This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the countries in this dataset have a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS:

unid: ISO numeric country code (used by the United Nations)

wbid: ISO alpha country code (used by the World Bank)

SES: Country socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174)

country: Short country name

year: Survey year

gdppc: GDP per capita: Single time-series (imputed)

yrseduc: Completed years of education in the adult (15+) population

region5: Five category regional coding schema

regionUN: United Nations regional coding schema

DATA SOURCES:

The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita:

1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls.

2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm

3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/

4. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.

5. United Nations Population Division. 2009.

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

Logistic regression models of socioeconomic status and psychosocial resources, adjusted for age, sex, country of birth, employment status and other measures of SES.

Analysis of ‘Country Socioeconomic Status Scores: 1880-2010’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sdorius/globses on 14 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the world’s people live in a country with a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS: UNID: ISO numeric country code (used by the United Nations) WBID: ISO alpha country code (used by the World Bank) SES: Socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174) country: Short country name year: Survey year SES: Socioeconomic status score (1-99) for each of 174 countries gdppc: GDP per capita: Single time-series (imputed) yrseduc: Completed years of education in the adult (15+) population popshare: Total population shares

DATA SOURCES: The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita: 1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. Maddison population data in 000s; GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls. 2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm 3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/ Total Population 1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.
2. United Nations Population Division. 2009.

--- Original source retains full ownership of the source dataset ---

Low-and middle-income countries (LMICs) are experiencing a nutritional transition in which the burden of obesity tends to shift towards the lower-socioeconomic status (SES) group. We propose a system dynamics (SD) model for assessing the nutritional stage dynamics of the Colombian urban population by age and SES projected to 2030. This SD model captures the ageing population according to body mass index (BMI) categories and SES. In this model, the transference rates (TRs) between BMI categories by age and SES are estimated using a heuristic based on data obtained from national surveys. The simulation results show that the Colombian population, particularly those aged 20 to 39 years with a lower SES, is moving towards the overweight and obese categories. The TRs for overweight and obese categories in the lower SES group (the mean TR from not overweight to overweight = 0.0215 (per year) and mean TR from overweight to obese = 0.0098 (per year)) are increasing more rapidly than the those in the middle (the mean TR from not overweight to overweight = 0.0162 (per year) and mean TR from overweight to obese = 0.0065 (per year)) and higher SES groups (the mean TR from not overweight to overweight = 0.0166 and mean TR from overweight to obese = 0.0054 (per year)). Additionally, from 2005 to 2010, individuals aged 20 to 39 years had the highest TRs towards the overweight and obese categories (from 0.026 to 0.036 per year and from 0.0064 to 0.012 per year, respectively). The TRs also indicated that children aged 0 to 14 years are moving from the obese to overweight and from the overweight to not overweight categories. These TRs show that the Colombian population is experiencing an SES-related nutritional transition that is affecting the lower SES population. The proposed model could be implemented to assess the nutritional transitions experienced in other LMICs.

This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” by Chicago community area, for the years 2008 – 2012. The indicators are the percent of occupied housing units with more than one person per room (i.e., crowded housing); the percent of households living below the federal poverty level; the percent of persons in the labor force over the age of 16 years that are unemployed; the percent of persons over the age of 25 years without a high school diploma; the percent of the population under 18 or over 64 years of age (i.e., dependency); and per capita income. Indicators for Chicago as a whole are provided in the final row of the table.

In 2021, 20.1% of people from the Indian ethnic group were in higher managerial and professional occupations – the highest percentage out of all ethnic groups in this socioeconomic group.

Annual Household income per capita and population share by socioeconomic status*.

This data collection and its 1940 counterpart were assembled through a collaborative effort between the United States Bureau of the Census and the Center for Demography and Ecology of the University of Wisconsin. The 1940 and 1950 Census Public Use Sample Project was supported by The National Science Foundation under Grant SES-7704135. The collections contain a stratified 1-percent sample of households, with separate records for each household, for each \'sample line\' respondent, and for each person in the household. These records were encoded from microfilm copies of original handwritten enumeration schedules from the 1940 and 1950 Censuses of Population. The universe for the sample included all persons and households within the United States. Geographic identification of the location of the sampled households includes Census regions and divisions, States (except Alaska and Hawaii), Standard Metropolitan Areas (SMA\'s), and State Economic Areas (SEA\'s). The SMA\'s and SEA\'s are comparable for both the 1940 and 1950 Public Use Microdata Samples (PUMS). The data collections were constructed from and consist of 20 independently-drawn subsamples stored in 20 discrete physical files. Each of the 20 subsamples contains three record types (household, \'sample line\', and person). Both collections had both a complete-count and a sample component. Individuals selected for the sample component were asked a set of additional questions. Only households with a \'sample line\' person were included in the public use microdata sample. The collections also contain records of group quarters members who were also on the Census \'sample line\'. For the 1940 and 1950 collections, each household record contains variables describing the location and composition of the household. The \'sample line\' records for 1950 contain variables describing demographic characteristics such as nativity, marital status, number of children, veteran status, education, income, and occupation. The person records for 1950 contain such demographic variables as nativity, marital status, family membership, and occupation. Accompanying the data collections are code books which include an abstract, descriptions of sample design, processing procedures and file structure, a data dictionary (record layout), category code lists, and a glossary. The data collections are arranged by subsample with each subsample stored as a separate physical file of information. The 20 subsamples were selected randomly. Within each of the 20 subsamples, records are sequenced by State. Extracting all of the records for one State entails reading through all of the 20 physical files and selecting that State\'s records from each of the 20 subsamples. Record types are ordered within household (household characteristics first, \'sample line\' next, and person records last). The 1950 collection consists of a total of 2,844,458 data records: 461,130 household records, 461,130 \'sample line\' records, and 1,922,198 person records. Each record type has a logical record length of 133.;

HCP M90 Session 1 Right Hemisphere

Collection description

***PLEASE READ OUR PUBLISHED PAPER CAREFULLY AND ENSURE YOU UNDERSTAND THESE IMAGES BEFORE USING ANY OF THIS INFORMATION CLINICALLY. WE CAN BE CONTACTED FOR CLARIFICATIONS.***

This collection contains images of the outer loop, and partial middle loop, of the optic radiation. These are population averages, displayed as percentages of participants, as single subject maps cannot be released for privacy reasons.

Please read our paper for how these images were generated. In brief, the CONSULT system created binarised tractography for each subject. We take the average of these binary maps *in MNI space* to create the images appearing here. and multiply the result by 100. The MNI template used is attached.

Data are from multiple sources and filed as they appear in the paper. These sources are:
1) HCP-*: Human Connectome Project data. These data were modified from their originals to test CONSULT using different quality data. Raw HCP data can be downloaded from the HCP website.
2) Hospital-*: Data acquired by us on two hospital campuses. Some of these data are from neurosurgical patients. Three different scanners and acquisition protocols were used.
3) MASSIVE-*: Data from the MASSIVE dataset. These data were modified from their originals to test CONSULT using different quality data. The original data can be downloaded from the MASSIVE website.

Subject species

homo sapiens

Modality

Diffusion MRI

Analysis level

group

Cognitive paradigm (task)

None / Other

Map type

Other

Abstract

The survey provides information relevant in setting up the anti-poverty policy of the country. Its primary objective is to collect information on household income and consumption expenditures, changes in assets and liabilities, housing characteristics as well as the ownership of some durable goods.

Geographic coverage

National

Analysis unit

Household, Individual

Universe

The survey covered all private, non-institutional households residing permanently in municipal areas, sanitary districts and villages. Excluded from the survey are population living in transient hotels and rooming houses, boarding schools, military barracks, wats, hospitals, prisons and other such institutions, as well as households of foreign diplomats and other temporary residents.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design A stratified two-stage sampling was adopted for the survey. Provinces in all regions were constituted strata and types of local administration in each province were sub-strata. The primary and secondary sampling units were blocks for municipal areas/villages for non-municipal areas and private households respectively. Stratification There were altogether 76 strata (provinces). Each stratum was divided into three parts according to type of local administration, namely, Municipal Areas, Sanitary Districts and Non-municipal Areas - outside sanitary districts. Selection of Primary Sampling Unit In each type of local administration, the sample selection of blocks/villages was performed separately and independently in each part by using probability proportional to size (the total number of households). The total sample blocks/villages was 3,359 from 79,835 blocks/villages. Selection of Secondary Sampling Unit Private households are our ultimate sampling units. A new listing of households was made for every sample block/village to serve as the sampling frame. A systematic sample of 15 private households was selected from each of the sample blocks, while 9 and 7 private households were selected from each of the sample villages in sanitary districts and non-municipal areas - outside sanitary districts respectively. Before selecting sample households, the list of households was rearranged by household's size, i.e. the member of the household and type of economic household. The total number of sample private households selected for enumeration by region and type of local administration was 31,143.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were used: - SES 2: for household composition, demographic and economic characteristics of household members; income and housing facilities - SES 3: for household expenditures and 7-day food consumption

Cleaning operations

Stages at which data editing took place:

a) In the field: - Each completed interview was subjected to a thorough field edit, followed by a follow-up interview if the information was found to be incomplete or internally inconsistent. - A balance sheet was prepared for each completed interview. This balance compared total money "disbursements" with total money "receipts" for the preceding month. If the account was more than 15 per cent out of balance, a revisit to the household was expected to reconcile the difference. - Members of the Central Office Staff conducted periodic visits to the field to review questionable reports and clarify data collection procedures. b) At the Central Office by subject matter staff of the Economic Statistics Division
c) At the Central Office by the Data processing Operations Division d) During data entry e) Prior to tabulations

Descriptive information was coded numerically for computer processing. All annual expenditure and income values were converted to a one-month base by dividing annual values by 12. For 7-day food consumption, values were multiplied by 4.3 which was the average number of weeks per month (52 weeks/12 months = 4.3 weeks/month). Income from farm or non-farm enterprises was calculated on the basis of total annual value of production less operating expenses. From this estimate, the value of products held or withdrawn for household consumption was subtracted to arrive at an estimate of money income.

Sampling error estimates

Formulas for coefficient of variations (CV) for totals and averages for characteristics (Y) of households is presented in Chapter 2 of the Report of the 1994 Household Socio-Economic Survey.

This dataset contains the data on which the conclusions of the study "Impact of neighbourhood-level socioeconomic status, traditional coronary risk factors, and ancestry on age at myocardial infarction onset: A population-based register study" rely. We collected data registered in the Norwegian Myocardial Infarction Register for all patients admitted to Diakonhjemmet Hospital with a non-ST elevation myocardial infarction (NSTEMI) in 2014-2017 (n=840). Using the patients' registered postal codes, we identified in which city district in Oslo, Norway the patients were residing. Patients from districts other than Frogner, Vestre Aker, Ullern, Stovner, Grorud, and Alna were excluded (n=60), and the remaining patients were grouped according to whether they were residing in the western (high neighbourhood-level socioeconomic status (SES)) or north-eastern (low neighbourhood-level SES) city districts. Using the patients' registered social security numbers and the electronic medical record system at Diakonhjemmet Hospital, patients were grouped according to whether or not they had presumed Northwest-European ancestry based on their names and other information found in their medical records. Patients with undecidable ancestry (n=2) were excluded. Furthermore, patients with type 2 myocardial infarction (n=117) were excluded since we aimed to investigate the risk for coronary heart disease (CHD). Re-admissions in the period (n=55) were excluded, and we were left with 606 patients. The dataset contains patient data on city district group, presumed ancestry group, age at hospital admission with NSTEMI, history of previous acute myocardial infarction (AMI), prior diagnosis of diabetes, prior diagnosis of hypertension, cigarette smoking status, use of statins, body mass index (BMI), and serum levels of low-density lipoprotein (LDL) cholesterol. Raw data from the Norwegian Myocardial Infarction Register, which was used to generate variables on the patients' presumed ancestry and city-district group, is not made available as it contains personal data, but can be applied for at helsedata.no. Previous AMI was defined regardless of infarction type and ECG diagnosis, prior diagnosis of diabetes was defined as known diagnosis with diabetes mellitus type 1 or 2, prior diagnosis of hypertension was defined as prior or ongoing treatment for hypertension, and cigarette smoking was defined as patients that had been smoking the last month. BMI and LDL cholesterol were measured at hospital admission. Registration of all cases of AMI in Norway in the Norwegian Myocardial Infarction Register is mandatory and does not require informed consent. The Norwegian Myocardial Infarction Register is part of the National Register of Cardiovascular Diseases and is authorized in the Section 11 h of the Norwegian Health Register Act. The study was approved by the Institutional Review Board of Diakonhjemmet Hospital and the data privacy representative for Diakonhjemmet Hospital, and all methods were in accordance with the ethical standards of the institution and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

HCP M60 Session 2 Right Hemisphere

Collection description

***PLEASE READ OUR PUBLISHED PAPER CAREFULLY AND ENSURE YOU UNDERSTAND THESE IMAGES BEFORE USING ANY OF THIS INFORMATION CLINICALLY. WE CAN BE CONTACTED FOR CLARIFICATIONS.***

This collection contains images of the outer loop, and partial middle loop, of the optic radiation. These are population averages, displayed as percentages of participants, as single subject maps cannot be released for privacy reasons.

Please read our paper for how these images were generated. In brief, the CONSULT system created binarised tractography for each subject. We take the average of these binary maps *in MNI space* to create the images appearing here. and multiply the result by 100. The MNI template used is attached.

Data are from multiple sources and filed as they appear in the paper. These sources are:
1) HCP-*: Human Connectome Project data. These data were modified from their originals to test CONSULT using different quality data. Raw HCP data can be downloaded from the HCP website.
2) Hospital-*: Data acquired by us on two hospital campuses. Some of these data are from neurosurgical patients. Three different scanners and acquisition protocols were used.
3) MASSIVE-*: Data from the MASSIVE dataset. These data were modified from their originals to test CONSULT using different quality data. The original data can be downloaded from the MASSIVE website.

Subject species

homo sapiens

Modality

Diffusion MRI

Analysis level

group

Cognitive paradigm (task)

None / Other

Map type

Other

Simulated age-standardized rates per 10000 person-years of incident coronary heart disease and CHD deaths in adults aged 35 to 64 years with low or high socioeconomic status, by gender.

Also the welfare state of Sweden has prominent socioeconomic (SES) differences in health. These are seen for most measures of SES, i e for education, occupation and income, and also for most health outcomes: for all-cause mortality, for morbidity in most diseases and for self-rated health. SES differences are, in particular, evident for coronary heart disease (CHD) with a two-fold difference in incidence and death between high and low SES groups. Causes for this are not clear. It is well known that an unhealthy lifestyle is more common in low SES but this can only explain a part of observed SES differences.

One possible explanation is effects of psychosocial factors. High levels of psychosocial risk factors and low availability of psychosocial resources are well documented predictors of CHD and more common in low SES. We and others have demonstrated that these factors are related to poor function of the HPA axis with reduced cortisol reactivity and with higher levels of markers for inflammation and plaque vulnerability, which also are known predictors of CHD.

The overall objective of the research program is to analyse, in a prospective design, to what extent socioeconomic differences in CHD incidence and death can be explained by psychosocial factors, especially psychological resources, and if observed effects are mediated by biological markers of stress, inflammation and plaque vulnerability. Our data builds on two cohorts, using the same design of a random sample from a normal middle-aged Swedish population. Data collection: cohort I 2003-2004 (n=1007); cohort II 2012-2015 (n=2051), used a comprehensive design with broad questionnaires on SES, psychosocial risk factors, psychological resources, lifestyle and present disease, anthropometrics, saliva and blood samples. Primary outcome is symptomatic CHD. In a nested case control design data for cases shall be compared to controls.

While CHD incidence is falling, SES differences in CHD incidence and mortality remain and causes for this are not clear. This is one of few prospective studies linking the chain from SES, via psychosocial factors to biological markers of stress, inflammation and plaque vulnerability and CHD. The study has, therefore, the potential to generate important knowledge on causes behind SES disparities in CHD and on “how stress gets under your skin” More information about study design, study populations and timeline is available in document under the tab Documentation.

Cohort 1: Data collection is conducted in collaboration with 10 Primary Health Care centers (PHCs) in Östergötland county council and sampling was done from the normal population of the catchment area for each PHC. The study is build on a comprehensive design with broad questionnaires on SES, psychosocial risk factors, psychological resources, lifestyle and present disease, anthropometrics, saliva and blood samples.

Cohort 2: Data collection is conducted in collaboration with 27 PHCs in Östergötland and 19 PHCs in Jönköping county council. The same methods are used for collecting data, as described for cohort 1.

Intercorrelation matrix for measures of socioeconomic status and psychosocial resources.

Abstract

Kenya hosts over half a million refugees, who, along with their hosts in urban and camp areas, face difficult living conditions and limited socioeconomic opportunities. Most refugees in Kenya live in camps located in the impoverished counties of Turkana (40 percent) and Garissa (44 percent), while 16 percent inhabit urban areas—mainly in Nairobi but also in Mombasa and Nakuru. Refugees in Kenya are not systematically included in national surveys, creating a lack of comparable socioeconomic data on camp-based and urban refugees, and their hosts. As the third of a series of surveys focusing on closing this gap, this Socioeconomic Survey of Urban Refugees's aim is to understand the socioeconomic needs of urban refugees in Kenya, especially in the face of ongoing conflicts, environmental hazards, and others shocks, as well as the recent government announcement to close Kenya’s refugee camps, which highlights the potential move of refugees from camps into urban settings. The SESs are representative of urban refugees and camp-based refugees in Turkana County. For the Kalobeyei 2018 and Urban 2020–21 SESs, households were randomly selected from the UNHCR registration database (proGres), while a complete list of dwellings, obtained from UNHCR’s dwelling mapping exercise, was used to draw the sample for the Kakuma 2019 SES. The Kalobeyei SES and Kakuma SES were done via Computer-Assisted Personal Interviews (CAPI). Due to COVID-19 social distancing measures, the Urban SES was collected via Computer Assisted Telephone Interviewing (CATI). The Kalobeyei SES covers 6,004 households; the Kakuma SES covers 2,127 households; and the Urban SES covers 2,438 households in Nairobi, Nakuru, and Mombasa. Questionnaires are aligned with national household survey instruments, while additional modules are added to explore refugee-specific dynamics. The SES includes modules on demographics, household characteristics, assets, employment, education, consumption, and expenditure, which are aligned with the Kenya Integrated Household Budget Survey (KIHBS) 2015–16 and the recent Kenya Continuous Household Survey (KCHS) 2019. Additional modules on access to services, vulnerabilities, social cohesion, mechanisms for coping with lack of food, displacement trajectories, and durable solutions are administered to capture refugee-specific challenges.

Geographic coverage

Nairobi, Mombasa, Nakuru

Analysis unit

Households and individuals

Universe

All refugees registered with UNHCR via ProGres, verified via the Verification Exercise conducted in 2021

Kind of data

Sample survey data [ssd]

Sampling procedure

– The survey was conducted using the UNHCR proGres data as the sampling frame. Due to the COVID-19 lockdown, the survey data was collected via telephone. Hence, the survey is representative of households with active phone numbers registered by UNHCR in urban Kenya – Nairobi, Mombasa and Nakuru. A sample size of 2,500 was needed to ensure a margin of error of less than 5 percent at a confidence level of 95 percent for groups represented by at least 50 percent of the population. The sample for the urban SES is designed to estimate socioeconomic indicators, such as food insecurity, for groups whose share represents at least 50 percent of the population. Considering the total urban refugee population as of August 2020 and the proportions of main countries of origin, as well as a 10 percent nonresponse rate, the target sample size is 2,500 households in total, with 1,250 in Nairobi, 700 in Nakuru, and 550 in Mombasa. A total of 2,438 households were reached: 1,300 in Nairobi, 409 in Nakuru, and 729 in Mombasa. The units in ProGres list are UNHCR proGres families, which are different from households as defined in standard household surveys. Upon registration, UNHCR groups individuals into ‘proGres’ families which do not necessarily meet the criteria to be considered a household. A proGres family is usually comprised by no more than one household. In turn, a household can be integrated by one or more proGres families. Households were selected as the unit of observation to ensure comparability with national household surveys. Households are a set of related or unrelated people (either sharing the same dwelling or not) who pool ration cards and regularly cook and eat together. As proGres families were sampled, the identification of households was done by an introductory section that confirms that each member of the selected proGres family is a member of the household and whether there are other members in the households that belong to other ProGres families. Thus, the introductory section documents the number of proGres families present in the household under observation. Before selecting the survey strata, the team attempted to better understand the type of bias observed by focusing on refugees with access to phones. From the proGres data, phone penetration in urban areas is high (Nairobi and Mombasa: 93 percent, Nakuru: 95 percent). To understand the type of bias observed by focusing on refugees with access to phone, we looked at socio-economic outcomes for proGres family refugees with access to a phone number and those without

Mode of data collection

Computer Assisted Telephone Interview [cati]

Hospital B Left Hemisphere

Collection description

***PLEASE READ OUR PUBLISHED PAPER CAREFULLY AND ENSURE YOU UNDERSTAND THESE IMAGES BEFORE USING ANY OF THIS INFORMATION CLINICALLY. WE CAN BE CONTACTED FOR CLARIFICATIONS.***

This collection contains images of the outer loop, and partial middle loop, of the optic radiation. These are population averages, displayed as percentages of participants, as single subject maps cannot be released for privacy reasons.

Please read our paper for how these images were generated. In brief, the CONSULT system created binarised tractography for each subject. We take the average of these binary maps *in MNI space* to create the images appearing here. and multiply the result by 100. The MNI template used is attached.

Data are from multiple sources and filed as they appear in the paper. These sources are:
1) HCP-*: Human Connectome Project data. These data were modified from their originals to test CONSULT using different quality data. Raw HCP data can be downloaded from the HCP website.
2) Hospital-*: Data acquired by us on two hospital campuses. Some of these data are from neurosurgical patients. Three different scanners and acquisition protocols were used.
3) MASSIVE-*: Data from the MASSIVE dataset. These data were modified from their originals to test CONSULT using different quality data. The original data can be downloaded from the MASSIVE website.

Subject species

homo sapiens

Modality

Diffusion MRI

Analysis level

group

Cognitive paradigm (task)

None / Other

Map type

Pa

TABLE 2 | AMOVA of Hippocampus reidi testing different hypothesis by mitochondrial data. Significant p values (p <0.05). BR: represents all sample sites in one single group; NNE represents PA, PI, CE, RN, PB, PE, AL populations; SES represents ES, RJ, SP, SC populations; BA represents Bahia population; ES represents Espírito Santo population.

Water companies in the UK are responsible for testing the quality of drinking water. This dataset contains the results of samples taken from the taps in domestic households by {Company Name} to make sure they meet the standards set out by UK and European legislation. This data shows the location, date, and measured levels of determinands set out by the Drinking Water Inspectorate (DWI).Many UK water companies provide a search tool on their websites where you can search for water quality in your area by postcode. The results of the search may identify the water supply zone that supplies the postcode searched. Water supply zones are not linked to LSOAs which means the results may differ to this datasetSome sample results are influenced by internal plumbing and may not be representative of drinking water quality in the wider area.Some samples are tested on site and others are sent to scientific laboratories.This is the dataset used for LSOA to postcode conversion. Lower layer Super Output Area population estimates (supporting information) - Office for National Statistics