This dataset includes the attendance rate for public school students PK-12 by student group and by district during the 2021-2022 school year. Student groups include: Students experiencing homelessness Students with disabilities Students who qualify for free/reduced lunch English learners All high needs students Non-high needs students Students by race/ethnicity (Hispanic/Latino of any race, Black or African American, White, All other races) Attendance rates are provided for each student group by district and for the state. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

Attendance boundaries for middle schools in the Chicago Public Schools district for school year 2023-2024. Note: only 22 middle schools have attendance boundaries in school year 2023-2024. Middle school boundaries are often established to relieve overcrowding at nearby elementary schools. Generally, all students in the applicable middle school grades who live within one of these boundaries may attend the school.

​​​​​This dataset is in a forma​​t for spatial datasets that is inherently tabular but allows for a map as a derived view. Please click the indicated link below for such a map.

To export the data in either tabular or geographic format, please use the Export button on this dataset.

2018 DC School Report Card. The sum of the student group scores using all applicable STAR framework metrics. This is a number from 0 – 100 points. Overall STAR score for the school based on all applicable framework scores and student groups. Star value assigned to the school based on the STAR score.1, 2, 3, 4, or 5. Supplemental:Metric scores are not reported for n-sizes less than 10; metrics that have an n-size less than 10 are not included in calculation of STAR scores and ratings.At the state level, teacher data is reported on the DC School Report Card for all schools, high-poverty schools, and low-poverty schools. The definition for high-poverty and low-poverty schools is included in DC's ESSA State Plan. At the school level, teacher data is reported for the entire school, and at the LEA-level, teacher data is reported for all schools only.On the STAR Framework, 203 schools received STAR scores and ratings based on data from the 2017-18 school year. Of those 203 schools, 2 schools closed after the completion of the 2017-18 school year (Excel Academy PCS and Washington Mathematics Science Technology PCHS). Because those two schools closed, they do not receive a School Report Card and report card metrics were not calculated for those schools.Schools with non-traditional grade configurations may be assigned multiple school frameworks as part of the STAR Framework. For example, a K-8 school would be assigned the Elementary School Framework and the Middle School Framework. Because a school may have multiple school frameworks, the total number of school framework scores across the city will be greater than the total number of schools that received a STAR score and rating.Detailed information about the metrics and calculations for the DC School Report Card and STAR Framework can be found in the 2018 DC School Report Card and STAR Framework Technical Guide (https://osse.dc.gov/publication/2018-dc-school-report-card-and-star-framework-technical-guide).

GSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers. The dataset is segmented into 7.5K training problems and 1K test problems. These problems take between 2 and 8 steps to solve, and solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer. A bright middle school student should be able to solve every problem. It can be used for multi-step mathematical reasoning.

District-run high schools in CPS are organized into 4 Geographic Networks, which provide administrative support, strategic direction, and leadership development to the schools within each Network. ​​​​​ This dataset is in a forma​​t for spatial datasets that is inherently tabular but allows for a map as a derived view. Please click the indicated link below for such a map. To export the data in either tabular or geographic format, please use the Export button on this dataset.

This dataset attempts to represent the point locations of every educational program in the state of Minnesota that is currently operational and reporting to the Minnesota Department of Education. It can be used to identify schools, various individual school programs, school districts (by office location), colleges, and libraries, among other programs. Please note that not all school programs are statutorily required to report, and many types of programs can be reported at any time of the year, so this dataset is by nature an incomplete snapshot in time.

Maintenance of these locations are a result of an ongoing project to identify current school program locations where Food and Nutrition Services Office (FNS) programs are utilized. The FNS Office is in the Minnesota Department of Education (MDE). GIS staff at MDE maintain the dataset using school program and physical addresses provided by local education authorities (LEAs) for an MDE database called "MDE ORG". MDE GIS staff track weekly changes to program locations, along with comprehensive reviews each summer. All records have been reviewed for accuracy or edited at least once since January 1, 2020.

Note that there may remain errors due to the number of program locations and inconsistency in reporting from LEAs and other organizations. In particular, some organization types (such as colleges and treatment programs) are not subject to annual reporting requirements, so some records included in this file may in fact be inactive or inaccurately located.

Note that multiple programs may occur at the same location and are represented as separate records. For example, a junior and a senior high school may be in the same building, but each has a separate record in the data layer. Users leverage the "CLASS" and "ORGTYPE" attributes to filter and sort records according to their needs. In general, records at the same physical address will be located at the same coordinates.

This data is now available in CSV format. For that format only, OBJECTID and Shape columns are removed, and the Shape column is replaced by Latitude and Longitude columns.

The District Analysis and Review Tools (DARTs) offer snapshots of district and school performance, allowing users to easily track select data elements over time, and make sound, meaningful comparisons to the state or to "comparable" organizations. The waterfall data shows a cohort of high school students and their progression through high school graduation, college enrollment and persistence in higher education to a second year or college completion.

This is a companion dataset to the main DART: Success After High School dataset. It contains two indicators published separately from the main dataset since the data are in a different format: "Student progression from high school through second year of postsecondary education" and "Student progression from high school through postsecondary degree completion". For all other DART: Success After High School indicators, please visit the main DART: Success After High School dataset.

This dataset contains the same data that is also published on our DART Detail: Success After High School Online Dashboard

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 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.

Comprehensive dataset of 1,173 Middle schools in Pennsylvania, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 330 Middle schools in Utah, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

In this example lesson plan, students will learn how to use the How’s My Waterway tool to explore and visualize water quality data and determine the health of their local waterbodies.

Comprehensive dataset of 264 Middle schools in Idaho, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 3,103 Middle schools in Texas, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This dataset was developed to provide states with comprehensive data on both middle school and high school students regarding tobacco use, exposure to environmental tobacco smoke, smoking cessation, school curriculum, minors' ability to purchase or otherwise obtain tobacco products, knowledge and attitudes about tobacco, and familiarity with pro-tobacco and anti-tobacco media messages. The dataset uses a two-stage cluster sample design to produce representative samples of students in middle schools (grades 6–8) and high schools (grades 9–12)

This dataset is valuable for data science due to its coverage of youth tobacco use over nearly two decades. Its rich demographic details and broad geographical spread enable researchers and policymakers to identify trends, behaviors, and risk factors associated with tobacco use among the youth.

For instance, it can help in understanding how tobacco use prevalence varies across different age groups, genders, races, and educational backgrounds. The stratification of data by location and demographic characteristics allows for targeted analysis that can inform public health strategies and educational campaigns aimed at reducing tobacco use among young people.

Some analysis of this dataset can include:

• Statistical assessments of tobacco use trends, examining changes in attitudes towards tobacco, and identifying high-risk groups based on demographic characteristics.
• Performing time-series analyses to understand how tobacco use has evolved over the years or spatial analyses to identify geographical variations in tobacco use trends.
• Correlation studies can help uncover associations between tobacco use and factors like education levels, race, and gender.
• Advanced machine learning models could predict future trends in youth tobacco use or evaluate the potential impact of new tobacco control measures.

This dataset includes the attendance rate for public school students PK-12 by town during the 2021-2022 school year. Attendance rates are provided for each town for the overall student population and for the high needs student population. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

2018 DC School Report Card. School enrollment by school and student group. For enrollment the metrics are either total enrollment or percent of total enrollment. Supplemental: Metric scores are not reported for n-sizes less than 10; metrics that have an n-size less than 10 are not included in calculation of STAR scores and ratings. At the state level, teacher data is reported on the DC School Report Card for all schools, high-poverty schools, and low-poverty schools. The definition for high-poverty and low-poverty schools is included in DC's ESSA State Plan. At the school level, teacher data is reported for the entire school, and at the LEA-level, teacher data is reported for all schools only. On the STAR Framework, 203 schools received STAR scores and ratings based on data from the 2017-18 school year. Of those 203 schools, 2 schools closed after the completion of the 2017-18 school year (Excel Academy PCS and Washington Mathematics Science Technology PCHS). Because those two schools closed, they do not receive a School Report Card and report card metrics were not calculated for those schools. Schools with non-traditional grade configurations may be assigned multiple school frameworks as part of the STAR Framework. For example, a K-8 school would be assigned the Elementary School Framework and the Middle School Framework. Because a school may have multiple school frameworks, the total number of school framework scores across the city will be greater than the total number of schools that received a STAR score and rating. Detailed information about the metrics and calculations for the DC School Report Card and STAR Framework can be found in the 2018 DC School Report Card and STAR Framework Technical Guide (https://osse.dc.gov/publication/2018-dc-school-report-card-and-star-framework-technical-guide).

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The purpose of this was to leverage an existing randomized controlled trial of The Second Step anti-bullying program, which was implemented when the sample of students was in middle school, by measuring related aggressive behaviors (e.g. bullying, cyberbullying, sexual violence) during the high school years. The objectives of this study were to determine treatment effects of the Second Step middle school program on reductions in youth aggression (including bullying), sexual violence, substance use, and teen dating violence when in high school, as well as to assess middle school belonging as a mediator of these treatment effects on targeted problem behaviors in high school. Demographic variables included as part of this collection are students' age, gender, race, and household characteristics. The collection contains 3 SPSS data files: analysis4_de-identified_2.sav (n=2143; 304 variables) RCT-WAVE-1-4-ITEMS_RECODED_de-identified_2.sav (n=4718; 741 variables) RCT---WAVE-5-7-ITEMS_RECODED_de-identified_2.sav (n=3064; 887 variables)

Dataset Card for GSM8K

  Dataset Summary

GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.

These problems take between 2 and 8 steps to solve. Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k.

2018 DC School Report Card. A school framework is a set of metrics and weighted domains based on the school's grade configuration or school designations. The STAR Framework contains four school frameworks: Elementary School Framework, Middle School Framework, High School Framework, and Alternative School Framework. The Elementary School Framework has two versions, one for schools with Pre-Kindergarten and one for schools without Pre-Kindergarten.Supplemental:Metric scores are not reported for n-sizes less than 10; metrics that have an n-size less than 10 are not included in calculation of STAR scores and ratings.At the state level, teacher data is reported on the DC School Report Card for all schools, high-poverty schools, and low-poverty schools. The definition for high-poverty and low-poverty schools is included in DC's ESSA State Plan. At the school level, teacher data is reported for the entire school, and at the LEA-level, teacher data is reported for all schools only.On the STAR Framework, 203 schools received STAR scores and ratings based on data from the 2017-18 school year. Of those 203 schools, 2 schools closed after the completion of the 2017-18 school year (Excel Academy PCS and Washington Mathematics Science Technology PCHS). Because those two schools closed, they do not receive a School Report Card and report card metrics were not calculated for those schools.Schools with non-traditional grade configurations may be assigned multiple school frameworks as part of the STAR Framework. For example, a K-8 school would be assigned the Elementary School Framework and the Middle School Framework. Because a school may have multiple school frameworks, the total number of school framework scores across the city will be greater than the total number of schools that received a STAR score and rating.Detailed information about the metrics and calculations for the DC School Report Card and STAR Framework can be found in the 2018 DC School Report Card and STAR Framework Technical Guide (https://osse.dc.gov/publication/2018-dc-school-report-card-and-star-framework-technical-guide).

The National Education Longitudinal Study of 1988 (NELS:88) is a study that is part of the Longitudinal Studies Branch (LSB) program; program data is available since 1988 at . NELS:88 (https://nces.ed.gov/surveys/nels88/) is a longitudinal study that is designed to provide trend data about critical transitions experienced by students as they leave middle or junior high school, and progress through high school and into postsecondary institutions or the work force. A nationally representative sample of eighth-graders were first surveyed in the spring of 1988. A sample of these respondents were then resurveyed through four follow-ups in 1990, 1992, 1994, and 2000. Overall weighted response rate was unavailable as of December 2014. Key statistics produced from NELS:88 data can be used for policy-relevant research about educational processes and outcomes, for example: student learning; early and late predictors of dropping out; and school effects on students' access to programs and equal opportunity to learn.