The East Asian Social Survey (EASS) is a biennial social survey project that serves as a cross-national network of the following four General Social Survey type surveys in East Asia: Chinese General Social Survey (CGSS), Japanese General Social Survey (JGSS), Korean General Social Survey (KGSS), Taiwan Social Change Survey (TSCS), and comparatively examines diverse aspects of social life in these regions. Survey information in this module focused on issues that affected overall health, such as specific conditions, physical functioning, aid received from family members or friends when needed, and lifestyle choices. Topics included activities respondents were able to perform and how they were affected socially in light of specific physical and mental health conditions. Respondents were asked to provide health conditions they were suffering from, such as hypertension, diabetes, heart disease, and how these conditions were limiting with respect to general health, physical functioning, emotional and mental health, as well as social functioning. Other topics included participation and frequency of lifestyle habits that affected overall health, as well as how often respondents visited the doctor. Respondents were also queried on whether they sought out alternative, non-traditional homeopathic care and whether family, friends, or co-workers listened to their personal problems and provided support financially. Additional topics include the environment and pollution, neighborhood amenities, fear of aging, addiction, and body image. Demographic information specific to the respondent and their spouse includes age, sex, marital status, education, employment status and hours worked, occupation, earnings and income, religion, class, size of community, and region.

The National Sample Survey of Registered Nurses (NSSRN) Download makes data from the survey readily available to users in a one-stop download. The Survey has been conducted approximately every four years since 1977. For each survey year, HRSA has prepared two Public Use File databases in flat ASCII file format without delimiters. The 2008 data are also offerred in SAS and SPSS formats. Information likely to point to an individual in a sparsely-populated county has been withheld. General Public Use Files are State-based and provide information on nurses without identifying the County and Metropolitan Area in which they live or work. County Public Use Files provide most, but not all, the same information on the nurse from the General Public Use File, and also identifies the County and Metropolitan Areas in which the nurses live or work. NSSRN data are to be used for research purposes only and may not be used in any manner to identify individual respondents.

The 1966-2023 North American Breeding Bird Survey (BBS) dataset contains avian point count data for more than 700 North American bird taxa (species, races, and unidentified species groupings). These data are collected annually during the breeding season, primarily in June, along thousands of randomly established roadside survey routes in the United States and Canada. Routes are roughly 24.5 miles (39.2 km) long with counting locations placed at approximately half-mile (800-m) intervals, for a total of 50 stops. At each stop, a citizen scientist highly skilled in avian identification conducts a 3-minute point count, recording all birds seen within a quarter-mile (400-m) radius and all birds heard. Surveys begin 30 minutes before local sunrise and take approximately 5 hours to complete. Routes are surveyed once per year, with the total number of routes sampled per year growing over time; just over 500 routes were sampled in 1966, while in recent decades approximately 3000 routes have been sampled annually. No data are provided for 2020. BBS field activities were cancelled in 2020 because of the coronavirus disease (COVID-19) global pandemic and observers were directed to not sample routes. In addition to avian count data, this dataset also contains survey date, survey start and end times, start and end weather conditions, a unique observer identification number, route identification information, and route location information including country, state, and BCR, as well as geographic coordinates of route start point, and an indicator of run data quality.

SSURGO consists of spatial data and a comprehensive relational database with tables that describe soil properties, interpretations and productivity values. The USDA Natural Resources Conservation Service (NRCS, formerly Soil Conservation Service) provides a download of the statewide SSURGO database that includes vector and raster spatial data, database tables and their relationship classes, and a user guide. To access SSURGO, go to the USDA NRCS Geospatial Data Gateway. To download the database, on the right side of the page, click on the Direct Data Download link under, I Want To... The Direct Data / NAIP Download page will then open. Click on the Soils Geographic Databases link. Then click on the folder named gSSURGO by State (date in folder name). Scroll through the list and select gSSURGO_NJ.zip. Then click on the Download button on the upper right. A message will open that Your Download is In Progress. You will then be prompted to select a file download location.

In addition to respondents’ highest educational qualification, some surveys also collect data on their main field of education. Current measurement practice involves either a closed question with highly aggregated response categories, which are difficult to use for respondents, or an open question, requiring expensive post-coding. Therefore, a measurement tool for fields of education was developed in the SERISS-project in work package 8, Task 8.3. In deliverable D8.9 we provide a database of fields of education and training in 34 languages, including the definition of a search tree interface to facilitate navigation of categories for respondents. All 120 standard categories and classification codes are taken from UNESCO's International Standard Classification of Education for Fields of Education and Training (ISCED-F). For most languages, detailed 3-digit information is available. The database, including a live search feature, is available at the surveycodings website at https://surveycodings.org/articles/codings/fields-of-education. The search tree can be used for respondents’ self-identification of fields of education and training in computer-assisted surveys. The live search feature can also be used for post-coding open answers in already collected data.

sflagg/Kaggle-Mental-Health-Survey-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

The Inshore Beam Trawl Surveys include data collected by 4 countries (BE, DE, NL, UK) cover cover most of the coastal and estuarine waters along the continental coast, and are also known as the Youngfish Surveys. Although, the surveys target plaice and sole, composition of the whole catch is analyzed. Responsible survey group is WGBEAM

The National Sample Survey contains a variety of socio-economic data for India and is collected by the Ministry of Statistics and Programme Implementation for planning and policy formulation. The National Sample Survey Office (NSSO) conducts the Socio-Economic (SE) Surveys, nationwide sample surveys relating to various socio-economic topics. Surveys are conducted in the form of Rounds, each Round being normally of one-year duration and occasionally for a period of six months.The National Sample Survey website provides further information about the survey, coverages and methodology.

Abstract

The primary objective of the 2017 Indonesia Dmographic and Health Survey (IDHS) is to provide up-to-date estimates of basic demographic and health indicators. The IDHS provides a comprehensive overview of population and maternal and child health issues in Indonesia. More specifically, the IDHS was designed to: - provide data on fertility, family planning, maternal and child health, and awareness of HIV/AIDS and sexually transmitted infections (STIs) to help program managers, policy makers, and researchers to evaluate and improve existing programs; - measure trends in fertility and contraceptive prevalence rates, and analyze factors that affect such changes, such as residence, education, breastfeeding practices, and knowledge, use, and availability of contraceptive methods; - evaluate the achievement of goals previously set by national health programs, with special focus on maternal and child health; - assess married men’s knowledge of utilization of health services for their family’s health and participation in the health care of their families; - participate in creating an international database to allow cross-country comparisons in the areas of fertility, family planning, and health.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-54

Universe

The survey covered all de jure household members (usual residents), all women age 15-49 years resident in the household, and all men age 15-54 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2017 IDHS sample covered 1,970 census blocks in urban and rural areas and was expected to obtain responses from 49,250 households. The sampled households were expected to identify about 59,100 women age 15-49 and 24,625 never-married men age 15-24 eligible for individual interview. Eight households were selected in each selected census block to yield 14,193 married men age 15-54 to be interviewed with the Married Man's Questionnaire. The sample frame of the 2017 IDHS is the Master Sample of Census Blocks from the 2010 Population Census. The frame for the household sample selection is the updated list of ordinary households in the selected census blocks. This list does not include institutional households, such as orphanages, police/military barracks, and prisons, or special households (boarding houses with a minimum of 10 people).

The sampling design of the 2017 IDHS used two-stage stratified sampling: Stage 1: Several census blocks were selected with systematic sampling proportional to size, where size is the number of households listed in the 2010 Population Census. In the implicit stratification, the census blocks were stratified by urban and rural areas and ordered by wealth index category.

Stage 2: In each selected census block, 25 ordinary households were selected with systematic sampling from the updated household listing. Eight households were selected systematically to obtain a sample of married men.

For further details on sample design, see Appendix B of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2017 IDHS used four questionnaires: the Household Questionnaire, Woman’s Questionnaire, Married Man’s Questionnaire, and Never Married Man’s Questionnaire. Because of the change in survey coverage from ever-married women age 15-49 in the 2007 IDHS to all women age 15-49, the Woman’s Questionnaire had questions added for never married women age 15-24. These questions were part of the 2007 Indonesia Young Adult Reproductive Survey Questionnaire. The Household Questionnaire and the Woman’s Questionnaire are largely based on standard DHS phase 7 questionnaires (2015 version). The model questionnaires were adapted for use in Indonesia. Not all questions in the DHS model were included in the IDHS. Response categories were modified to reflect the local situation.

Cleaning operations

All completed questionnaires, along with the control forms, were returned to the BPS central office in Jakarta for data processing. The questionnaires were logged and edited, and all open-ended questions were coded. Responses were entered in the computer twice for verification, and they were corrected for computer-identified errors. Data processing activities were carried out by a team of 34 editors, 112 data entry operators, 33 compare officers, 19 secondary data editors, and 2 data entry supervisors. The questionnaires were entered twice and the entries were compared to detect and correct keying errors. A computer package program called Census and Survey Processing System (CSPro), which was specifically designed to process DHS-type survey data, was used in the processing of the 2017 IDHS.

Response rate

Of the 49,261 eligible households, 48,216 households were found by the interviewer teams. Among these households, 47,963 households were successfully interviewed, a response rate of almost 100%.

In the interviewed households, 50,730 women were identified as eligible for individual interview and, from these, completed interviews were conducted with 49,627 women, yielding a response rate of 98%. From the selected household sample of married men, 10,440 married men were identified as eligible for interview, of which 10,009 were successfully interviewed, yielding a response rate of 96%. The lower response rate for men was due to the more frequent and longer absence of men from the household. In general, response rates in rural areas were higher than those in urban areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors result from mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2017 Indonesia Demographic and Health Survey (2017 IDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2017 IDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2017 IDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2017 IDHS is a STATA program. This program used the Taylor linearization method for variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix C of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar year - Reporting of age at death in days - Reporting of age at death in months

See details of the data quality tables in Appendix D of the survey final report.

These data reflect results of a household survey implemented in the summer of 2014. The survey randomly sampled households from 23 neighborhoods (census block groups) across 12 cities and 3 counties. Neighborhoods were purposively selected to represent different configurations of social, built, and natural environmental characteristics using the "iUTAH Urban Typology" (https://www.hydroshare.org/resource/84f00a1d8ae641a8af2d994a74f4ccfb/). Data were collected using a drop-off/pick-up methodology, and produced an overall response rate of over 62% (~2,400 respondents). The questionnaire included detailed questions related to household water use and landscaping behaviors, perceptions of water supply and quality, participation in water based recreation, concerns about water issues, and preferences for a range of local and state water policies.

Here we are making public an anonymized version of the large household survey dataset. To protect the identity of respondents, we have removed a few variables and truncated other variables.

Files included here: englishsurveys and spanishsurveys: These folders contain the survey questionnaires used specific to each neighborhood. Codebook in various formats: Tables (xls and csv files) with a list and definition of questions/variables, which correspond to the columns in the data files, and the encoding of the responses. Dataset in various formats: Tables (csv, xls, sas, sav, dta files) containing numeric responses to each question. Each participant's responses correspond to a row of data. Each question corresponds to a column of data. Interpretation of the coded responses is found in the data codebook. Maps: maps of the neighborhoods surveyed. SummaryReports: Summaries of the results that compare across three counties, summary reports for each county, highlight reports for each city.

Summary reports are also available at http://data.iutahepscor.org/mdf/Data/household_survey/ including an overall report that provides comparisons of how these vary across the three counties where we collected data (Cache, Salt Lake, and Wasatch) as well as summary reports for each county and highlights reports for each city.

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The universe of inference includes all formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys. All eligible establishments must be registered with the registration agency. In the case of Indonesia, registration are those establishments in possession of TDP (Company registration Certificate)/NIB (Business Identification Number). Both TDP and NIB are included as the implementation of the Omnibus Law on Job Creation from 2020 was being implemented and businesses were transitioning to the new definitions.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination). Starting in 2022, the WBES bases the industry classification on ISIC Rev. 4 (with earlier surveys using ISIC Rev. 3.1). For regional coverage within a country, the WBES has national coverage.

Note: Refer to Sampling Structure section in "The Indonesia 2023 World Bank Enterprise Survey Implementation Report" for detailed methodology on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

In addition to the standard set of questions administered to all respondents, the sample was randomly split with two different modules that cover different set of questions: Version A – B-Ready contains additional questions tailored for the Business Ready Report covering infrastructure, trade, government regulations, finance, labor, and other topics. Version B – Green Economy and Taxation covers questions with regards to taxes, green economy, and maternity policies.

The different modules in the dataset are reflected in variable q_version.

Response rate

Overall survey response rate was 41.2%.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

ASS: Exp: FI: SCOR: Commodity Contracts Brokerage data was reported at 3.399 USD bn in 2016. This records a decrease from the previous number of 3.602 USD bn for 2015. ASS: Exp: FI: SCOR: Commodity Contracts Brokerage data is updated yearly, averaging 3.602 USD bn from Dec 2003 (Median) to 2016, with 11 observations. The data reached an all-time high of 5.100 USD bn in 2008 and a record low of 2.960 USD bn in 2003. ASS: Exp: FI: SCOR: Commodity Contracts Brokerage data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H021: Annual Services Survey: Employer Firms Expense.

ABOUT THE COMMUNITY SURVEY DATASETFinal Reports for ETC Institute conducted annual community attitude surveys for the City of Tempe. These survey reports help determine priorities for the community as part of the City's on-going strategic planning process. In many of the survey questions, survey respondents are asked to rate their satisfaction level on a scale of 5 to 1, where 5 means "Very Satisfied" and 1 means "Very Dissatisfied" (while some questions follow another scale). The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level.This data is the weighted data provided by the ETC Institute, which is used in the final published PDF report. PERFORMANCE MEASURESData collected in these surveys applies directly to a number of performance measures for the City of Tempe including the following (as of 2021): 1. Safe and Secure Communities1.04 Fire Services Satisfaction1.06 Victim Not Reporting Crime to Police1.07 Police Services Satisfaction1.09 Victim of Crime1.10 Worry About Being a Victim1.11 Feeling Safe in City Facilities1.23 Feeling of Safety in Parks2. Strong Community Connections2.02 Customer Service Satisfaction2.04 City Website Quality Satisfaction2.05 Online Services Satisfaction Rate2.15 Feeling Invited to Participate in City Decisions2.21 Satisfaction with Availability of City Information3. Quality of Life3.16 City Recreation, Arts, and Cultural Centers3.17 Community Services Programs3.19 Value of Special Events3.23 Right of Way Landscape Maintenance3.36 Quality of City Services4. Sustainable Growth & DevelopmentNo Performance Measures in this category presently relate directly to the Community Survey5. Financial Stability & VitalityNo Performance Measures in this category presently relate directly to the Community Survey Additional InformationSource: Community Attitude SurveyContact (author): Wydale HolmesContact E-Mail (author): wydale_holmes@tempe.govContact (maintainer): Wydale HolmesContact E-Mail (maintainer): wydale_holmes@tempe.govData Source Type: Excel tablePreparation Method: Data received from vendorPublish Frequency: AnnualPublish Method: Manual

The Fisheries Research Survey team proposes to conduct the West Coast Groundfish Bottom Trawl Survey from May to October 2019. The goal of the survey is to ensure the sustainability of marine fisheries with a focus on ending overfishing. The groundfish fishery supports management for 90+ commercially fished stocks off Washington, Oregon, and California and is the primary source of fishery-indep...

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.

This dataset groups all the tables supplementing the contents of the article "Data Journals: A Survey", which is going to be published by the Journal of the Association for Information Science and Technology (JASIST). Tables are published with no header. Any details can be found in the article.

Abstract Data occupy a key role in our information society. However, although the amount of published data continues to grow and terms like “data deluge” and “big data” today characterize numerous (research) initiatives, a lot of work is still needed in the direction of publishing data in order to make them effectively discoverable, available, and reusable by others. Several barriers hinder data publishing, from lack of attribution and rewards, vague citation practices, quality issues, to a rather general lack of data sharing culture. Lately, data journals came forward as a solution to overcome some of these barriers. In this study of more than 100 currently existing data journals, we describe the approaches they promote for description, availability, citation, quality and open access or datasets. We close by identifying ways to expand and strengthen the data journals approach as a means to actually promote datasets access and exploitation.

This is a data product to support state indicators that are based from groundfish biological data, derived using primary data from surveys undertaken in the Northeast Atlantic between 1983 and 2020. Catch records by taxonomic group and by length category in terms of biomass and numbers of fish standardised to duration (per hour) or to the area swept by the haul. Data are available from multiple surveys using data downloaded from the ICES database of trawl surveys (DATRAS) once quality-controlled and standardised following procedures detailed in Greenstreet and Moriarty 2017. Data file names reflect the OSPAR region sampled, country conducting the sampling, fishing gear and time of years of sampling (as defined by Greenstreet and Moriarty 2017), e.g.: BBICFraBT4 refers to Bay of Biscay and Iberian Coast data from France by a Beam Trawl survey in quarter 4 of the year and GNSIntOT3 refers to Greater North Sea data from International (multiple countries) sampling by an Otter Trawl survey in quarter 3 of the year etc. Greenstreet, S.P.R. and Moriarty, M. (2017) OSPAR Interim Assessment 2107 Fish Indicator Data Manual (Relating to Version 2 of the Groundfish Survey Monitoring and Assessment Data Product). Scottish Marine and Freshwater Science Vol 8 No 17, 83pp. DOI: 10.7489/1985-1 Scientific survey data collected by multiple countries and made available through ICES DATRAS (https://www.ices.dk/data/data-portals/Pages/DATRAS.aspx). Swept-area estimates were generated by ICES 2021ab (ICES. 2021a. Workshop on the production of swept-area estimates for all hauls in DATRAS for biodiversity assessments (WKSAE-DATRAS). ICES Scientific Reports. 3:74. https://doi.org/10.17895/ices.pub.8232; ICES. 2021b. Workshop on the production of abundance estimates for sensitive species (WKABSENS); ICES Scientific Reports. 3:96. https://doi.org/10.17895/ices.pub.8299). ICES Data Centre host the database of trawl surveys (DATRAS) for groundfish and beam trawl data. DATRAS has an integrated quality check utility. All data, before entering the database, have to pass an extensive quality check. Despite this errors and missing data arise, which are subsequently dealt with by the data submitters from the contributing countries as required. However, this screening process was implemented in 2009 for data from 2004 onwards. Since some survey time-series extend back to the 1960s, historic data (unless re-evaluated and re-submitted by contributing countries) may not have been subject to the same level of quality control as these more recent data. Furthermore, the type of information collected, the level of detail and resolution in the data, has gradually evolved over time. In order to derive a single format, quality assured monitoring programme data product covering the entire Northeast Atlantic region inconsistencies in the datasets required resolution. These corrections are detailed in ICES 2021a,b: Biological data for trawl surveys are downloaded directly from DATRAS in raw exchange format (known as “HL data”). Ancillary data were processed by ICES 2021a,b to create the “SweptAreaAssessmentOutput” (which replaces the “HH data”) and these were downloaded from the same location: https://datras.ices.dk/Data _ products/Download/Download _ Data _ public.aspx Data are processed to create a standalone data product to be used for indicator assessments of fish and elasmobranchs. Initially, hauls are subset to determine the Standard Monitoring Programme (i.e. excluding invalid hauls including those of duration shorter than 13 minutes or longer than 66 minutes, following Greenstreet and Moriarty 2017) and these hauls are used to define the Standard Survey Area by excluding areas sampled infrequently over time). Biological data were accepted with ICES SpecVal of 1, 4, 7, 10 (see http://vocab.ices.dk/ for further information on SpecVal categories). Additional QA/QC is made at this step to determine if species identification issues are present in the raw biological data and these were discussed and agreed with the Chief Scientist for each survey.

From the site: "This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties."

Pew Research Center conducted random probability-based surveys among a total of 10,390 adults (ages 18 and older) in five places: Hong Kong, Japan, South Korea, Taiwan and Vietnam. Interviewing in Japan, South Korea and Taiwan was carried out under the direction of Langer Research Associates, and interviewing in Hong Kong and Vietnam was carried out under the direction of D3 Systems. In Hong Kong, Japan, South Korea and Taiwan, interviews were conducted via computer-assisted telephone interviewing (CATI). In Vietnam, interviews were administered face-to-face using tablet devices, also known as computer-assisted personal interviewing (CAPI). All surveys were conducted between June 2 and Sept. 17, 2023.

This project was produced by Pew Research Center as part of the Pew-Templeton Global Religious Futures project, which analyzes religious change and its impact on societies around the world. Funding for the Global Religious Futures project comes from The Pew Charitable Trusts and the John Templeton Foundation (grant 62287). This publication does not necessarily reflect the views of the John Templeton Foundation.

As of June 2024, one report has been published that focuses on the findings from this data: Religion and Spirituality in East Asian Societies: https://www.pewresearch.org/religion/2024/06/17/religion-and-spirituality-in-east-asian-societies