This collection contains measures of primary and secondary roads (highways and main arteries) per United States census tract and per United States ZIP code tabulation area (ZCTA) in 2010 and 2020. These measures may be used as a proxy for heavy traffic, high traffic speeds, and impediments to walking or biking. Variables include: counts of primary, secondary, and all streets per tract and per ZCTA; total length of primary, secondary, and all streets per tract and per ZCTA; ratio of primary and/or secondary road counts to all roads; and ratio of length of primary/secondary roads to all streets.

Gross enrolment ratio, primary and secondary, male (%) in Myanmar was reported at 87.07 % in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Myanmar - Gross enrolment ratio, primary and secondary, male - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not bedivided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

In 2023, ** years of free primary and secondary school education in Sri Lanka, Australia, and New Zealand were guaranteed in legal frameworks. In comparison, only **** years of free pre-tertiary school education were guaranteed in Bangladesh, Myanmar, and Vietnam that year.

Primary and secondary energy by fuel type in natural units (coal, natural gas, steam, etc.) and supply and demand characteristics (production, exports, imports, inter-regional transfers, etc.).

In the "transition from PRIMary to Secondary education (PRIMS)" project we collected data to supplement the Netherlands Cohort Study on Education (NCO, Nationaal Cohortonderzoek Onderwijs, for more information see Haelermans et al. 2020). The PRIMS project was funded by the Nationaal Regieorgaan Onderwijs (NRO) and carried out by the University of Groningen (RUG) and the University of Amsterdam (UvA), in collaboration with NCO. PRIMS aims to study the role of peers in the transition from primary to secondary school. More specifically, we want to shed light on educational inequalities in the transition from primary to secondary education, as well as students’ social integration during this school transition. Using the PRIMS data set, we can address questions on the influence of peers on educational decisions, aspirations, and performance, as well as the impact of positive and negative peer relationships on school well-being and academic achievement.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not be divided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

As of May 2023, there were around * million pupils enrolled in elementary school across Japan. The figures showed a continuous decrease in the past two decades, indicating a clear impact of the low fertility rate in the country. On the other hand, compulsory education schools that operate in the newest type of school year system established in 2016 showed an increasing number of students.

This dataset contains a list of primary and secondary schools, with their addresses.

Note: For the moment, only the geographical coordinates of secondary schools are available. Missing geolocations will be added in the near future, so that we can offer you the most complete data set possible.

Primary and secondary energy by fuel type in terajoules (coal, natural gas, steam, etc.) and supply and demand characteristics (production, exports, imports, inter-regional transfers, etc.).

Data on school land area of national primary and secondary schools across the country

The schools, districts, and other educational institutions in the Detroit Educational Institutions datasets were identified from the State of Michigan Center for Educational Performance and Information (CEPI) Educational Entity Master (EEM) database. Schools of all statuses (Open - Active; Open - Pending; Open - Inactive; Open - Under construction/remodeling; Open - Vacant/empty; Closed – Pending; Closed) are included in the dataset to provide access to information about schools that currently and previously operated in Detroit. Educational institutions with a mailing address in Detroit but a physical location outside the City are not included in this dataset. Each record in the dataset represents an educational entity, which may be a school, a district, or other entity directly associated with an educational institution. The word, "entity" is used in field (i.e., column) names and descriptions when a field is applicable to multiple types units associated with an educational entity (e.g., if applicable to schools, districts, and other facilities).Link to metadata: https://cepi.state.mi.us/eem/Documents/ColumnDescriptions.pdf

In 2022, 53.8 percent of the population aged between 3 and 34 were enrolled in either primary or secondary school in the United States. Of those aged 3 to 4 years old, 53.3 percent were enrolled in school.

Surveyed at the beginning of the 2023/2024 school year, only ********* of French people considered the current functioning of the education system in middle and high schools to be good. In contrast, preschools were the ones in which education worked best. However, the share of Emmanuel Macron's voters who believed that the education system was working well was higher than that of the population as a whole.

The paper discussed sources of data. Data is a set of values of qualitative or quantitative variables. Data is facts or figures from which conclusions can be drawn. Before one can present and interpret information, there has to be a process of gathering and sorting data. Just as trees are the raw material from which paper is produced, so too, can data be viewed as the raw material from which information is obtained. It is evident from the above discussion that primary data is an original and unique data, which is directly collected by the researcher from a source such as observations, surveys, questionnaires, case studies and interviews according to his requirements

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not bedivided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

International students’ mental health has become an increasing concern in recent years, as more students leave their country for better education. They experience a wide range of challenges while studying abroad that have an impact on their psychological well-being. These challenges can include language obstacles, cultural differences, homesickness, financial issues and other elements that could severely impact the mental health of international students. Given the limited research on the demographic, cultural, and psychosocial variables that influence international students’ mental health, and the scarcity of studies on the use of machine learning algorithms in this area, this study aimed to analyse data to understand the demographic, cultural factors, and psychosocial factors that impact mental health of international students. Additionally, this paper aimed to build a machine learning-based model for predicting depression among international students in the United Kingdom. This study utilized both primary data gathered through an online survey questionnaire targeted at international students and secondary data was sourced from the ’A Dataset of Students’ Mental Health and Help-Seeking Behaviors in a Multicultural Environment,’ focusing exclusively on international student data within this dataset. We conducted data analysis on the primary data and constructed models using the secondary data for predicting depression among international students. The secondary dataset is divided into training (70%) and testing (30%) sets for analysis, employing four machine learning models: Logistic Regression, Decision Tree, Random Forest, and K Nearest Neighbor. To assess each algorithm’s performance, we considered metrics such as Accuracy, Sensitivity, Specificity, Precision and AU-ROC curve. This study identifies significant demographic variables (e.g., loan status, gender, age, marital status) and psychosocial factors (financial difficulties, academic stress, homesickness, loneliness) contributing to international students’ mental health. Among the machine learning models, the Random Forest model demonstrated the highest accuracy, achieving an 80% accuracy rate in predicting depression.

School enrollment, primary and secondary (gross), gender parity index (GPI) in Honduras was reported at 1.0565 % in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Honduras - Ratio of girls to boys in primary and secondary education - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

School enrollment, primary and secondary (gross), gender parity index (GPI) in United States was reported at 0.98683 % in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Ratio of girls to boys in primary and secondary education - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.