This file, part of a data collection effort carried out annually from 1968-1974 to look at issues of school desegregation, contains selected school district-level racial and ethnic data about students and staff for the academic year 1974-1975. The data were collected using OCR Form OS/CR 101. Each district record for each separate year of the series is identical, containing fields for all district data elements surveyed in every year. Where a particular item was not surveyed for a specific year, its corresponding field is zero (for numeric fields) or blank (for alphanumeric fields). Counts of students in various racial and ethnic groups are provided and then further categorized across additional dimensions, including whether resident or non-resident, emotionally disturbed, physically or learning disabled, or requiring special education. Other categories include school-age children in public and non-public schools or not in school, dropouts, and those expelled or suspended. Racial and ethnic counts of full-time classroom teachers and full-time instructional staff are also supplied. Other variables focus on the number of schools in the district that used ability grouping, whether a district had single-sex schools, whether students of different sexes were required to take different courses, the number of students whose language was not English, whether bilingual instruction was used, the number of schools being newly built or modified to increase capacity, the racial composition of new schools, and whether there was litigation. Some computed data were included on the 1974 district file which were not on the district files for years 1968-1973.

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated school district boundary composite files that include public elementary, secondary, and unified school district boundaries clipped to the U.S. shoreline. School districts are special-purpose governments and administrative units designed by state and local officials to provide public education for local residents. District boundaries are collected for NCES by the U.S. Census Bureau to develop demographic estimates and to support educational research and program administration. The NCES Common Core of Data (CCD) program is an annual collection of basic administrative characteristics for all public schools, school districts, and state education agencies in the United States. These characteristics are reported by state education officials and include directory information, number of students, number of teachers, grade span, and other conditions. The administrative attributes in this layer were developed from the most current CCD collection available. For more information about NCES school district boundaries, see: https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries. For more information about CCD school district attributes, see: https://nces.ed.gov/ccd/files.asp.Notes:-1 or MIndicates that the data are missing.-2 or NIndicates that the data are not applicable.-9Indicates that the data do not meet NCES data quality standards.Collections are available for the following years:2021-222020-212019-202018-192017-18All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

Database is provided by ASL Marketing and covers the United States of America. With ASL Marketing Reaching GenZ has never been easier. Current high school student data customized by: Class year Date of Birth Gender GPA Geo Household Income Ethnicity Hobbies College-bound Interests College Intent Email

School Districts data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain three area geographic files of boundaries for elementary school districts, secondary school districts, and unifed school districts, each with associated Census and American Community Survey demographic data.

The National Center for Education Statistics' (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for public elementary and secondary schools included in the NCES Common Core of Data (CCD). The CCD program annually collects administrative and fiscal data about all public schools, school districts, and state education agencies in the United States. The data are supplied by state education agency officials and include basic directory and contact information for schools and school districts, as well as characteristics about student demographics, number of teachers, school grade span, and various other administrative conditions. CCD school and agency point locations are derived from reported information about the physical location of schools and agency administrative offices. The point locations and administrative attributes in this data layer represent the most current CCD collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations. For more information about these CCD attributes, as well as additional attributes not included, see: https://nces.ed.gov/ccd/files.asp.Notes:-1 or MIndicates that the data are missing.-2 or NIndicates that the data are not applicable.-9Indicates that the data do not meet NCES data quality standards.Collections are available for the following years:2022-232021-222020-212019-202018-192017-18All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data. Collections are available for the following years:

CSCD defines the entire school campus of all public schools to allow spatial analysis, including the full extent of lands used for public education in California. CSCD is suitable for a wide range of planning, assessment, analysis, and display purposes.The lands in CSCD are defined by the parcels owned, rented, leased, or used by a public California school district for the primary purpose of educating youth. CSCD provides vetted polygons representing each public school in the state.Data layers include: K-12 schools, university lands, community college campusesFull documentation is available in the User ManualSuggested improvements welcome via MapCollaborator (TM)

United States US: School Enrollment: Primary: Male: % Net data was reported at 92.425 % in 2015. This records an increase from the previous number of 91.790 % for 2014. United States US: School Enrollment: Primary: Male: % Net data is updated yearly, averaging 93.015 % from Dec 1986 (Median) to 2015, with 25 observations. The data reached an all-time high of 98.628 % in 1991 and a record low of 90.756 % in 2004. United States US: School Enrollment: Primary: Male: % Net data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Education Statistics. Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

The file set is a freely downloadable aggregation of information about Australian schools. The individual files represent a series of tables which, when considered together, form a relational database. The records cover the years 2008-2014 and include information on approximately 9500 primary and secondary school main-campuses and around 500 subcampuses. The records all relate to school-level data; no data about individuals is included. All the information has previously been published and is publicly available but it has not previously been released as a documented, useful aggregation. The information includes: (a) the names of schools (b) staffing levels, including full-time and part-time teaching and non-teaching staff (c) student enrolments, including the number of boys and girls (d) school financial information, including Commonwealth government, state government, and private funding (e) test data, potentially for school years 3, 5, 7 and 9, relating to an Australian national testing programme know by the trademark 'NAPLAN'

Documentation of this Edition 2016.1 is incomplete but the organization of the data should be readily understandable to most people. If you are a researcher, the simplest way to study the data is to make use of the SQLite3 database called 'school-data-2016-1.db'. If you are unsure how to use an SQLite database, ask a guru.

The database was constructed directly from the other included files by running the following command at a command-line prompt: sqlite3 school-data-2016-1.db < school-data-2016-1.sql Note that a few, non-consequential, errors will be reported if you run this command yourself. The reason for the errors is that the SQLite database is created by importing a series of '.csv' files. Each of the .csv files contains a header line with the names of the variable relevant to each column. The information is useful for many statistical packages but it is not what SQLite expects, so it complains about the header. Despite the complaint, the database will be created correctly.

Briefly, the data are organized as follows. (a) The .csv files ('comma separated values') do not actually use a comma as the field delimiter. Instead, the vertical bar character '|' (ASCII Octal 174 Decimal 124 Hex 7C) is used. If you read the .csv files using Microsoft Excel, Open Office, or Libre Office, you will need to set the field-separator to be '|'. Check your software documentation to understand how to do this. (b) Each school-related record is indexed by an identifer called 'ageid'. The ageid uniquely identifies each school and consequently serves as the appropriate variable for JOIN-ing records in different data files. For example, the first school-related record after the header line in file 'students-headed-bar.csv' shows the ageid of the school as 40000. The relevant school name can be found by looking in the file 'ageidtoname-headed-bar.csv' to discover that the the ageid of 40000 corresponds to a school called 'Corpus Christi Catholic School'. (3) In addition to the variable 'ageid' each record is also identified by one or two 'year' variables. The most important purpose of a year identifier will be to indicate the year that is relevant to the record. For example, if one turn again to file 'students-headed-bar.csv', one sees that the first seven school-related records after the header line all relate to the school Corpus Christi Catholic School with ageid of 40000. The variable that identifies the important differences between these seven records is the variable 'studentyear'. 'studentyear' shows the year to which the student data refer. One can see, for example, that in 2008, there were a total of 410 students enrolled, of whom 185 were girls and 225 were boys (look at the variable names in the header line). (4) The variables relating to years are given different names in each of the different files ('studentsyear' in the file 'students-headed-bar.csv', 'financesummaryyear' in the file 'financesummary-headed-bar.csv'). Despite the different names, the year variables provide the second-level means for joining information acrosss files. For example, if you wanted to relate the enrolments at a school in each year to its financial state, you might wish to JOIN records using 'ageid' in the two files and, secondarily, matching 'studentsyear' with 'financialsummaryyear'. (5) The manipulation of the data is most readily done using the SQL language with the SQLite database but it can also be done in a variety of statistical packages. (6) It is our intention for Edition 2016-2 to create large 'flat' files suitable for use by non-researchers who want to view the data with spreadsheet software. The disadvantage of such 'flat' files is that they contain vast amounts of redundant information and might not display the data in the form that the user most wants it. (7) Geocoding of the schools is not available in this edition. (8) Some files, such as 'sector-headed-bar.csv' are not used in the creation of the database but are provided as a convenience for researchers who might wish to recode some of the data to remove redundancy. (9) A detailed example of a suitable SQLite query can be found in the file 'school-data-sqlite-example.sql'. The same query, used in the context of analyses done with the excellent, freely available R statistical package (http://www.r-project.org) can be seen in the file 'school-data-with-sqlite.R'.

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated school district boundary composite files that include public elementary, secondary, and unified school district boundaries clipped to the U.S. shoreline. School districts are special-purpose governments and administrative units designed by state and local officials to provide public education for local residents. District boundaries are collected for NCES by the U.S. Census Bureau to develop demographic estimates and to support educational research and program administration. The NCES Common Core of Data (CCD) program is an annual collection of basic administrative characteristics for all public schools, school districts, and state education agencies in the United States. These characteristics are reported by state education officials and include directory information, number of students, number of teachers, grade span, and other conditions. The administrative attributes in this layer were developed from the 2021-2022 CCD collection. For more information about NCES school district boundaries, see: https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries. For more information about CCD school district attributes, see: https://nces.ed.gov/ccd/files.asp. Notes: -1 or MIndicates that the data are missing. -2 or N Indicates that the data are not applicable. -9 Indicates that the data do not meet NCES data quality standards. All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

This report shares information about school performance, sets expectations for schools, and promotes school improvement. School Quality Report Educator Guides can be found here.

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.

CR: Gender Parity Index (GPI): Tertiary School Enrollment: Gross data was reported at 1.186 Ratio in 2019. This records an increase from the previous number of 1.181 Ratio for 2018. CR: Gender Parity Index (GPI): Tertiary School Enrollment: Gross data is updated yearly, averaging 1.211 Ratio from Dec 1970 (Median) to 2019, with 13 observations. The data reached an all-time high of 1.251 Ratio in 2015 and a record low of 0.790 Ratio in 1970. CR: Gender Parity Index (GPI): Tertiary School Enrollment: Gross data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Social: Education Statistics. Gender parity index for gross enrollment ratio in tertiary education is the ratio of women to men enrolled at tertiary level in public and private schools.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;

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Vietnam Grade School: Class: Primary data was reported at 280.000 Unit th in 2017. This records an increase from the previous number of 277.500 Unit th for 2016. Vietnam Grade School: Class: Primary data is updated yearly, averaging 281.200 Unit th from Sep 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 327.300 Unit th in 1998 and a record low of 265.100 Unit th in 2008. Vietnam Grade School: Class: Primary data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.G050: Education Statistics.

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SCHOOL PROFICIENCY INDEXSummaryThe school proficiency index uses school-level data on the performance of 4th grade students on state exams to describe which neighborhoods have high-performing elementary schools nearby and which are near lower performing elementary schools. The school proficiency index is a function of the percent of 4th grade students proficient in reading (r) and math (m) on state test scores for up to three schools (i=1,2,3) within 1.5 miles of the block-group centroid. S denotes 4th grade school enrollment:Elementary schools are linked with block-groups based on a geographic mapping of attendance area zones from School Attendance Boundary Information System (SABINS), where available, or within-district proximity matches of up to the three-closest schools within 1.5 miles. In cases with multiple school matches, an enrollment-weighted score is calculated following the equation above. Please note that in this version of the data (AFFHT0004), there is no school proficiency data for jurisdictions in Kansas, West Virginia, and Puerto Rico because no data was reported for jurisdictions in these states in the Great Schools 2013-14 dataset. InterpretationValues are percentile ranked and range from 0 to 100. The higher the score, the higher the school system quality is in a neighborhood. Data Source: Great Schools (proficiency data, 2015-16); Common Core of Data (4th grade school addresses and enrollment, 2015-16); Maponics (attendance boundaries, 2016).Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 7.

To learn more about the School Proficiency Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

ATTOM’s school attendance areas data provide a nationwide layer of boundaries that identify the geographic areas served by each regular public school. This product enables potential home buyers to easily identify the school attendance zones that their children would attend.

CCSD is a GIS data set that contains detailed outlines of the lands used by public schools for educational purposes. The campus boundaries of schools with kindergarten through 12th grade instruction are each accurately mapped at the assessor parcel level. CCSD is the first statewide database of this information and is available for use without restriction.

This dataset contains information on all projects funded under the School Facility Program. The data is provided by the Office of Public School Construction under the authority of the Department of General Services. As staff to the State Allocation Board (SAB), the Office of Public School Construction (OPSC) implements and administers the $42 billion voter-approved school facilities construction program, known as the School Facility Program.

BY USING THIS WEBSITE OR THE CONTENT THEREIN, YOU AGREE TO THE TERMS OF USE. A spatial representation of school district boundaries. This polygon feature class was initially derived from the 1998 Parcel polygon feature class, based on a common school district code as maintained in the Oakland County Land Records database. The key attribute is Name (the school district name).