100+ datasets found
  1. d

    Middle School Overstaffing Query

    • data.gov.tw
    json
    Updated Apr 22, 2024
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    Bureau of Education, Tainan City Government (2024). Middle School Overstaffing Query [Dataset]. https://data.gov.tw/en/datasets/98803
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    jsonAvailable download formats
    Dataset updated
    Apr 22, 2024
    Dataset authored and provided by
    Bureau of Education, Tainan City Government
    License

    https://data.gov.tw/licensehttps://data.gov.tw/license

    Description

    This dataset provides excessive allocation data for junior high schools (Note: Only providing data for the current year and the previous year).

  2. n

    Public Schools

    • nconemap.gov
    • nccip.org
    • +4more
    Updated Aug 6, 2012
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    NC OneMap / State of North Carolina (2012). Public Schools [Dataset]. https://www.nconemap.gov/datasets/public-schools
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    Dataset updated
    Aug 6, 2012
    Dataset authored and provided by
    NC OneMap / State of North Carolina
    License

    https://www.nconemap.gov/pages/termshttps://www.nconemap.gov/pages/terms

    Area covered
    Description

    This dataset is an updated version of the 2008 dataset containing all public school locations in North Carolina. New schools were geocoded in ArcGIS 10 and merged with the existing dataset. Closed schools were removed,and existing attribute information was updated with current data. Some new fields were added, including designations for Pre-K, Elementary, Middle, High, and Early College schools. A review process was performed to move incorrect existing school points to new correct locations. Not all points were able to be individually reviewed,and may represent approximate school locations. This update was a collaborative effort among the Center for Geographic Information and Analysis, Institute for Transportation Research and Education, and NC Department of Public Instruction. Current school data was provided by NC DPI, using the Educational Directory and Demographical Information Exchange (EDDIE), found at http://apps.schools.nc.gov/eddie. ITRE used this dataset to conduct the update process, which included all steps of GIS analysis listed above. CGIA consulted on processing steps, lineage, and quality control to prepare the dataset for the NC OneMap Geospatial Portal.

  3. Legislative Lookup for California Schools and Districts

    • data-mountainview.opendata.arcgis.com
    • data.ca.gov
    • +1more
    Updated May 2, 2023
    + more versions
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    California Department of Education (2023). Legislative Lookup for California Schools and Districts [Dataset]. https://data-mountainview.opendata.arcgis.com/documents/358ec40eb1974adfb5f74215464899c6
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    Dataset updated
    May 2, 2023
    Dataset authored and provided by
    California Department of Educationhttps://www.cde.ca.gov/
    Area covered
    California
    Description

    This spreadsheet identifies the legislative districts assigned to California's K-12 public school sites and school district areas. Legislative districts assignments for schools are based on the geographic intersection of the legislative territories and school facility locations. Legislative district assignments for school districts are determined by overlaying, or combining, the geographic territories of both areas and looking at where they intersect.The schools in this report include all K-12 public schools from the California Department of Education's (CDE) public school directory that were open and had students enrolled on Fall Census Day in 2022 (first Wednesday in October). The school district boundaries are current as of Fall 2023. The state assembly, state senate and US congressional boundaries were approved by the California Citizens Redistricting Commission in 2021 following the completion of the 2020 United States Census and will be valid until the next decennial US Census in 2030.

  4. e

    Schools List

    • data.europa.eu
    • data.wu.ac.at
    csv, excel xlsx, html +2
    Updated Sep 12, 2020
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    Calderdale Metropolitan Borough Council (2020). Schools List [Dataset]. https://data.europa.eu/data/datasets/schools-list1?locale=bg
    Explore at:
    html, unknown, json, csv, excel xlsxAvailable download formats
    Dataset updated
    Sep 12, 2020
    Dataset authored and provided by
    Calderdale Metropolitan Borough Council
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Calderdale schools including status, type, principals / head teachers, address, website, number on roll as at May census, longitude and latitude, and eastings and northings. Routinely updated when notified of changes.

    You can also search online for school details, maps, performance tables and ofsted reports - Search for Schools

    Also see - School Clusters and Federations and Other schools data

  5. Census Data Explorer | USDA-FNS Farm to School Census

    • agdatacommons.nal.usda.gov
    bin
    Updated Feb 16, 2024
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    USDA Farm to School Program (2024). Census Data Explorer | USDA-FNS Farm to School Census [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/Census_Data_Explorer_USDA-FNS_Farm_to_School_Census/25234120
    Explore at:
    binAvailable download formats
    Dataset updated
    Feb 16, 2024
    Dataset provided by
    United States Department of Agriculturehttp://usda.gov/
    Food and Nutrition Servicehttps://www.fns.usda.gov/
    Authors
    USDA Farm to School Program
    License

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

    Description

    The Farm to School Census measures USDA's progress toward improving access to local foods in schools. The web-based interface allows users to run customized searches using data from the Farm to School Census. From a total of 18,104 public, private, and charter school districts in the target list frame, 12,585 schools and school districts completed usable responses for a response rate of 70%. Visualizations display national and state level data, and explanatory notes for each portion of the survey questionnaire are provided. Users can focus their search by location/state/school district/zip code, participation level, local food purchased category (fruit, vegetables, fluid milk, other dairy, meat/poultry, eggs, seafood, plant-based protein, grains/flour, baked goods, herbs), and sources (purchased directly or through intermediary). Resources in this dataset:Resource Title: Census Data Explorer | USDA-FNS Farm to School Census. File Name: Web Page, url: https://farmtoschoolcensus.fns.usda.gov/census-results/census-data-explorer This searchable database allows users to run customized searches using data from the Farm to School Census.

  6. College Map

    • catalog.data.gov
    • hub.arcgis.com
    Updated Jan 5, 2021
    + more versions
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    NCES (2021). College Map [Dataset]. https://catalog.data.gov/id/dataset/college-map-f9d60
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    Dataset updated
    Jan 5, 2021
    Dataset provided by
    National Center for Education Statisticshttps://nces.ed.gov/
    Description

    Finding Schools is now easier than ever with the College Map, the first geographic search tool published by IPEDS (Integrated Postsecondary Education Data System) providing access to over 7,000 certificate, undergraduate and graduate-level schools. This all-in-one tool enables students, parents and counselors to filter potential programs for location, major, tuition and more. Including both certificate-level programs and advanced degrees, this public application makes the often overwhelming process of school searching simple, and it’s available on mobile devices.Once the results are narrowed down, users can share their lists on social media or download in excel format. Additionally, the College Map integrates with the College Navigator, a research based search tool providing data from the complete list of IPEDS Survey indicators.

  7. e

    Dataset school search gent

    • data.europa.eu
    csv, esri shape +2
    Updated Mar 29, 2023
    + more versions
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    (2023). Dataset school search gent [Dataset]. https://data.europa.eu/data/datasets/918c476c-3fcb-32dc-a357-e440dec25a9c?locale=en
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    esri shape, json, csv, geojsonAvailable download formats
    Dataset updated
    Mar 29, 2023
    License

    http://data.gov.be/.well-known/genid/326c28fdfa9d4391b97f3d14655abddb131-335949b716ae2ccehttp://data.gov.be/.well-known/genid/326c28fdfa9d4391b97f3d14655abddb131-335949b716ae2cce

    http://data.gov.be/.well-known/genid/326c28fdfa9d4391b97f3d14655abddb131-335949b716ae2ccdhttp://data.gov.be/.well-known/genid/326c28fdfa9d4391b97f3d14655abddb131-335949b716ae2ccd

    http://data.gov.be/.well-known/genid/326c28fdfa9d4391b97f3d14655abddb131-335949b716ae2ccchttp://data.gov.be/.well-known/genid/326c28fdfa9d4391b97f3d14655abddb131-335949b716ae2ccc

    http://data.gov.be/.well-known/genid/326c28fdfa9d4391b97f3d14655abddb131-335949b716ae2ccfhttp://data.gov.be/.well-known/genid/326c28fdfa9d4391b97f3d14655abddb131-335949b716ae2ccf

    Description

    Urban education offers a wide range of training courses in general, technical, vocational and art education. This dataset contains all Ghent urban secondary schools.

  8. a

    School District Lookup

    • map-nebraska.hub.arcgis.com
    • nebraskamap.gov
    Updated Nov 4, 2019
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    State of Nebraska (2019). School District Lookup [Dataset]. https://map-nebraska.hub.arcgis.com/datasets/school-district-lookup
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    Dataset updated
    Nov 4, 2019
    Dataset authored and provided by
    State of Nebraska
    Description

    This application is a tool to find School Districts in Nebraska by County or Education Service Unit (ESU). Information for each School District includes: Address, District Name and Number, ESU Number, and Student Population. Search results may be filtered by School District Name and ESU. School District and ESU information are derived from Tax Unit Group (TUG) parcel data. The parcels used herein reflect County assessor data at the end of the 2018 calendar year.

  9. Independent special schools and post-16 institutions

    • gov.uk
    Updated Apr 30, 2025
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    Department for Education (2025). Independent special schools and post-16 institutions [Dataset]. https://www.gov.uk/government/publications/independent-special-schools-and-colleges
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    Dataset updated
    Apr 30, 2025
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department for Education
    Description

    A list of all independent schools and special post-16 institutions for children with special educational needs or disabilities (SEND) approved under section 41 of the Children and Families Act 2014 in England and Wales.

    You can filter the list by local authority or by type of setting.

    Our guide for independent special schools and special post-16 institutions explains how to apply for approval under section 41.

    Voluntary removal

    Contact hns.sos@education.gov.uk to request removal from the approved list, stating your reason. We will remove your institution in the next update and notify local authorities. The published list includes all removed institutions.

    Once removed, you cannot re-apply for one full academic year.

    Other special schools

    Details of all special schools in England are available on the https://www.get-information-schools.service.gov.uk/Search" class="govuk-link">Department for Education’s Get Information about Schools system. This includes:

    • maintained schools
    • academies
    • independent schools
    • non-maintained schools

    The SEND guide for parents and carers explains how parents can ask for one of these schools or special post-16 institutions to be named in their child’s education, health and care plan.

  10. d

    Replication data for: The High Returns to Private Schooling in a Low-Income...

    • search.dataone.org
    Updated Nov 21, 2023
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    Bold, Tessa; Kimenyi, Mwangi; Mwabu, Germano; Sandefur, Justin (2023). Replication data for: The High Returns to Private Schooling in a Low-Income Country [Dataset]. http://doi.org/10.7910/DVN/28145
    Explore at:
    Dataset updated
    Nov 21, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Bold, Tessa; Kimenyi, Mwangi; Mwabu, Germano; Sandefur, Justin
    Time period covered
    Jan 1, 1998 - Jan 1, 2005
    Description

    Existing studies from the United States, Latin America, and Asia provide scant evidence that private schools dramatically improve academic performance relative to public schools. Using data from Kenya—a poor country with weak public institutions—we find a large effect of private schooling on test scores, equivalent to one full standard deviation. This finding is robust to endogenous sorting of more able pupils into private schools. The magnitude of the effect dwarfs the impact of any rigorously tested intervention to raise performance within public schools. Furthermore, nearly two-thirds of private schools operate at lower cost than the median government school.

  11. g

    Common Core of Data: Public School Universe Data, 1986-1987 - Version 1

    • search.gesis.org
    Updated May 7, 2021
    + more versions
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    United States Department of Education. National Center for Education Statistics (2021). Common Core of Data: Public School Universe Data, 1986-1987 - Version 1 [Dataset]. http://doi.org/10.3886/ICPSR02426.v1
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    Dataset updated
    May 7, 2021
    Dataset provided by
    GESIS search
    ICPSR - Interuniversity Consortium for Political and Social Research
    Authors
    United States Department of Education. National Center for Education Statistics
    License

    https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de434761https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de434761

    Description

    Abstract (en): This dataset contains records for each public elementary and secondary school in the 50 states, the District of Columbia, United States territories (American Samoa, Guam, Puerto Rico, the Virgin Islands, and the Marshall Islands), and Department of Defense schools outside the United States for 1986-1987. Data were reported to the the National Center for Education Statistics by the state education agencies. Records in this file provide the name and address of the school, the name of the school district or other education agency that operates the school, a type code for the school, number of full-time equivalent classroom teachers, and membership/enrollment by grade. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. All public elementary and secondary education agencies in the 50 states, the District of Columbia, United States territories (American Samoa, Guam, Puerto Rico, the Virgin Islands, and the Marshall Islands), and Department of Defense schools outside the United States. The codebook is provided as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided through the ICPSR Website on the Internet.

  12. g

    Schools List | gimi9.com

    • gimi9.com
    Updated Sep 8, 2020
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    (2020). Schools List | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_schools-list1/
    Explore at:
    Dataset updated
    Sep 8, 2020
    Description

    Calderdale schools including status, type, principals / head teachers, address, website, number on roll as at May census, longitude and latitude, and eastings and northings. Routinely updated when notified of changes. You can also search online for school details, maps, performance tables and ofsted reports - Search for Schools Also see - School Clusters and Federations and Other schools data

  13. o

    School information and student demographics

    • data.ontario.ca
    • datasets.ai
    • +1more
    xlsx
    Updated May 22, 2025
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    Education (2025). School information and student demographics [Dataset]. https://data.ontario.ca/dataset/school-information-and-student-demographics
    Explore at:
    xlsx(1565910), xlsx(1550796), xlsx(1566878), xlsx(1565304), xlsx(1562805), xlsx(1459001), xlsx(1475787), xlsx(1462006), xlsx(1460629), xlsx(1547704), xlsx(1567330), xlsx(1580734), xlsx(1492217), xlsx(1462064)Available download formats
    Dataset updated
    May 22, 2025
    Dataset authored and provided by
    Education
    License

    https://www.ontario.ca/page/open-government-licence-ontariohttps://www.ontario.ca/page/open-government-licence-ontario

    Time period covered
    May 1, 2025
    Area covered
    Ontario
    Description

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

    How Are We Protecting Privacy?

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

      * Percentages depicted as 0 may not always be 0 values as in certain situations the values have been randomly rounded down or there are no reported results at a school for the respective indicator. * Percentages depicted as 100 are not always 100, in certain situations the values have been randomly rounded up.
    The school enrolment totals have been rounded to the nearest 5 in order to better protect and maintain student privacy.

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

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

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

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

  14. p

    Trends in Hispanic Student Percentage (2021-2023): Life Work/project Search...

    • publicschoolreview.com
    Updated Jun 4, 2025
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    Public School Review (2025). Trends in Hispanic Student Percentage (2021-2023): Life Work/project Search vs. Florida vs. St. Johns School District [Dataset]. https://www.publicschoolreview.com/life-work-project-search-profile
    Explore at:
    Dataset updated
    Jun 4, 2025
    Dataset authored and provided by
    Public School Review
    License

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

    Area covered
    St. Johns County School District, Florida
    Description

    This dataset tracks annual hispanic student percentage from 2021 to 2023 for Life Work/project Search vs. Florida and St. Johns School District

  15. p

    Trends in Asian Student Percentage (2021-2023): Life Work/project Search vs....

    • publicschoolreview.com
    Updated Jun 4, 2025
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    Public School Review (2025). Trends in Asian Student Percentage (2021-2023): Life Work/project Search vs. Florida vs. St. Johns School District [Dataset]. https://www.publicschoolreview.com/life-work-project-search-profile
    Explore at:
    Dataset updated
    Jun 4, 2025
    Dataset authored and provided by
    Public School Review
    License

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

    Area covered
    St. Johns County School District, Florida
    Description

    This dataset tracks annual asian student percentage from 2021 to 2023 for Life Work/project Search vs. Florida and St. Johns School District

  16. School District Data Book (SDDB), 1990: [United States] - Archival Version

    • search.gesis.org
    Updated Feb 26, 2021
    + more versions
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    United States Department of Education. National Center for Education Statistics (2021). School District Data Book (SDDB), 1990: [United States] - Archival Version [Dataset]. http://doi.org/10.3886/ICPSR02953
    Explore at:
    Dataset updated
    Feb 26, 2021
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    GESIS search
    Authors
    United States Department of Education. National Center for Education Statistics
    License

    https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de435696https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de435696

    Area covered
    United States
    Description

    Abstract (en): The School District Data Book (SDDB) is an education database and information system. It contains an extensive set of data on children, their households, and the nation's school systems. Under the sponsorship of the National Center for Education Statistics, the Bureau of the Census has produced special tabulation files using the basic record files of the 1990 Census of Population and Housing by school district. These tabulation files contain aggregated data describing attributes of children and households in school districts. Data are organized by seven types of tabulation records: (1) characteristics of all households, (2) characteristics of all persons, (3) characteristics of households with children, (4) characteristics of parents living with children, (5) children's household characteristics, (6) children's parents' characteristics, and (7) children's own characteristics. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. All public elementary and secondary education agencies in operation during 1990-1991 in the 50 states and the District of Columbia. 2006-10-27 Variable names were corrected in SAS and SPSS setup files. The processing note in the codebook was also updated to reflect the corrections.2006-01-12 All files were removed from dataset 139 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 138 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 137 and flagged as study-level files, so that they will accompany all downloads.2002-05-29 Seventeen additional datasets (Parts 140-156) were added to the collection, including data for two states previously not covered -- Vermont and Washington -- and additional data for Arkansas, California, Illinois, Massachusetts, Michigan, Minnesota, New Jersey, Pennsylvania, and Texas. (1) Some states have multiple data files because they have large numbers of cases. (2) Two data files are not included in this release. They are: Washington, Part 3, and Wisconsin, Part 4.

  17. School Learning Modalities, 2020-2021

    • healthdata.gov
    • data.virginia.gov
    • +3more
    application/rdfxml +5
    Updated Nov 1, 2022
    + more versions
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    Centers for Disease Control and Prevention (2022). School Learning Modalities, 2020-2021 [Dataset]. https://healthdata.gov/National/School-Learning-Modalities-2020-2021/a8v3-a3m3
    Explore at:
    application/rdfxml, tsv, csv, xml, json, application/rssxmlAvailable download formats
    Dataset updated
    Nov 1, 2022
    Dataset authored and provided by
    Centers for Disease Control and Prevention
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Description

    The 2020-2021 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2020-2021 school year, from August 2020 – June 2021.

    These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the https://nces.ed.gov/ccd/files.asp#Fiscal:2,LevelId:5,SchoolYearId:35,Page:1">National Center for Educational Statistics (NCES) for 2020-2021.

    School learning modality types are defined as follows:

      • In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels.
      • Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels.
      • Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students.

    Data Information

      • School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the location, number of schools and number of students in each district comes from NCES [21].
      • You can read more about the model in the CDC MMWR: https://www.cdc.gov/mmwr/volumes/70/wr/mm7039e2.htm" target="_blank">COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021.
      • The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes:
        • Public school district that is NOT a component of a supervisory union
        • Public school district that is a component of a supervisory union
        • Independent charter district
      • “BI” in the state column refers to school districts funded by the Bureau of Indian Education.

    Technical Notes

      • Data from September 1, 2020 to June 25, 2021 correspond to the 2020-2021 school year. During this timeframe, all four sources of data were available. Inferred modalities with a probability below 0.75 were deemed inconclusive and were omitted.
      • Data for the month of July may show “In Person” status although most school districts are effectively closed during this time for summer break. Users may wish to exclude July data from use for this reason where applicable.

    Sources

  18. c

    California Public Schools and Districts Map

    • gis.data.ca.gov
    • data.ca.gov
    • +2more
    Updated Oct 24, 2018
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    California Department of Education (2018). California Public Schools and Districts Map [Dataset]. https://gis.data.ca.gov/maps/169b581b560d4150b03ce84502fa5c72
    Explore at:
    Dataset updated
    Oct 24, 2018
    Dataset authored and provided by
    California Department of Education
    License

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

    Area covered
    Description

    This web map displays the California Department of Education's (CDE) core set of geographic data layers. This content represents the authoritative source for all statewide public school site locations and school district service areas boundaries for the 2018-19 academic year. The map also includes school and district layers enriched with student demographic and performance information from the California Department of Education's data collections. These data elements add meaningful statistical and descriptive information that can be visualized and analyzed on a map and used to advance education research or inform decision making.

  19. d

    A spatial database of public primary day schools in western Kenya

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 9, 2023
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    Peter M Macharia; Angela Moturi; Eda Mumo; Robert W Snow (2023). A spatial database of public primary day schools in western Kenya [Dataset]. http://doi.org/10.7910/DVN/5N8LW2
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    Dataset updated
    Nov 9, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Peter M Macharia; Angela Moturi; Eda Mumo; Robert W Snow
    Description

    Understanding the location of primary schools relative to population is important to contextualize the time, or distance, that students must travel and in defining school catchment areas for planning. However, such analyses are limited due to the perennial problem for absence of geocoded school databases. We, therefore, assembled existing school databases in western Kenya, merged and cleaned them to a unique list of 2170 public day primary school in 2009 and 4682 in 2020. We focused only on PPS managed by local authorities, community, Ministry of Education, non-governmental and religious organisations. These are more accessible by the general public since the introduction of free and compulsory primary education by the Kenyan government in 2003. We also excluded special schools catering for the deaf, blind, and neurologically impaired. The database was geocoded via Google Earth, OpenStreetMap and Geonames while ensuring no schools were located within protected areas or in water bodies by carefully rechecking the coordinates derived from online gazetteers.

  20. g

    Data from: The Implementation Of Curriculum Management And School-Based...

    • search.gesis.org
    • dataverse-staging.rdmc.unc.edu
    Updated Nov 3, 2017
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    Mustiningsih (2017). The Implementation Of Curriculum Management And School-Based Learning In Indonesian Elementary School [Dataset]. https://search.gesis.org/research_data/datasearch-httpsdataverse-unc-eduoai--doi10-15139S3NKVNUC
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    Dataset updated
    Nov 3, 2017
    Dataset provided by
    UNC Dataverse
    GESIS search
    Authors
    Mustiningsih
    License

    https://search.gesis.org/research_data/datasearch-httpsdataverse-unc-eduoai--doi10-15139S3NKVNUChttps://search.gesis.org/research_data/datasearch-httpsdataverse-unc-eduoai--doi10-15139S3NKVNUC

    Description

    Manajemen Berbasis Sekolah or School-based Management (SBM) in Indonesian elementary school is aimed at optimizing the quality of curriculum and learning management, because learning is the core activity in school. Development and improvement of SBM in Indonesian elementary school was implemented since 1999 and has been continuing until now. This study is generally aimed at describing the implementation of curriculum management and school-based learning in Indonesian elementary school. This study employs descriptive quantitative approach. The population of this study was all of the elementary schools in Indonesia which are spread into 34 provinces with three regions namely Western Indonesia Region (WIB), Central Indonesia Region (WITA) and Eastern Indonesia Region (WIT). The sample technique used in this study was purposive random sampling area. The samples of this study were 16 provinces, 32 districts/cities, and 96 elementary schools. The results show that the implementation of curriculum management and school-based learning in Indonesian elementary school is categorized as the excellent qualification. Of the 23 elements/aspects that become the scope of curriculum management and school-based learning in elementary school, all schools have excellent qualifications.

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Bureau of Education, Tainan City Government (2024). Middle School Overstaffing Query [Dataset]. https://data.gov.tw/en/datasets/98803

Middle School Overstaffing Query

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jsonAvailable download formats
Dataset updated
Apr 22, 2024
Dataset authored and provided by
Bureau of Education, Tainan City Government
License

https://data.gov.tw/licensehttps://data.gov.tw/license

Description

This dataset provides excessive allocation data for junior high schools (Note: Only providing data for the current year and the previous year).

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