About this dataset

Context

The dataset tabulates the data for the Social Circle, GA population pyramid, which represents the Social Circle population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Social Circle, GA, is 28.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Social Circle, GA, is 21.7.
• Total dependency ratio for Social Circle, GA is 50.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Social Circle, GA is 4.6.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Social Circle population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Social Circle for the selected age group is shown in the following column.
• Population (Female): The female population in the Social Circle for the selected age group is shown in the following column.
• Total Population: The total population of the Social Circle for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Social Circle Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Social Circle, GA population pyramid, which represents the Social Circle population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Social Circle, GA, is 26.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Social Circle, GA, is 18.1.
• Total dependency ratio for Social Circle, GA is 44.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Social Circle, GA is 5.5.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Social Circle population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Social Circle for the selected age group is shown in the following column.
• Population (Female): The female population in the Social Circle for the selected age group is shown in the following column.
• Total Population: The total population of the Social Circle for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Social Circle Population by Age. You can refer the same here

Note: Taken from the Agincourt HDSS 2007. +A household is defined as “a group of people who reside and eat together, plus the linked temporary migrants who would eat with them on return” [62]. Location refers to the physical dwelling place at which an individual is located.

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau... Visit https://dataone.org/datasets/sha256%3A05707c1dc04a814129f751937a6ea56b08413546b18b351a85bc96da16a7f8b5 for complete metadata about this dataset.

Note: age-cut-offs to define the younger and older groups were 88 years for males and 91 years for females, as reported in Materials and Methods section.SD: standard deviation.

This dataset is made available under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). See LICENSE.pdf for details.

Dataset description

Parquet file, with:

• 35694 rows
• 154 columns

The file is indexed on [participant]_[month], such that 34_12 means month 12 from participant 34. All participant IDs have been replaced with randomly generated integers and the conversion table deleted.

Column names and explanations are included as a separate tab-delimited file. Detailed descriptions of feature engineering are available from the linked publications.

File contains aggregated, derived feature matrix describing person-generated health data (PGHD) captured as part of the DiSCover Project (https://clinicaltrials.gov/ct2/show/NCT03421223). This matrix focuses on individual changes in depression status over time, as measured by PHQ-9.

The DiSCover Project is a 1-year long longitudinal study consisting of 10,036 individuals in the United States, who wore consumer-grade wearable devices throughout the study and completed monthly surveys about their mental health and/or lifestyle changes, between January 2018 and January 2020.

The data subset used in this work comprises the following:

• Wearable PGHD: step and sleep data from the participants’ consumer-grade wearable devices (Fitbit) worn throughout the study
• Screener survey: prior to the study, participants self-reported socio-demographic information, as well as comorbidities
• Lifestyle and medication changes (LMC) survey: every month, participants were requested to complete a brief survey reporting changes in their lifestyle and medication over the past month
• Patient Health Questionnaire (PHQ-9) score: every 3 months, participants were requested to complete the PHQ-9, a 9-item questionnaire that has proven to be reliable and valid to measure depression severity

From these input sources we define a range of input features, both static (defined once, remain constant for all samples from a given participant throughout the study, e.g. demographic features) and dynamic (varying with time for a given participant, e.g. behavioral features derived from consumer-grade wearables).

The dataset contains a total of 35,694 rows for each month of data collection from the participants. We can generate 3-month long, non-overlapping, independent samples to capture changes in depression status over time with PGHD. We use the notation ‘SM0’ (sample month 0), ‘SM1’, ‘SM2’ and ‘SM3’ to refer to relative time points within each sample. Each 3-month sample consists of: PHQ-9 survey responses at SM0 and SM3, one set of screener survey responses, LMC survey responses at SM3 (as well as SM1, SM2, if available), and wearable PGHD for SM3 (and SM1, SM2, if available). The wearable PGHD includes data collected from 8 to 14 days prior to the PHQ-9 label generation date at SM3. Doing this generates a total of 10,866 samples from 4,036 unique participants.

This data set used in the CoIL 2000 Challenge contains information on customers of an insurance company. The data consists of 86 variables and includes product usage data and socio-demographic data

DETAILED DATA DESCRIPTION

THE INSURANCE COMPANY (TIC) 2000

(c) Sentient Machine Research 2000

DISCLAIMER

This dataset is owned and supplied by the Dutch data mining company Sentient Machine Research, and is based on real-world business data. You are allowed to use this dataset and accompanying information for non-commercial research and education purposes only. It is explicitly not allowed to use this dataset for commercial education or demonstration purposes. For any other use, please contact Peter van der Putten, info@smr.nl.

This dataset has been used in the CoIL Challenge 2000 data mining competition. For papers describing results on this dataset, see the TIC 2000 homepage: http://www.wi.leidenuniv.nl/~putten/library/cc2000/

REFERENCE P. van der Putten and M. van Someren (eds). CoIL Challenge 2000: The Insurance Company Case. Published by Sentient Machine Research, Amsterdam. Also a Leiden Institute of Advanced Computer Science Technical Report 2000-09. June 22, 2000. See http://www.liacs.nl/~putten/library/cc2000/

RELEVANT FILES

tic_2000_train_data.csv: Dataset to train and validate prediction models and build a description (5822 customer records). Each record consists of 86 attributes, containing sociodemographic data (attribute 1-43) and product ownership (attributes 44-86). The sociodemographic data is derived from zip codes. All customers living in areas with the same zip code have the same sociodemographic attributes. Attribute 86, "CARAVAN: Number of mobile home policies", is the target variable.

tic_2000_eval_data.csv: Dataset for predictions (4000 customer records). It has the same format as TICDATA2000.txt, only the target is missing. Participants are supposed to return the list of predicted targets only. All datasets are in CSV format. The meaning of the attributes and attribute values is given dictionary.csv

tic_2000_target_data.csv Targets for the evaluation set.

dictionary.txt: Data description with numerical labeled categories descriptions. It has columnar description data and the labels of the dummy/Labeled encoding.

Original Task description Link: http://liacs.leidenuniv.nl/~puttenpwhvander/library/cc2000/problem.html UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/datasets/Insurance+Company+Benchmark+%28COIL+2000%29

Top model set for the individual risk analysis. Twenty-seven models fell within 2 AIC of the top model and were included in the average model for the cohort dataset analysis. Covariates not included in a model are denoted with “NA“.

As of July 2nd, 2024 the COVID-19 Deaths by Population Characteristics Over Time dataset has been retired. This dataset is archived and will no longer update. We will be publishing a cumulative deaths by population characteristics dataset that will update moving forward.

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics and by date. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals for previous days may increase or decrease. More recent data is less reliable.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health.

Data on the population characteristics of COVID-19 deaths are from: *Case reports *Medical records *Electronic lab reports *Death certificates

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

To protect resident privacy, we summarize COVID-19 data by only one characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more.

Data notes on each population characteristic type is listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Gender * The City collects information on gender identity using these guidelines.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of deaths on each date.

New deaths are the count of deaths within that characteristic group on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

This data may not be immediately available for more recent deaths. Data updates as more information becomes available.

To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - on this date, we began using an updated definition of a COVID-19 death to align with the California Department of Public Health. This change was applied to COVID-19 deaths retrospectively beginning on 1/1/2023. More information about the recommendation by the Council of State and Territorial Epidemiologists that motivated this change can be found https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">here.
• 6/6/2023 - data on deaths by transmission type have been removed. See section ARCHIVED DATA for more detail.
• 5/16/2023 - data on deaths by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 1/31/2023 - column “population_estimate” added.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.

The Cardiff Travel Survey is a longitudinal survey that aims to (a) establish current and previous (before the coronavirus outbreak) travel habits; (b) explore how travel-related attitudes, social norms and perceptions change over time; and (c) examine the interplay between individual (perceptual) and environmental (infrastructural) factors in travel mode choice, in particular in relation to the uptake of active travel such as walking and cycling in the City of Cardiff, Wales. The Cardiff Travel Survey 2024 (Wave 4) is an opportunity sample that was collected in 2024 (n=2,427) by the Centre for Climate Change and Social Transformations (CAST), and is the fourth of a longitudinal series of surveys to be held annually for the duration of the centre. Data for the Cardiff Travel Survey 2024 were collected between 11 April 2023 and 05 June 2024. Participants of the Cardiff Travel Survey 2023 who consented (n=1,324) were recontacted via email to invite them to take part in the 2024 survey. Furthermore, participants were recruited through posts on social media, such as Facebook® and Twitter®. Invitations were posted on CAST and investigator accounts. The survey was hosted on the Qualtrics online survey platform and available in both English and Welsh. Inclusion criteria were that participants had to be at least 18 years of age and live in or travel regularly to Cardiff. The English version of the survey was completed by 2,628 respondents and the Welsh version by 9 respondents. There was evidence of bot activity in the English survey. This led to 1,630 responses to be removed. Incomplete responses (n=129), defined as those without any answers beyond socio-demographic, were removed from the dataset. A further 81 respondents did not complete the first section on current travel behaviours and were also removed. This left a final sample of n=797 adults. Participants were asked to create a unique code that can be used match this survey to the previous and next surveys without knowing their identity. Main topic areas of the questionnaire were: Demographics, Travel behaviours, Physical activity, Physical health and mental wellbeing, Perceptions of infrastructure and environmental quality, Travel-related social identity, Attitudes to active travel, Active travel related social norms, Support for active travel policies, and Unique ID.

The Centre for Climate Change Transformations (C3T) will be a global hub for understanding the profound changes required to address climate change. At its core, is a fundamental question of enormous social significance: how can we as a society live differently - and better - in ways that meet the urgent need for rapid and far-reaching emission reductions? While there is now strong international momentum on action to tackle climate change, it is clear that critical targets (such as keeping global temperature rise to well within 2 degrees Celsius relative to pre-industrial levels) will be missed without fundamental transformations across all parts of society. C3T's aim is to advance society's understanding of how to transform lifestyles, organisations and social structures in order to achieve a low-carbon future, which is genuinely sustainable over the long-term. Our Centre will focus on people as agents of transformation in four challenging areas of everyday life that impact directly on climate change but have proven stubbornly resistant to change: consumption of goods and physical products, food and diet, travel, and heating/cooling. We will work across multiple scales (individual, community, organisational, national and global) to identify and experiment with various routes to achieving lasting change in these challenging areas. In particular, we will test how far focussing on 'co-benefits' will accelerate the pace of change. Co-benefits are outcomes of value to individuals and society, over and above the benefits from reducing greenhouse gas emissions. These may include improved health and wellbeing, reduced waste, better air quality, greater social equality, security, and affordability, as well as increased ability to adapt and respond to future climate change. For example, low-carbon travel choices (such as cycling and car sharing) may bring health, social and financial benefits that are important for motivating behaviour and policy change. Likewise, aligning environmental and social with economic objectives is vital for behaviour and organisational change within businesses. Our Research Themes recognise that transformative change requires: inspiring yet workable visions of the future (Theme 1); learning lessons from past and current societal shifts (Theme 2); experimenting with different models of social change (Theme 3); together with deep and sustained engagement with communities, business and governments, and a research culture that reflects our aims and promotes action (Theme 4). Our Centre integrates academic knowledge from disciplines across the social and physical sciences with practical insights to generate widespread impact. Our team includes world-leading researchers with expertise in climate change behaviour, choices and governance. We will use a range of theories and research methods to fill key gaps in our understanding of transformation at different spatial and social scales, and show how to target interventions to impactful actions, groups and moments in time.

Abstract

The Health and Demographic Surveillance System (HDSS) in Niakhar, a rural area of Senegal, is located 135 km east of Dakar. This HDSS has been set up in 1962 by the Institut de Recherche pour le Développement (IRD) to face the shortcomings of the civil registration system and provide demographic indicators.

Some 65 villages were followed annually in the Niakhar area from 1962 to 1969. The study zone was reduced to eight villages from 1969 to 1983, and from then on the HDSS was extended to include 22 other villages, covering a total of 30 villages for a population estimated at 45,000 in December 2013. Thus 8 villages have been under demographic surveillance for almost 50 years and 30 villages for 30years.

Vital events, migrations, marital changes, pregnancies, immunization are routinely recorded (every four months). The database also includes epidemiological, economic and environmental information coming from specific surveys. Data were collected through annual rounds from 1962 to 1987; rounds became weekly from 1987 to 1997; routine visits were conducted every three months between 1997and 2007 and every four months since then.

The current objectives are 1) to obtain a long-term assessment of demographic and socio-economic indicators necessary for bio-medical and social sciences research, 2) to keep up epidemiological and environmental monitoring, 3) to provide a research platform for clinical and interdisciplinary research (medical, social and environmental sciences). Research projects during the last 5 years are listed in Table 2. The Niakhar HDSS has institutional affiliation with the Institut de Recherche pour le Développement (IRD, formerly ORSTOM).

Geographic coverage

The study zone of Niakhar is located in Senegal, 14.5ºN Latitude and 16.5ºW Longitude in the department of Fatick (Sine-Saloum), 135 km east of Dakar. The Niakhar study zone covers 203 square kilometres and is located in the continental Sahelian-Sudanese climatic zone. For thirty years the region has suffered from drought. The average annual rainfall has decreased from 800 mm in the 1950s to 500 mm in the 1980s. Increasing amounts of precipitation have been observed since the mid-2000s with an average annual rainfall of 600 mm between 2005 and 2010. The area is 203 square kilometers.

Analysis unit

Individual

Universe

Members of households reside within the demographic surveillance area. Inmigrants are defined by intention to become resident, but actual residence episodes of less than 180 days are censored. Outmigrants are defined by intention to become resident elsewhere, but actual periods of non-residence less than 180 days are censored, except seasonal work migrants, worker with a wife resident, pupils or students. Children born to resident women are considered resident by default, irrespective of actual place of birth. The dataset contains the events of all individuals ever resident during the study period (1 Jan 1990 to 31 Dec 2013).

The Niakhar HDSS collects for each resident the following basic data: individual, household and compound identifying information, mother and father identification, relationship to the head of household and spousal relationship. From 1983 to 2007, the HDSS routinely monitored deaths, pregnancies, births, miscarriages, stillbirths, weaning, migrations, changes of marital status, immunizations, and cases of measles and whooping cough. For the last 5 years, the HDSS only recorded demographic events related to each resident including cause of death. Verbal autopsies have been conducted after all deaths except for those that occurred between 1999 and 2004 where only deaths for people aged 0-55 years were investigated. The Niakhar HDSS also registers visitors as well as all the demographic events related to them in case of in-migration. Household characteristics (living conditions, domestic equipment, etc.) were collected in 1998 and 2003, and community equipment (schools, boreholes, etc.) in 2003. Economic and environmental data will be collected in 2013. Table 3 presents further details on the data items collected. The Niakhar HDSS interviewers collect data with tablet PCs that are loaded with the last updated database linked to a user-friendly interface indicating the household members and the questionnaire. Daily backups are performed on an external hard drive and weekly synchronizations are scheduled during the round, helping to update the database and check data consistency (i.e. residential moves within the study area or marriages). Applications are Developed in Visual Basic.Net and the database is managed with Microsoft Access.

Kind of data

Event history data

Frequency of data collection

This dataset contains rounds 1 to 18 of demographic surveillance data covering the period from 1 Jan 1983 to 31 December 2015.

From 1983 to 1987, data were collected through annual rounds during the dry season. Demographic events were collected by interviewers using a printed list of compound residents with their characteristics. From 1987 to 1997, rounds became weekly because of the need for continuous birth registration for vaccine trials. Annual censuses were carried out to check data collection, particularly relative to in- and out-migration. Routine visits were conducted in the 30 villages of the study area every three months between 1997and 2007 and every four months between 2008 and 2012 and every six month since then.

Sampling procedure

This dataset is not based on a sample; it contains information from the complete demographic surveillence area.

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

List of questionnaires:

Compound Registration or update Form Houshold Registration or update Form Household Membership Registration or update Form External Migration Registration Form Internal Migration Registration Form Individual Registration Form Birth Registration Form Death Registration Form

Cleaning operations

On data entry data consistency and plausibility were checked by 455 data validation rules at database level. If data validaton failure was due to a data collection error, the questionnaire was referred back to the field for revisit and correction. If the error was due to data inconsistencies that could not be directly traced to a data collection error, the record was referred to the data quality team under the supervision of the senior database scientist. This could request further field level investigation by a team of trackers or could correct the inconsistency directly at database level.

No imputations were done on the resulting micro data set, except for:

a. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is greater than 180 days, the ENT event was changed to an in-migration event (IMG). b. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is less than 180 days, the OMG event was changed to an homestead exit event (EXT) and the ENT event date changed to the day following the original OMG event. c. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is greater than 180 days, the EXT event was changed to an out-migration event (OMG). d. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is less than 180 days, the IMG event was changed to an homestead entry event (ENT) with a date equal to the day following the EXT event. e. If the last recorded event for an individual is homestead exit (EXT) and this event is more than 180 days prior to the end of the surveillance period, then the EXT event is changed to an out-migration event (OMG)

In the case of the village that was added (enumerated) in 2006, some individuals may have outmigrated from the original surveillance area and setlled in the the new village prior to the first enumeration. Where the records of such individuals have been linked, and indivdiual can legitmately have and outmigration event (OMG) forllowed by and enumeration event (ENU). In a few cases a homestead exit event (EXT) was followed by an enumeration event in these cases. In these instances the EXT events were changed to an out-migration event (OMG).

Response rate

On an average the response rate is about 99% over the years for each round

Sampling error estimates

Not Applicable

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate SN013 MicroDataCleaned Starts 86883 2017-05-19 15:12
SN013 MicroDataCleaned Transitions 241970 241970 0 2017-05-19 15:12
SN013 MicroDataCleaned Ends 86883 2017-05-19 15:12
SN013 MicroDataCleaned SexValues 32 241938 241970 0 2017-05-19 15:12
SN013 MicroDataCleaned DoBValues 241970 2017-05-19 15:12

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals may increase or decrease.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health.

Data on the population characteristics of COVID-19 deaths are from: *Case reports *Medical records *Electronic lab reports *Death certificates

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

To protect resident privacy, we summarize COVID-19 data by only one population characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more.

Data notes on select population characteristic types are listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Gender * The City collects information on gender identity using these guidelines.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a dataset based on the San Francisco Population and Demographic Census dataset.These population estimates are from the 2018-2022 5-year American Community Survey (ACS).

This dataset includes several characteristic types. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cumulative deaths.

Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset.

E. CHANGE LOG

ABSTRACT: Socio-demographic and economic indicators are very important clues to scientists and institutions to direct development programs for the empowerment of rural women. The goal of this study is to determine the status of rural women living in the Taurus mountainous villages of Turkey according to socio-demographic and economic indicators as related to gender structure. In this research area, there is not enough official data related to socio-demographic and economic indicators about mountainous rural women, like in most developing countries. For this study, the authors collected data from 146 women and 133 men using questionnaires and face-to-face interviews. In this research area, we found that women occupy positions of low power compared to men when taking socio-demographic and economic indicators into account. Women tend to define themselves as housewives instead of farmers, and their tendency to migrate to cities were higher than men’s.

The data present the selection of Census topics and breakdowns of the 2021 EU population and housing census, as defined by Regulation (EC) 763/2008 and its three implementing regulations for the 2021 round of censuses: Regulation (EU) 2017/543; Regulation (EU) 2017/712 and Regulation (EU) 2017/881.

The 2021 data presented in the tables for European Union Member States (27) and EFTA countries (4) are taken from a broader collection of data composed of 119 hypercubes (organized into 41 groups) mandated by Annex I to the Regulation (EU) 2017/712. These hypercubes provide a highly detailed dataset, aligning with the key census features of individual enumeration, simultaneity, universality, availability of small-area data, and defined periodicity. This structure allows detailed cross-tabulation of demographic, socioeconomic, and housing characteristics across various geographic levels (National, NUTS 1, NUTS 2 and NUTS 3).

The census data presented here adhere to the same definitions, technical specifications, and breakdowns as the detailed hypercubes, which can be accessed via the Eurostat Census Hub.

The tables presented here provide key breakdowns and cross-tabulations.

The data tables are structured based on a 2021 Census Hub data topic design, where each table represents a multidimensional breakdown of census data.

The 2021 Census data offer a statistical overview of population, households, families, and dwellings. Datasets are organized around three core areas:

1.Population Characteristics:

Comprehensive demographic details, including sex, age, marital status, and family structures.

Socioeconomic indicators such as education, employment, occupation, and activity status.

Migration-related characteristics, covering citizenship, country of birth, year of arrival, and previous residence. Geographical breakdowns are offered at NUTS 2 in 23 tables and NUTS 3 levels in 12 tables.

2.Families and Households:

Household composition and family structures.

Features family nucleus size, tenure status, and household composition and size.

These tables are primarily provided for NUTS 3 geographical regions in the 3 tables.

3.Dwellings:

Dwelling characteristics, including ownership status, building types, occupancy, and construction periods among others.

Geographical detail, with data split between NUTS 2 in one table and NUTS 3 in 4 tables.

The statistical data are supplemented by national metadata files that facilitate interpretation of the numerical data, including country-specific definitions, information on the data sources and on methodological issues.

Current Population Survey - August 2016

Context

The Current Population Survey (CPS) is one of the oldest, largest, and most well-recognized surveys in the United States. It is immensely important, providing information on many of the things that define us as individuals and as a society – our work, our earnings, and our education.

• Frequency: Monthly
• Period: August 2016

Content

In addition to being the primary source of monthly labor force statistics, the CPS is used to collect data for a variety of other studies that keep the nation informed of the economic and social well-being of its people. This is done by adding a set of supplemental questions to the monthly basic CPS questions. Supplemental inquiries vary month to month and cover a wide variety of topics such as child support, volunteerism, health insurance coverage, and school enrollment. Supplements are usually conducted annually or biannually, but the frequency and recurrence of a supplement depend completely on what best meets the needs of the supplement’s sponsor.

Data Dictionary: http://thedataweb.rm.census.gov/pub/cps/basic/201501-/January_2015_Record_Layout.txt

Acknowledgements

The Current Population Survey (CPS) is administered, processed, researched and disseminated by the U.S. Census Bureau on behalf of the Bureau of Labor Statistics (BLS).

This dataset contains information about trending YouTube videos, including details about the videos and their respective channels. The data is collected daily and provides insights into video performance, audience engagement, and channel characteristics across different countries. Below is a detailed description of each column:

Video Information

1. video_id: Unique identifier for the video on YouTube.
2. video_published_at: The date and time when the video was published.
3. video_trending_date: The date when the video was identified as trending.
4. video_trending_country: The country where the video is trending (ISO 3166-1 alpha-2 country code, e.g., "US" for the United States).
5. video_title: The title of the video as displayed on YouTube.
6. video_description: The description provided by the video creator.
7. video_default_thumbnail: URL of the default thumbnail for the video.
8. video_category_id: Numeric ID representing the category of the video (e.g., Music, Gaming, etc.).
9. video_tags: List of tags associated with the video for categorization and discoverability.
10. video_duration: Duration of the video in ISO 8601 format (e.g., "PT10M15S" for 10 minutes and 15 seconds).
11. video_dimension: Dimension of the video (e.g., "2d", "3d").
12. video_definition: Video resolution quality (e.g., "hd" for high definition, "sd" for standard definition).
13. video_licensed_content: Boolean indicating if the video contains licensed content.
14. video_view_count: Total number of views for the video.
15. video_like_count: Total number of likes for the video.
16. video_comment_count: Total number of comments on the video.

Channel Information

1. channel_id: Unique identifier for the YouTube channel.
2. channel_title: The name/title of the channel.
3. channel_description: Description provided by the channel owner.
4. channel_custom_url: Custom URL for the channel (if available).
5. channel_published_at: The date and time when the channel was created.
6. channel_country: The country associated with the channel (if specified by the creator).
7. channel_view_count: Total number of views across all videos on the channel.
8. channel_subscriber_count: Total number of subscribers to the channel.
9. channel_have_hidden_subscribers: Boolean indicating if the channel has hidden its subscriber count.
10. channel_video_count: Total number of videos uploaded by the channel.
11. channel_localized_title: The localized title of the channel (if available in a different language).
12. channel_localized_description: The localized description of the channel (if available in a different language).

Potential Applications

This dataset is a rich resource for analyzing YouTube video and channel trends. Here are some potential use cases:

1. Trend Analysis:

   • Identify trending content in specific countries or globally.
   • Explore the relationship between publishing time and trending status.

2. Audience Engagement Insights:

   • Analyze the correlation between video views, likes, and comments.
   • Understand the impact of video tags, duration, or definition on engagement.

3. Content Category Insights:

   • Explore how different video categories perform over time.
   • Analyze the popularity of certain categories in specific regions.

4. Channel Growth Analysis:

   • Study how trending videos impact channel subscriber growth.
   • Analyze the characteristics of successful channels (e.g., average video duration, view count, etc.).

5. Machine Learning Projects:

   • Predict the likelihood of a video trending based on its metadata.
   • Cluster videos or channels based on similarities in tags, categories, and engagement metrics.

6. Business Applications:

   • Help content creators and marketers understand audience preferences.
   • Use data to guide video production strategies for better engagement.

Additional Notes

This dataset can be combined with other external datasets, such as demographic or social media engagement data, for broader analyses. It is particularly suitable for projects related to content strategy, audience analysis, or even recommendation system development for platforms similar to YouTube.

Here's a step-by-step guide on how to approach user segmentation for FitTrackr:

Define your segmentation goals: Start by determining what you want to achieve with user segmentation. For example, you might want to identify the most engaged users, understand the demographics of your user base, or target specific user groups with personalized promotions.

Gather data: Collect relevant data about your app users. This can include demographic information (age, gender, location), app usage data (frequency of app usage, time spent on different features), user behavior (types of workouts, goals set, achievements unlocked), and any other relevant data points available to you.

Identify relevant segmentation variables: Based on the goals you defined, identify the key variables that will help you segment your user base effectively. For FitTrackr, potential variables could include age, gender, fitness goals (e.g., weight loss, muscle gain), workout preferences (e.g., cardio, strength training), and user engagement level.

Segment the user base: Use clustering techniques or segmentation algorithms to divide your user base into distinct segments based on the identified variables. You can employ methods such as k-means clustering, hierarchical clustering, or even machine learning algorithms like decision trees or random forests.

Analyze and profile each segment: Once the segmentation is done, analyze each segment to understand their characteristics, preferences, and needs. Create detailed user profiles for each segment, including demographic information, app usage patterns, fitness goals, and any other relevant attributes. This will help you tailor your marketing messages and app features to each segment's specific requirements.

Develop targeted strategies: Based on the insights gained from user profiles, develop targeted marketing strategies and app features for each segment. For example, if you have a segment of users who primarily focus on weight loss, you might create personalized workout plans or send them motivational content related to weight management.

Implement and evaluate: Implement the targeted strategies and monitor their effectiveness. Continuously evaluate and refine your segmentation approach based on user feedback, engagement metrics, and the achievement of your goals.

Socio-demographic and clinical characteristics of the study community at first visit.

Participants socio-demographics.