Location of private elementary, middle, and high schools in Montgomery County. Public school locations can be downloaded from the public high school, middle school, and elementary school downloads.For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimates (EDGE) program develops bi-annually updated point locations (latitude and longitude) for private schools included in the NCES Private School Survey (PSS). The PSS is conducted to provide a biennial count of the total number of private schools, teachers, and students. The PSS school location and associated geographic area assignments are derived from reported information about the physical location of private schools. The school geocode file includes supplemental geographic information for the universe of schools reported in the most current PSS school collection, and they can be integrated with the survey files through use of institutional identifiers included in both sources. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations and https://nces.ed.gov/programs/edge/Geographic/LocaleBoundaries

Previous collections are available for the following years:

2021-22 2019-20 2017-18 2015-16

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.

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops bi-annually updated point locations (latitude and longitude) for private schools included in the NCES Private School Survey (PSS). The PSS is conducted to generate biennial data on the total number of private schools, teachers, and students, and to build an accurate and complete list of private schools to serve as a sampling frame for NCES surveys. The PSS school _location and associated geographic area assignments are derived from reported information about the physical _location of private schools. The school geocode file includes supplemental geographic information for the universe of schools reported in the 2017-2018 PSS school sample, and they can be integrated with the survey files through use of institutional identifiers included in both sources. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations and https://nces.ed.gov/programs/edge/Geographic/LocaleBoundaries.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.

2017 NYC School Survey parent data for all schools; Each year, all parents, teachers, and students in grades 6-12 take the NYC School Survey. The survey is aligned to the DOE's Framework for Great Schools. It is designed to collect important information about each school's ability to support student success. To understand the perceptions of families, students, and teachers regarding their school. School leaders use feedback from the survey to reflect and make improvements to schools and programs. Also, results from the survey used to help measure school quality.

KML file of public and private schools located in Chicago. To view or use these files, special GIS software such as Google Earth is required.

2017 NYC School Survey teacher data for all schools; To understand the perceptions of families, students, and teachers regarding their school. School leaders use feedback from the survey to reflect and make improvements to schools and programs. Also, results from the survey used to help measure school quality. Each year, all parents, teachers, and students in grades 6-12 take the NYC School Survey. The survey is aligned to the DOE's Framework for Great Schools. It is designed to collect important information about each school's ability to support student success.

Note: This version supersedes version 1: https://doi.org/10.15482/USDA.ADC/1522654. In Fall of 2019 the USDA Food and Nutrition Service (FNS) conducted the third Farm to School Census. The 2019 Census was sent via email to 18,832 school food authorities (SFAs) including all public, private, and charter SFAs, as well as residential care institutions, participating in the National School Lunch Program. The questionnaire collected data on local food purchasing, edible school gardens, other farm to school activities and policies, and evidence of economic and nutritional impacts of participating in farm to school activities. A total of 12,634 SFAs completed usable responses to the 2019 Census. Version 2 adds the weight variable, “nrweight”, which is the Non-response weight. Processing methods and equipment used The 2019 Census was administered solely via the web. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. This process involved examining the data for logical errors, contacting SFAs and consulting official records to update some implausible values, and setting the remaining implausible values to missing. The study team linked the 2019 Census data to information from the National Center of Education Statistics (NCES) Common Core of Data (CCD). Records from the CCD were used to construct a measure of urbanicity, which classifies the area in which schools are located. Study date(s) and duration Data collection occurred from September 9 to December 31, 2019. Questions asked about activities prior to, during and after SY 2018-19. The 2019 Census asked SFAs whether they currently participated in, had ever participated in or planned to participate in any of 30 farm to school activities. An SFA that participated in any of the defined activities in the 2018-19 school year received further questions. Study spatial scale (size of replicates and spatial scale of study area) Respondents to the survey included SFAs from all 50 States as well as American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and Washington, DC. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) No sampling was involved in the collection of this data. Level of subsampling (number and repeat or within-replicate sampling) No sampling was involved in the collection of this data. Study design (before–after, control–impacts, time series, before–after-control–impacts) None – Non-experimental Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains SFA-level responses to the Census questionnaire for SFAs that responded. This file includes information from only SFAs that clicked “Submit” on the questionnaire. (The dataset used to create the 2019 Farm to School Census Report includes additional SFAs that answered enough questions for their response to be considered usable.) In addition, the file contains constructed variables used for analytic purposes. The file does not include weights created to produce national estimates for the 2019 Farm to School Census Report. The dataset identified SFAs, but to protect individual privacy the file does not include any information for the individual who completed the questionnaire. Description of any gaps in the data or other limiting factors See the full 2019 Farm to School Census Report [https://www.fns.usda.gov/cfs/farm-school-census-and-comprehensive-review] for a detailed explanation of the study’s limitations. Outcome measurement methods and equipment used None Resources in this dataset:Resource Title: 2019 Farm to School Codebook with Weights. File Name: Codebook_Update_02SEP21.xlsxResource Description: 2019 Farm to School Codebook with WeightsResource Title: 2019 Farm to School Data with Weights CSV. File Name: census2019_public_use_with_weight.csvResource Description: 2019 Farm to School Data with Weights CSVResource Title: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets. File Name: Farm_to_School_Data_AgDataCommons_SAS_SPSS_R_STATA_with_weight.zipResource Description: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets

Private school (elementary, secondary, and combined*) enrolment numbers are organized by student gender and school level for each private school. The number captures the enrolment as of October 31st for the given school year. To be included, a student must be actively enrolled to attend the private school as their main school as of October 31. Data includes: * academic year * school number * school name * school level * elementary male enrolment * elementary female enrolment * secondary male enrolment * secondary female enrolment * total male enrolment * total female enrolment Source: As reported by private schools in the Ontario School Information System (OnSIS), October submission. Data includes private, First Nations, overseas, secondary and combined schools. *Combined schools offer both elementary and secondary education. Data does not include publicly funded elementary and secondary schools, hospital and provincial schools and care, treatment and correctional facilities. Small cells have been suppressed: * where fewer than 10 students are in a given category, the data is depicted with (<10) * suppressed totals are depicted with (SP) * the report may not be used in any way that could lead to the identification of an individual Note: * starting 2018-2019, enrolment numbers have been rounded to the nearest five. * where sum/totals are required, actual totals are calculated and then rounded to the nearest 5. As such, rounded numbers may not add up to the reported rounded totals.

The 2022 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. School Districts are single-purpose administrative units within which local officials provide public educational services for the area's residents. The Census Bureau obtains the boundaries, names, local education agency codes, grade ranges, and school district levels for school districts from state officials for the primary purpose of providing the U.S. Department of Education with estimates of the number of children in poverty within each school district. This information serves as the basis for the Department of Education to determine the annual allocation of Title I funding to states and school districts. The cartographic boundary files include separate files for elementary, secondary and unified school districts. The generalized school district boundaries in this file are based on those in effect for the 2021-2022 school year, i.e., in operation as of January 1, 2022.

The 2022 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. School Districts are single-purpose administrative units within which local officials provide public educational services for the area's residents. The Census Bureau obtains the boundaries, names, local education agency codes, grade ranges, and school district levels for school districts from state officials for the primary purpose of providing the U.S. Department of Education with estimates of the number of children in poverty within each school district. This information serves as the basis for the Department of Education to determine the annual allocation of Title I funding to states and school districts. The cartographic boundary files include separate files for elementary, secondary and unified school districts. The generalized school district boundaries in this file are based on those in effect for the 2021-2022 school year, i.e., in operation as of January 1, 2022.

NCES relies on information about school location to construct school-based surveys, support program administration, identify associations with other types of geographic entities, and to help investigate the social and spatial context of education. EDGE creates and assigns address geocodes (estimated latitude/latitude values) and other geographic indicators to public schools, public local education agencies, private schools, and postsecondary schools. The geographic data are provided for download as shapefiles and are also directly accessible as GIS web services.Geocodes for public schools are based on data reported in the NCES Common Core of Data (CCD), an annual collection of administrative data about enrollment, staffing, and program participation for schools, local education agencies (LEAs), and state education agencies (SEAs). SEAs report these data to the United States Department of Education in a series of file submissions throughout the year. Additional information about the CCD collection and data resources for public schools are available at https://nces.ed.gov/ccd/ccddata.asp.

This survey collected data from postsecondary institutions offering vocational education programs. This data file provides detailed information on the individual program offerings of the responding institutions, including enrollments and completions in various program categories and other outcomes. Information such as school name, city, ZIP code, sampling weight, and institutional and program characteristics are also contained in the file.

2016 NYC School Survey teacher data for all schools To understand the perceptions of families, students, and teachers regarding their school. School leaders use feedback from the survey to reflect and make improvements to schools and programs. Also, results from the survey used to help measure school quality. Each year, all parents, teachers, and students in grades 6-12 take the NYC School Survey. The survey is aligned to the DOE's Framework for Great Schools. It is designed to collect important information about each school's ability to support student success.

This is an ESRI shape file of school point locations based on the official address. It includes some additional basic and pertinent information needed to link to other data sources. It also includes some basic school information such as Name, Address, Principal, and Principal’s contact information.

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'.

Description of the experiment setting: location, influential climatic conditions, controlled conditions (e.g. temperature, light cycle)In Fall of 2023 the USDA Food and Nutrition Service (FNS) conducted the fourth Farm to School Census. The 2023 Census was sent via email to 18,833 school food authorities (SFAs) including all public, private, and charter SFAs, as well as residential care institutions, participating in the National School Lunch Program. The questionnaire collected data on local food purchasing, edible school gardens, other farm to school activities and policies, and outcomes and challenges of participating in farm to school activities. A total of 12,559 SFAs submitted a response to the 2023 Census.Processing methods and equipment usedThe 2023 Census was administered solely via the web. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. This process involved examining the data for logical errors and removing implausible values. The study team linked the 2023 Census data to information from the National Center of Education Statistics (NCES) Common Core of Data (CCD). Records from the CCD were used to construct a measure of urbanicity, which classifies the area in which schools are located.Study date(s) and durationData collection occurred from October 2, 2023 to January 7, 2024. Questions asked about activities prior to, during and after SY 2022-23. The 2023 Census asked SFAs whether they currently participated in, had ever participated in or planned to participate in any of 32 farm to school activities. Based on those answers, SFAs received a defined set of further questions.Study spatial scale (size of replicates and spatial scale of study area)Respondents to the survey included SFAs from all 50 States as well as American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and Washington, DC.Level of true replicationUnknownSampling precision (within-replicate sampling or pseudoreplication)No sampling was involved in the collection of this data.Level of subsampling (number and repeat or within-replicate sampling)No sampling was involved in the collection of this data.Study design (before–after, control–impacts, time series, before–after-control–impacts)None – Non-experimentalDescription of any data manipulation, modeling, or statistical analysis undertakenEach entry in the dataset contains SFA-level responses to the Census questionnaire for SFAs that responded. This file includes information from only SFAs that clicked “Submit” on the questionnaire. (The dataset used to create the 2023 Farm to School Census Report includes additional SFAs that answered enough questions for their response to be considered usable.)In addition, the file contains constructed variables used for analytic purposes. The file does not include weights created to produce national estimates for the 2023 Farm to School Census Report.The dataset identified SFAs, but to protect individual privacy the file does not include any information for the individual who completed the questionnaire. All responses to open-ended questions (i.e., containing user-supplied text) were also removed to protect privacy.Description of any gaps in the data or other limiting factorsSee the full 2023 Farm to School Census Report [https://www.fns.usda.gov/research/f2s/2023-census] for a detailed explanation of the study’s limitations.Outcome measurement methods and equipment usedNone

This data collection constitutes the third wave of data in the High School and Beyond series. The base-year data (ICPSR 7896) were collected in 1980, and the first follow-up (ICPSR 8297) was conducted in 1982. The series is a longitudinal study of students who were high school sophomores and seniors in 1980. As with the first follow-up, the structure and documentation of High School and Beyond Second Follow-Up data files represent a departure from base-year (1980) practices. While the base-year student file contains data from both the senior and sophomore cohorts, the two follow-up surveys provide separate student files for the two cohorts. Each of the cohort files for this collection merges the base year and first follow-up data with second follow-up data. Data collected for the sophomore cohort second follow-up differ substantially from data collected for the first follow-up since by 1984 the majority of respondents were out of high school and enrolled in postsecondary school, working, or looking for work. File 1, the Sophomore Cohort Second Follow-up Sample File, includes detailed questionnaire responses on background information, education, other training, military experience, work experience, periods unemployed, family information, income, experiences, and opinions. Information is also presented on the kind of school attended, hours per week spent in class, degree, certificate or diploma being sought, and requirements completed. Financial information in this file includes items on tuition and fees, scholarships, and financial aid from parents to the respondent and to any siblings. Work history data, including occupation, industry, gross starting salary, gross income, hours per week worked, and job satisfaction, are available along with data on the family, including the spouse's occupation and education, date of marriage(s), and number of children. File 5, the Senior Cohort Second Follow-up Sample File, repeats many of the same variables that are present in the first follow-up for this cohort. Respondents were asked to update background information, to provide information about postsecondary education, work experience, military service, family, income, and life goals. New items include a limited series on computer literacy (e.g., use of computers and software, knowledge of computer language), detailed information on financial assistance received from parents for pursuing postsecondary education, education and training outside of regular school, college or military programs (on-the-job and other employer-provided training), and periods of unemployment. Files 9,11,12, and 13 contain transcript data from each postsecondary institution reported by sample members of the High School and Beyond elder cohort (1980 senior cohort) in their responses to the High School and Beyond First Follow-up (1982) and Second Follow-up (1984) surveys. Data are available for several types of postsecondary institutions, ranging from short-term vocational or occupational programs through major universities with graduate programs and professional schools. Data in these four rectangular files--Student, Transcript, Term, and Course Files--are organized to be used in combination hierarchically. Information is available on terms of attendance, fields of study, specific courses taken, and grades and credits earned. A supplementary survey, the Administrator and Teacher Survey (ATS), was conducted in 1984 in approximately half of the schools sampled in the original High School and Beyond study. The ATS was designed to explore findings from research on effective schools, which were defined as those schools in which students perform at higher levels than would be expected from their backgrounds and other factors. The ATS provides measures of staff goals, school climates, and other processes identified in the effective schools literature as being important for achieving educational excellence. Separate questionnaires were administered to teachers, administrators, vocational education coordinators, and heads of guidance. Items in the questionnaires were selected to complement information already in the High School and Beyond database. Included were questions on staff goals, pedagogic practices, interpersonal relations of staff, work load of teachers, staff attitudes, availability and use of guidance services, planning processes, hiring practices, specia

As of February 1, ********* applications were submitted to the most selective higher education institutions, who admit less than 25 percent of their applicants, during the 2023/24 academic year in the United States. In comparison, only ******* applications had been submitted to highly selective institutions who admit between 25 and 49 percent of their applicants.

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 1971-1972. 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.