The Institute of Education Sciences (IES) is the statistics, research, and evaluation arm of the U.S. Department of Education. We are independent and non-partisan. Our mission is to provide scientific evidence on which to ground education practice and policy and to share this information in formats that are useful and accessible to educators, parents, policymakers, researchers, and the public.

IES conducts six broad types of work that addresses school readiness and education from infancy through adulthood and includes special populations such as English Learners and students with disabilities.

• We provide data that describes how well the United States is educating its students. We collect and analyze official statistics on the condition of education, including adult education and literacy; support international assessments; and carry out the National Assessment of Educational Progress (NAEP).

 • We conduct surveys and sponsor research projects to understand where education needs improvement and how these improvements might be made.  Our longitudinal surveys provide nationally representative data on how students are progressing through school and entering the workforce. Our cross-sectional surveys provide a snapshot of how students and the education system are doing at specific points in time. We fund research that uses these and other data to gain a deeper understanding of the nature and context of needed education improvements.

 • We fund development and rigorous testing of new approaches for improving education outcomes for all students.  We support development of practical solutions for education from the earliest design stages through pilot studies and rigorous testing at scale. With IES support, researchers are learning what works for improving instruction, student behavior, teacher learning, and school and system organization.

 • We conduct large-scale evaluations of federal education programs and policies.  Our evaluations address complex issues of national importance, such as the impact of alternative pathways to teacher preparation, teacher and leader evaluation systems, school improvement initiatives, and school choice programs.

 • We provide resources to increase use of data and research in education decision making.  Through the What Works Clearinghouse, we conduct independent reviews of research on what works in education. The Regional Educational Laboratories offer opportunities to learn what works as well as coaching, training, and other support for research use. Our Statewide Longitudinal Data System grants enable states to more efficiently track education outcomes and provide useful, timely information to decision makers.

 • We support advancement of statistics and research through specialized training and development of methods and measures.  We fund pre-doctoral and post-doctoral training programs, as well as database training and short courses on cutting-edge topics for working statisticians and researchers. Our empirical work on new methods and measures ensures continued advances in the accuracy, usefulness, and cost-effectiveness of education data collections and research.

This dataset contains statistically weighted estimates of initial education levels, highest education levels, and initial education locations for 43 key health workforce professions actively licensed in California as of July 1st, 2023. These metrics can be compared by workforce category, license type, time since license issue date (in years), race & ethnicity group, assigned sex at birth, and CHIS region.

The Higher Education Research and Development (HERD) Survey is the primary source of information on R&D expenditures at U.S. colleges and universities. The survey collects information on R&D expenditures by field of research and source of funds and also gathers information on types of research, expenses, and R&D personnel. The survey is an annual census of institutions that expended at least $150,000 in separately accounted for R&D in the fiscal year. This dataset includes HERD assets for 2021.

Subject: EducationSpecific: Online Learning and FunType: Questionnaire survey data (csv / excel)Date: February - March 2020Content: Students' views about online learning and fun Data Source: Project OLAFValue: These data provide students' beliefs about how learning occurs and correlations with fun. Participants were 206 students from the OU

The National Indian Education Study, 2009 (NIES 2009), is a study that is part of the National Indian Education Study (NIES), which is a part of National Assessment of Educational Progress (NAEP) program; program data is available since 2005 at https://nces.ed.gov/nationsreportcard/nies/. NIES 2009 (https://nces.ed.gov/nationsreportcard/nies/) is a cross-sectional survey that is designed to describe the condition of education for American Indian and Alaska Native (AI/AN) students in the United States. Students in public, private, Department of Defense, and Bureau of Indian Education-funded schools were sampled using paper-and-pencil assessment. Key statistics produced from NIES 2009 provides educators, policymakers, and the public with information about the academic performance in reading and mathematics of AI/AN fourth- and eighth-graders as well as their exposure to Native American culture.

This data collection presents follow-up data for the NATIONAL EDUCATION LONGITUDINAL STUDY, 1988 (ICPSR 9389). The base-year study collected information from student surveys and tests and from surveys of parents, school administrators, and teachers. It was designed to provide trend data about critical transitions experienced by students as they leave elementary school and progress through high school and postsecondary institutions or the work force. This collection provides the first opportunity for longitudinal measurement of the 1988 baseline samples. It also provides a point of comparison with high school sophomores from ten years before, as studied in HIGH SCHOOL AND BEYOND, 1980: A LONGITUDINAL SURVEY OF STUDENTS IN THE UNITED STATES (ICPSR 7896). Further, the study captures the population of early dropouts (those who leave school prior to the end of the tenth grade), while monitoring the transition of the student population into secondary schooling. The student component (Part 1) collected basic background information about students' school and home environments, participation in classes and extracurricular activities, current jobs, and students' goals, aspirations, and opinions about themselves. The student component also measured tenth-grade achievement and cognitive growth between 1988 and 1990 in the subject areas of mathematics, science, reading, and social studies. The school component (Part 3) supplies general descriptive information about the educational setting and environment in which surveyed students were enrolled. These data were collected from the chief administrator of each base-year school and cover school characteristics, grading and testing structure, school culture and academic climate, program and facilities information, parental interactions and involvement, and teaching staff characteristics. The dropout component (Part 5) provides data on the process of dropping out of school as it occurs from eighth grade on. Variables include school attendance, determinants of leaving school, self-perceptions and attitudes, work history, and relationships with school personnel, peers, and family. The teacher component (Part 7) was administered to teachers of follow-up students in four basic subject areas: mathematics, science, English, and history. The questionnaire elicited teacher evaluations of student characteristics and performance in the classroom, curriculum information about the classes taught, teacher demographic and professional characteristics, information about parent-teacher interactions, time spent on various tasks, and perceptions of school climate and culture.

This study contains data obtained from one-third of a national sample of college and university faculty surveyed under the sponsorship of the Carnegie Commission on Higher Education (see CARNEGIE COMMISSION NATIONAL SURVEY OF HIGHER EDUCATION: FACULTY STUDY, 1969 [ICPSR 7501]). The original data were collected by the Survey Research Center, University of California at Berkeley, while the subsample was provided by the Social Science Data Center at the University of Connecticut. The subsample for the present study was randomly drawn and the 20,008 selected respondents were weighted to 148,372. The variables provide information on the faculty's social and educational backgrounds and professional activities, their views on a wide range of social and political issues, and opinions on educational policy. Demographic data cover age, sex, race, marital status, number of children, religion, income, and parents' levels of education and occupations. In addition to the original survey data, this study includes a number of derived measures in the form of indexes and scales.

Quality criteria, analysed data sets, and criteria assessment results of a study in Jul 2019 and Mar 2021. The study was part of a Master Thesis in Information Science (Autor: Gabriel Schneider, Titel: Qualität von Forschungsdaten der Bildungsforschung in offenen Repositorien).

This data set contains an expanded data set and a sub-set (14) of the original 16 quality criteria. Original criteria were translated into English for a presentation at LIDA 21.

Quality criteria were developed based on expertise from the Research Data Centre for Education and the FAIR principles.

In the first study 2019, 29 data sets from Zenodo (search=keyword:Education and type:dataset) were analysed according to the criteria. 20 data sets were excluded due to access restrictions or topic (non educational research).

In 2021, 11 data sets uploaded at Zenodo 2021 were analysed according to the criteria. As search function at Zenodo changed, the search was adapted ((search=keyword:*Education OR keyword:*education) AND type:dataset AND accessright:open). Some data sets were excluded due to topic (non educational research), year published (2020 excluded), language barrieres or insufficient avalaible data.

The files include:

- Criteria: The criteria and assessment points applied

- Dataset: The search terms for the retrieved data sets, and exclusion criteria

- Results: The assessment points given for each criteria to each data set (without further details on decision with regard to specifics of data sets)

Open Science in (Higher) Education – data of the February 2017 survey

This data set contains:

• Full raw (anonymised) data set (completed responses) of Open Science in (Higher) Education February 2017 survey. Data are in xlsx and sav format.
• Survey questionnaires with variables and settings (German original and English translation) in pdf. The English questionnaire was not used in the February 2017 survey, but only serves as translation.
• Readme file (txt)

Survey structure

The survey includes 24 questions and its structure can be separated in five major themes: material used in courses (5), OER awareness, usage and development (6), collaborative tools used in courses (2), assessment and participation options (5), demographics (4). The last two questions include an open text questions about general issues on the topics and singular open education experiences, and a request on forwarding the respondent’s e-mail address for further questionings. The online survey was created with Limesurvey[1]. Several questions include filters, i.e. these questions were only shown if a participants did choose a specific answer beforehand ([n/a] in Excel file, [.] In SPSS).

Demographic questions

Demographic questions asked about the current position, the discipline, birth year and gender. The classification of research disciplines was adapted to general disciplines at German higher education institutions. As we wanted to have a broad classification, we summarised several disciplines and came up with the following list, including the option “other” for respondents who do not feel confident with the proposed classification:

• Natural Sciences
• Arts and Humanities or Social Sciences
• Economics
• Law
• Medicine
• Computer Sciences, Engineering, Technics
• Other

The current job position classification was also chosen according to common positions in Germany, including positions with a teaching responsibility at higher education institutions. Here, we also included the option “other” for respondents who do not feel confident with the proposed classification:

• Professor
• Special education teacher
• Academic/scientific assistant or research fellow (research and teaching)
• Academic staff (teaching)
• Student assistant
• Other

We chose to have a free text (numerical) for asking about a respondent’s year of birth because we did not want to pre-classify respondents’ age intervals. It leaves us options to have different analysis on answers and possible correlations to the respondents’ age. Asking about the country was left out as the survey was designed for academics in Germany.

Remark on OER question

Data from earlier surveys revealed that academics suffer confusion about the proper definition of OER^[2]. Some seem to understand OER as free resources, or only refer to open source software (Allen & Seaman, 2016, p. 11). Allen and Seaman (2016) decided to give a broad explanation of OER, avoiding details to not tempt the participant to claim “aware”. Thus, there is a danger of having a bias when giving an explanation. We decided not to give an explanation, but keep this question simple. We assume that either someone knows about OER or not. If they had not heard of the term before, they do not probably use OER (at least not consciously) or create them.

Data collection

The target group of the survey was academics at German institutions of higher education, mainly universities and universities of applied sciences. To reach them we sent the survey to diverse institutional-intern and extern mailing lists and via personal contacts. Included lists were discipline-based lists, lists deriving from higher education and higher education didactic communities as well as lists from open science and OER communities. Additionally, personal e-mails were sent to presidents and contact persons from those communities, and Twitter was used to spread the survey.

The survey was online from Feb 6^th to March 3^rd 2017, e-mails were mainly sent at the beginning and around mid-term.

Data clearance

We got 360 responses, whereof Limesurvey counted 208 completes and 152 incompletes. Two responses were marked as incomplete, but after checking them turned out to be complete, and we added them to the complete responses dataset. Thus, this data set includes 210 complete responses. From those 150 incomplete responses, 58 respondents did not answer 1st question, 40 respondents discontinued after 1st question. Data shows a constant decline in response answers, we did not detect any striking survey question with a high dropout rate. We deleted incomplete responses and they are not in this data set.

Due to data privacy reasons, we deleted seven variables automatically assigned by Limesurvey: submitdate, lastpage, startlanguage, startdate, datestamp, ipaddr, refurl. We also deleted answers to question N^o 24 (email address).

References

Allen, E., & Seaman, J. (2016). Opening the Textbook: Educational Resources in U.S. Higher Education, 2015-16.

First results of the survey are presented in the poster:

Heck, Tamara, Blümel, Ina, Heller, Lambert, Mazarakis, Athanasios, Peters, Isabella, Scherp, Ansgar, & Weisel, Luzian. (2017). Survey: Open Science in Higher Education. Zenodo. http://doi.org/10.5281/zenodo.400561

Contact:

Open Science in (Higher) Education working group, see http://www.leibniz-science20.de/forschung/projekte/laufende-projekte/open-science-in-higher-education/.

[1] https://www.limesurvey.org

[2] The survey question about the awareness of OER gave a broad explanation, avoiding details to not tempt the participant to claim “aware”.

PEELS is a study that is part of the Pre-Elementary Education Longitudinal Study. PEELS (https://ies.ed.gov/ncser/projects/peels/) is a longitudinal survey that is designed to describe the characteristics of children receiving preschool special education, their educational programs and services, and their transitions from preschool programs to elementary schools. The study was conducted using CATI, paper questionnaires, and child assessments. The study followed a nationally representative sample of children with disabilities who were 3 to 5 years old at the start of the study in 2003 through 2009, examining the achievement of students with disabilities in preschool, kindergarten, and elementary school and determining the factors associated with this achievement. Key statistics produced from PEELS are characteristics of children and their families; characteristics of educational services and providers; transitions from early intervention to preschool, and preschool to elementary school; and school-related readiness and behavior.

This dataset contains data from the National Center for Education Statistics' Academic Library Survey, which was gathered every two years from 1996 - 2014, and annually in IPEDS starting in 2014 (this dataset has continued to only merge data every two years, following the original schedule). This data was merged, transformed, and used for research by Starr Hoffman and Samantha Godbey.This data was merged using R; R scripts for this merge can be made available upon request. Some variables changed names or definitions during this time; a view of these variables over time is provided in the related Figshare Project. Carnegie Classification changed several times during this period; all Carnegie classifications were crosswalked to the 2000 classification version; that information is also provided in the related Figshare Project. This data was used for research published in several articles, conference papers, and posters starting in 2018 (some of this research used an older version of the dataset which was deposited in the University of Nevada, Las Vegas's repository).SourcesAll data sources were downloaded from the National Center for Education Statistics website https://nces.ed.gov/. Individual datasets and years accessed are listed below.[dataset] U.S. Department of Education, National Center for Education Statistics, Academic Libraries component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Academic Libraries Survey (ALS) Public Use Data File, Library Statistics Program, (2012, 2010, 2008, 2006, 2004, 2002, 2000, 1998, 1996), https://nces.ed.gov/surveys/libraries/aca_data.asp[dataset] U.S. Department of Education, National Center for Education Statistics, Institutional Characteristics component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Fall Enrollment component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014, 2012, 2010, 2008, 2006, 2004, 2002, 2000, 1998, 1996), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Human Resources component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014, 2012, 2010, 2008, 2006), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Employees Assigned by Position component, Integrated Postsecondary Education Data System (IPEDS), (2004, 2002), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Fall Staff component, Integrated Postsecondary Education Data System (IPEDS), (1999, 1997, 1995), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7

This dataset consists of an evaluation form, resulting data, and a slide deck from a case study describing the development, implementation, and evaluation of a 1.5 hour clinical research data management workshop for an academic medical center research community. This workshop was developed by the health sciences library.

The Education Longitudinal Study of 2002 (ELS:2002; https://nces.ed.gov/surveys/els2002/) is a study that is a part of the Education Longitudinal Study program. It is a longitudinal survey that monitors the transitions of a national sample of young people as they progress from tenth grade to, eventually, the world of work. In 2004, the sample was augmented to make it representative of seniors as well. The study was conducted using self-administered questionnaires and cognitive tests of students, parents, teachers, librarians, and school administrators. Students and their high school administrators, library media coordinators, mathematics and English teachers, and parents in the spring term of the 2002 school year were sampled. The study's base year weighted response rate was 87.3 percent for students, 98.5 percent for school administrators, 95.9 percent for library media coordinators, 91.6 percent for both mathematics and English teachers, 87.5 percent for parents, and 67.8 percent for schools. Key statistics produced from ELS:2002 focus on the changes taking place in the lives of students which can be understood by life achievements, aspirations, and experiences.

This dataset represents an inventory of research data services at 120 US colleges and universities. The data was collected using a systematic web content analysis process in late 2019. This dataset underlies the following report: Jane Radecki and Rebecca Springer, "Research Data Services in US Higher Education: A Web-Based Inventory," Ithaka S+R, Nov. 2020, https://doi.org/10.18665/sr.314397.

We defined research data services as any concrete, programmatic offering intended to support researchers (including faculty, postdoctoral researchers, and graduate students) in working with data, and identified services within the following campus units: library, IT department/research computing, independent research centers and facilities, academic departments, medical school, business school, and other professional schools. We also recorded whether the institution offered local high performance computing facilities. For detailed definitions, exclusions, and data collection procedures, please see the report referenced above.

The rapid advancements in generative AI models present new opportunities in the education sector. However, it is imperative to acknowledge and address the potential risks and concerns that may arise with their use. We collected Twitter data to identify key concerns related to the use of ChatGPT in education. This dataset is used to support the study "ChatGPT in education: A discourse analysis of worries and concerns on social media."

In this study, we particularly explored two research questions. RQ1 (Concerns): What are the key concerns that Twitter users perceive with using ChatGPT in education? RQ2 (Accounts): Which accounts are implicated in the discussion of these concerns? In summary, our study underscores the importance of responsible and ethical use of AI in education and highlights the need for collaboration among stakeholders to regulate AI policy.

The Children's Environmental Health Initiative (CEHI) at Rice University provided access to blood lead data from the North Carolina Childhood Lead Poisoning Prevention Program surveillance registry; data on end-of-grade standardized achievement tests in reading and mathematics from the North Carolina Education Research Data Center (NCERDC); and birth certificate data from the North Carolina Department of Health and Human Services. Test score, blood lead, and birth certificate data were linked using a common child identifier created by CEHI for matching purposes. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The data was made available by the Children's Environmental Health Initiative (CEHI) at Rice University. Contact Claire Osgood (ceo1@rice.edu), CEHI's Data Manager, to learn how the data can be accessed. Format: The Children's Environmental Health Initiative (CEHI) at Rice University provided access to blood lead data from the North Carolina Childhood Lead Poisoning Prevention Program surveillance registry; data on end-of-grade standardized achievement tests in reading and mathematics from the North Carolina Education Research Data Center (NCERDC); and birth certificate data from the North Carolina Department of Health and Human Services. Test score, blood lead, and birth certificate data were linked using a common child identifier created by CEHI for matching purposes. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

Comprehensive dataset of 5,680 Education in 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 collection represents the first stage of a major longitudinal effort to provide trend data about critical transitions experienced by students as they leave elementary school and progress through high school and into college or their careers. The 1988 eighth-grade cohort will be followed at two-year intervals as this group passes through high school and postsecondary education. The longitudinal data collected will yield policy-relevant information about educational processes and outcomes, early and later predictors of dropping out, and students' access to programs and equal opportunity. The study has four types of data files. The Parent Component was designed to collect information about the factors that influence educational attainment and participation, including questions exploring family background and socioeconomic conditions and character of the home educational system. The School Administrator component was designed to gather general descriptive information about the educational settings in which the surveyed students were enrolled in the winter and spring of 1988. These data were collected from the chief administrator of each base-year school and concern school characteristics, grading and testing structure, school culture and academic climate, program and facilities information, parental interactions and involvement, and teaching staff characteristics. The Student Component collected information on school work, aspirations, social relationships, and basic achievement areas such as reading, mathematics, science, and social studies. The Teacher Component provided data that could be used to analyze the behaviors and outcomes of the student sample. Teachers were surveyed about the base-year students' characteristics and performance in the classroom, curriculum and classes for eighth graders, and teacher demographics, professional characteristics, and relationships with other teachers, students, and parents.

Abstract (en): The National Center for Early Development and Learning (NCEDL) combined the data of two major studies in order to understand variations among state-funded pre-kindergarten (pre-k) programs and in turn, how these variations relate to child outcomes at the end of pre-k and in kindergarten. The Multi-State Study of Pre-Kindergarten and the State-Wide Early Education Programs (SWEEP) Study provide detailed information on pre-kindergarten teachers, children, and classrooms in 11 states. By combining data from both studies, information is available from 721 classrooms and 2,982 pre-kindergarten children in these 11 states. Pre-kindergarten data collection for the Multi-State Study of Pre-Kindergarten took place during the 2001-2002 school year in six states: California, Georgia, Illinois, Kentucky, New York, and Ohio. These states were selected from among states that had committed significant resources to pre-k initiatives. States were selected to maximize diversity with regard to geography, program settings (public school or community setting), program intensity (full-day vs. part-day), and educational requirements for teachers. In each state, a stratified random sample of 40 centers/schools was selected from the list of all the school/centers or programs (both contractors and subcontractors) provided to the researchers by each state's department of education. In total, 238 sites participated in the fall and two additional sites joined the study in the spring. Participating teachers helped the data collectors recruit children into the study by sending recruitment packets home with all children enrolled in the classroom. On the first day of data collection, the data collectors determined which of the children were eligible to participate. Eligible children were those who (1) would be old enough for kindergarten in the fall of 2002, (2) did not have an Individualized Education Plan, according to the teacher, and (3) spoke English or Spanish well enough to understand simple instructions, according to the teacher. Pre-kindergarten data collection for the SWEEP Study took place during the 2003-2004 school year in five states: Massachusetts, New Jersey, Texas, Washington, and Wisconsin. These states were selected to complement the states already in the Multi-State Study of Pre-K by including programs with significantly different funding models or modes of service delivery. In each of the five states, 100 randomly selected state-funded pre-kindergarten sites were recruited for participation in the study from a list of all sites provided by the state. In total, 465 sites participated in the fall. Two sites declined to continue participation in the spring, resulting in 463 sites participating in the spring. Participating teachers helped the data collectors recruit children into the study by sending recruitment packets home with all children enrolled in the classroom. On the first day of data collection, the data collectors determined which of the children were eligible to participate. Eligible children were those who (1) would be old enough for kindergarten in the fall of 2004, (2) did not have an Individualized Education Plan, according to the teacher, and (3) spoke English or Spanish well enough to understand simple instructions, according to the teacher. Demographic information collected across both studies includes race, teacher gender, child gender, family income, mother's education level, and teacher education level. The researchers also created a variable for both the child-level data and the class-level data which allows secondary users to subset cases according to either the Multi-State or SWEEP study. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed recodes and/or calculated derived variables.. Response Rates: Multi-State: Of the 40 sites per state, 78 percent of eligible sites agreed to participate (fall of pre-k, n = 238). For fall of pre-k (n = 238), 94 percent of the one classroom per site selected agreed to participate. For fall (n = 940) and spring (n = 960) of pre-k, 61 percent of the parents of eligible children consented.; SWEEP: Of the 10...