Pupil Identity Data and School Enrolment Data for all post-primary school learners

The number of pupils recorded with an exemption from the study of Irish each academic year from 2017-2018 onwards.Circular 0055/2022 outlines the grounds where an exemption from the study of Irish may be granted and is in place since 1/9/2022. Exemptions from the study of Irish were granted to pupils in primary schools under Circular 0010/1994 up to 1/9/2019 when that circular was superseded by Circular 0053/2019. OwnerCurriculum ＆ Assessment Policy UnitContact informationCAP_helpdesk@education.gov.ie +353 (0)1 889 2257Time Period Covered2017 - 2025Update FrequencyYearlyGeographic ResolutionNationalData Source(s)Post-Primary Online DatabaseQuality AssuranceData are extracted at point in time Known LimitationsData is self-reported by schools to the Post-Primary Online Database. Details of the total exemptions granted are those which were recorded on the Post-Primary Online Database on 31 July following the academic year reported. Details of the total exemptions recorded to be in place at the time of that academic year’s school census (1 October following the commencement of the academic year reported)Related Datasets(auto managed)Supporting Documentation (max 100 words)An exemption from the study of Irish may be granted by school management in the exceptional circumstances outlined in circular 0054/2022 (Primary) and 0055/2022 (Post Primary). The government website has guidelines and Frequently Asked Questions on exemptions from the study of Irish which can be accessed here: https://www.gov.ie/en/service/irish-exemption/

1. Reference Year: 2022/20232. Data Source: Department of Education's Primary Online Database (POD) system3. Data is reported on an academic year basis which starts in September of a year and finishes in June of the following year (First week of June for post-primary schools and last week of June for primary schools)

Focus of this study is the data of the educational system, collected in different European countries. Mainly there is information about the pupils’ number in primary schools, secondary schools and in universities collected. There is no information about the vocational education and adult education, because the differences between these systems are too strong.

In order to get comparable data, the pupils’ registration quota (relative school-/high-school attendance) were collected (this means the proportion of pupils or students of the corresponding population’s age cohort).

For each country the information are presented in three different tables:

• Information about the primary school-system,
• Information about the secondary school-system,
• Information about the higher education (universities).

Furthermore, time series about the population of the countries as well as about the population’s alphabetisation quota in Prussia/the German Empire, France, England and Wales and Russia are available.

Topics:

Time Series available via HISTAT

In the ZA-Online-Database HISTAT are tables available containing the following information for each country:

Primary-Schools: Number of pupils in all schools, abs. Number of pupils in public schools, abs. Number of pupils in private schools in % of all pupils in primary schools. Number of pupils in % of the population’s 5-14 age group in primary schools. Number of pupils in % of the population’s 5-14 age group in public primary schools. Number of teachers in all primary schools. Number of teachers in public primary schools. Pupils per teacher in all primary schools, in public schools and in private schools. Primary Teacher Schools: Number of students and number of female students.

Secondary Schools: Number of pupils in post-primary schools, abs. Number of pupils in lower secondary schools, abs. Number of pupils in % of the population’s 10-14 age group in lower secondary schools. Number of pupils in all schools of general higher secondary education, abs. Number of pupils in public schools of general higher secondary education, abs. Number of pupils in private schools of general higher secondary education in % of all schools. Number of pupils in % of the population’s 10-19 age group in all schools of general higher secondary education. Number of pupils in % of the population’s 10-19 age group in public schools of general higher secondary education. Number of female pupils in all schools of general higher secondary education. Percentage of female pupils in all schools of general higher secondary education. Number of pupils in Technical / Commercial higher secondary schools. Number of pupils in all higher secondary schools, abs. Number of pupils in all higher secondary schools in % of the population’s 10-19 age group.

Universities, higher education: Number of students in technological institutes of higher education, abs. Number of students in commercial institutes of higher education, abs. Number of students in other institutes of higher education, abs. Number of students in universities, abs. Number of students in universities in % of the population’s 20-24 age group. Number of female students in % of all students. Students by faculty in percent of all students: theology, law medicine, philosophy, mathematics/science, economics/social sciences, technology. Total number of students in higher education, abs. Total number of students in higher education in % of the population’s 20-24 age group.

Additional: Estimated population (including the USA and Russia). Concerning Prussia/German Empire, France, England and Wales, Russia: Alphabetisation-quota. Development of the primary education per 100 inhabitants / development of the secondary education per 1000 inhabitants / Development of the higher education per 10 000 inhabitants.

The number of pupils recorded with an exemption from the study of Irish each academic year from 2017-2018 onwards.Circular 0054/2022 outlines the grounds where an exemption from the study of Irish may be granted and is in place since 1/9/2022. Exemptions from the study of Irish were granted to pupils in primary schools under Circular 0012/1996 up to 1/9/2019 when that circular was superseded by Circular 0052/2019. OwnerCurriculum ＆ Assessment Policy UnitContact informationCAP_helpdesk@education.gov.ie +353 (0)1 889 2257Time Period Covered2017 - 2025Update FrequencyYearlyGeographic ResolutionNationalData Source(s)Primary Online DatabaseQuality AssuranceData are extracted at point in time Known Limitations Data is self-reported by schools to the Primary Online Database. Details of the total exemptions granted are those which were recorded on the Primary Online Database on 31 July following the academic year reported. Details of the total exemptions recorded to be in place at the time of that academic year’s school census (1 October following the commencement of the academic year reported)Related Datasets(auto managed)Supporting DocumentationAn exemption from the study of Irish may be granted by school management in the exceptional circumstances outlined in circular 0054/2022 (Primary) and 0055/2022 (Post Primary). The government website has guidelines and Frequently Asked Questions on exemptions from the study of Irish which can be accessed here: https://www.gov.ie/en/service/irish-exemption/

Social Media News Sentiment Dataset Source: [Insert your Kaggle Link Here]

Context & Purpose: This dataset is a subset of data available in the UCI Machine Learning Repository. It contains approximately 100,000 news items spanning from November 2015 to July 2016, covering four major topics: Economy, Microsoft, Obama, and Palestine. In this assignment, this dataset is used to populate the Posts and Users tables with realistic data. Using real-world headlines and sentiment scores allows us to test the database's ability to handle text-heavy content and analyze social media engagement metrics.

Significance of Columns: According to the assignment requirements, here is how the dataset attributes map to our database structure:

IDLink (Numeric): This serves as the Primary Key (e.g., post_id) for the Posts table. It ensures that every news item has a unique identifier, maintaining entity integrity.

Title & Headline (String): These columns are used to populate the content attributes in the Posts table. They represent the core information that users consume on the platform. Source (String): This column identifies the publisher (e.g., a news outlet). In our database design, this maps to the Users table (username), establishing a Foreign Key relationship (1:M) between a User and their Posts.

Publish-Date (Timestamp): This maps to the created_at column. It is crucial for maintaining the chronological order of posts and for queries that filter content by date. Facebook / Google-Plus / LinkedIn (Numeric): These values represent the popularity or engagement level of a post. In the database, these figures simulate data for the Likes table or engagement statistics, demonstrating how social interactions are stored. SentimentTitle & SentimentHeadline: These provide metadata about the emotional tone of the content. While not a structural key, storing this data allows for advanced database queries, such as filtering for "positive" or "negative" news.

Scientific discovery can be aided when data is shared following the principles of findability, accessibility, interoperability, reusability (FAIR) data (Wilkinson et al., 2016). Recent discussions in the palaeoclimate literature have focussed on defining the ideal database format for storing data and associated metadata. Here, we highlight an often overlooked primary process in widespread adoption of FAIR data, namely the systematic creation of machine readable data at source (i.e. at the field and laboratory level). We detail a file naming and structuring method that was used at LSCE to store data in text file format in a way that is machine-readable, and also human-friendly to persons of all levels of computer proficiency, thus encouraging the adoption of a machine-readable ethos at the very start of a project. Thanks to the relative simplicity of downcore palaeoclimate data, we demonstrate the power of this simple but powerful file format to function as a basic database in itself: we provide a Matlab-based GUI tool that allows users to search and visualise data by sediment core location, proxy type and species type. The adoption of similarily accessible, machine-readable file formats at other laboratories will promote data sharing within projects, while also allowing for the automation of submission of data to online database repositories with particular formatting and/or metadata requirements, thus reducing post-hoc workload.

Andrew Wharton's Actionable US Consumer Email Database hosts over 650 million email addresses that have been active within the last 36 months. This database is fully CAN-SPAM compliant and 100% opted-in for Third Party Use.

This Email Address database successfully connects you with your customers and/or prospects at their most recent, deliverable online address. and Increase impression rates, deliverability, and engagement in your digital campaigns.

The Email Address Data is 100% populated with email address, HEMS (MD5, Sha1, Sha256) first name, last name, postal address (primary and secondary), IP Address, Time Stamp(s) for Last Registration, Verification, and First Seen. An enhanced version of the database is available with Date-of-Birth (where available), Phone (mobile and landline) and MAIDs to Hashed email conversion.

The Andrews Wharton Actionable US Consumer Email Database is updated monthly. A complete replacement database or new adds are available as update files.

Contact us at successdelivered@andrewswharton.com or visit us at www.andrewswharton.com to learn more about this dataset.

NUOnet Vision: Efficient use of nutrients to optimize production and product quality of food for animals and humans, fuel and fiber in a sustainable manner that contributes to ecosystem services. This record contains the DET and Data Dictionary for NUOnet - the data files may be found at https://usdaars.maps.arcgis.com/apps/MapSeries/index.html?appid=e90392a99d5c427487c6c37cf6d47844 Best nutrient management practices are critical for maintaining profitable economic returns, sustaining higher yields, lowering environmental impacts, optimizing nutritional quality, and providing ecosystem services. Best management practices that improve nutrient use efficiencies can reduce nutrient losses from agricultural systems. However, we need to improve our understanding of biological, physical and chemical influences on nutrient processes. For instance, crop use efficiency of nitrogen (N), the primary macronutrient regulating yield and protein content, can be reduced by processes such as denitrification (N2O and N2 emission), leaching (NH4-N, NO3-N, and organic-N), ammonia (NH3-N,) volatilization, surface runoff and erosion, disease, and non-crop competition. Similarly, we need to obtain more information about biological and physical cycles of nutrients, especially phosphorus (P), including factors that influence nutrient availability from fertilizers, crop residues, cover crops, manures, and other byproducts. We need a better understanding of relationships between soil biological communities and ecosystems, including plant roots and root exudates, and availability and uptake of macro- and micro-nutrients. In addition, we need information regarding how these practices impact yields, organoleptic qualities, and the macro- and micro-nutritional composition of plants. This information will improve our ability to develop best nutrient management practices. Optimal soil nutrient levels are critical for maximizing economic returns, increasing sustainable yields, lowering environmental impacts, sustaining ecosystem services and optimizing nutritional and organoleptic qualities of human and animal foods. Efficient management practices are crucial for increasing economic returns for land managers in a sustainable manner while producing high quality of food for animals and humans with reduced off-site transfer of nutrients from agricultural areas in watersheds. Optimizing N and P inputs requires more information about nutrient inputs from fertilizers, manures, composts, agricultural byproducts, cover crops, and other nutrient sources in addition to nutrient cycling within soils. This requires data from long-term nutrient management studies across a wide range of soils, crops, and environmental conditions. Land management needs are to connect nutrient management practices for crops with nutrient use efficiency; crop quality; crop chemical composition and nutritional value, quality and acceptability for animal and human health. Development of databases that enable the scientific exploration of connections among data generated from diverse research efforts such as nutrient management, fate and ecosystem service outcomes, nutritional composition of crops, and animal and human health, is needed. Nitrogen is a key nutrient that enhances agricultural yield and protein content, but multiple N loss pathways, as previously mentioned, reduce crop N use efficiency (NUE). Implementing proper management practices is needed to reduce N losses from agricultural systems. ARS has multidisciplinary scientific teams with expertise in soils, ecological engineering, hydrology, livestock management and nutrition, horticulture, crop breeding, human and animal nutrition, post-harvest management and processing, and other areas, and intentional collaboration among these teams offers opportunities to rapidly improve NUE and crop quality and reduce off-site N losses. Similarly, increased P use efficiencies are needed to enhance and ensure sustainable agricultural production and to reduce environmental degradation of water sources. Manure is a valuable source of P and it can be used as a soil amendment to reduce crop production costs. However, there is a need to improve our understanding of the biological and physical cycles of soil P, as well as to obtain more information about P supplies from fertilizer, crop residues, cover crops, manure, and byproducts, and livestock nutrition impacts on manure properties. There is also a need for a better understanding of soil biological communities and ecosystems, including plant roots and root exudates and how their interactions with crops and community ecology affect yield and the uptake of macro- and micro-nutrients and the ultimate nutritional composition and organoleptic qualities of the crop. Studies documenting the responses of crop-associated biological communities to management practices and genetic technologies implemented across multiple environments (e.g., soil types and chemistries, hydrologic regimes, climates) will improve our understanding of gaps in macro- and micro-nutrient management strategies. A goal of the USDA-ARS is to increase agricultural production and quality while reducing environmental impacts. The Nutrient Uptake and Outcomes (NUOnet) database will be able to help establish baselines on nutrient use efficiencies; processes contributing to nutrient losses; and processes contributing to optimal crop yield, nutritional and organoleptic quality. This national database could be used to calculate many different environmental indicators from a comprehensive understanding of nutrient stocks and flows. Increasing our understanding of stocks and flows could help in the identification of knowledge gaps as well as areas where increased efficiencies can be achieved at a national level. NUOnet could also be used to develop tools to derive cost-benefit curves associated with nutrient management improvement scenarios and assess local, regional and national impacts of off-site nutrient loss. Understanding how agricultural production impacts human health is a challenge, and the database could be used to link crop management strategies to crop chemical composition to human consumption patterns and ultimately to human health outcomes. A national database will also be very important for development and evaluation of new technologies such as real-time sensing or other proximal and remote sensing technologies that enable assessment of nutrient use efficiencies, particularly at the grower level. The database could also be used to develop analyses that will contribute to the recommendation of policies for resource allocations that will most effectively fulfill the goals of the Grand Challenge. Such a national database with contributions from peers across different national programs could also enhance collaborations between ARS, universities, and extension specialists, as well as with producers, industry, and other partners. See the NUOnet Home Page for more information about this database and strategic goals. Resources in this dataset:Resource Title: GRACEnet-NUOnet Data Dictionary. File Name: GRACEnet-NUOnet_DD.csvResource Title: NUOnet Data Entry Template. File Name: DET_NATRES_NUO.zipResource Description: A multi-tab worksheet for data entry. Users can customize fields to be mandatory, set minimum and maximum values, and run a validation on fields as specified by the user.

https://gpsr.ars.usda.gov/html/NUOnet_DET/DET_NATRES_NUO.xlsm

Photosynthesis, growth and plant maintenance respiration are closely related to tree tissue nitrogen (N) concentrations. Earlier studies of the variation in tissue N concentrations and underlying controls have mostly focused on leaves. Here we present a novel database of N concentrations in stems, roots and branches covering all common northern hemisphere boreal and temperate tree species, combined with data for leaves mostly available from existing databases. Additional information on mean annual temperature (MAT), mean annual precipitation (MAP), soil N concentration, tree height, age, and biomass is extracted from the considered studies, when available. The database can be used, for instance, for studying the controls of tree tissue N concentrations (as in Thurner et al., accepted for publication in Biogeosciences, https://doi.org/10.5194/egusphere-2024-1794), or for improving the implementation of the N cycle in dynamic global vegetation models (DGVMs), which currently usually assume fixed ratios between tissue N concentrations and thus oversimplify effects of N limitation on the carbon (C) cycle.

We collect a novel database of N concentration measurements in stems (i.e., trunks), roots and branches of northern hemisphere boreal and temperate trees by an extensive literature research. For this task, we search Web of Science for stem, root and branch nitrogen concentrations for all common boreal and temperate tree genera (for search criteria see Supporting Information S1 of the associated primary article). To a lesser extent, we also collect leaf N concentration measurements from the literature, because numerous measurements of leaf N concentration are already available from the TRY database (Kattge et al., 2020). Since measurements are rare in Russian boreal forests, we include own measurements for Larix gmelinii in the central part of the Nizhnyaya Tunguska River basin in Central Siberia (ca. 64° N 100° E; Larjavaara et al., 2017; Prokushkin et al., 2018). Moreover, data sources from the Russian and Chinese literature, the TRY database (Kattge et al., 2020) and the biomass and allometry database (BAAD; Falster et al., 2015) are considered.

Only measurements of N concentration under natural conditions (no greenhouses, no trees grown in pots, no fertilizer, and no other experiments) are included in the database. In addition, we only include studies with explicit information on the measurement location and the investigated tree species. We only analyse measurements of total root N concentration, but do not include measurements of N concentration specifically for fine roots. In cases where separate measurements are available for (stem) sapwood and heartwood, we include only N concentrations of sapwood. Replicate measurements, if available from the studies, are retained. All tissue N concentrations are expressed in g N / g dry weight. In total, the compiled database comprises 1048 stem, 267 root, 599 branch, and 5944 leaf N concentration measurements. While almost all of the stem (911 collected from literature, 1 own, 52 from TRY, 84 from BAAD), root (266 collected from literature, 1 own) and branch (all collected from literature) N concentration measurements have been collected from in total 192 studies from the literature, leaf N concentration measurements are to a large extent available from existing databases (188 collected from literature, 5 own, 5522 from TRY, 229 from BAAD). The spatial distribution of N concentration measurements is shown in Fig. 1 of the associated primary article.

Information on MAT, MAP, soil N concentration, tree height, age, and biomass is extracted from the respective studies, when available. Growth / leaf type classes categorise tree species according to their growth rate (fast-growing, slow-/medium-growing) and leaf type (broadleaf deciduous, needleleaf deciduous, needleleaf evergreen). We exclude data without information on tree species as well as broadleaf evergreen trees from the analysis since available measurements for this leaf type are scarce. Due to missing information on actual growth rates of the species at the specific measurement sites, we assign their typical growth rate (slow/medium: <= 2 feet/year; fast: > 2 feet/year) to each investigated tree species based on our expert judgement and an online research (see Supporting Information S2 of the associated primary article). As a measure of dryness, we calculate the aridity index (AI = MAP / potential evapotranspiration) from CHELSA Version 2.1 long-term climate data at the study locations (1981-2010; 30 arcsec resolution; Brun et al., 2022), as information on potential evapotranspiration is usually not available from the compiled studies.

References:

Brun P, Zimmermann NE, Hari C, Pellissier L, Karger DN. 2022. Global climate-related predictors at kilometer resolution for the past and future. Earth System Science Data 14(12): 5573-5603.

Falster DS, Duursma RA, Ishihara MI, Barneche DR, FitzJohn RG, Vårhammar A, Aiba M, Ando M, Anten N, Aspinwall MJ, et al. 2015. BAAD: a biomass and allometry database for woody plants. Ecology 96(5): 1445.

Kattge J, Bonisch G, Diaz S, Lavorel S, Prentice IC, Leadley P, Tautenhahn S, Werner GDA, Aakala T, Abedi M, et al. 2020. TRY plant trait database - enhanced coverage and open access. Glob Chang Biol 26(1): 119-188.

Larjavaara M, Berninger F, Palviainen M, Prokushkin A, Wallenius T. 2017. Post-fire carbon and nitrogen accumulation and succession in Central Siberia. Sci Rep 7(1): 12776.

Prokushkin A, Hagedorn F, Pokrovsky O, Viers J, Kirdyanov A, Masyagina O, Prokushkina M, McDowell W. 2018. Permafrost Regime Affects the Nutritional Status and Productivity of Larches in Central Siberia. Forests 9(6).

Related works: We sincerely thank the TRY initiative on plant traits (http://www.try-db.org) for contributing to leaf N and the Biomass And Allometry Database (BAAD; https://github.com/dfalster/baad) for contributing to leaf and stem N concentration data of this database. The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. Boenisch (Max Planck Institute for Biogeochemistry, Jena, Germany). The BAAD is hosted, developed and maintained by D. Falster (University of New South Wales, Sydney, Australia).

This domain covers statistics and indicators on key aspects of the education systems across Europe. The data show entrants and enrolments in education levels, education personnel and the cost and type of resources dedicated to education.

For a general technical description of the UOE Data Collection see UNESCO OECD Eurostat (UOE) joint data collection – methodology - Statistics Explained (europa.eu).

The standards on international statistics on education and training systems are set by the three international organisations jointly administering the annual UOE data collection:

• The United Nations Educational, Scientific, and Cultural Organisation Institute for Statistics (UNESCO-UIS),
• The Organisation for Economic Co-operation and Development (OECD) and,
• The Statistical Office of the European Union (EUROSTAT).

The following topics are covered:

• Pupils and students – Enrolments and Entrants,
• Learning mobility,
• Education personnel,
• Education finance,
• Graduates,
• Language learning.

Data on enrolments in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Age or age group,
• NUTS1 and NUTS2 regions,
• Type of educational institution (public or private) – referred to as the ‘sector’ in Eurobase,
• Intensity of participation (full-time, part-time, full-time equivalent) – referred to as ‘working time’ in Eurobase,
• Programme orientation (general/academic or vocational/professional),
• Type of vocational programme (school-based only or combined school and work-based),
• Level of attainment that can be achieved upon programme completion (e.g. insufficient for level completion or partial level completion, sufficient for partial level completion without direct access to tertiary education),
• Field of education (ISCED-F13).

Additionally, the following types of indicators on enrolments are calculated (all indicators using population data use Eurostat’s population database (demo_pjan)):

• Participation rates by age or by age groups as % of corresponding age population.
• Participation rates by age as % of total population.
• Pupils from age 0, 3, 4 and 5 to the starting age of compulsory education at primary level, as % of the population of the corresponding age. In some countries, the start of primary education is not compulsory and in some countries compulsory education starts at pre-primary level. This indicator calculates the participation rates of pupils up until (but not including) the starting age of formal education that is both compulsory and at the primary level. This age varies from 5 years to 7 years across countries and the national starting ages for compulsory primary education used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
• Pupils under the age of 3 as % of corresponding age population. This indicator does not include 3 year olds (includes ages 0, 1 and 2).
• Out-of-school rates at different ages. This indicator is calculated as 100 – (students of a particular age who are enrolled in education at any ISCED level / Total population of that age *100).
  • Out-of-school rates in population of lower secondary school age and in population of upper secondary school age. This indicator is calculated as 100 – (students who are of the official age range for ISCED X who are enrolled in education at any ISCED level / Total population in the official age range for ISCED X *100). The official age range for each ISCED level varies across countries, and national age ranges for lower and upper secondary used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
  • Students in education of post-compulsory school age - as % of the total population of post-compulsory school age. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students participation at the end of compulsory education - as % of the corresponding age population. Indicator is calculated for age (X-1), (X), (X+1), (X+2) where X = the final age at which formal education is compulsory in national education systems. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students in education aged 30 and over - per 1000 of corresponding age population
    • Expected school years of pupils and students at different levels of education
    • Distribution of pupils and students enrolled in general and vocational programmes by education level and NUTS2 regions
    • Distribution of students in different fields of education
    • Ratio of the proportion of the population who are tertiary students in NUTS1 regions to the proportion of the population who are tertiary students in NUTS2 regions

Data on entrants in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Programme orientation (general/academic or vocational/professional),
• Sex,
• Age or age group,
• Field of education (ISCED-F13).

Additionally the following indicator on entrants is calculated:

• Distribution of new entrants in different fields of education.

Data on learning mobility is available for degree mobile students, degree mobile graduates and credit mobile graduates. Degree mobility means that students/graduates are/were enrolled as regular students in any semester/term of a programme taught in the country of destination with the intention of graduating from it in the country of destination. Credit mobility is defined as temporary tertiary education or/and study-related traineeship abroad within the framework of enrolment in a tertiary education programme at a "home institution" (usually) for the purpose of gaining academic credit (i.e. credit that will be recognised in that home institution). Further definitions are in Section 2.8 of the UOE manual.

Degree mobile students are referred to as just ‘mobile students’ in UOE learning mobility tables. Data is disseminated for degree mobile students and degree mobile graduates in absolute numbers with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Field of education (ISCED-F13),
• Country of origin (defined as the country of education prior to entering tertiary although there may be national deviations. These are listed in the Helpsheet of the latest footnotes report available to download in the Annexes section of this page) – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Additionally the following types of indicators on degree mobile students and degree mobile graduates are calculated ((all indicators using population data use Eurostat’s population database (demo_pjan)):

• Share of all students/graduates who are mobile students/degree mobile graduates from abroad,
• Distribution of mobile students/degree mobile graduates from abroad in different fields of education.

For credit mobile graduates, data are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Type of mobility scheme (e.g. Credit mobility under EU programmes i.e. ERASMUS, Credit mobility in other international/national programmes),
• Type of mobility (study period only or study period combined with work placement),
• Country of destination – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Data on personnel in education are available for classroom teachers/academic staff, teacher aides and school-management personnel. Teachers are employed in a professional capacity to guide and direct the learning experiences of students, irrespective of their training, qualifications or delivery mechanism. Teacher aides support teachers in providing instruction to students. Academic staff are personnel employed at the tertiary level of education whose primary assignment is instruction and/or research. School management personnel covers professional personnel who are responsible for school management/administration (ISCED 0-4) or whose primary or major responsibility is the management of the institution, or a recognised department or subdivision of the institution (tertiary levels). Full definitions of these statistical units are in Section 3.5 of the UOE manual.

Data are disseminated on teachers and academic staff in absolute numbers, with breakdowns available for the following dimensions:

• ISCED

PROSITE consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them [More... / References / Commercial users ]. PROSITE is complemented by ProRule , a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids [More...].

This domain covers statistics and indicators on key aspects of the education systems across Europe. The data show entrants and enrolments in education levels, education personnel and the cost and type of resources dedicated to education.

For a general technical description of the UOE Data Collection see UNESCO OECD Eurostat (UOE) joint data collection – methodology - Statistics Explained (europa.eu).

The standards on international statistics on education and training systems are set by the three international organisations jointly administering the annual UOE data collection:

• The United Nations Educational, Scientific, and Cultural Organisation Institute for Statistics (UNESCO-UIS),
• The Organisation for Economic Co-operation and Development (OECD) and,
• The Statistical Office of the European Union (EUROSTAT).

The following topics are covered:

• Pupils and students – Enrolments and Entrants,
• Learning mobility,
• Education personnel,
• Education finance,
• Graduates,
• Language learning.

Data on enrolments in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Age or age group,
• NUTS1 and NUTS2 regions,
• Type of educational institution (public or private) – referred to as the ‘sector’ in Eurobase,
• Intensity of participation (full-time, part-time, full-time equivalent) – referred to as ‘working time’ in Eurobase,
• Programme orientation (general/academic or vocational/professional),
• Type of vocational programme (school-based only or combined school and work-based),
• Level of attainment that can be achieved upon programme completion (e.g. insufficient for level completion or partial level completion, sufficient for partial level completion without direct access to tertiary education),
• Field of education (ISCED-F13).

Additionally, the following types of indicators on enrolments are calculated (all indicators using population data use Eurostat’s population database (demo_pjan)):

• Participation rates by age or by age groups as % of corresponding age population.
• Participation rates by age as % of total population.
• Pupils from age 0, 3, 4 and 5 to the starting age of compulsory education at primary level, as % of the population of the corresponding age. In some countries, the start of primary education is not compulsory and in some countries compulsory education starts at pre-primary level. This indicator calculates the participation rates of pupils up until (but not including) the starting age of formal education that is both compulsory and at the primary level. This age varies from 5 years to 7 years across countries and the national starting ages for compulsory primary education used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
• Pupils under the age of 3 as % of corresponding age population. This indicator does not include 3 year olds (includes ages 0, 1 and 2).
• Out-of-school rates at different ages. This indicator is calculated as 100 – (students of a particular age who are enrolled in education at any ISCED level / Total population of that age *100).
  • Out-of-school rates in population of lower secondary school age and in population of upper secondary school age. This indicator is calculated as 100 – (students who are of the official age range for ISCED X who are enrolled in education at any ISCED level / Total population in the official age range for ISCED X *100). The official age range for each ISCED level varies across countries, and national age ranges for lower and upper secondary used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
  • Students in education of post-compulsory school age - as % of the total population of post-compulsory school age. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students participation at the end of compulsory education - as % of the corresponding age population. Indicator is calculated for age (X-1), (X), (X+1), (X+2) where X = the final age at which formal education is compulsory in national education systems. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students in education aged 30 and over - per 1000 of corresponding age population
    • Expected school years of pupils and students at different levels of education
    • Distribution of pupils and students enrolled in general and vocational programmes by education level and NUTS2 regions
    • Distribution of students in different fields of education
    • Ratio of the proportion of the population who are tertiary students in NUTS1 regions to the proportion of the population who are tertiary students in NUTS2 regions

Data on entrants in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Programme orientation (general/academic or vocational/professional),
• Sex,
• Age or age group,
• Field of education (ISCED-F13).

Additionally the following indicator on entrants is calculated:

• Distribution of new entrants in different fields of education.

Data on learning mobility is available for degree mobile students, degree mobile graduates and credit mobile graduates. Degree mobility means that students/graduates are/were enrolled as regular students in any semester/term of a programme taught in the country of destination with the intention of graduating from it in the country of destination. Credit mobility is defined as temporary tertiary education or/and study-related traineeship abroad within the framework of enrolment in a tertiary education programme at a "home institution" (usually) for the purpose of gaining academic credit (i.e. credit that will be recognised in that home institution). Further definitions are in Section 2.8 of the UOE manual.

Degree mobile students are referred to as just ‘mobile students’ in UOE learning mobility tables. Data is disseminated for degree mobile students and degree mobile graduates in absolute numbers with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Field of education (ISCED-F13),
• Country of origin (defined as the country of education prior to entering tertiary although there may be national deviations. These are listed in the Helpsheet of the latest footnotes report available to download in the Annexes section of this page) – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Additionally the following types of indicators on degree mobile students and degree mobile graduates are calculated ((all indicators using population data use Eurostat’s population database (demo_pjan)):

• Share of all students/graduates who are mobile students/degree mobile graduates from abroad,
• Distribution of mobile students/degree mobile graduates from abroad in different fields of education.

For credit mobile graduates, data are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Type of mobility scheme (e.g. Credit mobility under EU programmes i.e. ERASMUS, Credit mobility in other international/national programmes),
• Type of mobility (study period only or study period combined with work placement),
• Country of destination – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Data on personnel in education are available for classroom teachers/academic staff, teacher aides and school-management personnel. Teachers are employed in a professional capacity to guide and direct the learning experiences of students, irrespective of their training, qualifications or delivery mechanism. Teacher aides support teachers in providing instruction to students. Academic staff are personnel employed at the tertiary level of education whose primary assignment is instruction and/or research. School management personnel covers professional personnel who are responsible for school management/administration (ISCED 0-4) or whose primary or major responsibility is the management of the institution, or a recognised department or subdivision of the institution (tertiary levels). Full definitions of these statistical units are in Section 3.5 of the UOE manual.

Data are disseminated on teachers and academic staff in absolute numbers, with breakdowns available for the following dimensions:

• ISCED

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All schools (public & private) in the state are listed. Contact details such as postal, street address and telephone numbers are also available. For further information or changes contact (08) 9264 4562. Show full description