The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. 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 feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

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 school enrolment totals have been rounded to the nearest 5 in order to better protect and maintain student privacy. 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.

This dataset displays the location of schools that are overseen by the Bureau of Indian Education. There are 183 Bureau-funded elementary and secondary schools on 64 reservations in 23 states, serving approximately 40,000 Indian students. Of these, 55 are BIE-operated and 128 are tribally controlled under BIE contracts or grants. The Bureau also funds or operates off-reservation boarding schools and peripheral dormitories near reservations for public school students. The BIE also serves American Indian and Alaska Native post-secondary students through higher education scholarships and support funding for tribal colleges and universities. The BIE directly operates two post-secondary institutions: the Haskell Indian Nations University (HINU) in Lawrence, Kansas, and the Southwestern Indian Polytechnic Institute (SIPI) in Albuquerque, New Mexico. Native American boarding schools and dormitories were established in the United States during the late 19th and early 20th centuries. The land where the schools are located is administered by the Bureau of Indian Affairs while the facilities and there operation is under the jurisdiction of the Bureau of Indian Education. As stated in Title 25 CFR Part 32.3, BIE’s mission is to provide quality education opportunities from early childhood through life in accordance with a tribe’s needs for cultural and economic well-being, in keeping with the vast diversity of Indian tribes and Alaska Native villages as distinct cultural and governmental entities. Further, the BIE is to manifest consideration of the whole person by considering the individual's spiritual, mental, physical, and cultural aspects within his or her family and tribal or village context. The BIE school system employs thousands of teachers, administrators and support personnel, while many more work in tribal school systems.

The Master datasets comprise of four datasets: on children, schools, teachers and households. These master datasets contain key variables and identifiers which will allow users of the data to determine the progression of sample sizes and attrition of children, households, schools and teachers across the four years of the LEAPS panel data. The children dataset contains round-by-round status of children's grades, enrollment, promotion etc. It also has variables indicating the panel child belongs to (the first panel being grade 3 children LEAPS started following in 2003, and the second one being 3rd graders followed starting in 2005 i.e. round 3 of the survey) as well as whether child was randomly selected for child questionnaire in class. The school dataset contains information such as school type, survey status, construction date. Note that there is only one schoolid variable and it is constant across all rounds. To capture the fact that there is merging of some schools going on across the rounds, refer to the school_merged_into and school_merged_with variables. The school_merged_into variable only exists for the small schools that merged into a larger school whereas the school_merged_with variable exists for the larger schools that the smaller schools merged in to. The teachers dataset contains information such as their round-by-round school, teaching status. The household dataset contains a Mauza indicator, and a variable on whether the household was surveyed in a particular round.

The number of children, youth, and adults not attending schools or universities because of COVID-19 is soaring. Governments all around the world have closed educational institutions in an attempt to contain the global pandemic. According to UNESCO monitoring, over 100 countries have implemented nationwide closures, impacting over half of the world’s student population. Several other countries have implemented localized school closures and, should these closures become nationwide, millions of additional learners will experience education disruption. Method Data taken from: UNESCO Caveats / Comments Note: Figures correspond to total number of learners enrolled at pre-primary, primary, lower-secondary, and upper-secondary levels of education [ISCED levels 0 to 3], as well as at tertiary education levels [ISCED levels 5 to 8] who could be affected should localized closures become countrywide. Enrollment figures based on latest UNESCO Institute of Statistics data.

Abstract

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

Geographic coverage

National

Analysis unit

Schools, teachers, students, public officials

Kind of data

Sample survey data [ssd]

Sampling procedure

The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level. We also wish to detect differences by urban/rural location. For our school survey, we will employ a two-stage random sample design, where in the first stage a sample of typically around 200 schools, based on local conditions, is drawn, chosen in advance by the Bank staff. In the second stage, a sample of teachers and students will be drawn to answer questions from our survey modules, chosen in the field. A total of 10 teachers will be sampled for absenteeism. Five teachers will be interviewed and given a content knowledge exam. Three 1st grade students will be assessed at random, and a classroom of 4th grade students will be assessed at random. Stratification will be based on the school’s urban/rural classification and based on region. When stratifying by region, we will work with our partners within the country to make sure we include all relevant geographical divisions. For our Survey of Public Officials, we will sample a total of 200 public officials. Roughly 60 officials are typically surveyed at the federal level, while 140 officials will be surveyed at the regional/district level. For selection of officials at the regional and district level, we will employ a cluster sampling strategy, where roughly 10 regional offices (or whatever the secondary administrative unit is called) are chosen at random from among the regions in which schools were sampled. Then among these 10 regions, we also typically select around 10 districts (tertiary administrative level units) from among the districts in which schools werer sampled. The result of this sampling approach is that for 10 clusters we will have links from the school to the district office to the regional office to the central office. Within the regions/districts, five or six officials will be sampled, including the head of organization, HR director, two division directors from finance and planning, and one or two randomly selected professional employees among the finance, planning, and one other service related department chosen at random. At the federal level, we will interview the HR director, finance director, planning director, and three randomly selected service focused departments. In addition to the directors of each of these departments, a sample of 9 professional employees will be chosen in each department at random on the day of the interview.

Sampling deviation

The sample for the Global Education Policy Dashboard in SLE was based in part on a previous sample of 260 schools which were part of an early EGRA study. Details from the sampling for that study are quoted below. An additional booster sample of 40 schools was chosen to be representative of smaller schools of less than 30 learners.

EGRA Details:

"The sampling frame began with the 2019 Annual School Census (ASC) list of primary schools as provided by UNICEF/MBSSE where the sample of 260 schools for this study were obtained from an initial list of 7,154 primary schools. Only schools that meet a pre-defined selection criteria were eligible for sampling.

To achieve the recommended sample size of 10 learners per grade, schools that had an enrolment of at least 30 learners in Grade 2 in 2019 were considered. To achieve a high level of confidence in the findings and generate enough data for analysis, the selection criteria only considered schools that: • had an enrolment of at least 30 learners in grade 1; and • had an active grade 4 in 2019 (enrolment not zero)

The sample was taken from a population of 4,597 primary schools that met the eligibility criteria above, representing 64.3% of all the 7,154 primary schools in Sierra Leone (as per the 2019 school census). Schools with higher numbers of learners were purposefully selected to ensure the sample size could be met in each site.

As a result, a sample of 260 schools were drawn using proportional to size allocation with simple random sampling without replacement in each stratum. In the population, there were 16 districts and five school ownership categories (community, government, mission/religious, private and others). A total of 63 strata were made by forming combinations of the 16 districts and school ownership categories. In each stratum, a sample size was computed proportional to the total population and samples were drawn randomly without replacement. Drawing from other EGRA/EGMA studies conducted by Montrose in the past, a backup sample of up to 78 schools (30% of the sample population) with which enumerator teams can replace sample schools was also be drawn.

In the distribution of sampled schools by ownership, majority of the sampled schools are owned by mission/religious group (62.7%, n=163) followed by the government owned schools at 18.5% (n=48). Additionally, in school distribution by district, majority of the sampled schools (54%) were found in Bo, Kambia, Kenema, Kono, Port Loko and Kailahun districts. Refer to annex 9. for details on the population and sample distribution by district."

Because of the restriction that at least 30 learners were available in Grade 2, we chose to add an additional 40 schools to the sample from among smaller schools, with between 3 and 30 grade 2 students. The objective of this supplement was to make the sample more nationally representative, as the restriction reduced the sampling frame for the EGRA/EGMA sample by over 1,500 schools from 7,154 to 4,597.

The 40 schools were chosen in a manner consistent with the original set of EGRA/EGMA schools. The 16 districts formed the strata. In each stratum, the number of schools selected were proportional to the total population of the stratum, and within stratum schools were chosen with probability proportional to size.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

More information pertaining to each of the three instruments can be found below: - School Survey: The School Survey collects data primarily on practices (the quality of service delivery in schools), but also on some de facto policy indicators. It consists of streamlined versions of existing instruments—including Service Delivery Surveys on teachers and inputs/infrastructure, Teach on pedagogical practice, Global Early Child Development Database (GECDD) on school readiness of young children, and the Development World Management Survey (DWMS) on management quality—together with new questions to fill gaps in those instruments. Though the number of modules is similar to the full version of the Service Delivery Indicators (SDI) Survey, the number of items and the complexity of the questions within each module is significantly lower. The School Survey includes 8 short modules: School Information, Teacher Presence, Teacher Survey, Classroom Observation, Teacher Assessment, Early Learner Direct Assessment, School Management Survey, and 4th-grade Student Assessment. For a team of two enumerators, it takes on average about 4 hours to collect all information in a given school. For more information, refer to the Frequently Asked Questions.

• Policy Survey: The Policy Survey collects information to feed into the policy de jure indicators. This survey is filled out by key informants in each country, drawing on their knowledge to identify key elements of the policy framework (as in the SABER approach to policy-data collection that the Bank has used over the past 7 years). The survey includes questions on policies related to teachers, school management, inputs and infrastructure, and learners. In total, there are 52 questions in the survey as of June 2020. The key informant is expected to spend 2-3 days gathering and analyzing the relavant information to answer the survey

Understanding the location of primary schools relative to population is important to contextualize the time, or distance, that students must travel and in defining school catchment areas for planning. However, such analyses are limited due to the perennial problem for absence of geocoded school databases. We, therefore, assembled existing school databases in western Kenya, merged and cleaned them to a unique list of 2170 public day primary school in 2009 and 4682 in 2020. We focused only on PPS managed by local authorities, community, Ministry of Education, non-governmental and religious organisations. These are more accessible by the general public since the introduction of free and compulsory primary education by the Kenyan government in 2003. We also excluded special schools catering for the deaf, blind, and neurologically impaired. The database was geocoded via Google Earth, OpenStreetMap and Geonames while ensuring no schools were located within protected areas or in water bodies by carefully rechecking the coordinates derived from online gazetteers.

There is no description for this dataset.

Educational Youth Indicators

School Enrollment, Attendance, Achievement, and Engagement

By City of Baltimore [source]

About this dataset

This dataset from the Baltimore Neighborhood Indicators Alliance-Jacob France Institute (BNIA-JFI) gathers information about education and youth across Baltimore. Through tracking 27 indicators grouped into seven categories - student enrollment and demographics, dropout rate and high school completion, student attendance, suspensions and expulsions, elementary and middle school student achievement, high school performance, youth labor force participation, and youth civic engagement - BNIA-JFI paints a comprehensive picture of education trends within the city limits. Data sourced from the Baltimore City Public School System (BCPSS), American Community Survey (ACS), as well as Maryland Department of Education allows for cross program comparison to better map connections between educational outcomes affected by neighborhood context. The 2009-2010 school year was used based on readily available data with an approximated 3.4% of address unable to be matched or geocoded and therefore not included in these calculations. Leveraging this data provides perspective to help guide decisions made at local government level that could impact thousands of lives in years ahead

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How to use the dataset

This dataset contains valuable information about the educational performance and youth engagement in Baltimore City. It provides data on 27 indicators, grouped into seven categories: student enrollment and demographics; dropout rate and high school completion; student attendance, suspensions and expulsions; elementary and middle school student achievement; high school performance; youth labor force participation; and youth civic engagement. This dataset can be used to answer important questions about education in Baltimore, such as examining the relationship between community conditions and educational outcomes.

Before using this dataset, it’s important to understand the source of data for each indicator (e.g., Baltimore City Public School System, American Community Survey) so you can understand potential limitations inherent in each data set. Additionally, keep in mind that this dataset does not include students whose home address cannot be geocoded or matched between datasets due to inconsistency of information or other issues - this means that comparisons between some of these indicators may not be as accurate as is achievable with other datasets available from sources such as the Maryland Department of Education or the Baltimore City Public Schools System.

Once you are familiar with where the data comes from you can use it to answer these questions by exploring different trends within Baltimore city over time:

• How have student enrollment numbers changed over time?
• What has been the overall trend in dropout rates across elementary schools?
• Are there any differences in student attendance based on school type?
• What correlations exist between neighborhood community characteristics (such as crime rates or poverty levels), and academic achievement scores?
• How have rates of labor force participation among adolescents shifted year-over-year?

And more! By looking at trends by geography within this diverse city we can gain valuable insight into what factors may play a role influencing educational outcomes for children growing up in different areas around Baltimore City - an essential step for developing methodologies for successful policy interventions targeting our most vulnerable populations!

Research Ideas

• Analyzing the correlation between student achievement and socio-economic status of the neighborhoods in which students live.
• Creating targeted policies that are tailored to address specific educational issues showcased in each Baltimore neighborhood demographic.
• Using data visualizations to demonstrate to residents and community leaders how their area is performing compared to other communities in terms of education, dropout rates, suspension rates, and more

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. [See Other Information](https://creativecommons.org/public...

School Districts are administrative units within which local officials provide public educational services for the area's residents. The Census Bureau obtains school district boundaries, names, local education agency codes, grade ranges, and school district levels annually from state education officials. The Census Bureau collects this information for the primary purpose of providing the U.S. Department of Education with annual estimates of the number of children aged 5 through 17 in families in poverty within each school district, county, and state. This information serves as the basis for the Department of Education to determine the annual allocation of Title I funding to states and school districtsThe Census Bureau tabulates data for four types of school districts: elementary, secondary, unified, and administrative. Each school district is assigned a five-digit code that is unique within state. School district codes are the local education agency number assigned by the Department of Education and are not necessarily in alphabetical order by school district name.Unified school districts provide education to children of all school ages. In general, if there is a unified school district, no elementary or secondary school district exists. If there is an elementary school district, the secondary school district may or may not exist. Administrative school districts were added in 2022 and provide administrative, planning, and educational services for all grade ranges. Currently, the Census Bureau maintains administrative school districts only in Vermont, and they represent supervisory unions and supervisory districts.The Census Bureau categorizes school districts based on the grade ranges for which the school district is financially responsible. These may or may not be the same as the grade ranges that a school district operates. A typical example would be a school district that operates schools for children in grades Kindergarten (KG)-8 and pays a neighboring school district to educate children in grades 9-12. The first school district is operationally responsible for grades KG-8, but financially responsible for grades KG-12. Therefore, the Census Bureau would define the grade range for that school district as KG-12. If an elementary school district is financially responsible for grades KG-12 or Pre-Kindergarten (PK)-12, there will be no secondary school district represented for that area. In cases, where an elementary school district is financially responsible for only lower grades, there is generally a secondary school district that is financially responsible for providing educational services for the upper grades.Download: https://www2.census.gov/geo/tiger/TGRGDB24/tlgdb_2024_a_us_school.gdb.zip Layer: School_District_UnifiedMetadata: https://meta.geo.census.gov/data/existing/decennial/GEO/GPMB/TIGERline/Current_19115/series_tl_2023_unsd.shp.iso.xml

Layer includes school name and address, County-District-School code (CDS) from the California Department of Education (CDE), and county and district (public only) in which each school is located. Other variables include TYPE (public/private), subtype, grade span, and 10 years of employment and enrollment numbers where available (2012/2013 school year to 2021-2022). New in this version is the notation in the STATUS field for schools that are primarily or exclusively virtual. One school - Walt Tyler Elementary in El Dorado County - burned in the 2021 Caldor Fire. CDE lists the school as "active" with employees and students accounted for at the physical location, so it is listed the same here.Unlike previous versions, this database does not include schools that have closed. Closed schools (such as we had them to this version) are available by request, but users should keep in mind that some campuses have hosted multiple schools over the years this database has been produced. There could and sometimes are multiple closed schools on a given campus. All attempts have been made to include all K-12 schools in this database, but especially with private schools, which are not held to the same reporting standards as public schools, some may have been missed.*12/11/23 Update: Added Title by year designation and SACOG Environmental Justice Boundary

Green-Schools, known internationally as Eco-Schools, is an environmental education programme run by An Taisce in partnership with local authorities. It promotes long-term, whole-school action for the environment. There are approximately 253 Dublin City Council schools registered with An Taisce Green-Schools. Schools carry out a number of tasks, run educational programs and environmental projects which are incorporated into everyday school-life. Many of them having already achieved Green-School status and proudly fly the Green Flag outside their school throughout the school year. Following the award of their first Green Flag for the Litter & Waste theme, schools renew their Green Flag award every two years by working on a new theme: Energy, Water, Travel, Biodiversity and Global Citizenship. Dublin City Council supports schools by providing ongoing guidance and support, and also carrying out Green-Schools renewal visits.

United Arab Emirates Number of Schools data was reported at 1,226.000 Unit in 2017. This records a decrease from the previous number of 1,230.000 Unit for 2016. United Arab Emirates Number of Schools data is updated yearly, averaging 1,027.000 Unit from Jun 1976 (Median) to 2017, with 41 observations. The data reached an all-time high of 1,238.000 Unit in 2005 and a record low of 227.000 Unit in 1976. United Arab Emirates Number of Schools data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under Global Database’s United Arab Emirates – Table AE.G005: Education Statistics.

There are 187 Bureau-funded elementary and secondary schools on 64 reservations in 23 states, serving approximately 40,000 Indian students. Of these, 58 are BIE-operated and 129 are tribally controlled under BIE contracts or grants. The Bureau also funds or operates off-reservation boarding schools and peripheral dormitories near reservations for public school students. The BIE also serves American Indian and Alaska Native post-secondary students through higher education scholarships and support funding for tribal colleges and universities. The BIE directly operates two post-secondary institutions: the Haskell Indian Nations University (HINU) in Lawrence, Kansas, and the Southwestern Indian Polytechnic Institute (SIPI) in Albuquerque, New Mexico. Native American boarding schools and dormitories were established in the United States during the late 19th and early 20th centuries. The land where the schools are located is administered by the Bureau of Indian Affairs while the facilities and there operation is under the jurisdiction of the Bureau of Indian Education. As stated in Title 25 CFR Part 32.3, BIE’s mission is to provide quality education opportunities from early childhood through life in accordance with a tribe’s needs for cultural and economic well-being, in keeping with the vast diversity of Indian tribes and Alaska Native villages as distinct cultural and governmental entities. Further, the BIE is to manifest consideration of the whole person by considering the individual's spiritual, mental, physical, and cultural aspects within his or her family and tribal or village context. The BIE school system employs thousands of teachers, administrators and support personnel, while many more work in tribal school systems.

India Number of Schools: Secondary School data was reported at 252,176.000 Unit in 2015. This records an increase from the previous number of 244,653.000 Unit for 2014. India Number of Schools: Secondary School data is updated yearly, averaging 114,629.000 Unit from Sep 1950 (Median) to 2015, with 34 observations. The data reached an all-time high of 252,176.000 Unit in 2015 and a record low of 7,416.000 Unit in 1950. India Number of Schools: Secondary School data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDC001: Number of Schools: Secondary School.

Dataset Overview 📝

The dataset includes the following key indicators, collected for over 200 countries:

• Government Expenditure on Education (% of GDP): Shows the percentage of a country’s GDP allocated to education.
• Literacy Rate (Adult Total): Represents the percentage of the population aged 15 and above who can read and write.
• Primary Completion Rate: The percentage of children who complete their primary education within the official age group.
• Pupil-Teacher Ratio (Primary and Secondary Education): Indicates the average number of students per teacher at the primary and secondary levels.
• School Enrollment Rates (Primary, Secondary, Tertiary): Reflects the percentage of the relevant age group enrolled in schools across different education levels.

Data Source 🌐

World Bank: This dataset is compiled from the World Bank's educational database, providing reliable, updated statistics on educational progress worldwide.

Potential Use Cases 🔍 This dataset is ideal for anyone interested in:

Educational Research: Understanding how education spending and policies impact literacy, enrollment, and overall educational outcomes. Predictive Modeling: Building models to predict educational success factors, such as completion rates and literacy. Global Education Analysis: Analyzing trends in global education systems and how different countries allocate resources to education. Policy Development: Helping governments and organizations make data-driven decisions regarding educational reforms and funding.

Key Questions You Can Explore 🤔

How does government expenditure on education correlate with literacy rates and school enrollment across different regions? What are the trends in pupil-teacher ratios over time, and how do they affect educational outcomes? How do education indicators differ between low-income and high-income countries? Can we predict which countries will achieve universal primary education based on current trends?

Important Notes ⚠️ - Missing Data: Some values may be missing for certain years or countries. Consider using techniques like forward filling or interpolation when working with time series models. - Data Limitations: This dataset provides global averages and may not capture regional disparities within countries.

The number of students in regular programs for youth, general programs for adults, and vocational programs for youth and adults in public and private/independent schools, and home-schooling at the elementary-secondary level, by school type and program type.

This survey by the United Nations Educational, Scientific and Cultural Organization (UNESCO), the United Nations Children's Fund (UNICEF) and the World Bank seeks to collect information on national education responses to school closures related to the COVID-19 pandemic. The questionnaire is designed for Ministry of Education officials at central or decentralized level in charge of school education. The questionnaire does not cover higher education or technical and vocational education and training. Analysis of results will allow for policy learning across the diversity of country settings in order to better inform local/national responses and prepare for the reopening of schools. The survey will be run on a regular basis to ensure that the latest impact and responses are captured. In light of the current education crisis, the COVID-19 education response coordinated by UNESCO with our partners is deemed urgent. A first wave of data collection started in May and lasted until mid-June 2020. A second wave of data collection will start at the beginning of July. A link to the online survey questionnaire, as well as other formats, will be available shortly.

School locations in Kenya. It comprises Primary and Secondary Schools. The dataset was provided by Kenya Ministry of Education.

Green-Schools, known internationally as Eco-Schools, is an environmental education programme run by An Taisce and local authorities, designed to promote and acknowledge whole school action for the environment. Schools undertake long term projects on environmental issues such as waste and litter management, energy, water, sustainable transport and biodiversity. On a practical front the Green-Schools programme helps schools to reduce waste and save money on waste charges and it also helps schools to conserve energy and water, therefore saving on utility bills. There are approximately 100 schools in the Dún Laoghaire-Rathdown County Council area registered with An Taisce Green-Schools. Many of these schools have achieved Green-Schools status and proudly fly the internationally recognised Green Flag. Following the award of their first Green Flag for the Litter & Waste theme schools renew their Green Flag award every two years by working on a new theme: Energy, Water, Travel, Biodiversity and Global Citizenship.