The mid-year estimates refer to the population on 30 June of the reference year and are produced in line with the standard United Nations (UN) definition for population estimates. They are the official set of population estimates for the UK and its constituent countries, the regions and counties of England, and local authorities and their equivalents.

EUROPOP2019 are the latest Eurostat population projections produced at national and subnational levels for 31 countries: all 27 European Union (EU) Member States and four European Free Trade Association (EFTA) countries, covering the time horizon from 2019 to 2100.

Population projections are 'what-if scenario' that aim to show the hypothetically developments of the population size and its structure based on a sets of assumptions for fertility, mortality and net migration; they are presented for a long time period that covers more than a half-century (50 years).

The datasets at national level are composed by the baseline population projections and five sensitivity tests, namely:

• no migration – it is assumed that the net migration is set to zero in each year of the entire horizon of projections;
• lower migration – it is assumed that the net migration is 33% lower than in the baseline assumptions, in each year of the entire horizon of projections;
• higher migration – it is assumed that the net migration is 33% higher than in the baseline assumptions, in each year of the entire horizon of projections;
• lower fertility - it is assumed that the fertility rates are lower 20% than in the baseline assumptions, in each year of the entire horizon of projections;
• lower mortality - it is assumed that the mortality rates are decreased such that the life expectancy at birth will increase of about two years by 2070 when compared with the baseline assumptions.

Data are available by single year time interval, as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected life expectancy by age (in completed years) and sex.

Moreover, the demographic balances and indicators are available for the baseline projections and the five sensitive variants:

• Total numbers of the projected live births and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The dataset at regional level is composed by the baseline population projections and covers all 1169 regions classified as NUTS level 3 corresponding to the NUTS-2016 classification (the Nomenclature of Territorial Units for Statistics) and the 47 Statistical Regions (SR) agreed between European Commission and EFTA countries. Statistical regions are defined according to principles similar to those used in the establishment of the NUTS classification.

For all 1216 regions NUTS-3 level, data are available by single year time interval as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected deaths by age and sex;
• Projected life expectancy by age (reached during the year) and sex, which is computed according to the method described in the https://ec.europa.eu/eurostat/cache/metadata/Annexes/proj_19n_esms_an_24.pdf" target="_self">Technical note - Alternative life table (with annex)

In addition to the baseline projections, datasets on projected population at regional level are available for two sensitivity tests:

• no migration - it is assumed that migration is zero for both international and internal components in each year of the entire horizon of projections;
• no inter-regional migration - it is assumed that only internal migration is zero in each year of the entire horizon of projections.

Moreover, the demographic balances and indicators are available for the baseline projections and the two sensitive variants:

• Total numbers of the projected live births by sex and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The additional dataset called ‘Short-term update of the projected population (2022-2032)’ [proj_stp22] was published on 28 September 2022. While EUROPOP2019 remain the main set of reference for population projections, this new dataset includes updates of baseline projections for the total population, population in the age group 15 to 74 years (considered as the population in the working-age group), and its share in the total population. In addition, two sensitivity tests are carried out – high and very high number of refugees – by introducing in the baseline projections a shock due to the mass-influx of refugees fleeing the war in Ukraine, and who have received temporary protection in the EU countries.

The updated EUROPOP2019 projections were constructed from cumulative sums of weighted averages of annual population changes of two series: the original EUROPOP2019 projection and a new short-term population projection computed from the latest available data over the period of 10 years.

The two sensitivity tests were built on the following assumptions:

• High number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 only, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 10% of the total influx in 2022;
• Very high number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 and 2023, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 15% of the cumulated influx in 2022 and 2023.

Dataset Overview

This dataset contains comprehensive birth and death statistics for New Zealand regions spanning 18 years (2005-2022).

Content

• Period: Years from 2005 to 2022 (18 years)
• Regions: 16 regions across New Zealand
• Metrics: Birth counts and death counts per region per year
• Total Records: 576+ data points

Columns

• Period - Year (2005-2022)
• Birth_Death - Category (Births or Deaths)
• Region - New Zealand region name
• Count - Number of births or deaths

Potential Use Cases

✅ Demographic trend analysis ✅ Regional population studies
✅ Time series forecasting ✅ Machine learning prediction models ✅ COVID-19 impact analysis ✅ Statistical analysis practice

Related Work

This dataset is used in my comprehensive analysis project: - GitHub: https://github.com/0luwanishola/New-Zealand-birth-analysis-and-model - Kaggle Notebooks: [Links will be added after publishing]

Data Source

New Zealand official statistics (December 2022)

Inspiration

What patterns can you find in New Zealand's demographic trends? How did COVID-19 impact birth rates? Can you predict future birth rates using machine learning? Tags: Add these tags (type and press Enter after each) demographics new zealand births time series analysis beginner healthcare social science License: Select CC0: Public Domain or CC BY-SA 4.0

EUROPOP2019 are the latest Eurostat population projections produced at national and subnational levels for 31 countries: all 27 European Union (EU) Member States and four European Free Trade Association (EFTA) countries, covering the time horizon from 2019 to 2100.

Population projections are 'what-if scenario' that aim to show the hypothetically developments of the population size and its structure based on a sets of assumptions for fertility, mortality and net migration; they are presented for a long time period that covers more than a half-century (50 years).

The datasets at national level are composed by the baseline population projections and five sensitivity tests, namely:

• no migration – it is assumed that the net migration is set to zero in each year of the entire horizon of projections;
• lower migration – it is assumed that the net migration is 33% lower than in the baseline assumptions, in each year of the entire horizon of projections;
• higher migration – it is assumed that the net migration is 33% higher than in the baseline assumptions, in each year of the entire horizon of projections;
• lower fertility - it is assumed that the fertility rates are lower 20% than in the baseline assumptions, in each year of the entire horizon of projections;
• lower mortality - it is assumed that the mortality rates are decreased such that the life expectancy at birth will increase of about two years by 2070 when compared with the baseline assumptions.

Data are available by single year time interval, as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected life expectancy by age (in completed years) and sex.

Moreover, the demographic balances and indicators are available for the baseline projections and the five sensitive variants:

• Total numbers of the projected live births and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The dataset at regional level is composed by the baseline population projections and covers all 1169 regions classified as NUTS level 3 corresponding to the NUTS-2016 classification (the Nomenclature of Territorial Units for Statistics) and the 47 Statistical Regions (SR) agreed between European Commission and EFTA countries. Statistical regions are defined according to principles similar to those used in the establishment of the NUTS classification.

For all 1216 regions NUTS-3 level, data are available by single year time interval as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected deaths by age and sex;
• Projected life expectancy by age (reached during the year) and sex, which is computed according to the method described in the https://ec.europa.eu/eurostat/cache/metadata/Annexes/proj_19n_esms_an_24.pdf" target="_self">Technical note - Alternative life table (with annex)

In addition to the baseline projections, datasets on projected population at regional level are available for two sensitivity tests:

• no migration - it is assumed that migration is zero for both international and internal components in each year of the entire horizon of projections;
• no inter-regional migration - it is assumed that only internal migration is zero in each year of the entire horizon of projections.

Moreover, the demographic balances and indicators are available for the baseline projections and the two sensitive variants:

• Total numbers of the projected live births by sex and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The additional dataset called ‘Short-term update of the projected population (2022-2032)’ [proj_stp22] was published on 28 September 2022. While EUROPOP2019 remain the main set of reference for population projections, this new dataset includes updates of baseline projections for the total population, population in the age group 15 to 74 years (considered as the population in the working-age group), and its share in the total population. In addition, two sensitivity tests are carried out – high and very high number of refugees – by introducing in the baseline projections a shock due to the mass-influx of refugees fleeing the war in Ukraine, and who have received temporary protection in the EU countries.

The updated EUROPOP2019 projections were constructed from cumulative sums of weighted averages of annual population changes of two series: the original EUROPOP2019 projection and a new short-term population projection computed from the latest available data over the period of 10 years.

The two sensitivity tests were built on the following assumptions:

• High number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 only, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 10% of the total influx in 2022;
• Very high number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 and 2023, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 15% of the cumulated influx in 2022 and 2023.

Content & Context:

This dataset provides a comprehensive collection of annual country-level indicators spanning social, economic, environmental, financial, and demographic domains for 265 countries and regions from 1960 to 2024. Each row corresponds to a unique combination of country/region, indicator, sex, and age group, with values reported annually in wide format. The indicators cover areas such as population demographics, health, education, labor, income, trade, government finance, agriculture, energy, environmental sustainability, and infrastructure, making it suitable for trend analysis, cross-country comparisons, policy research, and predictive modeling. Missing values are marked as NaN, and metadata columns provide additional context including units, aggregation methods, and data sources.

It is ideal for:

• Trend analysis over time (e.g., population growth, GDP, education levels)
• Comparisons between countries or regions
• Research and policy studies in development, economics, health, agriculture, and sustainability
• Machine learning and predictive modeling using historical indicators

Example Usage:

• Visualizing trends: Compare GDP per capita or fertility rates over decades.
• Country comparisons: Examine health or education indicators across regions.
• Predictive modeling: Forecast future economic or demographic trends.

Notes:

• Wide format is suitable for trend analysis; can be converted to long format using pd.melt() if needed.
• Early years (1960s–1970s) have more missing data.
• Dataset is cleaned and contains no duplicate rows.

Columns:

• FREQ – Data frequency code (e.g., 'A' = Annual)
• FREQ_LABEL – Frequency label (e.g., 'Annual')
• REF_AREA – Country/region ISO code
• REF_AREA_LABEL – Country/region name
• INDICATOR – Indicator code (WDI code)
• INDICATOR_LABEL – Indicator name and description
• SEX – Sex code (_T=Total, M=Male, F=Female)
• SEX_LABEL – Sex description
• AGE – Age group code (_T=All ages or no breakdown)
• AGE_LABEL – Age group description
• UNIT_MEASURE – Unit code
• UNIT_MEASURE_LABEL – Unit description
• AGG_METHOD – Aggregation method code
• AGG_METHOD_LABEL – Aggregation method description
• DECIMALS – Number of decimal places reported
• DECIMALS_LABEL – Decimal description
• DATABASE_ID – Source database code
• DATABASE_ID_LABEL – Source database name
• UNIT_MULT – Unit multiplier (e.g., 1, 1000, 1e6)
• UNIT_MULT_LABEL – Multiplier description
• DATA_SOURCE – Data source code
• DATA_SOURCE_LABEL – Data source name
• OBS_STATUS – Observation status code
• OBS_STATUS_LABEL – Observation status description
• OBS_CONF – Confidence level code
• OBS_CONF_LABEL – Confidence level description
• 1960, 1961, 1962, …, 2024 – Annual values for each indicator (float64, missing values marked as NaN)

Dataset 1: Wikipedia Article Metadata and Content Distribution (2019–2023)

This dataset represents metadata and structural information extracted from Wikipedia articles across multiple language editions between January 2019 and December 2023. The data was collected through the Wikimedia REST API and Wikidata Query Service, focusing on high-level article characteristics such as content length, number of references, topic classification, and readership activity. Each row corresponds to a unique Wikipedia article identified by an article_id and includes metadata describing its topic category (e.g., Politics, Science, Culture), geographic focus, and quality assessment.

The dataset was designed to help quantify content inequality and topic bias across languages. For example, English and German editions tend to have more extensive coverage of scientific and technological topics, while Swahili and Arabic editions show higher representation of local cultural and geographical content but fewer high-quality (“Featured Article”) designations. Article-level metrics like word_count, references_count, and page_views were gathered to provide indicators of article depth, credibility, and public engagement. The last_edit_date variable helps capture how frequently articles are updated, indicating editorial activity over time.

Temporal coverage: 2019–2023 Data sources: Wikimedia REST API, Wikidata Query Service, Pageview Analytics Primary purpose: To analyze disparities in article depth, topic diversity, and regional focus across Wikipedia’s major language editions.

Dataset 2: Wikipedia Editor Demographics and Contribution Data (2018–2023)

This dataset summarizes demographic and contribution patterns of active Wikipedia editors from 2018 to 2023, based on public edit histories available through the Wikimedia Dumps and MediaWiki API. Each record corresponds to a unique editor identified by editor_id, containing attributes such as country, primary language of editing, total edit counts, and dominant topic area.

Although Wikipedia does not directly record personal information, country and language data were inferred using IP-based geolocation for anonymous edits and user-declared data for registered contributors. The dataset was sampled to capture editors across seven major languages (English, French, Spanish, German, Swahili, Arabic, and Chinese). Demographic variables like gender and education_level are approximations derived from community surveys conducted by the Wikimedia Foundation in 2019 and 2021, used here to represent broad participation trends rather than individual identities.

This dataset provides insight into editorial imbalance, highlighting, for example, that editors from Europe and North America contribute disproportionately more to technical and scientific topics compared to those from Africa or South America. Fields such as total_edits, articles_edited, and avg_edit_size reflect productivity and depth of engagement, while active_since helps trace editor retention and historical participation.

Temporal coverage: 2018–2023 Data sources: Wikimedia Dumps, MediaWiki API, Wikimedia Community Surveys (2019, 2021) Primary purpose: To analyze demographic participation gaps and editing activity distribution across languages and regions.

Dataset 3: Wikipedia Language and Geographic Coverage Statistics (2023)

This dataset presents aggregated statistics at the language edition level, representing Wikipedia’s overall content and contributor structure as of December 2023. The data was compiled from the Wikimedia Statistics Portal and Meta-Wiki language reports, which provide high-level metrics such as total number of articles, average article length, number of active editors, and editing intensity per language.

Each entry represents one Wikipedia language edition, capturing its global footprint and coverage balance. The column coverage_score is a composite index derived from article volume, diversity of covered topics, and proportional representation of countries and regions. underrepresented_regions indicates the number of global regions (out of ten defined by the UN geoscheme) that have low coverage or minimal article representation in that language edition. The dataset allows researchers to identify which language Wikipedias most effectively cover global topics and which remain regionally or linguistically constrained.

EUROPOP2019 are the latest Eurostat population projections produced at national and subnational levels for 31 countries: all 27 European Union (EU) Member States and four European Free Trade Association (EFTA) countries, covering the time horizon from 2019 to 2100.

Population projections are 'what-if scenario' that aim to show the hypothetically developments of the population size and its structure based on a sets of assumptions for fertility, mortality and net migration; they are presented for a long time period that covers more than a half-century (50 years).

The datasets at national level are composed by the baseline population projections and five sensitivity tests, namely:

• no migration – it is assumed that the net migration is set to zero in each year of the entire horizon of projections;
• lower migration – it is assumed that the net migration is 33% lower than in the baseline assumptions, in each year of the entire horizon of projections;
• higher migration – it is assumed that the net migration is 33% higher than in the baseline assumptions, in each year of the entire horizon of projections;
• lower fertility - it is assumed that the fertility rates are lower 20% than in the baseline assumptions, in each year of the entire horizon of projections;
• lower mortality - it is assumed that the mortality rates are decreased such that the life expectancy at birth will increase of about two years by 2070 when compared with the baseline assumptions.

Data are available by single year time interval, as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected life expectancy by age (in completed years) and sex.

Moreover, the demographic balances and indicators are available for the baseline projections and the five sensitive variants:

• Total numbers of the projected live births and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The dataset at regional level is composed by the baseline population projections and covers all 1169 regions classified as NUTS level 3 corresponding to the NUTS-2016 classification (the Nomenclature of Territorial Units for Statistics) and the 47 Statistical Regions (SR) agreed between European Commission and EFTA countries. Statistical regions are defined according to principles similar to those used in the establishment of the NUTS classification.

For all 1216 regions NUTS-3 level, data are available by single year time interval as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected deaths by age and sex;
• Projected life expectancy by age (reached during the year) and sex, which is computed according to the method described in the https://ec.europa.eu/eurostat/cache/metadata/Annexes/proj_19n_esms_an_24.pdf" target="_self">Technical note - Alternative life table (with annex)

In addition to the baseline projections, datasets on projected population at regional level are available for two sensitivity tests:

• no migration - it is assumed that migration is zero for both international and internal components in each year of the entire horizon of projections;
• no inter-regional migration - it is assumed that only internal migration is zero in each year of the entire horizon of projections.

Moreover, the demographic balances and indicators are available for the baseline projections and the two sensitive variants:

• Total numbers of the projected live births by sex and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The additional dataset called ‘Short-term update of the projected population (2022-2032)’ [proj_stp22] was published on 28 September 2022. While EUROPOP2019 remain the main set of reference for population projections, this new dataset includes updates of baseline projections for the total population, population in the age group 15 to 74 years (considered as the population in the working-age group), and its share in the total population. In addition, two sensitivity tests are carried out – high and very high number of refugees – by introducing in the baseline projections a shock due to the mass-influx of refugees fleeing the war in Ukraine, and who have received temporary protection in the EU countries.

The updated EUROPOP2019 projections were constructed from cumulative sums of weighted averages of annual population changes of two series: the original EUROPOP2019 projection and a new short-term population projection computed from the latest available data over the period of 10 years.

The two sensitivity tests were built on the following assumptions:

• High number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 only, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 10% of the total influx in 2022;
• Very high number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 and 2023, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 15% of the cumulated influx in 2022 and 2023.

EUROPOP2019 are the latest Eurostat population projections produced at national and subnational levels for 31 countries: all 27 European Union (EU) Member States and four European Free Trade Association (EFTA) countries, covering the time horizon from 2019 to 2100.

Population projections are 'what-if scenario' that aim to show the hypothetically developments of the population size and its structure based on a sets of assumptions for fertility, mortality and net migration; they are presented for a long time period that covers more than a half-century (50 years).

The datasets at national level are composed by the baseline population projections and five sensitivity tests, namely:

• no migration – it is assumed that the net migration is set to zero in each year of the entire horizon of projections;
• lower migration – it is assumed that the net migration is 33% lower than in the baseline assumptions, in each year of the entire horizon of projections;
• higher migration – it is assumed that the net migration is 33% higher than in the baseline assumptions, in each year of the entire horizon of projections;
• lower fertility - it is assumed that the fertility rates are lower 20% than in the baseline assumptions, in each year of the entire horizon of projections;
• lower mortality - it is assumed that the mortality rates are decreased such that the life expectancy at birth will increase of about two years by 2070 when compared with the baseline assumptions.

Data are available by single year time interval, as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected life expectancy by age (in completed years) and sex.

Moreover, the demographic balances and indicators are available for the baseline projections and the five sensitive variants:

• Total numbers of the projected live births and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The dataset at regional level is composed by the baseline population projections and covers all 1169 regions classified as NUTS level 3 corresponding to the NUTS-2016 classification (the Nomenclature of Territorial Units for Statistics) and the 47 Statistical Regions (SR) agreed between European Commission and EFTA countries. Statistical regions are defined according to principles similar to those used in the establishment of the NUTS classification.

For all 1216 regions NUTS-3 level, data are available by single year time interval as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected deaths by age and sex;
• Projected life expectancy by age (reached during the year) and sex, which is computed according to the method described in the https://ec.europa.eu/eurostat/cache/metadata/Annexes/proj_19n_esms_an_24.pdf" target="_self">Technical note - Alternative life table (with annex)

In addition to the baseline projections, datasets on projected population at regional level are available for two sensitivity tests:

• no migration - it is assumed that migration is zero for both international and internal components in each year of the entire horizon of projections;
• no inter-regional migration - it is assumed that only internal migration is zero in each year of the entire horizon of projections.

Moreover, the demographic balances and indicators are available for the baseline projections and the two sensitive variants:

• Total numbers of the projected live births by sex and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The additional dataset called ‘Short-term update of the projected population (2022-2032)’ [proj_stp22] was published on 28 September 2022. While EUROPOP2019 remain the main set of reference for population projections, this new dataset includes updates of baseline projections for the total population, population in the age group 15 to 74 years (considered as the population in the working-age group), and its share in the total population. In addition, two sensitivity tests are carried out – high and very high number of refugees – by introducing in the baseline projections a shock due to the mass-influx of refugees fleeing the war in Ukraine, and who have received temporary protection in the EU countries.

The updated EUROPOP2019 projections were constructed from cumulative sums of weighted averages of annual population changes of two series: the original EUROPOP2019 projection and a new short-term population projection computed from the latest available data over the period of 10 years.

The two sensitivity tests were built on the following assumptions:

• High number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 only, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 10% of the total influx in 2022;
• Very high number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 and 2023, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 15% of the cumulated influx in 2022 and 2023.

This is analytical proofs and raw data for research article, “The Role of Protection Motivation in the Adoption of Cloud-enabled Storage Service”. The original article aimed to investigate how the threat of data loss influences an individual’s intention to adopt cloud-enabled storage service as protection against data loss. This article includes analytical proofs, psychometric details of the measures and measurement items, analytic tables-related to the original article and raw data. Files included are as follows.

○ File 1 - Title: Details of prior studies (2009 to 2019) on the adoption of cloud-enabled storage at individual level - Description: This file presents a review of twenty-three studies (2009 to 2019) that focused on
the adoption of cloud-enabled storage service at the individual level. ○ File 2 - Title: Details of prior on applications of PMT in IS and IT areas - Description: This file presents a review of forty-seven studies (2009 to 2019) of PMT in IS/IT research areas. ○ File 3 - Title: Measurement items - Description: This file reports psychometric details of the measures and measurement items used in the original research article. ○ File 4 - Title: Sample characteristics - Description: This file reports the demographic characteristics of the respondents. ○ File 5 - Title: raw data for empirical analytics - Description: This file contains raw data for the original study: 392 samples were used for its final analysis. This data were collected through an online survey in South Korea.

Statistical open data on LAU regions of Slovakia, Czech Republic, Poland, Hungary (and other countries in the future). LAU1 regions are called counties, okres, okresy, powiat, járás, járási, NUTS4, LAU, Local Administrative Units, ... and there are 733 of them in this V4 dataset. Overall, we cover 733 regions which are described by 137.828 observations (panel data rows) and more than 1.760.229 data points.

This LAU dataset contains panel data on population, on age structure of inhabitants, on number and on structure of registered unemployed. Dataset prepared by Michal Páleník. Output files are in json, shapefiles, xls, ods, json, topojson or CSV formats. Downloadable at zenodo.org.

This dataset consists of:

data on unemployment (by gender, education and duration of unemployment),

data on vacancies,

open data on population in Visegrad counties (by age and gender),

data on unemployment share.

Combined latest dataset

dataset of the latest available data on unemployment, vacancies and population

dataset includes map contours (shp, topojson or geojson format), relation id in OpenStreetMap, wikidata entry code,

it also includes NUTS4 code, LAU1 code used by national statistical office and abbreviation of the region (usually license plate),

source of map contours is OpenStreetMap, licensed under ODbL

no time series, only most recent data on population and unemployment combined in one output file

columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies, pop_period, TOTAL, Y15-64, Y15-64-females, local_lau, osm_id, abbr, wikidata, population_density, area_square_km, way

Slovakia – SK: 79 LAU1 regions, data for 2024-10-01, 1.659 data,

Czech Republic – CZ: 77 LAU1 regions, data for 2024-10-01, 1.617 data,

Poland – PL: 380 LAU1 regions, data for 2024-09-01, 6.840 data,

Hungary – HU: 197 LAU1 regions, data for 2024-10-01, 2.955 data,

13.071 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 79 77 380 197

lau LAU code of the region 79 77 380 197

name name of the region in local language 79 77 380 197

registered_unemployed number of unemployed registered at labour offices 79 77 380 197

registered_unemployed_females number of unemployed women 79 77 380 197

disponible_unemployed unemployed able to accept job offer 79 77 0 0

low_educated unmployed without secondary school (ISCED 0 and 1) 79 77 380 197

long_term unemployed for longer than 1 year 79 77 380 0

unemployment_inflow inflow into unemployment 79 77 0 0

unemployment_outflow outflow from unemployment 79 77 0 0

below_25 number of unemployed below 25 years of age 79 77 380 197

over_55 unemployed older than 55 years 79 77 380 197

vacancies number of vacancies reported by labour offices 79 77 380 0

pop_period date of population data 79 77 380 197

TOTAL total population 79 77 380 197

Y15-64 number of people between 15 and 64 years of age, population in economically active age 79 77 380 197

Y15-64-females number of women between 15 and 64 years of age 79 77 380 197

local_lau region's code used by local labour offices 79 77 380 197

osm_id relation id in OpenStreetMap database 79 77 380 197

abbr abbreviation used for this region 79 77 380 0

wikidata wikidata identification code 79 77 380 197

population_density population density 79 77 380 197

area_square_km area of the region in square kilometres 79 77 380 197

way geometry, polygon of given region 79 77 380 197

Unemployment dataset

time series of unemployment data in Visegrad regions

by gender, duration of unemployment, education level, age groups, vacancies,

columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies

Slovakia – SK: 79 LAU1 regions, data for 334 periods (1997-01-01 ... 2024-10-01), 202.082 data,

Czech Republic – CZ: 77 LAU1 regions, data for 244 periods (2004-07-01 ... 2024-10-01), 147.528 data,

Poland – PL: 380 LAU1 regions, data for 189 periods (2005-03-01 ... 2024-09-01), 314.100 data,

Hungary – HU: 197 LAU1 regions, data for 106 periods (2016-01-01 ... 2024-10-01), 104.408 data,

768.118 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 26 386 18 788 71 772 20 882

lau LAU code of the region 26 386 18 788 71 772 20 882

name name of the region in local language 26 386 18 788 71 772 20 882

registered_unemployed number of unemployed registered at labour offices 26 386 18 788 71 772 20 882

registered_unemployed_females number of unemployed women 26 386 18 788 62 676 20 882

disponible_unemployed unemployed able to accept job offer 25 438 18 788 0 0

low_educated unmployed without secondary school (ISCED 0 and 1) 11 771 9855 41 388 20 881

long_term unemployed for longer than 1 year 24 253 9855 41 388 0

unemployment_inflow inflow into unemployment 26 149 16 478 0 0

unemployment_outflow outflow from unemployment 26 149 16 478 0 0

below_25 number of unemployed below 25 years of age 11 929 9855 17 100 20 881

over_55 unemployed older than 55 years 11 929 9855 17 100 20 882

vacancies number of vacancies reported by labour offices 11 692 18 788 62 676 0

Population dataset

time series on population by gender and 5 year age groups in V4 counties

columns: period, lau, name, gender, TOTAL, Y00-04, Y05-09, Y10-14, Y15-19, Y20-24, Y25-29, Y30-34, Y35-39, Y40-44, Y45-49, Y50-54, Y55-59, Y60-64, Y65-69, Y70-74, Y75-79, Y80-84, Y85-89, Y90-94, Y_GE95, Y15-64

Slovakia – SK: 79 LAU1 regions, data for 28 periods (1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 152.628 data,

Czech Republic – CZ: 78 LAU1 regions, data for 24 periods (2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 125.862 data,

Poland – PL: 382 LAU1 regions, data for 29 periods (1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 626.941 data,

Hungary – HU: 197 LAU1 regions, data for 11 periods (2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 86.680 data,

992.111 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 6636 5574 32 883 4334

lau LAU code of the region 6636 5574 32 883 4334

name name of the region in local language 6636 5574 32 883 4334

gender gender (male or female) 6636 5574 32 883 4334

TOTAL total population 6636 5574 32 503 4334

Y00-04 inhabitants between 00 to 04 years inclusive 6636 5574 32 503 4334

Y05-09 number of inhabitants between 05 to 09 years of age 6636 5574 32 503 4334

Y10-14 number of people between 10 to 14 years inclusive 6636 5574 32 503 4334

Y15-19 number of inhabitants between 15 to 19 years of age 6636 5574 32 503 4334

Y20-24 number of people between 20 to 24 years inclusive 6636 5574 32 503 4334

Y25-29 number of inhabitants between 25 to 29 years of age 6636 5574 32 503 4334

Y30-34 inhabitants between 30 to 34 years inclusive 6636 5574 32 503 4334

Y35-39 number of inhabitants between 35 to 39 years of age 6636 5574 32 503 4334

Y40-44 inhabitants between 40 to 44 years inclusive 6636 5574 32 503 4334

Y45-49 number of inhabitants younger than 49 and older than 45 years 6636 5574 32 503 4334

Y50-54 inhabitants between 50 to 54 years inclusive 6636 5574 32 503 4334

Y55-59 number of inhabitants between 55 to 59 years of age 6636 5574 32 503 4334

Y60-64 inhabitants between 60 to 64 years inclusive 6636 5574 32 503 4334

Y65-69 number of inhabitants younger than 69 and older than 65 years 6636 5574 32 503 4334

Y70-74 inhabitants between 70 to 74 years inclusive 6636 5574 24 670 4334

Y75-79 number of inhabitants between 75 to 79 years of age 6636 5574 24 670 4334

Y80-84 number of people between 80 to 84 years inclusive 6636 5574 24 670 4334

Y85-89 number of inhabitants younger than 89 and older than 85 years 6636 5574 0 0

Y90-94 inhabitants between 90 to 94 years inclusive 6636 5574 0 0

Y_GE95 number of people 95 years or older 6636 3234 0 0

Y15-64 number of people between 15 and 64 years of age, population in economically active age 6636 5574 32 503 4334

Notes

more examples at www.iz.sk

NUTS4 / LAU1 / LAU codes for HU and PL are created by me, so they can (and will) change in the future; CZ and SK NUTS4 codes are used by local statistical offices, so they should be more stable

NUTS4 codes are consistent with NUTS3 codes used by Eurostat

local_lau variable is an identifier used by local statistical office

abbr is abbreviation of region's name, used for map purposes (usually cars' license plate code; except for Hungary)

wikidata is code used by wikidata

osm_id is region's relation number in the OpenStreetMap database

Example outputs

you can download data in CSV, xml, ods, xlsx, shp, SQL, postgis, topojson, geojson or json format at 📥 doi:10.5281/zenodo.6165135

Counties of Slovakia – unemployment rate in Slovak LAU1 regions

Regions of the Slovak Republic

Unemployment of Czechia and Slovakia – unemployment share in LAU1 regions of Slovakia and Czechia

interactive map on unemployment in Slovakia

Slovakia – SK, Czech Republic – CZ, Hungary – HU, Poland – PL, NUTS3 regions of Slovakia

download at 📥 doi:10.5281/zenodo.6165135

suggested citation: Páleník, M. (2024). LAU1 dataset [Data set]. IZ Bratislava. https://doi.org/10.5281/zenodo.6165135

The data set records the statistical data of population change in different regions of Qinghai Province from 1998 to 2010, which is divided by region, total number of households, total population, birth population and death population. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains 10 data tables with different structures. For example, the data table in 1999 has five fields: Field 1: Region Field 2: total number of households Field 3: total population Field 4: birth population Field 5: death population

Abstract

The SHDS is a national sample survey designed to provide information on population, birth spacing, reproductive health, nutrition, maternal and child health, child survival, HIV/AIDS and sexually transmitted infections (STIs), in Somalia.. The main objective of the SHDS was to provide evidence on the health and demographic characteristics of the Somali population that will guide the development of programmes and formulation of effective policies. This information would also help monitor and evaluate national, sub-national and sector development plans, including the Sustainable Development Goals (SDGs), both by the government and development partners. The target population for SHDS was the women between 15 and 49 years of age, and the children less than the age of 5 years

Geographic coverage

The SHDS 2020 was a nationally representative household survey.

Analysis unit

The unit analysis of this survey are households, women aged 15-49 and children aged 0-5

Universe

This sample survey covered Women aged 15-49 and Children aged 0-5 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The sample for the SHDS was designed to provide estimates of key indicators for the country as a whole, for each of the eighteen pre-war geographical regions, which are the country's first-level administrative divisions, as well as separately for urban, rural and nomadic areas. With the exception of Banadir region, which is considered fully urban, each region was stratified into urban, rural and nomadic areas, yielding a total of 55 sampling strata. All three strata of Lower Shabelle and Middle Juba regions, as well as the rural and nomadic strata of Bay region, were completely excluded from the survey due to security reasons. A final total of 47 sampling strata formed the sampling frame. Through the use of up-to-date, high-resolution satellite imagery, as well as on-the-ground knowledge of staff from the respective ministries of planning, all dwelling structures were digitized in urban and rural areas. Enumeration Areas (EAs) were formed onscreen through a spatial count of dwelling structures in a Geographic Information System (GIS) software. Thereafter, a sample ground verification of the digitized structures was carried out for large urban and rural areas and necessary adjustments made to the frame.

Each EA created had a minimum of 50 and a maximum of 149 dwelling structures. A total of 10,525 EAs were digitized: 7,488 in urban areas and 3,037 in rural areas. However, because of security and accessibility constraints, not all digitized areas were included in the final sampling frame-9,136 EAs (7,308 in urban and 1,828 in rural) formed the final frame. The nomadic frame comprised an updated list of temporary nomadic settlements (TNS) obtained from the nomadic link workers who are tied to these settlements. A total of 2,521 TNS formed the SHDS nomadic sampling frame. The SHDS followed a three-stage stratified cluster sample design in urban and rural strata with a probability proportional to size, for the sampling of Primary Sampling Units (PSU) and Secondary Sampling Units (SSU) (respectively at the first and second stage), and systematic sampling of households at the third stage. For the nomadic stratum, a two-stage stratified cluster sample design was applied with a probability proportional to size for sampling of PSUs at the first stage and systematic sampling of households at the second stage. To ensure that the survey precision is comparable across regions, PSUs were allocated equally to all regions with slight adjustments in two regions. Within each stratum, a sample of 35 EAs was selected independently, with probability proportional to the number of digitized dwelling structures. In this first stage, a total of 1,433 EAs were allocated (to urban - 770 EAs, rural - 488 EAs, and nomadic - 175 EAs) representing about 16 percent of the total frame of EAs. In the urban and rural selected EAs, all households were listed and information on births and deaths was recorded through the maternal mortality questionnaire. The data collected in this first phase was cleaned and a summary of households listed per EA formed the sampling frames for the second phase. In the second stage, 10 EAs were sampled out of the possible 35 that were listed, using probability proportional to the number of households. All households in each of these 10 EAs were serialized based on their location in the EA and 30 of these households sampled for the survey. The serialization was done to ensure distribution of the households interviewed for the survey in the EA sampled. A total of 220 EAs and 150 EAs were allocated to urban and rural strata respectively, while in the third stage, an average of 30 households were selected from the listed households in every EA to yield a total of 16,360 households from 538 EAs covered (220 EAs in urban, 147 EAs in rural and 171 EAs in nomadic) out of the sampled 545 EAs. In nomadic areas, a sample of 10 EAs (in this case TNS) were selected from each nomadic stratum, with probability proportional to the number of estimated households. A complete listing of households was carried out in the selected TNS followed by the selection of 30 households for the main survey interview. In those TNS with less than 30 households, all households were interviewed for the main survey. All eligible ever-married women aged 12 to 49 and never-married women aged 15 to 49 were interviewed in the selected households, while the household questionnaire was administered to all households selected. The maternal mortality questionnaire was administered to all households in each sampled TNS.

Mode of data collection

Face-to-face [f2f]

Response rate

A total of 16,360 households were selected for the sample, of which 15,870 were occupied. Of the occupied households, 15,826 were successfully interviewed, yielding a response rate of 99.7 percent. The SHDS 2020 interviewed 16,486 women-11,876 ever-married women and 4,610 never-married women.

Sampling error estimates

Sampling errors are important data quality parameters which give measure of the precision of the survey estimates. They aid in determining the statistical reliability of survey estimates. The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the Somaliland Health and Demographic Survey ( SHDS 2020) to minimise this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically. Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the SHDS 2020 is only one of many samples that could have been selected from the same population, using the same design and sample size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results. Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design. If the sample of respondents had been selected by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the SHDS 2020 sample was the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The variance approximation procedure that account for the complex sample design used R program was estimated sampling errors in SHDS which is Taylor series linearization. The non-linear estimates are approximated by linear ones for estimating variance. The linear approximation is derived by taking the first-order Tylor series approximation. Standard variance estimation methods for linear statistics are then used to estimate the variance of the linearized estimator. The Taylor linearisation method treats any linear statistic such as a percentage or mean as a ratio estimate, r = y/x, where y represents the total sample value for variable y and x represents the total number of cases in the group or subgroup under consideration

Data appraisal

• Household age distribution
• Age distribution of eligible and interviewed women
• Pregnancy- related mortality trends Note: See detailed data quality tables in APPENDIX C of the report.

This data set records the statistical data of the proportion of urban population in various regions of China (2010-2018), which is divided by year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of three data tables Proportion of urban population in different regions of China (2010-2016). Xls Proportion of urban population in different regions of China (2011-2017). Xls The proportion of urban population in all regions of China (2011-2018). XLS, the data table structure is the same. For example, the data table in 2018 has two fields: Field 1: year Field 2: Region

Population (number of individuals) with a focus on Ar Riyadh Region, Ar Diriyah and Ar Riyadh governorates compared to the other governorates of Ar Riyadh Region, the other governorates in the Kingdom, and the Kingdom as a whole in the year 2022.1.MethodologyThe population data are taken from Gastat and aggregated at three levels:The Administrative Region,The Governorate,The City.The City level is not represented, as data is always aggregated at most to the Governorate level. This is due to the heterogeneity of availability of the information at the City level. Further, this dataset presents a breakdown for Ar Riyadh and Ad-Diriyah at the governorate level. Similarly, a breakdown exists at the Regional level, distinguishing Ar Riaydh and the other Regions. This presentation highlights the importance of Riyadh at both the governorate and region levels in the Kingdom.At the Administrative Region level,For Ar Riyadh Region, aggregation is provided at the Governorate Region for Ad Diriyah, Ar Riyadh, and the remaining governorates of Ar Riyadh Region combined under the label “Others (not Diri. And Riy.).For the regions other than Ar Riyadh Region, all governorates are aggregated together,Finally, the aggregate for the KSA "Total (KSA Govs.)" is provided in full for Ad Diriyah, Ar Riyadh, and all governorates other than Ad Diriyah and Ar Riyadh "Others (not Diri. and Riy.)", for all governorates in the Kingdom excluding Ar Riyadh Regions' governorates "Total (Govs. not Ar Riyadh Region)".2.Definition(s)Population: All individuals residing within the Kingdom's territory at a given date, including both Saudi citizens and permanent/temporary non-Saudi residents. Source: https://www.stats.gov.sa/en/term-details?id=2583312).Administrative Region: the 13 administrative regions of the Kingdom, which are administered by a government body directly affiliated with the Ministry of Interior (e.g. Ar Riyadh, Makkah). Every administrative region has a designated capital city. Governorate: The second level of administrative division within the Kingdom. Each region is subdivided into several governorates, varying in number from one region to another. Governorates are further divided into centres that report administratively to the governorate or emirate. For example, Al-Kharj Governorate within the Riyadh Region.3.Detailed breakdownRegion code: An identifier of the Region, not official (visible only in the downloadable version of the dataset).Region: The first level of administrative division within the Kingdom. After aggregation, ‘Ar Riaydh’, ‘Others (not Ar Riyadh Region)’ (aggregating every case where region is not Riyadh), Total (KSA Regions) (aggregating all regions) remains.Governorate: The second level of administrative division within the Kingdom. After aggregation, Ad Diriyah, Ar Riyadh (as a Governorate) and Others (not Diri. and Riy.) (for all the other governorates in Ar Riyadh Region) remains. The three together are forming Ar Riyadh Region. Outside Ar Riyadh Region, all governorates in a given Region have been aggregated, providing a sub-total which is finally collected in Total (Govs. not Ar Riyadh Region). Finally, Total (KSA Govs.) aggregating all governorates within the Kingdom.Citizenship: Saudi and Non-Saudi population.Gender: Male and femaleAge in 10-year groups: Age categorised into 10-year intervals, ranging from 0 to 90 years old and above.Age in 5-year groups: Age categorised into 5-year intervals, ranging from 0 to 100 years old and above.Age: Age recorded in individual year.Comments: The Open Data Team comments on the metadata publishedDsetIdx: >>182.

The goal of the study is to investigate the relationship between the HEXACO personality model and Disintegration—representing a broad spectrum of psychotic-like experiences and behavioral tendencies (Perceptual Distortions, General Executive/Cognitive Impairment, Enhanced Awareness, Paranoia, Mania, Flattened Affect, Apathy/Depression, Somatoform Dysregulation, and Magical Thinking) that are reconceptualized as a personality trait. In this preregistered study, we predicted that the Disintegration factor would separate from HEXACO. The replicability of the factorial structures of HEXACO and Disintegration subcomponents is investigated across the three national samples (UK, Germany, and Serbia), matched on key socio-demographic variables. Exploratory Structural Equation Modeling (ESEM) is used to study the invariance of the hypothesized seven-factor structure (six HEXACO plus Disintegration). Support for the metric invariance of the seven-factor structure based on HEXACO and Disintegration subcomponents/facets across the three nations was found. The Disintegration factor lied outside the HEXACO personality space with each of its nine subcomponents. The Disintegration factor appeared to be among the most coherent and replicable of the seven across the samples and units of measurement (facets and items). A broad spectrum of psychotic-like experiences/behavioral tendencies relevant in understanding and explaining many aspects of everyday and long-term (mal)adaptations is not captured by the HEXACO model. Dataset for: Knežević, G., Lazarević, L. B., Bosnjak, M., & Keller, J. (2022). Proneness to psychotic-like experiences as a basic personality trait complementing the HEXACO model—A preregistered cross-national study. Personality and Mental Health, 1– 19. https://doi.org/10.1002/pmh.1537

The first row gives the rate for coalescence between two lineages that are ancestral to both loci. The second row gives rate for two types of events, coalescences between two lineages ancestral to only locus a, and coalescences of a lineage ancestral only to a with a lineage ancestral to both. The third row reflects similar events for locus b. The last row gives the rate of recombination events. Note that these rates are defined to permit a maximum of 1 ancestral recombination event occurring between locus a and b.

Number of women of reproductive age (15–49 years) categorised by birth status -whether they gave birth or not- during the 12 months preceding the census, with a focus on Ar Riyadh Region, Ar Diriyah and Ar Riyadh governorates compared to the other governorates of Ar Riyadh Region, the other governorates in the Kingdom, and the Kingdom as a whole in the year 2022.1.MethodologyData are taken from Gastat and aggregated at three levels:The Administrative Region,The Governorate,The City.The City level is not represented, as data is always aggregated at most to the Governorate level. This is due to the heterogeneity of availability of the information at the City level. Further, this dataset presents a breakdown for Ar-Riyadh and Ad-Diriyah at the governorate level. Similarly, a breakdown exists at the Regional level, distinguishing Ar-Riaydh and the other Regions. This presentation highlights the importance of Riyadh at both the governorate and region levelst in the Kingdom.At the Administrative Region level,For Ar-Riyadh Region, aggregation is provided at the Governorate Region for Ad Diriyah, Ar Riyadh, and the remaining governorates of Ar Riyadh Region combined under the label “Others (not Diri. And Riy.).For the regions other than Ar-Riyadh Region, all governorates are aggregated together,Finally, the aggregate for the KSA "Total (KSA Govs.)" is provided in full for Ad Diriyah, Ar Riyadh, and all governorates other than Ad Diriyah and Ar Riyadh "Others (not Diri. and Riy.)", for all governorates in the Kingdom excluding Ar Riyadh Regions' governorates "Total (Govs. not Ar Riyadh Region)".2.Definition(s)Population: All individuals residing within the Kingdom's territory at a given date, including both Saudi citizens and permanent/temporary non-Saudi residents. Source: https://www.stats.gov.sa/en/term-details?id=2583312).Administrative Region: the 13 administrative regions of the Kingdom, which are administered by a government body directly affiliated with the Ministry of Interior (e.g. Ar Riyadh, Makkah). Every administrative region has a designated capital city. Governorate: The second level of administrative division within the Kingdom. Each region is subdivided into several governorates, varying in number from one region to another. Governorates are further divided into centres that report administratively to the governorate or emirate. For example, Al-Kharj Governorate within the Riyadh Region.3.Detailed breakdownRegion code: An identifier of the Region, not official (visible only in the downloadable version of the dataset).Region: The first level of administrative division within the Kingdom. After aggregation, ‘Ar Riaydh’, ‘Others (not Ar Riyadh Region)’ (aggregating every case where region is not Riyadh), Total (KSA Regions) (aggregating all regions) remains.Governorate: The second level of administrative division within the Kingdom. After aggregation, Ad Diriyah, Ar Riyadh (as a Governorate) and Others (not Diri. and Riy.) (for all the other governorates in Ar Riyadh Region) remains. The three together are forming Ar-Riyadh Region. Outside Ar Riyadh Region, all governorates in a given Region have been aggregated, providing a sub-total which is finally collected in Total (Govs. not Ar Riyadh Region). Finally, Total (KSA Govs.) aggregating all governorates within the Kingdom.Citizenship: Saudi and Non-Saudi population.Mother's age group: A demographic category that segments mothers by age at the time of birth, limited to the reproductive age range of 15 to 49 years. The age groups are divided into 5-year intervals.Gave birth in the last 12 months: Indicates the birth status during the 12 months preceding the census (Y- gave birth or N- did not give birth).Comments: The Open Data Team comments on the metadata publishedDsetIdx: >>186.

The ranges are given in generations before present.

Background and Aims: The determination of energy requirements is necessary to promote adequate growth and nutritional status in pediatric populations. Currently, several predictive equations have been designed and modified to estimate energy expenditure at rest. Our objectives were (1) to identify the equations designed for energy expenditure prediction and (2) to identify the anthropometric and demographic variables used in the design of the equations for pediatric patients who are healthy and have illness.Methods: A systematic search in the Medline/PubMed, EMBASE and LILACS databases for observational studies published up to January 2021 that reported the design of predictive equations to estimate basal or resting energy expenditure in pediatric populations was carried out. Studies were excluded if the study population included athletes, adult patients, or any patients taking medications that altered energy expenditure. Risk of bias was assessed using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies.Results: Of the 769 studies identified in the search, 39 met the inclusion criteria and were analyzed. Predictive equations were established for three pediatric populations: those who were healthy (n = 8), those who had overweight or obesity (n = 17), and those with a specific clinical situation (n = 14). In the healthy pediatric population, the FAO/WHO and Schofield equations had the highest R2 values, while in the population with obesity, the Molnár and Dietz equations had the highest R2 values for both boys and girls.Conclusions: Many different predictive equations for energy expenditure in pediatric patients have been published. This review is a compendium of most of these equations; this information will enable clinicians to critically evaluate their use in clinical practice.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=226270, PROSPERO [CRD42021226270].

The study of Indiana's Child Welfare reform was designed to identify community professionals' perceptions of the Department of Child Services (DCS) following the release of a pilot program to reform child welfare in the state of Indiana. In December, 2005, the pilot project was officially rolled out in three regions of the state. The three chosen regions of the state included 11 county agencies with both urban and rural population centers. Together these regions represented 28% of the state's CHINS (Child In Need of Service) population and 20% of the child fatalities for 2004. This study represents data collected to identify perceptions of the DCS by sending a survey to professionals in the 11 pilot and 12 comparison counties. The survey questions were arranged by categories of safety, permanency, well-being, DCS goals, the reform, team meetings, and demographics. Nine separate instruments were developed and disseminated for each community group. The community professionals surveyed included: Court Appointed Special Advocates (CASAs), foster parents, judges, Law Enforcement Agencies (LEAs), medical and public health professionals, schools, social service professionals, and mental health professionals. Survey instruments were tailored to each audience, with questions that were derived from the DCS "Framework for Individualized Needs-Based Child Welfare Service Provisions," which outlined the agency's core practice values and principles.