The Virginia Department of Education (VDOE) annually collects statistics on the number of students dropped out in public school on September 30 for students in grades 7-12 for the year 2020 - 2021

The Virginia Department of Education (VDOE) annually collects statistics on the number of students dropped out in public school on September 30 for students in grades 7-12 for the year 2021 - 2022.

This dataset contains records for each public elementary and secondary education agency in the 50 states, the District of Columbia, and United States territories (American Samoa, Guam, Puerto Rico, the Virgin Islands, and the Marshall Islands) for 1995-1996. Records in this file provide the National Center for Education Statistics and state identification numbers, agency name, address, and telephone number, county name, agency type (regular school district, component of supervisory union, headquarters of supervisory union, regional educational service agency, state-operated agency, federally-operated agency, other), metropolitan status, Metropolitan Statistical Area (MSA) code if applicable, number of students (ungraded/PK-12), number of students with special Individual Education Programs (IEPs), number of high school completers (regular diploma/other diploma/other completers), number of classroom teachers and staff, and grades 7-12 dropout data.

Local Law 14 (2016) requires that NYCDOE provide citywide Health Education data, disaggregated by community school district, city council district and each individual school. This provides information about the number and percent of students scheduled for at least one semester of health education as reported through the STARS database. NYSED mandates HIV/AIDS instruction with a set number of lessons for every student, every year. There are five required lessons per year for grade levels of K-6 and six required lessons for grade level 7-8; teachers must use the NYCDOE HIV\AIDS curriculum.

This data set contains the replication data and supplements for the article "Knowing, Doing, and Feeling: A three-year, mixed-methods study of undergraduates’ information literacy development." The survey data is from two samples: - cross-sectional sample (different students at the same point in time) - longitudinal sample (the same students and different points in time)Surveys were distributed via Qualtrics during the students' first and sixth semesters. Quantitative and qualitative data were collected and used to describe students' IL development over 3 years. Statistics from the quantitative data were analyzed in SPSS. The qualitative data was coded and analyzed thematically in NVivo. The qualitative, textual data is from semi-structured interviews with sixth-semester students in psychology at UiT, both focus groups and individual interviews. All data were collected as part of the contact author's PhD research on information literacy (IL) at UiT. The following files are included in this data set: 1. A README file which explains the quantitative data files. (2 file formats: .txt, .pdf)2. The consent form for participants (in Norwegian). (2 file formats: .txt, .pdf)3. Six data files with survey results from UiT psychology undergraduate students for the cross-sectional (n=209) and longitudinal (n=56) samples, in 3 formats (.dat, .csv, .sav). The data was collected in Qualtrics from fall 2019 to fall 2022. 4. Interview guide for 3 focus group interviews. File format: .txt5. Interview guides for 7 individual interviews - first round (n=4) and second round (n=3). File format: .txt 6. The 21-item IL test (Tromsø Information Literacy Test = TILT), in English and Norwegian. TILT is used for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know about information literacy. (2 file formats: .txt, .pdf)7. Survey questions related to interest - specifically students' interest in being or becoming information literate - in 3 parts (all in English and Norwegian): a) information and questions about the 4 phases of interest; b) interest questionnaire with 26 items in 7 subscales (Tromsø Interest Questionnaire - TRIQ); c) Survey questions about IL and interest, need, and intent. (2 file formats: .txt, .pdf)8. Information about the assignment-based measures used to measure what students do in practice when evaluating and using sources. Students were evaluated with these measures in their first and sixth semesters. (2 file formats: .txt, .pdf)9. The Norwegain Centre for Research Data's (NSD) 2019 assessment of the notification form for personal data for the PhD research project. In Norwegian. (Format: .pdf)

Trinidad and Tobago TT: Persistence to Grade 5: % of Cohort data was reported at 92.066 % in 2009. This records a decrease from the previous number of 96.144 % for 2008. Trinidad and Tobago TT: Persistence to Grade 5: % of Cohort data is updated yearly, averaging 94.703 % from Dec 1990 (Median) to 2009, with 7 observations. The data reached an all-time high of 96.906 % in 2003 and a record low of 82.081 % in 1990. Trinidad and Tobago TT: Persistence to Grade 5: % of Cohort data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Trinidad and Tobago – Table TT.World Bank: Education Statistics. Persistence to grade 5 (percentage of cohort reaching grade 5) is the share of children enrolled in the first grade of primary school who eventually reach grade 5. The estimate is based on the reconstructed cohort method.; ; United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.; Weighted average;

The Reagent Grade 7-Hydroxycoumarin Glucoside market is witnessing significant growth as industries increasingly recognize its value in various applications, particularly in pharmaceuticals and agricultural research. This compound, known for its antioxidant properties, is primarily utilized in biochemical assays, re

The data set records the statistical data of natural grassland grade area in Zeku County, Qinghai Province in 1988 and 2012. The data are classified and counted according to the grade code of natural grassland. The grassland is divided into five grades: excellent, good, medium, low and poor based on the grassland type. The classification criteria of each grade are as follows: Grade I (excellent) Grassland: the weight of excellent forage accounts for more than 60%; Grade II (good grade) Grassland: the weight of grass above good grade accounts for more than 60%, and that of other types accounts for 40%; Grade III (medium) Grassland: the weight of forages above the medium category accounts for more than 60%, and that of other categories accounts for 40%; Grade IV (low) Grassland: the weight of grass above the low category accounts for more than 60%, and that of other categories accounts for 40%; Grade V (inferior) Grassland: the weight of inferior forage accounts for more than 40% The grassland level is divided into 8 levels according to the fresh grass yield. Standards at all levels are as follows: Level 1 Grassland: more than 12000k g of fresh grass per hectare of grassland; Level 2 Grassland: 9000kg ~ 12000kg fresh grass per hectare; Level 3 Grassland: 6000kg ~ 9000kg fresh grass per hectare; Level 4 Grassland: 4500kg ~ 6000kg fresh grass per hectare; Level 5 Grassland: 30001kg ~ 4500kg fresh grass per hectare; Grade 6 Grassland: 1500kg ~ 3000kg fresh grass per hectare; Grade 7 Grassland: 750KG ~ 1500kg fresh grass per hectare; Grade 8 Grassland: fresh grass per hectare is less than 750KG. The data are compiled from the grassland station of Qinghai Province and the grassland resources statistics of Qinghai Province issued in 1988 and 2012. The data set contains two data tables: statistical data of natural grassland grade area in Zeku County (2012) and statistical data of natural grassland grade in Zeku County (1988). The data table structure is similar. For example, the statistical data of natural grassland grade area in Zeku County (2012) has 9 fields: Field 1: Total Field 2: Level 1 Field 3: Level 2 Field 4: Level 3 Field 5: Level 4 Field 6: Level 5 Field 7:6 level Field 8: Level 7 Field 9: level 8

In 2024, five percent of GCSE entries in England were awarded the highest grade of 9, with a further 7.1 percent of entries being awarded an 8, the second-highest grade. A 5 grade was the most common individual grade level achieved by GCSE students, at 16.6 percent of all entries.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

The data set records the statistical data of natural grassland grade area in Chengduo County, Qinghai Province in 1988 and 2012. The data are classified and counted according to the grade code of natural grassland. The grassland is divided into five grades: excellent, good, medium, low and poor with grassland type as the basic unit. The classification criteria of each grade are as follows: Grade I (excellent) Grassland: the weight of excellent forage accounts for more than 60%; Grade II (good grade) Grassland: the weight of grass above good grade accounts for more than 60%, and that of other types accounts for 40%; Grade III (medium) Grassland: the weight of forages above the medium category accounts for more than 60%, and that of other categories accounts for 40%; Grade IV (low) Grassland: the weight of grass above the low category accounts for more than 60%, and that of other categories accounts for 40%; Grade V (inferior) Grassland: the weight of inferior forage accounts for more than 40% The grassland level is divided into 8 levels according to the fresh grass yield. Standards at all levels are as follows: Level 1 Grassland: more than 12000k g of fresh grass per hectare of grassland; Level 2 Grassland: 9000kg ~ 12000kg fresh grass per hectare; Level 3 Grassland: 6000kg ~ 9000kg fresh grass per hectare; Level 4 Grassland: 4500kg ~ 6000kg fresh grass per hectare; Level 5 Grassland: 30001kg ~ 4500kg fresh grass per hectare; Grade 6 Grassland: 1500kg ~ 3000kg fresh grass per hectare; Grade 7 Grassland: 750KG ~ 1500kg fresh grass per hectare; Grade 8 Grassland: fresh grass per hectare is less than 750KG. The data are compiled from the grassland station of Qinghai Province and the grassland resources statistics of Qinghai Province issued in 1988 and 2012. The data set contains two data tables, namely: statistical data of natural grassland grade area in Chengduo county (2012) and statistical data of natural grassland grade in Chengduo county (1988). The data table structure is similar. For example, the statistical data of natural grassland grade area in Chengduo county (2012) has 9 fields: Field 1: Total Field 2: Level 1 Field 3: Level 2 Field 4: Level 3 Field 5: Level 4 Field 6: Level 5 Field 7:6 level Field 8: Level 7 Field 9: level 8

The Virginia Department of Education (VDOE) annually collects statistics on the number of students dropped out in public school on September 30 for students in grades 7-12 for the year 2019 - 2020

The data set records the statistical data of natural grassland grade area in Guinan County, Qinghai Province in 1988 and 2012. The data are classified and counted according to the grade code of natural grassland. The grassland is divided into five grades: excellent, good, medium, low and inferior based on the grassland type. The classification criteria of each grade are as follows: Grade I (excellent) Grassland: the weight of excellent forage accounts for more than 60%; Grade II (good grade) Grassland: the weight of grass above good grade accounts for more than 60%, and that of other types accounts for 40%; Grade III (medium) Grassland: the weight of forages above the medium category accounts for more than 60%, and that of other categories accounts for 40%; Grade IV (low) Grassland: the weight of grass above the low category accounts for more than 60%, and that of other categories accounts for 40%; Grade V (inferior) Grassland: the weight of inferior forage accounts for more than 40% The grassland level is divided into 8 levels according to the fresh grass yield. Standards at all levels are as follows: Level 1 Grassland: more than 12000k g of fresh grass per hectare of grassland; Level 2 Grassland: 9000kg ~ 12000kg fresh grass per hectare; Level 3 Grassland: 6000kg ~ 9000kg fresh grass per hectare; Level 4 Grassland: 4500kg ~ 6000kg fresh grass per hectare; Level 5 Grassland: 30001kg ~ 4500kg fresh grass per hectare; Grade 6 Grassland: 1500kg ~ 3000kg fresh grass per hectare; Grade 7 Grassland: 750KG ~ 1500kg fresh grass per hectare; Grade 8 Grassland: fresh grass per hectare is less than 750KG. The data are compiled from the grassland station of Qinghai Province and the grassland resources statistics of Qinghai Province issued in 1988 and 2012. The data set contains two data tables, namely, the statistical data of natural grassland grade area in Guinan county (2012) and the statistical data of natural grassland grade in Guinan county (1988). The data table structure is similar. For example, there are 9 fields in the statistical data of natural grassland grade area in Guinan county (2012): Field 1: Total Field 2: Level 1 Field 3: Level 2 Field 4: Level 3 Field 5: Level 4 Field 6: Level 5 Field 7:6 level Field 8: Level 7 Field 9: level 8

This statistic shows the percentage of Canadian students in grades 7 to 12 who used select illicit drugs in the past year as of 2017-2018. As of that time, around 2.2 percent of Canadian students had used cocaine in the past year.

In 2023, there were a total of 12,592 7-Eleven convenience stores in operation throughout the United States. The company had U.S. retail sales of 27.88 billion U.S. dollars that year.

Many students in secondary school find physical science and mathematics uninteresting and difficult to learn with understanding. This leaves important gaps in their education and narrows the range of careers open to them. This project will redesign key aspects of the teaching and learning of these subjects, devising a principled approach which is more effective in engaging students and guiding them towards understanding. Insights from several social scientific fields – concerned with conceptual growth, identity formation, classroom dialogue, collaborative learning, and relations between everyday and formal understanding – will guide the design of an intervention suitable for widespread use in normal school settings. This research project will generate tried-and-tested resources for training teachers and teaching students, and improve understanding of teaching and learning processes in science and mathematics. Phase 1 will involve collaboration with teacher co-researchers from several schools to devise and pilot the intervention. In Phase 2, classroom implementation by the teacher co-researchers will be analysed, and the intervention refined accordingly. Phase 3 will evaluate repeated implementation by the teacher co-researchers, alongside initial implementation by teachers from a wider range of schools, compared to the established practice of a control group of teachers from similar schools. This dataset was collected during the 2010/11 school year as part of a randomised field trial of the epiSTEMe intervention with Year 7 mathematics and science classes in English secondary schools. An open invitation was sent to schools across the Eastern region and into North London. Schools were invited to participate on the basis that teachers from schools later assigned to the intervention group would follow the associated 2-day training programme for the intervention (and receive the associated classroom materials) prior to the field trial; teachers nominated by schools later assigned to the control group would participate in the field trial using their normal teaching approach, before receiving training/materials after its completion. All schools completing the application process were assigned to an experimental group using an approach in which schools were paired according to school type and contextual value-added score, and then randomly allocated between the intervention or control group. One school withdrew prior to the start of the field trial because of staffing shortages. This yielded 25 participating schools; 12 in the intervention group, 13 in the control. Schools were requested to nominate 2 teachers of mathematics and 2 teachers of science, each with a Year 7 class; in the event, not all schools participated in both subjects or nominated 2 teachers in a subject. Schools were also recommended to choose classes in which a majority of pupils had attained level 4 in the relevant subject in end-of-KS2 assessment (a level achieved by around 80% of pupils nationally in mathematics and science). Because many schools did not timetable their Year 7 classes until close to the start of the school year, the assignment of teachers to classes had to take place vicariously within each school without any involvement of the research team. The field trial was scheduled to be undertaken by 70 teachers with a Year 7 mathematics or science class. After attrition of 10 teachers/classes (i.e. insufficient data returns made), the number of teachers/classes included in the analysis was 60: in Mathematics, 12 intervention, 16 control; in Science, 16 intervention, 16 control. In the case of 5 intervention group teachers, data was also collected in a second Year 7 class, but it did not prove necessary to fall back on this data for the analysis. A 25-item attitude questionnaire (in parallel mathematics and science versions) was administered to each participating class at the start and end of the school year. A series of pre-, immediate post- and deferred post-tests tailored to the particular topic were administered to each class when (and if) it studied each of the target topics over the course of the school year. A 20-item opinion questionnaire (in parallel versions for each topic) was also administered to each class after teaching of the target topic was complete. Background data about students was gathered from school records and/or student questionnaire. A table in the documentation shows the 75 classes that set out to participate in the study. Whatever data were collected from these classes are included in the Original data files. As explained above, 60 classes were retained for analysis. For these classes, the variables on which analysis was based are included in the Composite data files. For the 15 classes excluded from the analysis, the reason is shown in the table.

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

Sao Tome and Principe ST: Persistence to Grade 5: Female: % of Cohort data was reported at 88.519 % in 2010. This records an increase from the previous number of 81.211 % for 2008. Sao Tome and Principe ST: Persistence to Grade 5: Female: % of Cohort data is updated yearly, averaging 75.694 % from Dec 2001 (Median) to 2010, with 7 observations. The data reached an all-time high of 88.519 % in 2010 and a record low of 65.369 % in 2001. Sao Tome and Principe ST: Persistence to Grade 5: Female: % of Cohort data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sao Tome and Principe – Table ST.World Bank: Education Statistics. Persistence to grade 5 (percentage of cohort reaching grade 5) is the share of children enrolled in the first grade of primary school who eventually reach grade 5. The estimate is based on the reconstructed cohort method.; ; United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.; Weighted average;

Key Table Information.Table Title.Mining: Summary Statistics for the U.S., States, and Selected Geographies: 2022.Table ID.ECNBASIC2022.EC2221BASIC.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Summary Statistics for the U.S., States, and Selected Geographies: 2022.Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2024-12-05.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesProduction and/or development and exploration workers annual wages ($1,000)Production and/or development and exploration workers for pay period including March 12Construction, production and/or development and exploration workers annual hours (1,000)Other employees annual wages ($1,000)Other employees for pay period including March 12Total fringe benefits ($1,000)Employer's cost for health insurance ($1,000)Employer's cost for defined benefit pension plans ($1,000)Employer's cost for defined contribution plans ($1,000)Employer's cost for other fringe benefits ($1,000)Total cost of supplies and/or materials ($1,000)Cost of materials, components, packaging and/or supplies used, minerals received, or purchased machinery installed ($1,000)Cost of resales ($1,000)Cost of contract work ($1,000)Cost of purchased fuels consumed ($1,000)Cost of purchased electricity ($1,000)Quantity of electricity purchased for heat and power (1,000 kWh)Quantity of generated electricity (1,000 kWh)Quantity of electricity sold or transferred (1,000 kWh) Value added ($1,000)Total inventories, beginning of year ($1,000)Finished goods or minerals products, crude petroleum, and natural gas liquids inventories, beginning of year ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, beginning of year ($1,000)Total inventories, end of year ($1,000)Finished goods or minerals products, crude petroleum, and natural gas liquids inventories, end of year ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, end of year ($1,000)Capital expenditures (except land and mineral rights) ($1,000)Total capital expenditures for buildings, structures, machinery, and equipment (new and used) ($1,000)Capital expenditures for mineral exploration and development ($1,000)Capital expenditures for mineral land and rights ($1,000)Lease rents ($1,000)Expensed mineral exploration, development, land, and rights ($1,000)Current operating expenses for exploration, development, and mineral land and rights ($1,000)Current operating expenses for royalty payments ($1,000)Total rental payments or lease payments ($1,000)Rental payments or lease payments for buildings and other structures ($1,000)Rental payments or lease payments for machinery and equipment ($1,000)Total other operating expenses ($1,000)Temporary staff and leased employee expenses ($1,000)Expensed computer hardware and other equipment ($1,000)Expensed purchases of software ($1,000)Data processing and other purchased computer services ($1,000)Communication services ($1,000)Repair and maintenance services of buildings and/or machinery ($1,000) Refuse removal (including hazardous waste) services ($1,000)Advertising and promotional services ($1,000)Purchased professional and technical services ($1,000) Taxes and license fees ($1,000)All other operating expenses ($1,000)Range indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization...