8, 2022
    Description

    LinkOut is a service that allows you to link directly from PubMed and other NCBI databases to a wide range of information and services beyond the NCBI systems. LinkOut aims to facilitate access to relevant online resources in order to extend, clarify, and supplement information found in NCBI databases.

    Third parties can link directly from PubMed and other Entrez database records to relevant Web-accessible resources beyond the Entrez system. Includes full-text publications, biological databases, consumer health information and research tools.

  3. d

    U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2020)

    • catalog.data.gov
    Updated Jun 15, 2024
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    National Renewable Energy Laboratory (NREL) (2024). U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2020) [Dataset]. https://catalog.data.gov/dataset/u-s-electric-utility-companies-and-rates-look-up-by-zipcode-2020
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    Dataset updated
    Jun 15, 2024
    Dataset provided by
    National Renewable Energy Laboratory (NREL)
    Area covered
    United States
    Description

    This dataset, compiled by NREL using data from ABB, the Velocity Suite and the U.S. Energy Information Administration dataset 861, provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not average rates per zip code), but not the detailed rate structure data found in the OpenEI U.S. Utility Rate Database.

  4. Utility Energy Registry Monthly County Energy Use: 2016-2021

    • data.ny.gov
    • gimi9.com
    • +1more
    application/rdfxml +5
    Updated May 25, 2023
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    New York State Energy Research and Development Authority (NYSERDA) (2023). Utility Energy Registry Monthly County Energy Use: 2016-2021 [Dataset]. https://data.ny.gov/Energy-Environment/Utility-Energy-Registry-Monthly-County-Energy-Use-/47km-hhvs
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    application/rssxml, csv, json, xml, tsv, application/rdfxmlAvailable download formats
    Dataset updated
    May 25, 2023
    Dataset provided by
    New York State Energy Research and Development Authorityhttps://www.nyserda.ny.gov/
    Authors
    New York State Energy Research and Development Authority (NYSERDA)
    Description

    The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale utility-reported energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315. The order requires utilities under its regulation to develop and report community energy use data to the UER.

    This dataset includes electricity and natural gas usage data reported at the county level level collected under a data protocol in effect between 2016 and 2021. Other UER datasets include energy use data reported at the city, town, and village, and ZIP code level. Data collected after 2021 were collected according to a modified protocol. Those data may be found at https://data.ny.gov/Energy-Environment/Utility-Energy-Registry-Monthly-County-Energy-Use-/46pe-aat9.

    Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld.

    The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and accelerate economic growth. reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

  5. U.S. public libraries providing education resources and databases 2011/12

    • statista.com
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    Statista, U.S. public libraries providing education resources and databases 2011/12 [Dataset]. https://www.statista.com/statistics/250774/us-public-libraries-providing-education-resources-and-databases/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Sep 2011 - Nov 2011
    Area covered
    United States
    Description

    The statistic depicts the assessment of U.S. public libraries regarding the importance of public libraries in the U.S. as a service provider for education resources and databases. 35.2 percent of the responding public libraries stated that the this (Education resources and databases for home schooling) service was one of the most important.

  6. N

    Database of Short Genetic Variations (dbSNP)

    • datadiscovery.nlm.nih.gov
    • data.virginia.gov
    • +2more
    application/rdfxml +5
    Updated Jun 17, 2021
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    (2021). Database of Short Genetic Variations (dbSNP) [Dataset]. https://datadiscovery.nlm.nih.gov/Molecular-biology-Genetics/Database-of-Short-Genetic-Variations-dbSNP-/x4yw-gnzq
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    json, tsv, csv, application/rdfxml, xml, application/rssxmlAvailable download formats
    Dataset updated
    Jun 17, 2021
    Description

    Database of Short Genetic Variations (dbSNP) contains human single nucleotide variations, microsatellites, and small-scale insertions and deletions along with publication, population frequency, molecular consequence, and genomic and RefSeq mapping information for both common variations and clinical mutations.

  7. USGS USWTDB - U.S. Wind Turbine Database

    • zenodo.org
    json, zip
    Updated Jan 31, 2025
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    Catalyst Cooperative; Catalyst Cooperative (2025). USGS USWTDB - U.S. Wind Turbine Database [Dataset]. http://doi.org/10.5281/zenodo.14783215
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    zip, jsonAvailable download formats
    Dataset updated
    Jan 31, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Catalyst Cooperative; Catalyst Cooperative
    Description

    The United States Wind Turbine Database (USWTDB) provides the locations of land-based and offshore wind turbines in the United States, corresponding wind project information, and turbine technical specifications. Wind turbine records are collected and compiled from various public and private sources, digitized and position-verified from aerial imagery, and quality checked. The USWTDB is available for download in a variety of tabular and geospatial file formats, to meet a range of user/software needs. Dynamic web services are available for users that wish to access the USWTDB as a Representational State Transfer Services (RESTful) web service. Archived from https://energy.usgs.gov/uswtdb/

    This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. It is organized into "https://specs.frictionlessdata.io/data-package/">Frictionless Data Packages. For additional information about this data and PUDL, see the following resources:

  8. N

    Database of Genomic Structural Variation (dbVar)

    • datadiscovery.nlm.nih.gov
    • healthdata.gov
    • +2more
    application/rdfxml +5
    Updated Jun 17, 2021
    + more versions
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    (2021). Database of Genomic Structural Variation (dbVar) [Dataset]. https://datadiscovery.nlm.nih.gov/Molecular-biology-Genetics/Database-of-Genomic-Structural-Variation-dbVar-/pubs-rzki
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    csv, application/rdfxml, tsv, json, xml, application/rssxmlAvailable download formats
    Dataset updated
    Jun 17, 2021
    Description

    Database of Genomic Structural Variation (dbVar) is NCBI's database of human genomic Structural Variation — large variants >50 bp including insertions, deletions, duplications, inversions, mobile elements, translocations, and complex variants.

  9. Hosting of applications, databases, and services globally 2023

    • statista.com
    Updated Feb 11, 2025
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    Statista (2025). Hosting of applications, databases, and services globally 2023 [Dataset]. https://www.statista.com/statistics/1450945/application-database-service-hosting/
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    Dataset updated
    Feb 11, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    Worldwide
    Description

    The most common location for developed applications, databases, and services in 2023 was in a cloud service, as reported by 48 percent of all survey respondents worldwide. Next in line was locally with a share of 45 percent.

  10. H

    The Municipal Drinking Water Database, 2000-2018 [United States]

    • dataverse.harvard.edu
    • search.dataone.org
    Updated Aug 2, 2023
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    The Municipal Drinking Water Database, 2000-2018 [United States] [Dataset]. https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DFB6NG
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 2, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Sara Hughes; Christine Kirchhoff; Katelynn Conedera; Mirit Friedman
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    Jan 1, 2000 - Dec 31, 2018
    Area covered
    Alabama, Louisiana, Washington, Vermont, Utah, Arkansas, District of Columbia, Georgia, Oregon, Minnesota
    Dataset funded by
    National Science Foundation
    Description

    Drinking water services in the U.S. are critical for public health and economic development but face technical, political, and administrative challenges. Understanding the root cause of these challenges and how to overcome them is hindered by the lack of integrative, comprehensive data about drinking water systems and the communities they serve. The Municipal Drinking Water Database (MDWD) fills a critical gap by combining financial, institutional, political, and system conditions of U.S. municipalities and their community water systems (CWS) to enable researchers and practitioners interested in viewing or tracking drinking water spending, the financial condition of city governments, or myriad demographic, political, institutional, and physical characteristics of U.S. cities and their drinking water systems to access the data quickly and easily. The MDWD focuses on municipally owned and operated CWS, which are ubiquitous and play a critical role in ensuring safe, affordable drinking water services for most Americans. They also offer important opportunities for understanding municipal government behavior and decision making. The MDWD is a unique dataset of municipal CWSs in the U.S. that includes information about their residents, their city governments, and their drinking water systems.

  11. Neighborhood Utility Permits

    • hub.arcgis.com
    • virginiaroads.org
    • +1more
    Updated Dec 5, 2024
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    Virginia Department of Transportation (2024). Neighborhood Utility Permits [Dataset]. https://hub.arcgis.com/maps/bef29386e24e43948ff38be9f87a5cd5
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    Dataset updated
    Dec 5, 2024
    Dataset provided by
    Virginia Department Of Transportation
    Authors
    Virginia Department of Transportation
    Area covered
    Description

    This map is related to HB – 143 § 33.2-280.2 (Effective January 1, 2025) Utility work database which stipulates The Virginia Department of Transportation map any permits issued to utility companies for work in a residential neighborhood . § 33.2-280.2. (Effective January 1, 2025) Utility work database.A. As used in this section:"Service connection" means any utility facility installed overhead or underground between a distribution main, pipelines, conduits, lines, wires, or other sources of supply and the premises of the individual customer."Utility work" means the construction, installation, removal, or substantial maintenance of a privately, publicly, or cooperatively owned line, facility, or system for producing, transmitting, or distributing telecommunications, cable television, electricity, gas, oil, petroleum products, water, steam, storm water not connected with highway drainage, or any other similar commodity, including any fire or police signal system. "Utility work" does not include emergency maintenance or repairs or any work directly related to any individual service connection or service drop.B. The Department shall establish and maintain a publicly accessible database and map of all utility work that has been approved by the Department and will occur within a highway right-of-way in a residential neighborhood, as specified by the utility. Such database shall include the location where such work will occur, the start date of such work, the projected end date of such work, the company administering such work, and any other relevant information; however, under no circumstances shall such database and map provide information on any (i) utility work within a right-of-way not maintained by the Department; (ii) critical utility infrastructure, as designated by the utility, that, upon disclosure, has the potential to jeopardize security or critical services, including Critical Energy Infrastructure Information and Controlled Unclassified Information; or (iii) information the permittee has designated as confidential. Such information shall be available in the database at least seven days prior to the start date of any such utility work and shall be deleted from such database 90 days after the completion of such work.

  12. Water Quality Portal

    • catalog.data.gov
    • agdatacommons.nal.usda.gov
    • +1more
    Updated Mar 30, 2024
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    Agricultural Research Service (2024). Water Quality Portal [Dataset]. https://catalog.data.gov/dataset/water-quality-portal-a4e85
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    Dataset updated
    Mar 30, 2024
    Dataset provided by
    Agricultural Research Servicehttps://www.ars.usda.gov/
    Description

    The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Water quality data can be downloaded in Excel, CSV, TSV, and KML formats. Fourteen site types are found in the WQP: aggregate groundwater use, aggregate surface water use, atmosphere, estuary, facility, glacier, lake, land, ocean, spring, stream, subsurface, well, and wetland. Water quality characteristic groups include physical conditions, chemical and bacteriological water analyses, chemical analyses of fish tissue, taxon abundance data, toxicity data, habitat assessment scores, and biological index scores, among others. Within these groups, thousands of water quality variables registered in the EPA Substance Registry Service (https://iaspub.epa.gov/sor_internet/registry/substreg/home/overview/home.do) and the Integrated Taxonomic Information System (https://www.itis.gov/) are represented. Across all site types, physical characteristics (e.g., temperature and water level) are the most common water quality result type in the system. The Water Quality Exchange data model (WQX; http://www.exchangenetwork.net/data-exchange/wqx/), initially developed by the Environmental Information Exchange Network, was adapted by EPA to support submission of water quality records to the EPA STORET Data Warehouse [USEPA, 2016], and has subsequently become the standard data model for the WQP. Contributing organizations: ACWI The Advisory Committee on Water Information (ACWI) represents the interests of water information users and professionals in advising the federal government on federal water information programs and their effectiveness in meeting the nation's water information needs. ARS The Agricultural Research Service (ARS) is the U.S. Department of Agriculture's chief in-house scientific research agency, whose job is finding solutions to agricultural problems that affect Americans every day, from field to table. ARS conducts research to develop and transfer solutions to agricultural problems of high national priority and provide information access and dissemination to, among other topics, enhance the natural resource base and the environment. Water quality data from STEWARDS, the primary database for the USDA/ARS Conservation Effects Assessment Project (CEAP) are ingested into WQP via a web service. EPA The Environmental Protection Agency (EPA) gathers and distributes water quality monitoring data collected by states, tribes, watershed groups, other federal agencies, volunteer groups, and universities through the Water Quality Exchange framework in the STORET Warehouse. NWQMC The National Water Quality Monitoring Council (NWQMC) provides a national forum for coordination of comparable and scientifically defensible methods and strategies to improve water quality monitoring, assessment, and reporting. It also promotes partnerships to foster collaboration, advance the science, and improve management within all elements of the water quality monitoring community. USGS The United States Geological Survey (USGS) investigates the occurrence, quantity, quality, distribution, and movement of surface waters and ground waters and disseminates the data to the public, state, and local governments, public and private utilities, and other federal agencies involved with managing the United States' water resources. Resources in this dataset:Resource Title: Website Pointer for Water Quality Portal. File Name: Web Page, url: https://www.waterqualitydata.us/ The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Links to Download Data, User Guide, Contributing Organizations, National coverage by state.

  13. w

    Distribution System Upgrade Unit Cost Database

    • data.wu.ac.at
    • data.amerigeoss.org
    docx, xlsx
    Updated Nov 30, 2017
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    Department of Energy (2017). Distribution System Upgrade Unit Cost Database [Dataset]. https://data.wu.ac.at/schema/data_gov/NjliZGM1MWEtNzZiYy00NzNiLWE3ZjgtNDIyZThlOTgxMTg2
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    xlsx, docxAvailable download formats
    Dataset updated
    Nov 30, 2017
    Dataset provided by
    Department of Energy
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    4b3ba9bd6f9db26effda8c8b59ead1871e5455c8
    Description

    This database contains unit cost information for different components that may be used to integrate distributed photovotaic (D-PV) systems onto distribution systems. Some of these upgrades and costs may also apply to integration of other distributed energy resources (DER). Which components are required, and how many of each, is system-specific and should be determined by analyzing the effects of distributed PV at a given penetration level on the circuit of interest in combination with engineering assessments on the efficacy of different solutions to increase the ability of the circuit to host additional PV as desired. The current state of the distribution system should always be considered in these types of analysis.

    The data in this database was collected from a variety of utilities, PV developers, technology vendors, and published research reports. Where possible, we have included information on the source of each data point and relevant notes. In some cases where data provided is sensitive or proprietary, we were not able to specify the source, but provide other information that may be useful to the user (e.g. year, location where equipment was installed). NREL has carefully reviewed these sources prior to inclusion in this database.

    Additional information about the database, data sources, and assumptions is included in the "Unit_cost_database_guide.doc" file included in this submission. This guide provides important information on what costs are included in each entry. Please refer to this guide before using the unit cost database for any purpose.

  14. O

    Austin 311 Public Data

    • data.austintexas.gov
    • datahub.austintexas.gov
    Updated Mar 26, 2025
    + more versions
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    City of Austin, Texas - data.austintexas.gov (2025). Austin 311 Public Data [Dataset]. https://data.austintexas.gov/Utilities-and-City-Services/Austin-311-Public-Data/xwdj-i9he
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    csv, xml, application/rdfxml, tsv, application/rssxml, application/geo+json, kmz, kmlAvailable download formats
    Dataset updated
    Mar 26, 2025
    Dataset authored and provided by
    City of Austin, Texas - data.austintexas.gov
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    Data collected from CSR production system.
    Data begins 01/03/2014 and is refreshed daily at 8:00am.

  15. V

    Database of Genotype and Phenotype (dbGaP)

    • data.virginia.gov
    • datadiscovery.nlm.nih.gov
    • +2more
    html
    Updated Nov 4, 2024
    + more versions
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    National Library of Medicine (2024). Database of Genotype and Phenotype (dbGaP) [Dataset]. https://data.virginia.gov/dataset/database-of-genotype-and-phenotype-dbgap
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    htmlAvailable download formats
    Dataset updated
    Nov 4, 2024
    Dataset provided by
    National Library of Medicine
    Description

    Database of Genotype and Phenotype (dbGaP) was developed to archive and distribute the data and results from studies that have investigated the interaction of genotype and phenotype in Humans.

  16. d

    Protected Areas Database of the United States (PAD-US) 4.0

    • catalog.data.gov
    • data.usgs.gov
    Updated Jul 20, 2024
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    U.S. Geological Survey (2024). Protected Areas Database of the United States (PAD-US) 4.0 [Dataset]. https://catalog.data.gov/dataset/protected-areas-database-of-the-united-states-pad-us-4-0
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    Dataset updated
    Jul 20, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    United States
    Description

    The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://ngda-cadastre-geoplatform.hub.arcgis.com/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g., 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. PAD-US provides a full inventory geodatabase, spatial analysis, statistics, data downloads, web services, poster maps, and data submissions included in efforts to track global progress toward biodiversity protection. PAD-US integrates spatial data to ensure public lands and other protected areas from all jurisdictions are represented. PAD-US version 4.0 includes new and updated data from the following data providers. All other data were transferred from previous versions of PAD-US. Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in regular PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. Revisions associated with the federal estate in this version include updates to the Federal estate (fee ownership parcels, easement interest, management designations, and proclamation boundaries), with authoritative data from 7 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), and the U.S. Forest Service (USFS). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/federal-lands-workgroup/ ). This includes improved the representation of boundaries and attributes for the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. Additionally, National Cemetery boundaries were added using geospatial boundary data provided by the U.S. Department of Veterans Affairs and NASA boundaries were added using data contained in the USGS National Boundary Dataset (NBD). State Updates - USGS is committed to building capacity in the state data steward network and the PAD-US Team to increase the frequency of state land and NGO partner updates, as resources allow. State Lands Workgroup ( https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/state-lands-workgroup ) is focused on improving protected land inventories in PAD-US, increase update efficiency, and facilitate local review. PAD-US 4.0 included updates and additions from the following seventeen states and territories: California (state, local, and nonprofit fee); Colorado (state, local, and nonprofit fee and easement); Georgia (state and local fee); Kentucky (state, local, and nonprofit fee and easement); Maine (state, local, and nonprofit fee and easement); Montana (state, local, and nonprofit fee); Nebraska (state fee); New Jersey (state, local, and nonprofit fee and easement); New York (state, local, and nonprofit fee and easement); North Carolina (state, local, and nonprofit fee); Pennsylvania (state, local, and nonprofit fee and easement); Puerto Rico (territory fee); Tennessee (land trust fee); Texas (state, local, and nonprofit fee); Virginia (state, local, and nonprofit fee); West Virginia (state, local, and nonprofit fee); and Wisconsin (state fee data). Additionally, the following datasets were incorporated from NGO data partners: Trust for Public Land (TPL) Parkserve (new fee and easement data); The Nature Conservancy (TNC) Lands (fee owned by TNC); TNC Northeast Secured Areas; Ducks Unlimited (land trust fee); and the National Conservation Easement Database (NCED). All state and NGO easement submissions are provided to NCED. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/programs/gap-analysis-project/science/protected-areas . For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/programs/gap-analysis-project/science/protected-areas . For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-history/ for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B 9) Revised - April 2024 (Version 4.0) https://doi.org/10.5066/P96WBCHS Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

  17. m

    Database Platform as a Service Market Size | Trend and Forecast to 2031

    • marketresearchintellect.com
    Updated Mar 16, 2025
    + more versions
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    Market Research Intellect (2025). Database Platform as a Service Market Size | Trend and Forecast to 2031 [Dataset]. https://www.marketresearchintellect.com/product/global-database-platform-as-a-service-market-size-and-forecast/
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    Dataset updated
    Mar 16, 2025
    Dataset authored and provided by
    Market Research Intellect
    License

    https://www.marketresearchintellect.com/privacy-policyhttps://www.marketresearchintellect.com/privacy-policy

    Area covered
    Global
    Description

    The size and share of the market is categorized based on Application (Small-sized Enterprises, Medium-sized Enterprise, Large Enterprises) and Product (Public Cloud Service, Private Service, Software) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

  18. m

    Database Platform as a Service (DBPaaS) Solutions Market Size Forecast

    • marketresearchintellect.com
    Updated Mar 15, 2025
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    Market Research Intellect (2025). Database Platform as a Service (DBPaaS) Solutions Market Size Forecast [Dataset]. https://www.marketresearchintellect.com/product/global-database-platform-as-a-service-dbpaas-solutions-market-size-and-forecast/
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    Dataset updated
    Mar 15, 2025
    Dataset authored and provided by
    Market Research Intellect
    License

    https://www.marketresearchintellect.com/privacy-policyhttps://www.marketresearchintellect.com/privacy-policy

    Area covered
    Global
    Description

    The size and share of the market is categorized based on Application (Large Enterprises(1000+ Users), Medium-sized Enterprise(499-1000 Users), Small Enterprises(1-499 Users)) and Product (Cloud-based, On-premises) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

  19. T

    Nuclear Medicine National Headquarter System

    • datahub.va.gov
    • data.va.gov
    • +6more
    application/rdfxml +5
    Updated Sep 12, 2019
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    (2019). Nuclear Medicine National Headquarter System [Dataset]. https://www.datahub.va.gov/dataset/Nuclear-Medicine-National-Headquarter-System/x6z5-25xw
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    csv, xml, application/rssxml, json, tsv, application/rdfxmlAvailable download formats
    Dataset updated
    Sep 12, 2019
    Description

    The Nuclear Medicine National HQ System database is a series of MS Excel spreadsheets and Access Database Tables by fiscal year. They consist of information from all Veterans Affairs Medical Centers (VAMCs) performing or contracting nuclear medicine services in Veterans Affairs medical facilities. The medical centers are required to complete questionnaires annually (RCS 10-0010-Nuclear Medicine Service Annual Report). The information is then manually entered into the Access Tables, which includes: * Distribution and cost of in-house VA - Contract Physician Services, whether contracted services are made via sharing agreement (with another VA medical facility or other government medical providers) or with private providers. * Workload data for the performance and/or purchase of PET/CT studies. * Organizational structure of services. * Updated changes in key imaging service personnel (chiefs, chief technicians, radiation safety officers). * Workload data on the number and type of studies (scans) performed, including Medicare Relative Value Units (RVUs), also referred to as Weighted Work Units (WWUs). WWUs are a workload measure calculated as the product of a study's Current Procedural Terminology (CPT) code, which consists of total work costs (the cost of physician medical expertise and time), and total practice costs (the costs of running a practice, such as equipment, supplies, salaries, utilities etc). Medicare combines WWUs together with one other parameter to derive RVUs, a workload measure widely used in the health care industry. WWUs allow Nuclear Medicine to account for the complexity of each study in assessing workload, that some studies are more time consuming and require higher levels of expertise. This gives a more accurate picture of workload; productivity etc than using just 'total studies' would yield. * A detailed Full-Time Equivalent Employee (FTEE) grid, and staffing distributions of FTEEs across nuclear medicine services. * Information on Radiation Safety Committees and Radiation Safety Officers (RSOs). Beginning in 2011 this will include data collection on part-time and non VA (contract) RSOs; other affiliations they may have and if so to whom they report (supervision) at their VA medical center.Collection of data on nuclear medicine services' progress in meeting the special needs of our female veterans. Revolving documentation of all major VA-owned gamma cameras (by type) and computer systems, their specifications and ages. * Revolving data collection for PET/CT cameras owned or leased by VA; and the numbers and types of PET/CT studies performed on VA patients whether produced on-site, via mobile PET/CT contract or from non-VA providers in the community.* Types of educational training/certification programs available at VA sites * Ongoing funded research projects by Nuclear Medicine (NM) staff, identified by source of funding and research purpose. * Data on physician-specific quality indicators at each nuclear medicine service.* Academic achievements by NM staff, including published books/chapters, journals and abstracts. * Information from polling field sites re: relevant issues and programs Headquarters needs to address. * Results of a Congressionally mandated contracted quality assessment exercise, also known as a Proficiency study. Study results are analyzed for comparison within VA facilities (for example by mission or size), and against participating private sector health care groups. * Information collected on current issues in nuclear medicine as they arise. Radiation Safety Committee structures and membership, Radiation Safety Officer information and information on how nuclear medicine services provided for female Veterans are examples of current issues.The database is now stored completely within MS Access Database Tables with output still presented in the form of Excel graphs and tables.

  20. f

    Methods for digitizing sewersheds.

    • figshare.com
    • plos.figshare.com
    xls
    Updated Jun 10, 2023
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    Dustin T. Hill; David A. Larsen (2023). Methods for digitizing sewersheds. [Dataset]. http://doi.org/10.1371/journal.pgph.0001062.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 10, 2023
    Dataset provided by
    PLOS Global Public Health
    Authors
    Dustin T. Hill; David A. Larsen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Methods for digitizing sewersheds.

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World Bank Group (2023). Latin America and Caribbean - Utility Benchmarking Database [Dataset]. https://data.subak.org/dataset/latin-america-and-caribbean-utility-benchmarking-database-2008
Organization logoOrganization logo

Latin America and Caribbean - Utility Benchmarking Database

Explore at:
pdf, csvAvailable download formats
Dataset updated
Feb 16, 2023
Dataset provided by
World Bank Grouphttp://www.worldbank.org/
World Bankhttp://worldbank.org/
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Caribbean, Latin America
Description

Investments in infrastructure have been on the development agenda of Latin American and Caribbean (LCR) countries as they move towards economic and social progress. Investing in infrastructure is investing in human welfare by providing access to and quality basic infrastructure services. Improving the performance of the electricity sector is one such major infrastructure initiative and the focus of this benchmarking data. A key initiative for both public and private owned distribution utilities has been to upgrade their efficiency as well as to increase the coverage and quality of service. In order to accomplish this goal, this initiative serves as a clearing house for information regarding the country and utility level performance of electricity distribution sector. This initiative allows countries and utilities to benchmark their performance in relation to other comparator utilities and countries. In doing so, this benchmarking data contributes to the improvement of the electricity sector by filling in knowledge gaps for the identification of the best performers (and practices) of the region. This benchmarking database consists of detailed information of 25 countries and 249 utilities in the region. The data collected for this benchmarking project is representative of 88 percent of the electrification in the region. Through in-house and field data collection, consultants compiled data based on accomplishments in output, coverage, input, labor productivity, operating performance, the quality of service, prices, and ownership. By serving as a mirror of good performance, the report allows for a comparative analysis and the ranking of utilities and countries according to the indicators used to measure performance. Although significant efforts have been made to ensure data comparability and consistency across time and utilities, the World Bank and the ESMAP do not guarantee the accuracy of the data included in this work. Acknowledgement: This benchmarking database was prepared by a core team consisting of Luis Alberto Andres (Co-Task Team Leader), Jose Luis Guasch (Co-Task Team Leader), Julio A. Gonzalez, Georgeta Dragoiu, and Natalie Giannelli. The team was benefited by data contributions from Jordan Z. Schwartz (Senior Infrastructure Specialist, LCSTR), Lucio Monari (Lead Energy Economist, LCSEG), Katharina B. Gassner (Senior Economist, FEU), and Martin Rossi (consultant). Funding was provided by the Energy Sector Management Assistance Program (ESMAP) and the World Bank. Comments and suggestion are welcome by contacting Luis Andres (landres@worldbank.org)

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