Number and percentage of live births, by month of birth, 1991 to most recent year.

Demographic Trends and Health Outcomes in the U.S

Inequalities,Risk Factors and Access to Care

By Data Society [source]

About this dataset

This dataset contains key demographic, health status indicators and leading cause of death data to help us understand the current trends and health outcomes in communities across the United States. By looking at this data, it can be seen how different states, counties and populations have changed over time. With this data we can analyze levels of national health services use such as vaccination rates or mammography rates; review leading causes of death to create public policy initiatives; as well as identify risk factors for specific conditions that may be associated with certain populations or regions. The information from these files includes State FIPS Code, County FIPS Code, CHSI County Name, CHSI State Name, CHSI State Abbreviation, Influenza B (FluB) report count & expected cases rate per 100K population , Hepatitis A (HepA) Report Count & expected cases rate per 100K population , Hepatitis B (HepB) Report Count & expected cases rate per 100K population , Measles (Meas) Report Count & expected cases rate per 100K population , Pertussis(Pert) Report Count & expected case rate per 100K population , CRS report count & expected case rate per 100K population , Syphilis report count and expected case rate per 100k popuation. We also look at measures related to preventive care services such as Pap smear screen among women aged 18-64 years old check lower/upper confidence intervals seperately ; Mammogram checks among women aged 40-64 years old specified lower/upper conifence intervals separetly ; Colonosopy/ Proctoscpushy among men aged 50+ measured in lower/upper limits ; Pneumonia Vaccination amongst 65+ with loewr/upper confidence level detail Additionally we have some interesting trend indicating variables like measures of birth adn death which includes general fertility ratye ; Teen Birth Rate by Mother's age group etc Summary Measures covers mortality trend following life expectancy by sex&age categories Vressionable populations access info gives us insight into disablilty ratio + access to envtiromental issues due to poor quality housing facilities Finally Risk Factors cover speicfic hoslitic condtiions suchs asthma diagnosis prevelance cancer diabetes alcholic abuse smoking trends All these information give a good understanding on Healthy People 2020 target setings demograpihcally speaking hence will aid is generating more evience backed policies

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How to use the dataset

What the Dataset Contains

This dataset contains valuable information about public health relevant to each county in the United States, broken down into 9 indicator domains: Demographics, Leading Causes of Death, Summary Measures of Health, Measures of Birth and Death Rates, Relative Health Importance, Vulnerable Populations and Environmental Health Conditions, Preventive Services Use Data from BRFSS Survey System Data , Risk Factors and Access to Care/Health Insurance Coverage & State Developed Types of Measurements such as CRS with Multiple Categories Identified for Each Type . The data includes indicators such as percentages or rates for influenza (FLU), hepatitis (HepA/B), measles(MEAS) pertussis(PERT), syphilis(Syphilis) , cervical cancer (CI_Min_Pap_Smear - CI_Max\Pap \Smear), breast cancer (CI\Min Mammogram - CI \Max \Mammogram ) proctoscopy (CI Min Proctoscopy - CI Max Proctoscopy ), pneumococcal vaccinations (Ci min Pneumo Vax - Ci max Pneumo Vax )and flu vaccinations (Ci min Flu Vac - Ci Max Flu Vac). Additionally , it provides information on leading causes of death at both county levels & national level including age-adjusted mortality rates due to suicide among teens aged between 15-19 yrs per 100000 population etc.. Furthermore , summary measures such as age adjusted percentage who consider their physical health fair or poor are provided; vulnerable populations related indicators like relative importance score for disabled adults ; preventive service use related ones ranging from self reported vaccination coverage among men40-64 yrs old against hepatitis B virus etc...

Getting Started With The Dataset

To get started with exploring this dataset first your need to understand what each column in the table represents: State FIPS Code identifies a unique identifier used by various US government agencies which denote states . County FIPS code denotes counties wi...

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

Data Source: This dataset was compiled from multiple data sources

If this was helpful, a vote is appreciated ❤️ Thank you 🙂

This dataset provides a global gridded (5 arc-min resolution) detailed annual net-migration dataset for 2000-2019. We also provide global annual birth and death rate datasets – that were used to estimate the net-migration – for same years. The dataset is presented in details, with some further analyses, in the following publication. Please cite this paper when using data.

Niva et al. 2023. World's human migration patterns in 2000-2019 unveiled by high-resolution data. Nature Human Behaviour 7: 2023–2037. Doi: https://doi.org/10.1038/s41562-023-01689-4

You can explore the data in our online net-migration explorer: https://wdrg.aalto.fi/global-net-migration-explorer/

Short introduction to the data

For the dataset, we collected, gap-filled, and harmonised:

a comprehensive national level birth and death rate datasets for altogether 216 countries or sovereign states; and

sub-national data for births (data covering 163 countries, divided altogether into 2555 admin units) and deaths (123 countries, 2067 admin units).

These birth and death rates were downscaled with selected socio-economic indicators to 5 arc-min grid for each year 2000-2019. These allowed us to calculate the 'natural' population change and when this was compared with the reported changes in population, we were able to estimate the annual net-migration. See more about the methods and calculations at Niva et al (2023).

We recommend using the data either over multiple years (we provide 3, 5 and 20 year net-migration sums at gridded level) or then aggregated over larger area (we provide adm0, adm1 and adm2 level geospatial polygon files). This is due to some noise in the gridded annual data.

Due to copy-right issues we are not able to release all the original data collected, but those can be requested from the authors.

List of datasets

Birth and death rates:

raster_birth_rate_2000_2019.tif: Gridded birth rate for 2000-2019 (5 arc-min; multiband tif)

raster_death_rate_2000_2019.tif: Gridded death rate for 2000-2019 (5 arc-min; multiband tif)

tabulated_adm1adm0_birth_rate.csv: Tabulated sub-national birth rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)

tabulated_ adm1adm0_death_rate.csv: Tabulated sub-national death rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)

Net-migration:

raster_netMgr_2000_2019_annual.tif: Gridded annual net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_3yrSum.tif: Gridded 3-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_5yrSum.tif: Gridded 5-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_20yrSum.tif: Gridded 20-yr sum net-migration 2000-2019 (5 arc-min)

polyg_adm0_dataNetMgr.gpkg: National (adm 0 level) net-migration geospatial file (gpkg)

polyg_adm1_dataNetMgr.gpkg: Provincial (adm 1 level) net-migration geospatial file (gpkg) (if not adm 1 level division, adm 0 used)

polyg_adm2_dataNetMgr.gpkg: Communal (adm 2 level) net-migration geospatial file (gpkg) (if not adm 2 level division, adm 1 used; and if not adm 1 level division either, adm 0 used)

Files to run online net migration explorer

masterData.rds and admGeoms.rds are related to our online ‘Net-migration explorer’ tool (https://wdrg.aalto.fi/global-net-migration-explorer/). The source code of this application is available in https://github.com/vvirkki/net-migration-explorer. Running the application locally requires these two .rds files from this repository.

Metadata

Grids:

Resolution: 5 arc-min (0.083333333 degrees)

Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax)

Coordinate ref system: EPSG:4326 - WGS 84

Format: Multiband geotiff; each band for each year over 2000-2019

Units:

Birth and death rates: births/deaths per 1000 people per year

Net-migration: persons per 1000 people per time period (year, 3yr, 5yr, 20yr, depending on the dataset)

Geospatial polygon (gpkg) files:

Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax)

Temporal extent: annual over 2000-2019

Coordinate ref system: EPSG:4326 - WGS 84

Format: gkpk

Units:

Net-migration: persons per 1000 people per year

Live births and stillbirths annual summary statistics, by sex, age of mother, whether within marriage or civil partnership, percentage of non-UK-born mothers, birth rates and births by month and mothers' area of usual residence.

The 'Climate Just' Map Tool shows the geography of England’s vulnerability to climate change at a neighbourhood scale. The Climate Just Map Tool shows which places may be most disadvantaged through climate impacts. It aims to raise awareness about how social vulnerability combined with exposure to hazards, like flooding and heat, may lead to uneven impacts in different neighbourhoods, causing climate disadvantage. Climate Just Map Tool includes maps on: Flooding (river/coastal and surface water) Heat Fuel poverty. The flood and heat analysis for England is based on an assessment of social vulnerability in 2011 carried out by the University of Manchester. This has been combined with national datasets on exposure to flooding, using Environment Agency data, and exposure to heat, using UKCP09 data. Data is available at Middle Super Output Area (MSOA) level across England. Summaries of numbers of MSOAs are shown in the file named Climate Just-LA_summaries_vulnerability_disadvantage_Dec2014.xls Indicators include: Climate Just-Flood disadvantage_2011_Dec2014.xlsx Fluvial flood disadvantage indexPluvial flood disadvantage index (1 in 30 years)Pluvial flood disadvantage index (1 in 100 years)Pluvial flood disadvantage index (1 in 1000 years) Climate Just-Flood_hazard_exposure_2011_Dec2014.xlsx Percentage of area at moderate and significant risk of fluvial floodingPercentage of area at risk of surface water flooding (1 in 30 years)Percentage of area at risk of surface water flooding (1 in 100 years)Percentage of area at risk of surface water flooding (1 in 1000 years) Climate Just-SSVI_indices_2011_Dec2014.xlsx Sensitivity - flood and heatAbility to prepare - floodAbility to respond - floodAbility to recover - floodEnhanced exposure - floodAbility to prepare - heatAbility to respond - heatAbility to recover - heatEnhanced exposure - heatSocio-spatial vulnerability index - floodSocio-spatial vulnerability index - heat Climate Just-SSVI_indicators_2011_Dec2014.xlsx % children < 5 years old% people > 75 years old% people with long term ill-health/disability (activities limited a little or a lot)% households with at least one person with long term ill-health/disability (activities limited a little or a lot)% unemployed% in low income occupations (routine & semi-routine)% long term unemployed / never worked% households with no adults in employment and dependent childrenAverage weekly household net income estimate (equivalised after housing costs) (Pounds)% all pensioner households% households rented from social landlords% households rented from private landlords% born outside UK and IrelandFlood experience (% area associated with past events)Insurance availability (% area with 1 in 75 chance of flooding)% people with % unemployed% in low income occupations (routine & semi-routine)% long term unemployed / never worked% households with no adults in employment and dependent childrenAverage weekly household net income estimate (equivalised after housing costs) (Pounds)% all pensioner households% born outside UK and IrelandFlood experience (% area associated with past events)Insurance availability (% area with 1 in 75 chance of flooding)% single pensioner households% lone parent household with dependent children% people who do not provide unpaid care% disabled (activities limited a lot)% households with no carCrime score (IMD)% area not roadDensity of retail units (count /km2)% change in number of local VAT-based units% people with % not home workers% unemployed% in low income occupations (routine & semi-routine)% long term unemployed / never worked% households with no adults in employment and dependent childrenAverage weekly household net income estimate (Pounds)% all pensioner households% born outside UK and IrelandInsurance availability (% area with 1 in 75 chance of flooding)% single pensioner households% lone parent household with dependent children% people who do not provide unpaid care% disabled (activities limited a lot)% households with no carTravel time to nearest GP by walk/public transport (mins - representative time)% of at risk population (no car) outside of 15 minutes by walk/public transport to nearest GP Number of GPs within 15 minutes by walk/public transport Number of GPs within 15 minutes by car Travel time to nearest hospital by walk/public transport (mins - representative time)Travel time to nearest hospital by car (mins - representative time)% of at risk population outside of 30 minutes by walk/PT to nearest hospitalNumber of hospitals within 30 minutes by walk/public transport Number of hospitals within 30 minutes by car % people with % not home workersChange in median house price 2004-09 (Pounds)% area not green space Area of domestic buildings per area of domestic gardens (m2 per m2)% area not blue spaceDistance to coast (m)Elevation (m)% households with the lowest floor level: Basement or semi-basement% households with the lowest floor level: ground floor% households with the lowest floor level: fifth floor or higher

Permanent group fissions are thought to represent the tipping point at which a group has become too large and therefore splits into two, allowing for an evaluation of the consequences of living in too large a group and if fission can alleviate those costs. We first examined how adult female activity budgets (feeding, moving, resting) differed among periods surrounding (i.e., before and after) multiple fission events, accounting for seasonal variation, and using five mixed-effects beta regression models. We then assessed how rates of agonism differed among periods surrounding these fission events using two negative binomial models, one examining all agonistic interactions and one focusing on agonistic interactions that were lost. Our third analysis used a generalized linear mixed model to investigate a female’s likelihood of conception in a given month, based on her individual characteristics, which post-fission group size she joined, and whether that month fell before vs. after fission, vs. neither. Finally, we used a mixed effects Cox proportional hazards model to evaluate the relationship between infant survival, whether the infant’s mother joined the small vs. large post-fission group, and whether the month in which the infant was born fell before vs. after fission vs. neither. Here we present the three datasets used for these analyses, thus presenting individualized records of both behavioral and life history variables in relation to group fissions. Methods The datasets relate to seven fission events that occurred between 1999 and 2019 in the blue monkey population inhabiting the Kakamega Forest, western Kenya. We used data from all seven fissions for records of female conceptions and infant survival and data from the last five fissions only (2008 to 2019) for records of female behavior, because only these last five fissions occurred while the long-term monitoring protocol included focal animal follows of adult females, which allowed systematic recording of activity. Throughout the study period, a team of trained observers monitored the study groups for all or part of a day on a near daily basis. All group members could be identified as individuals. Observers documented which individuals were present and whether any sub-grouping occurred, meaning that group members were separated into two parties that traveled and foraged separately for at least part of the day. They also recorded all observed agonistic interactions, noting winners and losers when one and only one animal (the loser) showed submission. Beginning in September 2006, the team also conducted systematic 30-minute focal animal follows of adult females, selecting subjects to maintain even sampling across females and across the morning (until 10:30 AM), midday (10:30 AM-14:30 PM) and afternoon (14:30 and later). During focal follows, observers recorded the subject’s activity at 1-minute intervals: main activity categories included feeding (if the subject ingested food on or within 2 sec of the minute mark), moving (involving hindlimb locomotion), and resting. Observers also noted the food item if the focal subject was feeding and the identity of any social partner. Observers recorded all occurrences of agonistic interactions involving the focal subject during focal follows; agonistic interactions between the same opponents were considered separate events if there was a lull in aggressive behavior for at least 30 seconds. We used the census data to identify periods of sub-grouping. Specifically, we identified a sub-grouping period as when the group was split into spatially distinct parties on at least five days, and consecutive sub-grouping days were less than 14 days apart. We considered a fission to be complete when the two sub-groups had their first aggressive intergroup encounter. We designated four 60-day periods representing different times relative to each sub-grouping period. The earliest period was centered on the day that fell a year before the onset of sub-grouping. The last day of the second period fell immediately (a week) before the onset of sub-grouping, and the first day of the third period fell immediately (a week) after fission was complete. The fourth and latest period was centered on the day that fell one year after the date of fission. We aggregated activity records from focal follows for each female in each of the four periods. We calculated individuals’ activity budgets for each period by dividing the total number of instantaneous records when a female performed a given activity by the total number of instantaneous records when she was a focal subject. We accounted for seasonal variation by calculating a population-wide mean percentage for a given activity for each month using all focal follows from 2006 to 2013. We then calculated the mean during the time of year matching each 60-day analysis period as a weighted mean based on the number of days of each month that matched the analysis period. Finally, we expressed the percentage of a female’s activity budget as a deviation in percentage points from the mean time spent on that activity during the same time of year. To investigate how agonism rates varied by period, we aggregated all agonism that a female experienced during her focal samples in each period, breaking it down into total agonism and agonism losses. Agonistic interactions included aggressive (spatial displacements, threats, chases, contact aggression) and submissive (flee, cower, gecker, trill) behavior. Females did not need to be present in all four periods to be included in either analysis. However, we excluded females that were sampled for less than 6 hours in a given period, as these females were prone to having outlying data values. To analyze likelihood of conception, we focused on females who were adults at any time from October 1997 to December 2022. Females that were already reproductively mature (i.e., had already conceived their first offspring) in October 1997 were included in the dataset beginning that month. Females that matured after October 1997 were added to the dataset starting the month after their first confirmed conception. For females that died during the study period, the last month we included in the dataset was 7 months before their death or the month of their last birth, whichever occurred later. All other females remained in the data set through December 2022. We excluded the month of a female’s first conception because it had missing values for certain predictors, including time since last conception. Conceptions could be confirmed only if an offspring was born, whether it was first seen alive or dead (either stillbirth or peri-natal death). Therefore, the month of a female’s first conception fell 176 days before her first birth of a full-term infant (whether living or stillborn). For one female that had a miscarriage after her first confirmed birth, we omitted all months from seven months before the miscarriage to the month after the subsequent conception (because we could not confirm a value for the time since last conception for these months). We assigned each adult female a monthly reproductive status (pregnant, gave birth, conceived, or non-reproductive). We categorized a female as “pregnant” if she was pregnant the entire month, “gave birth” if she gave birth during that month, “conceived” if she conceived during that month, and “non-reproductive” if no other status applied. We created three categorical variables to assess the influence of fission on probability of conception at six months, one year, and two years. We calculated time since last conception and maternal age to the nearest month. We classified lactation stage as one of five categories based on the age of her most recent surviving infant: 1 (infant age < 5 months), 2 (infant age 5-9 months), 3 (infant age 10-15 months), 4 (infant age 15-32 months), and 5 (infant age > 32 months). We also created an exposure variable that equaled the number of days in each month in which a female could conceive. For months during which females gave birth, this value was the number of days remaining in the month after the birth. Pregnant females, who took a value of 0, were excluded from the model of conception probability. We added a variable identifying which post-fission group a female ended up in for months falling within 2 years before or after a fission event. For the infant survival analysis, we created three categorical variables to assess the influence of fission on infant survival, assigning each infant as being born before vs. after fission vs. neither, and using timescales of six months, one year, and two years to assess “before” and “after”. We used the infant’s mother’s age at the time of the infant’s birth and designated whether the infant was born during the peak birth season (December-March) or not. We added a variable identifying which post-fission group an infant’s mother ended up in for infants born two years before or after fission.

📦 About the Dataset This dataset provides a comprehensive snapshot of IMDb data up to March 2025, formatted as gzipped, tab-separated values (TSV) files encoded in UTF-8. Each file includes a header row detailing the columns, and missing values are denoted by \N.​

📁 Dataset Contents 1. title.akas.tsv.gz Contains alternative titles for films and shows, including:​

titleId: Unique identifier for the title.​

ordering: Sequence number for titles with the same titleId.​

title: Localized title.​

region: Region for this version of the title.​

language: Language of the title.​

types: Attributes like "alternative", "dvd", "festival", etc.​

attributes: Additional descriptors for the title.​

isOriginalTitle: Indicates if it's the original title (1) or not (0).​

1. title.basics.tsv.gz Provides fundamental details about each title:​

tconst: Unique identifier for the title.​

titleType: Type of title (e.g., movie, short, tvSeries).​

primaryTitle: Main title used in promotional materials.​

originalTitle: Original title in the original language.​

isAdult: Indicates if the title is adult content (1) or not (0).​

startYear: Release year of the title.​

endYear: End year for TV series; otherwise null.​ Kaggle

runtimeMinutes: Runtime in minutes.​ Kaggle

genres: Up to three genres associated with the title.​

1. title.principals.tsv.gz Details about principal cast and crew members:​

tconst: Unique identifier for the title.​

ordering: Sequence number for credits.​

nconst: Unique identifier for the person.​

category: Job category (e.g., actor, director).​

job: Specific job title, if applicable.​

characters: Names of characters played, if applicable.​

1. title.ratings.tsv.gz Contains IMDb ratings and vote counts:​

tconst: Unique identifier for the title.​

averageRating: Weighted average of user ratings.​

numVotes: Number of votes the title has received.​

1. name.basics.tsv.gz Information about individuals in the industry:​

nconst: Unique identifier for the person.​

primaryName: Name by which the person is most often credited.​

birthYear: Year of birth.​

deathYear: Year of death, if applicable.​

primaryProfession: Top three professions of the person.​

knownForTitles: Titles the person is known for.​

💡 Inspiration This dataset is ideal for various analytical and machine learning projects, such as:​

Building movie recommendation systems.​

Predicting movie ratings based on metadata.​

Analyzing trends in genres, runtimes, and release years.​

Studying the careers of actors, directors, and other professionals.​

Number of infant deaths and infant mortality rates, by age group (neonatal and post-neonatal), 1991 to most recent year.

Context

The Programme for International Student Assessment (PISA) is a test given every three years to 15-year-old students from around the world to evaluate their performance in mathematics, reading, and science. This test provides a quantitative way to compare the performance of students from different parts of the world. In this homework assignment, we will predict the reading scores of students from the United States of America on the 2009 PISA exam.

The datasets pisa2009train.csv and pisa2009test.csv contain information about the demographics and schools for American students taking the exam, derived from 2009 PISA Public-Use Data Files distributed by the United States National Center for Education Statistics (NCES). While the datasets are not supposed to contain identifying information about students taking the test, by using the data you are bound by the NCES data use agreement, which prohibits any attempt to determine the identity of any student in the datasets.

Each row in the datasets pisa2009train.csv and pisa2009test.csv represents one student taking the exam. The datasets have the following variables:

Content

grade: The grade in school of the student (most 15-year-olds in America are in 10th grade)

male: Whether the student is male (1/0)

raceeth: The race/ethnicity composite of the student

preschool: Whether the student attended preschool (1/0)

expectBachelors: Whether the student expects to obtain a bachelor's degree (1/0)

motherHS: Whether the student's mother completed high school (1/0)

motherBachelors: Whether the student's mother obtained a bachelor's degree (1/0)

motherWork: Whether the student's mother has part-time or full-time work (1/0)

fatherHS: Whether the student's father completed high school (1/0)

fatherBachelors: Whether the student's father obtained a bachelor's degree (1/0)

fatherWork: Whether the student's father has part-time or full-time work (1/0)

selfBornUS: Whether the student was born in the United States of America (1/0)

motherBornUS: Whether the student's mother was born in the United States of America (1/0)

fatherBornUS: Whether the student's father was born in the United States of America (1/0)

englishAtHome: Whether the student speaks English at home (1/0)

computerForSchoolwork: Whether the student has access to a computer for schoolwork (1/0)

read30MinsADay: Whether the student reads for pleasure for 30 minutes/day (1/0)

minutesPerWeekEnglish: The number of minutes per week the student spend in English class

studentsInEnglish: The number of students in this student's English class at school

schoolHasLibrary: Whether this student's school has a library (1/0)

publicSchool: Whether this student attends a public school (1/0)

urban: Whether this student's school is in an urban area (1/0)

schoolSize: The number of students in this student's school

readingScore: The student's reading score, on a 1000-point scale

Acknowledgements

MITx ANALYTIX

SFARI_EEG multi-paradigm dataset (Raw)

Overview

This dataset provides raw EEG from children recorded across seven complementary paradigms designed to assay neuro-oscillatory function spanning basic sensory through cognitive control and motor systems. This dataset contains data for three groups of children: 44 typically developing (TD), 66 autism spectrum disorder (ASD), and 28 sibling of individuals with ASD (SIB) participants, all between 8 and 13 years of age. Data are organized in BIDS 1.10.1 format (Dataset Type: raw).

The scientific motivation is to enable robust, large-sample mapping of oscillatory dysfunction in autism spectrum disorder (ASD) across the major frequency bands (theta, alpha, beta, gamma) using a common acquisition platform and harmonized annotations. By sampling multiple assays within the same participants, these data support both targeted hypothesis-driven analyses and data-driven discovery (e.g., network/feature selection approaches for biomarker development and predictive modeling of dimensional traits relevant to social cognition and motor function).

Acquisition

• System: BioSemi ActiveTwo, 64 channels (BioSemi64 montage)
• Sampling rate: 512 Hz
• Power line frequency: 60 Hz
• Trigger channel: Status (BioSemi)

Participants

• To be included in the ASD group, participants had to meet diagnostic criteria for ASD on the basis of the following measures: 1) autism diagnostic observation schedule 2 (ADOS-2) (Lord et al., 1994); 2) diagnostic criteria for autistic disorder from the Diagnostic and Statistical Manual of Mental Disorders (DSM-5); 3) clinical impression of a licensed clinician with extensive experience in diagnosis and evaluation of children with ASD. Due to precautions during the COVID-19 pandemic, a subset of ASD participants (n=9) was not able to complete the ADOS-2 evaluation, as masking requirements impacted administration. These participants instead underwent the Childhood Autism Rating Scale 2 (CARS-2) and Autism Diagnostic Interview-Revised (ADI-R) for diagnostic assessment. Participants in the TD group met the following inclusion criteria: no history of neurological, developmental, or psychiatric disorders, no first-degree relatives diagnosed with ASD, and enrollment in an age-appropriate grade in school. The SIB group participants met the same criteria as the TD group, except that they had a sibling diagnosed with ASD. Exclusion criteria for all groups included: (1) a known genetic syndrome associated with an IDD (including syndromic forms of ASD), (2) a history of or current use of medication for seizures in the past 2 years, (3) significant physical limitations (e.g., vision or hearing impairments, as screened over the phone and on the day of testing), (4) premature birth (<35 weeks) or having experienced significant prenatal/perinatal complications, or (5) a Full Scale IQ (FS-IQ) of less than 80.

• See participants.tsv and participants.json in each specific paradigm for more details.

Data quality & preprocessing notes

Raw EEG is provided without preprocessing.

Notes

• This work was supported by a grant from the Simons Foundation Autism Research Initiative (SFARI Award # 874845, SM). Support for recruitment and phenotyping of participants was provided by the Human Clinical Phenotyping Core of the NICHD funded Rose. F. Kennedy Intellectual and Developmental Disabilities Research Center (P50 HD105352, SM)

Description of the tasks

Auditory Steady-State Response (ASSR_run)

EEG recorded during an Auditory Steady-State Response (ASSR) in children.

• Participants were seated in a chair in an electrically shielded room (International Acoustics Company, Bronx, New York), 70 cm away from the visual display (Dell UltraSharp 1704FPT). Auditory stimuli were 500-ms binaural click trains at either 27- or 40-Hz, presented through HD 650 Sennheiser headphones at 60 dB SPL. Inter-stimulus interval was randomly jittered between 488-788 ms. On 15% of trials, an oddball stimulus presented at a different frequency (27-Hz for 40-Hz trials, 40-Hz for 27-Hz trials) was randomly intermixed among the standards. Participants were instructed to respond via button-press when they identified an oddball stimulus, to promote attention to the auditory stimuli. Stimuli were presented in four randomly presented blocks of 100 trials—blocked by stimulus type (40-Hz standard, 27-Hz standard), consisting of 170 trials per standard frequency and 30 trials per oddball stimulus.

  Events:

• Codes: '27_Hz_Standard': 21, '40_Hz_Oddball': 12, '40_Hz_Standard': 11, '27_Hz_Oddball': 22, 'Block_27_Hz_Standard': 27, 'Block_40_Hz_Standard': 40, 'Half_Block_Pause': 199, 'Response_button':1}

• Onsets are stimulus onsets derived from the Status channel.

• See each *_events.tsv for per-run details.

Notes: - Please cite:

Face Processing (FAST_run)

EEG recorded during a social attentional task (FAST) in children.

• Participants were seated in a chair in an electrically shielded room (International Acoustics Company, Bronx, New York), 70 cm away from the visual display (Dell UltraSharp 1704FPT). The stimuli, controlled by Presentation software (Neurobehavioral Systems), were faces ('Social') or objects (‘Non-Social’), each shown as upright and inverted images, along with shadow versions. Participants were instructed to press a button as quickly as possible upon detecting a shadow stimulus (presented at 20% probability). A jittered interstimulus interval (900–1100ms) reduced onset predictability. The task comprised 720 trials across 12 blocks (60 trials/block, ~3 minutes and 40 seconds each). Blocks were organized by stimulus category; each block contained only social stimuli (i.e., upright and inverted faces with their shadow versions) or non-social stimuli. There were six blocks of social and six blocks of non-social in total). Face and object images (upright/inverted) were randomly chosen from a pool of 28 stimuli. All faces depicted a positive emotion, (i.e., smiling faces; see the github folder for stimuli). Shadow faces and objects were chosen across a reduced pool of 5 stimuli. Responses were recorded using a response pad (Logitech Wingman Precision Gamepad), and stimulus and response triggers were sent from the PC acquisition computer via Presentation software.

Events: - Codes: Face_upright=21, Face_inverted=22, Face_upright_shadow=121, Face_inverted_shadow=122, Object_upright=31, Object_inverted=32, Object_upright_shadow=131, Object_inverted_shadow=132 - Onsets are stimulus onsets derived from the Status channel. - See each *_events.tsv for per-run details.

Note: - Please cite: in preparation

Intersensory Attention (Beepflash_run) Cued S1→S2 design indicating whether to attend visual or auditory targets. Primary measures: posterior alpha increases indexing suppression of task-irrelevant sensory input and intersensory attentional gating.

Audiovisual simple reaction time task (AVSRT_run)

EEG recorded during an audiovisual simple reaction-time task (AVSRT) in children.

• Participants were seated in a chair in an electrically shielded room (International Acoustics Company, Bronx, New York), 70 cm away from the visual display (Dell UltraSharp 1704FPT). The stimuli, controlled by Presentation software (Neurobehavioral Systems), included three types: a red disc ('Visual'), a 1000Hz tone ('Audio'), and their simultaneous presentation ('Audiovisual'). Participants were instructed to press a button as quickly as possible upon detecting any stimulus. The auditory stimulus was 1000Hz, 60ms tone presented binaurally (75 dB SPL). The visual stimulus was a red disc subtending to 1.5 degrees, displayed above a fixation cross. The audiovisual stimulus was a simultaneous presentation of both. Each trial presented a pseudo randomly chosen stimulus (A, V, or AV; represented equiprobably), with stimuli delivered through headphones (XX) and displayed on a flat-panel LCD (60Hz). A jittered randomly sampled interstimulus interval (1000-3000ms) reduced onset predictability. The task consisted of 400 trials across 4 blocks (100 trials per block), each block lasting approximately 3 minutes and 40 seconds. Button presses were recorded using a response pad (Logitech Wingman Precision Gamepad). Triggers indicating stimulus latency were sent from the PC acquisition computer via Presentation software.

Events: - Codes: AV=3, A=4, V=5 - Onsets are stimulus onsets derived from the Status channel. - See each *_events.tsv for per-run details.

Note: - Please cite: in preparation

Motor Processing (Motor_run)

EEG recorded during a mobile EEG paradigm in children. This paradigm is recorded using Lab Streaming Layer to synchronize the camera system (for gait measures) with the EEG recordings.

• Participants are either standing on a treadmill (30 sec recordings) or walking on a treadmill (5 min recordings) with either no flow (static background with white dots) or a flow (dots are moving toward the participant). The task is to ignore the white dots and to respond when the fixation cross at the center of the projected image rotate (45 degrees rotation).

Events: - Codes: - Onsets are stimulus onsets derived from the Status channel. - See each *_events.tsv for per-run details.

Cross-sensory attentional switching task (Beep-Flash_run)

EEG recorded during a cross-sensory attentional task (Beep-Flash) in children.

• Participants were seated in a chair in an electrically shielded room (International Acoustics Company, Bronx, New York), 70 cm away from the visual display (Dell UltraSharp 1704FPT). A cued intersensory attention task was employed in which each trial consisted of an instructional cue (S1), an

Life expectancy at birth and at age 65, by sex, on a three-year average basis.

Provisional counts of the number of deaths registered in England and Wales, by age, sex, region and Index of Multiple Deprivation (IMD), in the latest weeks for which data are available.

Permanent group fissions present rare opportunities for individuals in philopatric groups to select their groupmates. Studying post-fission group choice allows insights into how sociality influences animal decision-making and which social ties are important to group-living individuals. Our first analysis investigated which social ties influenced post-fission group choice in adult female blue monkeys by considering the strength and consistency of their ties with their original group’s members, as well as their dominance relations, relatedness to other female group members, and risk of infanticide. We used these dyadic and nodal characteristics in a separable temporal exponential random graph model to model edge persistence across two timesteps, before vs. after fission. Our second analysis used a conditional logit model to investigate the role of the original group’s resident male in a female’s post-fission group choice, assessing the strength of her tie to him and her vulnerability to i..., Observational data were collected from blue monkeys (Cercopithecus mitis stuhlmanni) in the Kakamega Forest, western Kenya surrounding five instances of group fission that occurred between 2008 and 2019. During the study period, trained observers monitored the different groups on a near daily basis, conducting focal animal samples on all adult females (classified as adults from the day they give birth to their first offspring). Focal animal samples were designed to last 30 min, and were retained in the dataset if they were at least 20 min long. On each day, females were chosen as subjects so that focal samples accumulated evenly across individuals and among different periods of the day (morning, midday, afternoon). Instantaneous recording of the focal subject’s behavior occurred at 1-minute intervals and included the identities of any social partners and individuals in proximity (within 1m). Agonistic interactions (with one individual showing submission) were recorded during focal sampl..., , # Social ties drive post-fission group choice in blue monkeys

https://doi.org/10.5061/dryad.xd2547drj

Permanent group fissions present rare opportunities for individuals in philopatric groups to select their groupmates, and so by studying post-fission group choice, we can gain insight into how sociality influences decision-making. Our study investigated which social ties and individual attributes influence post-fission group choice in blue monkeys by considering the strength and consistency of female’s ties to social affiliates, her relatedness to female peers, her relative rank, her vulnerability to infanticide, and her tie with the original group’s resident male. We found that females maintained different kinds of relationships during fission (those with social affiliates and the original group’s resident male). Here we present two datasets (and associated R code) used in this analysis, one (Dataset 1) including attributes of dyadic ties an...,

Context & Content

This dataset features the salaries of 874 nhl players for the 2016/2017 season. I have randomly split the players into a training (612 players) and test (262 players) populations. There are 151 predictor columns (described in column legend section, if you're not familiar with hockey the meaning of some of these may be a bit cryptic!) as well as a leading column with the players 2016/2017 annual salary. For the test population the actual salaries have been broken off into a separate .csv file.

Acknowledgements

Raw excel sheet was acquired http://www.hockeyabstract.com/

Inspiration

Can you build a model to predict NHL player's salaries? What are the best predictors of how much a player will make?

Column Legend

Acronym - Meaning

%FOT - Percentage of all on-ice faceoffs taken by this player.

+/- - Plus/minus

1G - First goals of a game

A/60 - Events Against per 60 minutes, defaults to Corsi, but can be set to another stat

A1 - First assists, primary assists

A2 - Second assists, secondary assists

BLK% - Percentage of all opposing shot attempts blocked by this player

Born - Birth date

C.Close - A player shot attempt (Corsi) differential when the game was close

C.Down - A player shot attempt (Corsi) differential when the team was trailing

C.Tied - A player shot attempt (Corsi) differential when the team was tied

C.Up - A player shot attempt (Corsi) differential when the team was in the lead

CA - Shot attempts allowed (Corsi, SAT) while this player was on the ice

Cap Hit - The player's cap hit

CBar - Crossbars hit

CF - The team's shot attempts (Corsi, SAT) while this player was on the ice

CF.QoC - A weighted average of the Corsi percentage of a player's opponents

CF.QoT - A weighted average of the Corsi percentage of a player's linemates

CHIP - Cap Hit of Injured Player is games lost to injury multiplied by cap hit per game

City - City of birth

Cntry - Country of birth

DAP - Disciplined aggression proxy, which is hits and takeaways divided by minor penalties

DFA - Dangerous Fenwick against, which is on-ice unblocked shot attempts weighted by shot quality

DFF - Dangerous Fenwick for, which is on-ice unblocked shot attempts weighted by shot quality

DFF.QoC - Quality of Competition metric based on Dangerous Fenwick, which is unblocked shot attempts weighted for shot quality

DftRd - Round in which the player was drafted

DftYr - Year drafted

Diff - Events for minus event against, defaults to Corsi, but can be set to another stat

Diff/60 - Events for minus event against, per 60 minutes, defaults to Corsi, but can be set to another stat

DPS - Defensive point shares, a catch-all stats that measures a player's defensive contributions in points in the standings

DSA - Dangerous shots allowed while this player was on the ice, which is rebounds plus rush shots

DSF - The team's dangerous shots while this player was on the ice, which is rebounds plus rush shots

DZF - Shifts this player has ended with an defensive zone faceoff

dzFOL - Faceoffs lost in the defensive zone

dzFOW - Faceoffs win in the defensive zone

dzGAPF - Team goals allowed after faceoffs taken in the defensive zone

dzGFPF - Team goals scored after faceoffs taken in the defensive zone

DZS - Shifts this player has started with an defensive zone faceoff

dzSAPF - Team shot attempts allowed after faceoffs taken in the defensive zone

dzSFPF - Team shot attempts taken after faceoffs taken in the defensive zone

E+/- - A player's expected +/-, based on his team and minutes played

ENG - Empty-net goals

Exp dzNGPF - Expected goal differential after faceoffs taken in the defensive zone, based on the number of them

Exp dzNSPF - Expected shot differential after faceoffs taken in the defensive zone, based on the number of them

Exp ozNGPF - Expected goal differential after faceoffs taken in the offensive zone, based on the number of them

Exp ozNSPF - Expected shot differential after faceoffs taken in the offensive zone, based on the number of them

F.Close - A player unblocked shot attempt (Fenwick) differential when the game was close

F.Down - A player unblocked shot attempt (Fenwick) differential when the team was trailing

F.Tied - A player unblocked shot attempt (Fenwick) differential when the team was tied

F.Up - A player unblocked shot attempt (Fenwick) differential when the team was in the lead. Not the best acronym.

F/60 - Events For per 60 minutes, defaults to Corsi, but can be set to another stat

FA - Unblocked shot attempts allowed (Fenwick, USAT) while this player was on the ice

FF - The team's unblocked shot attempts (Fenwick, USAT) while this player was on the ice

First Name -

FO% - Faceoff winning percentage

FO%vsL - Faceoff winning percentage against lefthanded opponents

FO%vsR - Faceoff winning percentage against righthanded opponents

FOL - The team's faceoff losses...

A single-centre retrospective case control study was conducted. The protocol defined cases as all neonatal deaths or NICU admissions occurring within an eight-year period from 2012 to 2020, although no neonatal deaths occurred during this period following a vaginal breech birth. Controls were identified as the two vaginal breech births directly prior to the case where no neonatal death nor NICU admission occurred. Two previous births were used to prevent bias on the understanding that an adverse outcome can affect clinical decision-making for subsequent births.12 Any NICU admission was included because this indicates a neonate which requires additional observation, tests and/or intervention. Neonates who are not admitted are deemed as generally well.13 Additionally, separation from the baby was considered an important outcome by our Patient and Public Involvement Group,14 who also requested more information on the timing of cord clamping.The study was conducted within the maternity unit at a London District General Hospital which serves a large population of 176,313 people. Two thirds are of white British ethnicity and one third from Black, Asian and Minority Ethnic (BAME) backgrounds. The community the hospital serves is thought of as affluent, with good employment rates, particularly employment in high-end jobs. The hospital itself serves a wider community than the borough it is situated within and has 5000 births per year. It has a level two NICU situated within the maternity unit. The Algorithm was not in use at the site, and none of the authors were employed by the Trust, during the time period covered by the study. Fifteen cases and thirty controls were identified from routine electronic health records. The Medical Record Numbers were sent to the Health Records Department for the complete files to be retrieved. Data were extracted by the lead researcher from the intrapartum care records and recorded anonymously in a Microsoft Excel spreadsheet.A structured data collection tool was developed based on Reitter et al.13 The data collection tool consisted of information usually recorded in the notes during a breech birth and included: lead professional, type of breech, position, epidural, fetal monitoring, meconium, what emerged first, time each part of the breech born, documented manoeuvres used, time performed and information related to the condition of the neonate at birth.To calculate our sample size, based on the work of Reitter et al,11 we hypothesised that the rate of exposure to a pelvis-to-head interval >3 minutes would be 25% among controls and 75% among cases. Using a case:control ratio of 1:2, we determined that 15 independent cases and 30 controls were required to infer an association between a pelvis-to-head interval >3 minutes and the composite neonatal outcome with a confidence interval of 95% and a power of 80%. First, we calculated the time to event interval for variables of interest. We then reported descriptive statistics for all variables, including means, medians and range for continuous variables. Exposures and confounders were converted into binary variables, reflecting the cut-offs used in the Algorithm. These were then tested against the primary outcome using the non-parametric chi-square, or Fisher’s Exact tests where cell frequencies were too small for the chi-square test. Logistic regression analysis was used to test the predictive values of meeting or exceeding the recommended time limits in the Physiological Breech Birth Algorithm. Further logistic regression analyses were conducted with all variables that showed an association with the composite neonatal outcome to determine their predictive value, and additional variables to explore their potential as confounding factors for investigation in future studies. Finally, a Receiver Operating Characteristics (ROC) curve analysis was conducted to compare the sensitivity and specificity of the 7-5-3 minute time limits. All statistical analyses were performed using IBM SPSS version 26.

The Mayors Health Inequalities Strategy sets out his plans to tackle unfair differences in health to make London a healthier, fairer city. This dataset reports the 14 headline population health indicators that will be used to monitor London’s progress in reducing health inequalities over the next ten years. The themes of the indicators are listed below. The measures will monitor an identified inequality gap between defined populations. Healthy life expectancy at birth – male Healthy life expectancy at birth – female Children born with low birth weight School readiness among children Excess weight in children at age 10-11 (year 6) Excess mortality in adults with serious mental illness Suicide Mortality caused by Particulate Matter (PM2.5) Employment Feeling of belonging to a community (provisional) HIV late diagnosis People diagnosed with TB Adults walking or cycling for two periods of ten minutes each day Smoking

Two online overviews offer comprehensive metadata on the EVS datasets and variables. The extended study description for the EVS 2008 provides country-specific information on the origin and outcomes of the national surveys The variable overview of the four EVS waves 1981 1990 1999/2000 and 2008 allows for identifying country specific deviations in the question wording within and across the EVS waves. These overviews can be found at: http://info1.gesis.org/EVS/Studies (Extended Study Description), http://info1.gesis.org/EVS/Variables (Variable Overview). Moral, religious, societal, political, work, and family values of Europeans. Topics: 1. Perceptions of life: importance of work, family, friends and acquaintances, leisure time, politics and religion; frequency of political discussions with friends; happiness; self-assessment of own health; memberships and unpaid work (volunteering) in: social welfare services, religious or church organizations, education, or cultural activities, labor unions, political parties, local political actions, human rights, environmental or peace movement, professional associations, youth work, sports clubs, women´s groups, voluntary associations concerned with health or other groups; tolerance towards minorities (people with a criminal record, of a different race, left/right wing extremists, alcohol addicts, large families, emotionally unstable people, Muslims, immigrants, AIDS sufferers, drug addicts, homosexuals, Jews, gypsies and Christians - social distance); trust in people; estimation of people´s fair and helpful behavior; internal or external control; satisfaction with life. 2. Work: reasons for people to live in need; importance of selected aspects of occupational work; employment status; general work satisfaction; freedom of decision-taking in the job; importance of work(work ethics, scale); important aspects of leisure time; attitude towards following instructions at work without criticism (obedience work); give priority to nationals over foreigners as well as men over women in jobs. 3. Religion: Individual or general clear guidelines for good and evil; religious denomination; current and former religious denomination; current frequency of church attendance and at the age of 12; importance of religious celebration at birth, marriage, and funeral; self-assessment of religiousness; churches give adequate answers to moral questions, problems of family life, spiritual needs and social problems of the country; belief in God, life after death, hell, heaven, sin and re-incarnation; personal God versus spirit or life force; own way of connecting with the divine; interest in the sacred or the supernatural; attitude towards the existence of one true religion; importance of God in one´s life (10-point-scale); experience of comfort and strength from religion and belief; moments of prayer and meditation; frequency of prayers; belief in lucky charms or a talisman(10-point-scale); attitude towards the separation of church and state. 4. Family and marriage: most important criteria for a successful marriage (scale); attitude towards childcare (a child needs a home with father and mother, a woman has to have children to be fulfilled, marriage is an outdated institution, woman as a single-parent); attitude towards marriage, children, and traditional family structure(scale); attitude towards traditional understanding of one´s role of man and woman in occupation and family (scale); attitude towards: respect and love for parents, parent´s responsibilities for their children and the responsibility of adult children for their parents when they are in need of long-term care; importance of educational goals; attitude towards abortion. 5. Politics and society: political interest; political participation; preference for individual freedom or social equality; self-assessment on a left-right continuum (10-point-scale); self-responsibility or governmental provision; free decision of job-taking of the unemployed or no permission to refuse a job; advantage or harmfulness of competition; liberty of firms or governmental control; equal incomes or incentives for individual efforts; attitude concerning capitalism versus government ownership; postmaterialism (scale); expectation of future development (less emphasis on money and material possessions, greater respect for authority); trust in institutions; satisfaction with democracy; assessment of the political system of the country as good or bad (10-point-scale); preferred type of political system(strong leader, expert decisions, army should rule the country, or democracy); attitude towards democracy (scale). 6. Moral attitudes (scale: claiming state benefits without entitlement, cheating on taxes, joyriding, taking soft drugs, lying, adultery, bribe money, homosexuality, abortion, divorce, euthanasia, suicide, corruption, paying cash, casual sex, avoiding fare on publictransport, prostitution, experiments with human embryos, geneticmanipulation of food, insemination or in-vitro fertilization and deathpenalty). 7. National identity: geographical group the respondent feels belonging to (town, region of country, country, Europe, the world); citizenship; national pride; fears associated with the European Union(the loss of social security and national identity, growing expenditure of the own country, the loss of power in the world for one´s own country and the loss of jobs); attitude towards the enlargement of the European Union (10-point-scale); voting intensions in the next election and party preference; party that appeals most; preferred immigrant policy; opinion on terrorism; attitude towards immigrants and their customs and traditions (take jobs away, undermine a country´s cultural life, make crime problems worse, strain on country´s welfare system, threat to society, maintain distinct customs and traditions); feeling like a stranger in one´s own country; too many immigrants; important aspects of national identity (being born in the country, to respect country´s political institutions and laws, to have country´s ancestry, to speak the national language, to have lived for a long time in the country); interest in politics in the media; give authorities information to help justice versus stick to own affairs; closeness to family, neighborhood, the people in the region, countrymen, Europeans and mankind; concerned about the living conditions of elderly people, unemployed, immigrants and sick or disabled people. 8. Environment: attitude towards the environment (scale: readiness to give part of own income for the environment, overpopulation, disastrous consequences from human interference with nature, human ingenuity remains earth fit to live in, the balance of nature is strong enough to cope with the impacts of modern industrial nations, humans were meant to rule over the rest of nature, an ecological catastrophe is inevitable). Demography: sex; age (year of birth); born in the country of interview; country of birth; year of immigration into the country; father and mother born in the country; country of birth of father and mother; current legal marital status; living together with the partner before marriage or before the registration of partnership; living together with a partner and living with a partner before; steady relationship; married to previous partner; living together with previous partner before marriage; end of relationship; number of children; year of birth of the first child; size and composition of household; experienced events: the death of a child, of father or mother, the divorce of a child, of the parents or of another relative; age of respondent when these events took place; age at completion of education; highest educational level attained; employment status; employed or self-employed in the last job; profession (ISCO-88) and occupational position; supervising function and span of control; size of company. Social origin and partner: respondent´s partner or spouse: partner was born in the country and partner´s country of birth; highest educational level; employment status of the partner; employment or self-employment of the partner in his/her last job; partner´s profession (ISCO-88) and occupational position; supervising function of the partner and span of control; unemployment and dependence on social-security of the respondent and his partner longer than three months in the last five years; scale of household income; living together with parents when the respondent was 14 years old; highest educational level of father/mother; employment status of father/mother when the respondent was 14 years old; profession of father/mother (ISCO-88) and kind of work; number of employees (size of business); supervising function and span of control of father and mother; characterization of the parents when respondent was 14 years old (scale: liked to read books, discussed politics at home with their child, liked to follow the news, had problems making ends meet, had problems replacing broken things); region the respondent lived at the age of 14, present place of residence (postal code); size of town; region. Interviewer rating: respondent´s interest in the interview. Additionally encoded: interviewer number; date of the interview; total length of the interview; time of the interview (start hour and start minute, end hour and end minute); language in which the interview was conducted. Additional country specific variables are included in this national dataset.

The data in this dataset were collected as part of the 100 Minutes of Art campaign. The aim of the campaign was to increase the meaning and importance of art and culture in people's lives and in social and health services communities. Participation in the campaign was possible for both individuals and communities. Art and culture and the activities related to them were defined by the participants. This dataset consists of responses to two forms that were completed by individual participants before and after participating in the campaign. The campaign was funded and conducted by the Arts Promotion Centre Finland (TAIKE). 250 individual participants completed the first form before participating in the campaign and consented to the archiving of their responses. The participants discussed the role of art and culture in their lives at the starting point of the campaign as well as their expectations for the campaign. Additionally, 25 individual participants completed the second form after participating in the campaign and consented to the archiving of their responses. In the responses to the second form, the participants discussed the role of art and culture in their lives after the campaign had ended as well as how their expectations regarding the campaign had realised and how they intended to continue including art and culture in their lives. Background information included the date when the form was completed and the participant's gender, year of birth and situation in life. The data were organised into an easy to use HTML version at FSD. This dataset can be connected to the two other datasets collected as part of the 100 Minutes of Art campaign: FSD3407 100 Minutes of Art 2019: Communities and FSD3410 100 Minutes of Art 2019: Entries in the Taidepassi Participation Diary. The dataset is only available in Finnish.

This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).