https://en.wikipedia.org/wiki/Public_domainhttps://en.wikipedia.org/wiki/Public_domain
This Public Schools feature dataset is composed of all Public elementary and secondary education facilities in the United States as defined by the Common Core of Data (CCD, https://nces.ed.gov/ccd/ ), National Center for Education Statistics (NCES, https://nces.ed.gov ), US Department of Education for the 2017-2018 school year. This includes all Kindergarten through 12th grade schools as tracked by the Common Core of Data. Included in this dataset are military schools in US territories and referenced in the city field with an APO or FPO address. DOD schools represented in the NCES data that are outside of the United States or US territories have been omitted. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 3065 new records, modifications to the spatial location and/or attribution of 99,287 records, and removal of 2996 records not present in the NCES CCD data.
The 2021-2022 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 2021-2022 school year and the Fall 2022 semester, from August 2021 – December 2022. 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 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: 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 August 1, 2021 to June 24, 2022 correspond to the 2021-2022 school year. During this time frame, data from the AEI/Return to Learn Tracker and most state dashboards were not available. Inferred modalities with a probability below 0.6 were deemed inconclusive and were omitted. During the Fall 2022 semester, modalities for districts with a school closure reported by Burbio were updated to either “Remote”, if the closure spanned the entire week, or “Hybrid”, if the closure spanned 1-4 days of the week. Data from August
https://en.wikipedia.org/wiki/Public_domainhttps://en.wikipedia.org/wiki/Public_domain
This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2017-2018 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 2675 new records, modifications to the spatial location and/or attribution of 19836 records, the removal of 254 records no longer applicable. Additionally, 10,870 records were removed that previously had a STATUS value of 2 (Unknown; not represented in the most recent PSS data) and duplicate records identified by ORNL.
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 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: 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 K-12 School Opening Tracker. Burbio 2021; https
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Book publishers Colleges Universities Religious Organizations Education Supply Companies Office Supply Companies Fundraising Product Companies
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset tracks annual american indian student percentage from 1992 to 2023 for Ennis K-12 Schools School District vs. Montana
https://www.icpsr.umich.edu/web/ICPSR/studies/38426/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/38426/terms
LearnPlatform is a technology platform in the kindergarten-12th grade (K-12) market providing a broadly interoperable platform to the breadth of educational technology (edtech) solutions in the United States K-12 field. A key component of edtech effectiveness is integrated reporting on tool usage and, where applicable, evidence of efficacy. With COVID closures, LearnPlatform is a resource to measure whether students are accessing digital resources within distance learning constraints. This platform provides a source of data to understand if students are accessing digital resources, and where resources have disparate usage and impact. This study includes educational technology usage across over 8,000 tools used in the education field in 2020.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset tracks annual american indian student percentage from 2014 to 2023 for Great Lakes K-12 School vs. Michigan and Suttons Bay Public Schools School District
The dataset contains information collected from 122 K-12 public school food service directors in Mississippi, USA, who completed an online survey designed for Mississippi school food service directors. The survey was created using Snap Surveys Desktop software. Information includes school size (number of enrolled students), percent of students participating in free or reduced-price lunch, foods sourced locally (defined as grown or produced in Mississippi), desire to purchase more or start purchasing locally sourced foods, fresh fruit and vegetable purchasing practices, experience purchasing fruits and vegetables from farmers, challenges purchasing from farmers, and interest in other farm to school (F2S) activities. School food service directors' demographic characteristics collected include gender, age, ethnicity/race, marital status, and education level. The data were collected from October 2021 to January 2022 using an online mobile and secure survey management system called Snap Online. The data were collected to obtain updated demographic and school purchasing characteristics from school food service directors in Mississippi and to determine their current abilities, experiences, and desires to engage in F2S activities. The dataset can be used to learn about K-12 public school food service directors in Mississippi but results should not be generalized to all school food service directors in Mississippi or elsewhere in the USA. Resources in this dataset:Resource Title: Mississippi Farm to School Food Service Director Dataset. File Name: MS F2S School Data Public.csvResource Description: The dataset contains information collected from 122 K-12 public school food service directors in Mississippi regarding their experience with and interest in farm to school, including purchasing local foods. It also contains demographic characteristics of the school food service directors and their fresh fruit and vegetable purchasing practices.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Mississippi Farm to School Food Service Director Data Dictionary. File Name: MS F2S School Data Dictionary Public.csvResource Description: The file contains information for variables contained in the associated dataset including names, brief descriptions, types, lengths, and values.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel
Local Law 15 (2016) requires that NYCDOE provide citywide Health Education instructor data, disaggregated by commuunity school district, city council district, and individual school data. Reports provide the number of licensed full-time and part-time instructors, the number of instructors assigned to teach at least one health education class, the number and percentage of instructors who received professional development training and the total number and percentage of instructors attending multiple sessions of professional development. This report includes number of licensed health instructors for 2015-16 school year. Counts of licensed health instructors represent all active, school-based teachers serving under an NYCDOE health license as of 10/31/2015.
https://www.usa.gov/government-workshttps://www.usa.gov/government-works
Historical Dataset starting with School Year 2016-2017 through the most Current School Year enrollments for all publicly funded schools in Pennsylvania as reported by school districts, area vocational-technical schools, charter schools, intermediate units, and state operated educational facilities. Local education agencies were asked to report those students who were enrolled and attending as of October 1, of the later year.
County and Statewide Totals Notes:
Statewide and county totals include counts of students attending education classes on a full-time basis outside their parents' district of residence. This data was obtained from the Bureau of Special Education.
Intermediate Unit and CTC Part-day enrollments are excluded from county and state totals.
Statewide and county totals are unique counts of students being educated by public Local Education Agencies. LEA and School level reports may not sum to the County and Statewide totals.
Source: Pennsylvania Information Management System (PIMS)
Notes regarding County Totals:
Enrollment for School Districts, Charter Schools, State Juvenile Correctional Institutions and Comprehensive CTCs are included. Enrollments for Occupational CTCs and IUs are not included.
Counts of students attending education classes on a full-time basis outside their parents' district of residence are included. This data was obtained from the Bureau of Special Education.
Morning and afternoon detail for Half day grades is not available in PENN Data. Therefore, PKH equals the sum of PKA and PKP enrollment, K4H equals the sum of K4A and K4P enrollment, and K5H equals the sum of K5A and K5P enrollment.
County totals are unique counts of students being educated by public Local Education Agencies. LEA and School level reports may not sum to the County total.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset tracks annual american indian student percentage from 2001 to 2023 for Custer K-12 Schools School District vs. Montana
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset tracks annual american indian student percentage from 1996 to 2020 for Lavina Elementary School vs. Montana and Lavina K-12 Schools School District
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
Dataset Description
Dataset Summary
The Fine-grained Error ANalysis for English Learners (FEANEL) dataset aims to advance research in fine-grained error analysis. The benchmark includes a large-scale dataset of 1,000 essays written by K-12 students, with 500 essays from elementary school students and 500 from secondary school students, covering a wide range of age groups and proficiency levels. Each error analysis has been meticulously annotated with an error type… See the full description on the dataset page: https://huggingface.co/datasets/Feanel/FEANEL.
Local Law 15 (2016) requires that the NYCDOE provide citywide Health Education Instructor data, disaggregated by community school district, city council district, and each individual school Data reported in this report is from the 2016-17 school year. This report provides the number of licensed full- time and part-time instructors, the number of instructors assigned to teach at least one health education class, the number and percentage of instructors who received professional development training and the total number and percentage of instructors attending multiple sessions of professional development. Data is reported from the 2016-17 school year.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset tracks annual total students amount from 2016 to 2023 for American Leadership Academy Ironwood K12
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset tracks annual american indian student percentage from 2022 to 2023 for Compass K-12 Online School vs. Oregon and Lincoln County School District
Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
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Updates
[May 1, 2025] Bounding Box Data: We have added bounding box version of Web-Hybrid. For everyone's convenience, no conversation template is applied to this version of data. All the coordinates (x1, y1, x2, y2) are as always normalized to [0,999].
Notes for Requests
If you have applied for access to this dataset but have not received approval, please contact us via email (Boyu Gou) with your name, institution, and research purpose. Typically, requests will be… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/UGround-V1-Data.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset tracks annual american indian student percentage from 2000 to 2022 for Opheim 7-8 vs. Montana and Opheim K-12 Schools School District
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset tracks annual american indian student percentage from 2000 to 2023 for Hysham School vs. Montana and Hysham K-12 Schools School District
https://en.wikipedia.org/wiki/Public_domainhttps://en.wikipedia.org/wiki/Public_domain
This Public Schools feature dataset is composed of all Public elementary and secondary education facilities in the United States as defined by the Common Core of Data (CCD, https://nces.ed.gov/ccd/ ), National Center for Education Statistics (NCES, https://nces.ed.gov ), US Department of Education for the 2017-2018 school year. This includes all Kindergarten through 12th grade schools as tracked by the Common Core of Data. Included in this dataset are military schools in US territories and referenced in the city field with an APO or FPO address. DOD schools represented in the NCES data that are outside of the United States or US territories have been omitted. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 3065 new records, modifications to the spatial location and/or attribution of 99,287 records, and removal of 2996 records not present in the NCES CCD data.