This dataset contains counts of live births for California as a whole based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

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

This dataset contains counts of live births for California counties based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

This is a publication on maternity activity in English NHS hospitals. This report examines data relating to delivery and birth episodes in 2022-23, and the booking appointments for these deliveries. This annual publication covers the financial year ending March 2023. Data is included from both the Hospital Episodes Statistics (HES) data warehouse and the Maternity Services Data Set (MSDS). HES contains records of all admissions, appointments and attendances for patients admitted to NHS hospitals in England. The HES data used in this publication are called 'delivery episodes'. The MSDS collects records of each stage of the maternity service care pathway in NHS-funded maternity services, and includes information not recorded in HES. The MSDS is a maturing, national-level dataset. In April 2019 the MSDS transitioned to a new version of the dataset. This version, MSDS v2.0, is an update that introduced a new structure and content - including clinical terminology, in order to meet current clinical practice and incorporate new requirements. It is designed to meet requirements that resulted from the National Maternity Review, which led to the publication of the Better Births report in February 2016. This is the fourth publication of data from MSDS v2.0 and data from 2019-20 onwards is not directly comparable to data from previous years. This publication shows the number of HES delivery episodes during the period, with a number of breakdowns including by method of onset of labour, delivery method and place of delivery. It also shows the number of MSDS deliveries recorded during the period, with breakdowns including the baby's first feed type, birthweight, place of birth, and breastfeeding activity; and the mothers' ethnicity and age at booking. There is also data available in a separate file on breastfeeding at 6 to 8 weeks. The count of Total Babies includes both live and still births, and previous changes to how Total Babies and Total Deliveries were calculated means that comparisons between 2019-20 MSDS data and later years should be made with care. Information on how all measures are constructed can be found in the HES Metadata and MSDS Metadata files provided below. In this publication we have also included an interactive Power BI dashboard to enable users to explore key NHS Maternity Statistics measures. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care. This report will also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. Any feedback on this publication or dashboard can be provided to enquiries@nhsdigital.nhs.uk, under the subject “NHS Maternity Statistics”.

A data set designed to provide a cross-sectional description of health, mental, and social status of the oldest-old segment of the elderly population in Israel, and to serve as a baseline for a multiple-stage research program to correlate demographic, health, and functional status with subsequent mortality, selected morbidity, and institutionalization. Study data are based on a sample of Jewish subjects aged 75+, alive and living in Israel on January 1, 1989, randomly selected from the National Population Register (NPR), a complete listing of the Israeli population maintained by the Ministry of the Interior. The NPR is updated on a routine basis with births, deaths, and in and out migration, and corrected by linkage with census data. The sample was stratified by age (five 5-year age groups: 75-79, 80-84, 85-89, 90-94, 95+), sex, and place of birth (Israel, Asia-Africa, Europe-America). One hundred subjects were randomly selected in each of the 30 strata. However, there were less than 100 individuals of each sex aged 95+ born in Israel, so all were selected for the sample. The total group included 2,891 individuals living both in the community and in institutions. A total of 1,820 (76%) of the 75-94 age group were interviewed during 1989-1992. An additional cognitive exam (Folstein) and a 24-hour dietary recall interview were added in the second round. Kibbutz Residents Sample The kibbutz is a social and economic unit based on equality among members, common property and work, collaborative consumption, and democracy in decision making. There are 250 kibbutzim in Israel, and their population constitutes about 3% of the country''s total population. All kibbutz residents in the country aged 85+, both members and parents, were selected for interviewing, of whom 80.4% (n=652) were interviewed. A matched sample aged 75-84 was selected, and 85.9% (n=674) were successfully interviewed. The original interview took approximately two hours to administer, and collected extensive information concerning the socio-demographic, physical, health, functioning, life events (including Holocaust), depression, mental status, and social network characteristics of the sample. The questionnaire used for kibbutz residents in the follow-up interview is identical to that utilized in the national random sample. Data Availability: Mortality data for both the national and kibbutz samples are available for analysis as a result of the linkage to the NPR file updated as of June 2000. The fieldwork for first follow up was completed as of September 1994 and for the second follow up as of December 2002. The data file of the three phases of the study is ready for analysis. * Dates of Study: 1989-1992 * Study Features: Longitudinal, International * Sample Size: 2,891

This is a publication on maternity activity in English NHS hospitals. This report examines data relating to delivery and birth episodes in 2023-24, and the booking appointments for these deliveries. This annual publication covers the financial year ending March 2024. Data is included from both the Hospital Episodes Statistics (HES) data warehouse and the Maternity Services Data Set (MSDS). HES contains records of all admissions, appointments and attendances for patients admitted to NHS hospitals in England. The HES data used in this publication are called 'delivery episodes'. The MSDS collects records of each stage of the maternity service care pathway in NHS-funded maternity services, and includes information not recorded in HES. The MSDS is a maturing, national-level dataset. In April 2019, the MSDS transitioned to a new version of the dataset. This version, MSDS v2.0, is an update that introduced a new structure and content - including clinical terminology, in order to meet current clinical practice and incorporate new requirements. It is designed to meet requirements that resulted from the National Maternity Review, which led to the publication of the Better Births report in February 2016. This is the fifth publication of data from MSDS v2.0 and data from 2019-20 onwards is not directly comparable to data from previous years. This publication shows the number of HES delivery episodes during the period, with a number of breakdowns including by method of onset of labour, delivery method and place of delivery. It also shows the number of MSDS deliveries recorded during the period, with a breakdown for the mother's smoking status at the booking appointment by age group. It also provides counts of live born term babies with breakdowns for the general condition of newborns (via Apgar scores), skin-to-skin contact and baby's first feed type - all immediately after birth. There is also data available in a separate file on breastfeeding at 6 to 8 weeks. For the first time information on 'Smoking at Time of Delivery' has been presented using annual data from the MSDS. This includes national data broken down by maternal age, ethnicity and deprivation. From 2025/2026, MSDS will become the official source of 'Smoking at Time of Delivery' information and will replace the historic 'Smoking at Time of Delivery' data which is to become retired. We are currently undergoing dual collection and reporting on a quarterly basis for 2024/25 to help users compare information from the two sources. We are working with data submitters to help reconcile any discrepancies at a local level before any close down activities begin. A link to the dual reporting in the SATOD publication series can be found in the links below. Information on how all measures are constructed can be found in the HES Metadata and MSDS Metadata files provided below. In this publication we have also included an interactive Power BI dashboard to enable users to explore key NHS Maternity Statistics measures. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care. This report will also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. Any feedback on this publication or dashboard can be provided to enquiries@nhsdigital.nhs.uk, under the subject “NHS Maternity Statistics”.

We assembled a retrospective, administrative cohort of singleton births in North Carolina from 2003-2015. We used US EPA EQUATES data to assign long-term SO2 gestational exposures to eligible births for the entire pregnancy and by trimester. We used multivariable generalized linear regression to estimate risk differences (RD (95%CI)) per 1-ppb increase in SO2, adjusted for gestational parent education, Medicaid status, marital status, and season of conception. Multi-pollutant models were additionally adjusted for other criteria air co-pollutants (O3, PM2.5, NO2). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The North Carolina Birth Cohort data are not publicly available as it contains personal identifiable information. Data may be requested through the NCDHHS, Division of Public Health with proper approvals. Data from EPA's CMAQ EQUATES model are publicly available. Format: Birth certificate data from the State Center for Health Statistics of the NC Department of Health and Human Services linked with data from the Birth Defects Monitoring Program (NC BDMP) to create a birth cohort of all infants born in NC between 2003-2015. The NC BDMP is an active surveillance system that follows NC births to obtain birth defect diagnoses up to 1 year after the date of birth as well as identify infant deaths during the first year of life and include relevant information from the death certificate. EPA's publicly available CMAQ EQUATES model provided data on predicted SO2 concentrations over critical windows of gestation. The model predicts daily 1-hour maximum SO2 concentrations for 12 km x 12 km grid cells across the study area. This dataset is associated with the following publication: Wilkie, A., T. Luben, K. Rappazzo, K. Foley, C. Woods, M. Serre, D. Richardson, and J. Daniels. Long-term ambient sulfur dioxide exposure during gestation and preterm birth in North Carolina, 2003-2015. ATMOSPHERIC ENVIRONMENT. Elsevier B.V., Amsterdam, NETHERLANDS, 333(September 15): 120669, (2024).

This dataset supports a publication on the determinants of infant growth in a birth cohort in the Nepal plains.This study aimed to identify the determinants of infant growth in terms of length-for-age z-score (LAZ) in a birth cohort (n=602) in the plains of Nepal. Children were enrolled within 72 hours of birth and followed-up every 28 days until they were 2 years. We fitted mixed-effects linear regression models controlling for multiple measurements within individuals to examine the impact of household and maternal factors, feeding practices and infection on infant LAZ. We conducted separate analyses for the age periods 0-6 months (exclusive breastfeeding period) and 7-24 months (complementary feeding period) to check whether the importance of determinants differed by child age.The data are useful to those seeking to understand the factors associated with longitudinal changes in nutritional status in children from birth to 2 years in the plains of Nepal.

AbstractThe NCKU CBIC ECG database collects ECG data from 6 different patients. The patients information have been processed for anonymization, and each patient has signed a consent form to ensure the legitimacy of data usage. Each patient collects lead II ECG for four hours a day to highlight patients' different physiological meanings at different times of the day, and the database provides the labels for motion artifact and baseline wandering, which are invalid signal for diagnosis. Prevent physicians from using the noise signal to diagnose. These data were collected using Patch[1] at Ministry of Health and Welfare Tainan Hospital, and the included data have been approved by the Institutional Review Board (IRB).BackgroundTechnology and medical treatment are highly developed in the 21st century, and people have more irregular daily routines and greater life pressure. Cardiovascular disease has become a tough nut to crack when the changing of lifestyle is coupled with the aging of society. The age distribution of patients is wider than ever. A wealth of health information can be obtained through electrocardiogram (ECG) measurement, including cardiac arrhythmias. Severe arrhythmias will lead to many life problems, including palpitations, chest tightness, dizziness, shock, and even life-threatening conditions. Therefore, the monitoring of ECG signal is quite essential.To do our part in the study of arrhythmia, our team started the patient enrollment after gaining the permission of the National Cheng Kung University Hospital Institutional Review Board (NCKUH IRB No. B-ER-104-379) from 2018. We have selected total 128 patients' 24 hours ECG data until now. The results of the arrhythmia label are confirmed by the cardiologist Ju-Yi Chen in NCKUH. Finally, We selected 6 patients from the received signals and made them into a database for researchers to access.MethodsThe NCKU CBIC ECG database contains the ECG recordings from 6 subjects. The signals were collected in Tainan Hospital (Ministry of Health and Welfare) via an ECG acquisition device[1] developed by Your health technology Co., Ltd. The sampling frequency is 400Hz, and the ADC resolution is 12 bits.The age distribution of subjects was from 24 to 76 years old, and each patient was measured at the lead II for 24 hours. After the signal is recorded, four cleaner segments in the morning, noon, evening, and midnight are selected, and each segment is one hour long. The heartbeat of human body is different when sleeping and awake, and some arrhythmia type occurs at sleeping period often. It's hard to detect some arrhythmia at specific time of a day, therefore, we choose signal segments from different time period for a patient, which is more representative of the daily heartbeat condition. It's worth mentioning that the ECG signals from the 6th subject contains too many noise signals in the daytime due to his career type, so the segments from 22:00 to 02:00 are selected.We have collected total 128 patients from Tainan Hospital since 2018. Since most of the ECG data of patients are normal beats, we finally selected the ECG data of six patients which contain clinically significant arrhythmia. The database provides two particular label type for motion artifact and baseline wandering, which are caused by body movement during ECG acquisition. In actual situations, cardiologist doesn't use the noise signals as a basis for diagnosis, therefore, these two specific labels prevent physicians from using noise to make a diagnosis.The original data is first compared with the holter report, and the R peak position and beat labels are manually marked. And then the data were given to a professional cardiologist, Ju-Yi, Chen, for verification. The cardiologist checked the correction and position of beat labels, and chose the acceptable signal segmentation for high quality.Introduction of Ju-Yi, Chen :JU-YI CHEN was born in Tainan, Taiwan, in 1974. He received the M.S. degree from Chang Gung University, Taoyuan City, Taiwan, in 1999 and the Ph.D. degree from the National Cheng Kung University, Tainan, in 2013. Since 2021, he has been a Professor at the Department of Internal Medicine, National Cheng Kung University. His current research interests include the cardiovascular diseases, including arrhythmias, hypertension, arterial stiffness, and cardiac implantable electric devices.Data DescriptionThe file structure and naming rule are described as follows :[The subject number]_[The measurement time] : The directory nameOUTPUT_ECG_data.csv : The one-hour ECG signals ( unit : 0.1V )OUTPUT_peak_label.csv : The arrhythmia type label of R-peakOUTPUT_peak_position.csv : The position of R-peakex : 1_0100 directory contains subject No. 1's data which is measured at 01:00.Arrhythmia diseases and the corresponding label codes :Code Arrhythmia Disease—————————————————————0 Normal1 Atrial Fibrillation2 Supraventricular Tachycardia3 Premature Ventricular Contraction4 Atrial Premature Contraction5 Motion Artifact6 Wandering7 First degree AV block8 Atrial FlutterPS : Wandering represents baseline drifted by 1mV.Patient information :Subject 1: Male，61 yearsSubject 2: Female，77 yearsSubject 3: Male，63 yearsSubject 4: Male，64 yearsSubject 5: Male，24 yearsSubject 6: Male，64 yearsUsage NotesFew public ECG databases provide long-term ECG, our goal in creating the database is to help understand what a person's ECG looks like in a day, and this database is more valuable in obtaining long-term ECG.EthicsOur team has cooperated with National Cheng Kung University Hospital and Tainan Hospital. All the patients enrolled gave their informed consent to participate in the study. The certification of safety-related IEC standards and human study approval are all acquired.Conflicts of InterestThe authors declare that there are no known conflicts of interest.ReferencesS.-Y. Lee, P.-W. Huang, M.-C. Liang, J.-H. Hong, and J.-Y. Chen, "Development of an arrhythmia monitoring system and human study," IEEE Transactions on Consumer Electronics, vol. 64, no. 4, pp. 442-451, 2018.

Abstract copyright UK Data Service and data collection copyright owner. The purpose of this study was to collect data defining the problems and needs of Small Heath, as seen by its residents, and to examine the attitudes of respondents to the area and to improvement policies for it. Main Topics: Attitudinal/Behavioural Questions 1. Household Questionnaire Length of residence, number of house moves since 1968, comparison of present and previous accommodation. Past and present residence: place, type, number of rooms, number of adults, tenure, amenities (whether shared). Reasons for moving. If owner occupiers: improvements made, knowledge of grants available and details of grants received. If tenants: satisfaction with landlord's council's standard of maintenance; details of alterations and repairs completed/needed. Knowledge of and attitude to plans for local area, whether further enquiries made, whether seriously considered moving with reasons. Action taken, proposed type and tenure of future residence, whether respondent was on council housing list or Transfer Exchange list (duration). Details of sons'/daughters' place of residence and occupation. Details of illness/disability suffered by any household member, severity of handicap, household's contact with doctor/hospital during previous 2 weeks. Attitude to proposed advice and information centre in Small Heath, knowledge of council departments, action taken to change plans for area. 2. Individual Questionnaire Length of residence, opinion of local district and change in the area over recent years. Respondents were asked to agree/disagree with a number of statements about the area. Opinion on social and racial mix in the area, adequacy of public services in district. Satisfaction with present residence, whether seriously considered moving, proximity of relatives and frequency of contact. Periods of unemployment during last 2 years. Occupation: job description, industry, responsibilities and qualifications, number of job changes in last 5 years, reasons for leaving previous job, length of service in present job, place of work, hours of work (shifts). Journey to work (method, time, distance), effect of three-day week on income. Reasons if women respondents were not working, pre-school child care arrangements and perceived advantages, preferred hours for child-care provision. Type of school children attended, satisfaction with education in area. Respondents were asked to agree/disagree with a number of statements about education in Small Heath. 3. Special Questions for Individuals Born Outside the UK Year of entry into UK, place of residence before emigration (urban/rural), number of close relatives in home country, occupation in home country, use of job skills in UK, place of birth of employer and colleagues, nationality of landlord. Background Variables Age, sex, marital status, place of birth, employment status, income, use of car/telephone. Interval sampling from local authority rating lists Face-to-face interview

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

New reweighting policy
Following the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly data; Secure Access datasets; household datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

Variables DISEA and LNGLST
Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018.

An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2022 Weighting

The population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.

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.

Abstract copyright UK Data Service and data collection copyright owner.BackgroundThe Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The Annual Population Survey, also held at the UK Data Archive, is derived from the LFS.The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.LFS DocumentationThe documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.LFS response to COVID-19From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.Occupation data for 2021 and 2022 data filesThe ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.2024 ReweightingIn February 2024, reweighted person-level data from July-September 2022 onwards were released. Up to July-September 2023, only the person weight was updated (PWT23); the income weight remains at 2022 (PIWT22). The 2023 income weight (PIWT23) was included from the October-December 2023 quarter. Users are encouraged to read the ONS methodological note of 5 February, Impact of reweighting on Labour Force Survey key indicators: 2024, which includes important information on the 2024 reweighting exercise.End User Licence and Secure Access QLFS dataTwo versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).The Secure Access version contains more detailed variables relating to:age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent childfamily unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of familynationality and country of originfiner detail geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district, and other categories;health: including main health problem, and current and past health problemseducation and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeshipsindustry: including industry, industry class and industry group for main, second and last job, and industry made redundant fromoccupation: including 5-digit industry subclass and 4-digit SOC for main, second and last job and job made redundant fromsystem variables: including week number when interview took place and number of households at addressother additional detailed variables may also be included.The Secure Access datasets (SNs 6727 and 7674) have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Latest edition informationFor the second edition (June 2022), 2022 weighting variable PWT22 was added to the study, and the 2020 weight removed. Main Topics:The QLFS questionnaire comprises a 'core' of questions which are included in every survey, together with some 'non-core' questions which vary from quarter to quarter.The questionnaire can be split into two main parts. The first part contains questions on the respondent's household, family structure, basic housing information and demographic details of household members. The second part contains questions covering economic activity, education and health, and also may include a few questions asked on behalf of other government departments (for example the Department for Work and Pensions and the Home Office). Until 1997, the questions on health covered mainly problems which affected the respondent's work. From that quarter onwards, the questions cover all health problems. Detailed questions on income have also been included in each quarter since 1993. The basic questionnaire is revised each year, and a new version published, along with a transitional version that details changes from the previous year's questionnaire. Four sampling frames are used. See documentation for details.

Crude birth rates, age-specific fertility rates and total fertility rates (live births), 2000 to most recent year.

Abstract

The NCKU CBIC ECG database collects ECG data from 6 different patients. Each patient collects lead II ECG for four hours a day to highlight patients' different physiological meanings at different times of the day, and the database provides the labels for motion artifact and baseline wandering, which are invalid signal for diagnosis. Prevent physicians from using the noise signal to diagnose. These data were collected using Patch[1] at Tainan Hospital.

Background

Technology and medical treatment are highly developed in the 21st century, and people have more irregular daily routines and greater life pressure. Cardiovascular disease has become a tough nut to crack when the changing of lifestyle is coupled with the aging of society. The age distribution of patients is wider than ever. A wealth of health information can be obtained through electrocardiogram (ECG) measurement, including cardiac arrhythmias. Severe arrhythmias will lead to many life problems, including palpitations, chest tightness, dizziness, shock, and even life-threatening conditions. Therefore, the monitoring of ECG signal is quite essential.
To do our part in the study of arrhythmia, our team started the patient enrollment after gaining the permission of the National Cheng Kung University Hospital Institutional Review Board (NCKUH IRB No. B-ER-104-379) from 2018. We have selected total 128 patients' 24 hours ECG data until now. The results of the arrhythmia label are confirmed by the cardiologist Ju-Yi Chen in NCKUH. Finally, We selected 6 patients from the received signals and made them into a database for researchers to access.

Methods

The NCKU CBIC ECG database contains the ECG recordings from 6 subjects. The signals were collected in Tainan Hospital (Ministry of Health and Welfare) via an ECG acquisition device[1] developed by Your health technology Co., Ltd. The sampling frequency is 400Hz, and the ADC resolution is 12 bits. The age distribution of subjects was from 24 to 76 years old, and each patient was measured at the lead II for 24 hours. After the signal is recorded, four cleaner segments in the morning, noon, evening, and midnight are selected, and each segment is one hour long. The heartbeat of human body is different when sleeping and awake, and some arrhythmia type occurs at sleeping period often. It's hard to detect some arrhythmia at specific time of a day, therefore, we choose signal segments from different time period for a patient, which is more representative of the daily heartbeat condition. It's worth mentioning that the ECG signals from the 6th subject contains too many noise signals in the daytime due to his career type, so the segments from 22:00 to 02:00 are selected.
We have collected total 128 patients from Tainan Hospital since 2018. Since most of the ECG data of patients are normal beats, we finally selected the ECG data of six patients which contain clinically significant arrhythmia. The database provides two particular label type for motion artifact and baseline wandering, which are caused by body movement during ECG acquisition. In actual situations, cardiologist doesn't use the noise signals as a basis for diagnosis, therefore, these two specific labels prevent physicians from using noise to make a diagnosis. The original data is first compared with the holter report, and the R peak position and beat labels are manually marked. And then the data were given to a professional cardiologist, Ju-Yi, Chen, for verification. The cardiologist checked the correction and position of beat labels, and chose the acceptable signal segmentation for high quality.

Introduction of Ju-Yi, Chen :
JU-YI CHEN was born in Tainan, Taiwan, in 1974. He received the M.S. degree from Chang Gung University, Taoyuan City, Taiwan, in 1999 and the Ph.D. degree from the National Cheng Kung University, Tainan, in 2013. Since 2021, he has been a Professor at the Department of Internal Medicine, National Cheng Kung University. His current research interests include the cardiovascular diseases, including arrhythmias, hypertension, arterial stiffness, and cardiac implantable electric devices.

Data Description

The file structure and naming rule are described as follows : [The subject number]_[The measurement time] : The directory name

OUTPUT_ECG_data.csv : The one-hour ECG signals ( unit : 0.1V ) OUTPUT_peak_label.csv : The arrhythmia type label of R-peak OUTPUT_peak_position.csv : The position of R-peak

ex : 1_0100 directory contains subject No. 1's data which is measured at 01:00.

Arrhythmia diseases and the corresponding label codes :

Code Arrhythmia Disease ————————————————————— 0 Normal 1 Atrial Fibrillation 2 Supraventricular Tachycardia 3 Premature Ventricular Contraction 4 Atrial Premature Contraction 5 Motion Artifact 6 Wandering 7 First degree AV block 8 Atrial Flutter

PS : Wandering represents baseline drifted by 1mV.

Patient information :

Subject 1: Male，61 years Subject 2: Female，77 years Subject 3: Male，63 years Subject 4: Male，64 years Subject 5: Male，24 years Subject 6: Male，64 years

Usage Notes

Few public ECG databases provide long-term ECG, our goal in creating the database is to help understand what a person's ECG looks like in a day, and this database is more valuable in obtaining long-term ECG.

Ethics

Our team has cooperated with National Cheng Kung University Hospital and Tainan Hospital. All the patients enrolled gave their informed consent to participate in the study. The certification of safety-related IEC standards and human study approval are all acquired.

Conflicts of Interest The authors declare that there are no known conflicts of interest.

References

S.-Y. Lee, P.-W. Huang, M.-C. Liang, J.-H. Hong, and J.-Y. Chen, "Development of an arrhythmia monitoring system and human study," IEEE Transactions on Consumer Electronics, vol. 64, no. 4, pp. 442-451, 2018.

Lund 80 + cohort is a longitudinal population based cohort with sequential design that started in 1988. All residents of Lund, born in 1908 and then 80 years, were invited for a first examination. The baseline population consisted of 315 individuals, of whom 212 agreed to be examined (146 females and 66 males). In the en, 165 participants (113 women and 52 men) were analyzed, after exclusion of 47 individuals due to imparied cognition, denied participation and incomplete data. Each survey, which included a general medical examination and psychological tests, took about 3 to 4 hours and was done by a doctor and a psychologist. The subjects were asked to answer questions if they felt depressed, how satisfied they were with life, trouble sleeping, irritability, nervousness, problems of the stomach and headache among other things. Additional information such as education and family relationships were collected by a nurse. Five years later, in 1993, the same individuals were invited for re-examination number 1. At the same time also individuals born in 1913, and then 80 years of age 1993, were invited for a first examination. This procedure has since then continued every five years, ie 1998 there were three Groups: re-examination nr 2 for individuals born in 1908, re-examination number 1 for individuals born in 1913 and the first examination for individuals born in 1918.

Purpose:

To investigate the health in the older population and explore whether helping them cope with problems can make them feel better.

Every five year, participants are invited to a follow up and new individuals (aged 80 years) are invited to participate in the study.

This dataset supports a publication on the determinants of infant growth in a birth cohort in the Nepal plains.This study aimed to identify the determinants of infant growth in terms of length-for-age z-score (LAZ) in a birth cohort (n=602) in the plains of Nepal. Children were enrolled within 72 hours of birth and followed-up every 28 days until they were 2 years. We fitted mixed-effects linear regression models controlling for multiple measurements within individuals to examine the impact of household and maternal factors, feeding practices and infection on infant LAZ. We conducted separate analyses for the age periods 0-6 months (exclusive breastfeeding period) and 7-24 months (complementary feeding period) to check whether the importance of determinants differed by child age.The data are useful to those seeking to understand the factors associated with longitudinal changes in nutritional status in children from birth to 2 years in the plains of Nepal.

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Abstract copyright UK Data Service and data collection copyright owner.Background The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.Longitudinal data The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary. Secure Access data Secure Access longitudinal datasets for the LFS are available for two-quarters (SN 7908) and five-quarters (SN 7909). The two-quarter datasets are available from April 2001 and the five-quarter datasets are available from June 2010. The Secure Access versions include additional, detailed variables not included in the standard 'End User Licence' (EUL) longitudinal datasets (see under GNs 33315 and 33316). Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to:day, month and year of birthstandard occupational classification (SOC) relating to second job, job made redundant from, last job, apprenticeships and occupation one year agofive digit industry subclass relating to main job, last job, second job and job one year agoThese extra variables are not available for every quarter or dataset. Users are advised to consult the 'LFS Variable Catalogue' file available in the Documentation section below for further information. Occupation data for 2021 and 2022 data filesThe ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.2022 WeightingThe population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust. DocumentationThe study documentation presented in the Documentation section includes data dictionaries for all years, and the most recent LFS documentation only, due to available space. Documentation for previous years is provided alongside the data for access and is also available upon request.Latest edition informationFor the fifteenth edition (July 2023), a data file covering January 2022 - March 2023 has been added to the study.

Dataset contains counts and measures for individuals from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 1.

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The variables included in this dataset are for the census usually resident population count (unless otherwise stated). All data is for level 1 of the classification.

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The variables for part 2 of the dataset are:

• Individual home ownership for the census usually resident population count aged 15 years and over
• Usual residence 1 year ago indicator
• Usual residence 5 years ago indicator
• Years at usual residence
• Average years at usual residence
• Years since arrival in New Zealand for the overseas-born census usually resident population count
• Average years since arrival in New Zealand for the overseas-born census usually resident population count
• Study participation
• Main means of travel to education, by usual residence address for the census usually resident population who are studying
• Main means of travel to education, by education address for the census usually resident population who are studying
• Highest qualification for the census usually resident population count aged 15 years and over
• Post-school qualification in New Zealand indicator for the census usually resident population count aged 15 years and over
• Highest secondary school qualification for the census usually resident population count aged 15 years and over
• Post-school qualification level of attainment for the census usually resident population count aged 15 years and over
• Sources of personal income (total responses) for the census usually resident population count aged 15 years and over
• Total personal income for the census usually resident population count aged 15 years and over
• Median ($) total personal income for the census usually resident population count aged 15 years and over
• Work and labour force status for the census usually resident population count aged 15 years and over
• Job search methods (total responses) for the unemployed census usually resident population count aged 15 years and over
• Status in employment for the employed census usually resident population count aged 15 years and over
• Unpaid activities (total responses) for the census usually resident population count aged 15 years and over
• Hours worked in employment per week for the employed census usually resident population count aged 15 years and over
• Average hours worked in employment per week for the employed census usually resident population count aged 15 years and over
• Industry, by usual residence address for the employed census usually resident population count aged 15 years and over
• Industry, by workplace address for the employed census usually resident population count aged 15 years and over
• Occupation, by usual residence address for the employed census usually resident population count aged 15 years and over
• Occupation, by workplace address for the employed census usually resident population count aged 15 years and over
• Main means of travel to work, by usual residence address for the employed census usually resident population count aged 15 years and over
• Main means of travel to work, by workplace address for the employed census usually resident population count aged 15 years and over
• Sector of ownership for the employed census usually resident population count aged 15 years and over
• Individual unit data source.

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Download lookup file for part 2 from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Te Whata

Under the Mana Ōrite Relationship Agreement, Te Kāhui Raraunga (TKR) will be publishing Māori descent and iwi affiliation data from the 2023 Census in partnership with Stats NZ. This will be available on Te Whata, a TKR platform.

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Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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Study participation time series

In the 2013 Census study participation was only collected for the census usually resident population count aged 15 years and over.

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Concept descriptions and quality ratings

Data quality ratings for 2023 Census variables has additional details about variables found within totals by topic, for example, definitions and data quality.

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Disability indicator

This data should not be used as an official measure of disability prevalence. Disability prevalence estimates are only available from the 2023 Household Disability Survey. Household Disability Survey 2023: Final content has more information about the survey.

Activity limitations are measured using the Washington Group Short Set (WGSS). The WGSS asks about six basic activities that a person might have difficulty with: seeing, hearing, walking or climbing stairs, remembering or concentrating, washing all over or dressing, and communicating. A person was classified as disabled in the 2023 Census if there was at least one of these activities that they had a lot of difficulty with or could not do at all.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Measures

Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value