This dataset contains data from the National Center for Education Statistics' Academic Library Survey, which was gathered every two years from 1996 - 2014, and annually in IPEDS starting in 2014 (this dataset has continued to only merge data every two years, following the original schedule). This data was merged, transformed, and used for research by Starr Hoffman and Samantha Godbey.This data was merged using R; R scripts for this merge can be made available upon request. Some variables changed names or definitions during this time; a view of these variables over time is provided in the related Figshare Project. Carnegie Classification changed several times during this period; all Carnegie classifications were crosswalked to the 2000 classification version; that information is also provided in the related Figshare Project. This data was used for research published in several articles, conference papers, and posters starting in 2018 (some of this research used an older version of the dataset which was deposited in the University of Nevada, Las Vegas's repository).SourcesAll data sources were downloaded from the National Center for Education Statistics website https://nces.ed.gov/. Individual datasets and years accessed are listed below.[dataset] U.S. Department of Education, National Center for Education Statistics, Academic Libraries component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Academic Libraries Survey (ALS) Public Use Data File, Library Statistics Program, (2012, 2010, 2008, 2006, 2004, 2002, 2000, 1998, 1996), https://nces.ed.gov/surveys/libraries/aca_data.asp[dataset] U.S. Department of Education, National Center for Education Statistics, Institutional Characteristics component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Fall Enrollment component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014, 2012, 2010, 2008, 2006, 2004, 2002, 2000, 1998, 1996), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Human Resources component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014, 2012, 2010, 2008, 2006), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Employees Assigned by Position component, Integrated Postsecondary Education Data System (IPEDS), (2004, 2002), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Fall Staff component, Integrated Postsecondary Education Data System (IPEDS), (1999, 1997, 1995), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7

This is a monthly report on publicly funded community services for people of all ages using data from the Community Services Data Set (CSDS) reported in England for September 2021. It has been developed to help achieve better outcomes and provide data that will be used to commission services in a way that improves health, reduces inequalities, and supports service improvement and clinical quality. This report uses the new version of the dataset, CSDS v1.5. As an uplift from v1.0, the v1.5 dataset collects additional data on a person's care plan details, employment status and social & personal circumstances. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. More information about experimental statistics can be found on the UK Statistics Authority website. A supplementary file including health visiting metrics now accompanies this publication. Due to the coronavirus illness (COVID-19) disruption, the quality and coverage of some of our statistics has been affected, for example, by an increase in non-submissions for some datasets. We are also seeing some different patterns in the submitted data. For example, fewer patients are being referred to hospital and more appointments being carried out via phone/telemedicine/email. Therefore, data should be interpreted with care over the COVID-19 period.

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

Abstract

Within the frame of PCBS' efforts in providing official Palestinian statistics in the different life aspects of Palestinian society and because the wide spread of Computer, Internet and Mobile Phone among the Palestinian people, and the important role they may play in spreading knowledge and culture and contribution in formulating the public opinion, PCBS conducted the Household Survey on Information and Communications Technology, 2014.

The main objective of this survey is to provide statistical data on Information and Communication Technology in the Palestine in addition to providing data on the following: -

· Prevalence of computers and access to the Internet. · Study the penetration and purpose of Technology use.

Geographic coverage

Palestine (West Bank and Gaza Strip) , type of locality (Urban, Rural, Refugee Camps) and governorate

Analysis unit

Household. Person 10 years and over .

Universe

All Palestinian households and individuals whose usual place of residence in Palestine with focus on persons aged 10 years and over in year 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of a list of enumeration areas adopted in the Population, Housing and Establishments Census of 2007. Each enumeration area has an average size of about 124 households. These were used in the first phase as Preliminary Sampling Units in the process of selecting the survey sample.

Sample Size The total sample size of the survey was 7,268 households, of which 6,000 responded.

Sample Design The sample is a stratified clustered systematic random sample. The design comprised three phases:

Phase I: Random sample of 240 enumeration areas. Phase II: Selection of 25 households from each enumeration area selected in phase one using systematic random selection. Phase III: Selection of an individual (10 years or more) in the field from the selected households; KISH TABLES were used to ensure indiscriminate selection.

Sample Strata Distribution of the sample was stratified by: 1- Governorate (16 governorates, J1). 2- Type of locality (urban, rural and camps).

Sampling deviation

-

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on persons (aged 10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Preparation of Data Entry Program: This stage included preparation of the data entry programs using an ACCESS package and defining data entry control rules to avoid errors, plus validation inquiries to examine the data after it had been captured electronically.

Data Entry: The data entry process started on 8 May 2014 and ended on 23 June 2014. The data entry took place at the main PCBS office and in field offices using 28 data clerks.

Editing and Cleaning procedures: Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

Response rate

Response Rates= 79%

Sampling error estimates

There are many aspects of the concept of data quality; this includes the initial planning of the survey to the dissemination of the results and how well users understand and use the data. There are three components to the quality of statistics: accuracy, comparability, and quality control procedures.

Checks on data accuracy cover many aspects of the survey and include statistical errors due to the use of a sample, non-statistical errors resulting from field workers or survey tools, and response rates and their effect on estimations. This section includes:

Statistical Errors Data of this survey may be affected by statistical errors due to the use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators.

Variance calculations revealed that there is no problem in disseminating results nationally or regionally (the West Bank, Gaza Strip), but some indicators show high variance by governorate, as noted in the tables of the main report.

Non-Statistical Errors Non-statistical errors are possible at all stages of the project, during data collection or processing. These are referred to as non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, and practical and theoretical training took place during the training course. Training manuals were provided for each section of the questionnaire, along with practical exercises in class and instructions on how to approach respondents to reduce refused cases. Data entry staff were trained on the data entry program, which was tested before starting the data entry process.

Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

The sources of non-statistical errors can be summarized as: 1. Some of the households were not at home and could not be interviewed, and some households refused to be interviewed. 2. In unique cases, errors occurred due to the way the questions were asked by interviewers and respondents misunderstood some of the questions.

Please note: This is a Synthetic data file, also known as a Dummy file - it is not real data. This synthetic file should not be used for purposes other than to develop an test computer programs that are to be submitted by remote access. Each record in the synthetic file matches the format and content parameters of the real Statistics Canada Master File with which it is associated, but the data themselves have been 'made up'. They do NOT represent responses from real individuals and should NOT be used for actual analysis. These data are provided solely for the purpose of testing statistical package 'code' (e.g. SPSS syntax, SAS programs, etc.) in preperation for analysis using the associated Master File in a Research Data Centre, by Remote Job Submission, or by some other means of secure access. If statistical analysis 'code' works with the synthetic data, researchers can have some confidence that the same code will run successfully against the Master File data in the Resource Data Centres. In the fall of 1991, the National Health Information Council recommended that an ongoing national survey of population health be conducted. This recommendation was based on consideration of the economic and fiscal pressures on the health care systems and the requirement for information with which to improve the health status of the population in Canada. Commencing in April 1992, Statistics Canada received funding for development of a National Population Health Survey (NPHS). The NPHS collects information related to the health of the Canadian population and related socio-demographic information to: aid in the development of public policy by providing measures of the level, trend and distribution of the health status of the population, provide data for analytic studies that will assist in understanding the determinants of health, and collect data on the economic, social, demographic, occupational and environmental correlates of health. In addition the NPHS seeks to increase the understanding of the relationship between health status and health care utilization, including alternative as well as traditional services, and also to allow the possibility of linking survey data to routinely collected administrative data such as vital statistics, environmental measures, community variables, and health services utilization. The NPHS collects information related to the health of the Canadian population and related socio-demographic information. It is composed of three components: the Households, the Health Institutions, and the North components. The Household component started in 1994/1995 and is conducted every two years. The first three cycles (1994/1995, 1996/1997, 1997/1998) were both cross-sectional and longitudinal. The NPHS longitudinal sample includes 17,276 persons from all ages in 1994/1995 and these same persons are to be interviewed every two years. Beginning in Cycle 4 (2000/2001) the survey became strictly longitudinal (collecting health information from the same individuals each cycle). The cross-sectional and longitudinal documentation of the Household component is presented separately as well as the documentation for the Health Institutions and North components. The cross-sectional component of the Population Health Survey Program has been taken over by the Canadian Community Health Survey (CCHS). With the introduction of the Canadian Community Health Survey (CCHS), there were many changes to the 2000-2001 National Population Health Survey - Household questionnaire. Since NPHS is strictly a longitudinal survey, some content was migrated to the CCHS (such as the two-week disability section and certain questions on place where health care was provided) or was dropped (e.g. certain chronic conditions), while the order of the questionnaire changed. As only the longitudinal respondent is now surveyed, it was no longer necessary to distinguish between the General questionnaire and the Health component. Health Canada, Public Health Agency of Canada and provincial ministries of health use NPHS longitudinal data to plan, implement and evaluate programs and health policies to improve health and the efficiency of health services. Non-profit health organizations and researchers in the academic fields use the information to move research ahead and to improve health.

The dataset collection in question is a comprehensive assembly of related data tables sourced from Statistics Finland (Tilastokeskus). This collection includes several tables that contain related data, structured in a format that utilizes columns and rows for organization. The data within these tables is derived from the Statistics Finland's service interface (WFS). This collection provides a wealth of statistical information, potentially spanning various years, as suggested by the inclusion of 2013 and 2015 in some of the table names. Given the source, this dataset collection is likely to contain a wealth of valuable statistical data pertinent to Finland. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

United States WE: Age 25 & Over: No. of Workers: Male: Some College or Degree data was reported at 14,404.000 Person th in Mar 2020. This records a decrease from the previous number of 14,881.000 Person th for Dec 2019. United States WE: Age 25 & Over: No. of Workers: Male: Some College or Degree data is updated quarterly, averaging 13,477.000 Person th from Mar 2000 (Median) to Mar 2020, with 81 observations. The data reached an all-time high of 14,881.000 Person th in Dec 2019 and a record low of 12,376.000 Person th in Mar 2010. United States WE: Age 25 & Over: No. of Workers: Male: Some College or Degree data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G030: Current Population Survey: Usual Weekly Earnings.

Graph and download economic data for Unemployment Rate - Some College, No Degree, 25 to 64 years, Women (SCND2564W) from Jan 2000 to Jun 2025 about 25 to 64 years, females, tertiary schooling, education, unemployment, rate, and USA.

This statistical release makes available the most recent monthly data on NHS-funded maternity services in England, using data submitted to the Maternity Services Data Set (MSDS). This is the latest report from the newest version of the data set, MSDS.v.2, which has been in place since April 2019. The new data set was a significant change which added support for key policy initiatives such as continuity of carer, as well as increased flexibility through the introduction of new clinical coding. This was a major change, so data quality and coverage initially reduced from the levels seen in earlier publications. MSDS.v.2 data completeness improved over time, and we are looking at ways of supporting further improvements. This publication also includes the National Maternity Dashboard, which can be accessed via the link below. Data derived from SNOMED codes is used in some measures such as those for birthweight, and others will follow in later publications. SNOMED data is also included in some of the published Clinical Quality Improvement Metrics (CQIMs), where rules have been applied to ensure measure rates are calculated only where data quality is high enough. System suppliers are at different stages of development and delivery to trusts. In some cases, this has limited the aspects of data that can be submitted in the MSDS. Since last month, this publication contains a new Clinical Quality Improvement Metric (CQIM) called CQIMReadmissions. This new metric reports the number of babies born in hospital then discharged home, who were then readmitted to hospital while still under 30 days old. This is supported by five new data quality metrics to ensure we only publish CQIMReadmissions figures where the underlying data is of sufficient completeness and quality. The new data quality metrics are CQIMDQ46 to CQIMDQ50. Further information about this new readmissions metric can found in this publication’s Data Quality Statement. This new data can be found in the Measures file available for download and in the CQIM and CQIM+ pages in the National Maternity Dashboard, and further information on the new metrics can be found in the accompanying Metadata file. To help Trusts understand to what extent they met the Clinical Negligence Scheme for Trusts (CNST) Maternity Incentive Scheme (MIS) Data Quality Criteria for Safety Action 2, we have been producing a CNST Scorecard Dashboard showing trust performance against this criteria. This dashboard has been updated following the release of CNST Y6 criteria, and can be accessed via the link below. The percentages presented in this report are based on rounded figures and therefore may not total to 100%.

Key Table Information.Table Title.Wholesale Trade: Summary Statistics for the U.S., States, and Selected Geographies: 2022.Table ID.ECNBASIC2022.EC2242BASIC.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Summary Statistics for the U.S., States, and Selected Geographies: 2022.Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2024-12-05.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesOperating expenses ($1,000)Total inventories, beginning of year ($1,000)Total inventories, end of year ($1,000)Range indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S., State, Combined Statistical Area, Metropolitan and Micropolitan Statistical Area, Metropolitan Division, Consolidated City, County (and equivalent), and Economic Place (and equivalent; incorporated and unincorporated) levels that vary by industry. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 7-digit 2022 NAICS code levels and selected 8-digit 2022 NAICS-based code levels. For information about NAICS, see Economic Census Code Lists..Business Characteristics.For Wholesale Trade (42), data are presented by Type of Operation (All establishments; Merchant Wholesalers, except Manufacturers’ Sales Branches and Offices; and Manufacturers’ Sales Branches and Offices)..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not sampled are represented with administrative data..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/.API Information.Economic census data are housed in the Census Bureau Appli...

1Coordinates of vertices (latitude, longitude): (45.538, −122.665), (45.534, −122.654), (45.527, −122.643), (45.51563, −122.647), (45.507, −122.656), (45.508, −122.687), (45.522, −122.695), (45.532, −122.684).

Current and updated dataset of NBA Playoff statistics since the 1949-1950 season!

All standard statistics like Assists Per Game, Minutes Per Game, etc. are present as well as advanced statistics like Player Efficiency Rating (PER), Value Over Replacement Player (VORP), Win Share, and more!

This dataset was web scraped from https://www.basketball-reference.com.

Feel free to let me know if there are any statistics or player information that isn't present that you think should be added!

If you want the regular season statistics check out my other data set.

For more details on how some statistics are calculated, please see the https://www.basketball-reference.com/about/glossary.html

Key Table Information.Table Title.Manufacturing: E-Commerce Statistics for the U.S.: 2022.Table ID.ECNECOMM2022.EC2231ECOMM.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Manufacturing: E-Commerce Statistics for the U.S.: 2022.Release Date.2025-01-23.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Sales, value of shipments, or revenue ($1,000)E-Shipments value ($1,000) E-Shipments as percent of total sales, value of shipments, or revenue (%) Range indicating imputed percentage of total sales, value of shipments, or revenueDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S. level only. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 3-digit 2022 NAICS code levels for the U.S. For information about NAICS, see Economic Census Code Lists..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not sampled are represented with administrative data..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector31/.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.X - Not applicableA - Relative standard error of 100% or morer - Reviseds - Relative standard error exceeds 40%For a complete list of symbols, see Economic Census Data Dictionary..Data-Specific Notes.Data users who create their own es...

As of March 2025, Google represented 79.1 percent of the global online search engine market on desktop devices. Despite being much ahead of its competitors, this represents the lowest share ever recorded by the search engine in these devices for over two decades. Meanwhile, its long-time competitor Bing accounted for 12.21 percent, as tools like Yahoo and Yandex held shares of over 2.9 percent each. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of 2.02 trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly 348.16 billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than 63 percent of internet users in Russia used Yandex, whereas Google users represented little over 33 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 21 percent of users in Mexico said they used Yahoo.

This collection contains time series data, spanning 35 years, on general social indicators. The file includes data on vital statistics, population, the labor force, health, education, and leisure activities. Variables on vital statistics cover birth and death rates and life expectancy. Variables on population cover marriage, divorce, and age by sex. Labor force data contain information on employment and unemployment, percentages of the population employed in certain occupations, labor turnover, and family income. Health-related data include hospital and doctor visits, prescription costs, and nursing home expenses. Education data cover degrees conferred, educational attainment rates, and number of children enrolled in kindergarten and primary and secondary schools. Data on leisure activities cover sporting event attendance, automobile ownership, and utility consumption. Most of the data are reported on an annual basis extending from 1946 through 1980. Some data, such as the education data, are based on a less frequent observation cycle, and other data, such as health data, do not extend back to 1946. The study consists of a documentation file and a data file. The documentation contains a list of the variables, an alphabetized listing of the variables, and SPSS descriptives. The data file contains both the SPSS control cards and the data.

Data provided here is used by WDFW’s partners, government entities, schools, private businesses, and the general public. WDFW actively promotes inter-agency data exchange and resource sharing. Every effort is made to provide accurate, complete, and timely information on this site. However, some content may be incomplete or out of date. The content on this site is subject to change without notice. The Washington Department of Fish and Wildlife (WDFW) shall not be liable for any activity involving this data with regard to lost profits or savings or any other consequential damages; or the fitness for use of the data for a particular purpose; or the installation of the data, its use, or the results obtained.

Errata: Due to a coding error, monthly files with "dma8epax" statistics were wrongly aggregated. This concerns all gridded files of this metric as well as the monthly aggregated csv files. All erroneous files were replaced with corrected versions on Jan, 16th, 2018. Each updated file contains a version label "1.1" and a brief description of the error. If you have made use of previous TOAR data files with the "dma8epax" metric, please exchange your data files. Furthermore, some data were exchanged in December 2018 in the yearly statistics and aggregated data sets to avoid user confusion. While metrics had been correctly calculated, most users expected to find average "dma8" values when in fact 4th highest (in the case of dma8eu 26-highest) values were provided. Therefore, some data columns of "dma8epax" were replaced with "avgdma8epax" and the variable descriptions in the metadata were improved. As no actual new calculation of metrics took place, we assigned the same version label 1.1 to these data. Please make sure to use "avgdma8epax" data when you want to analyze average daily 8-hour maximum concentration values.

Abstract

In 2001, the World Bank in co-operation with the Republika Srpska Institute of Statistics (RSIS), the Federal Institute of Statistics (FOS) and the Agency for Statistics of BiH (BHAS), carried out a Living Standards Measurement Survey (LSMS). The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows:

1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs.

2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household.

3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analyzed data.

The Department for International Development, UK (DFID) contributed funding to the LSMS and provided funding for a further two years of data collection for a panel survey, known as the Household Survey Panel Series (HSPS). Birks Sinclair & Associates Ltd. were responsible for the management of the HSPS with technical advice and support provided by the Institute for Social and Economic Research (ISER), University of Essex, UK. The panel survey provides longitudinal data through re-interviewing approximately half the LSMS respondents for two years following the LSMS, in the autumn of 2002 and 2003. The LSMS constitutes Wave 1 of the panel survey so there are three years of panel data available for analysis. For the purposes of this documentation we are using the following convention to describe the different rounds of the panel survey: - Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel
- Wave 2 Second interview of 50% of LSMS respondents in Autumn/ Winter 2002 - Wave 3 Third interview with sub-sample respondents in Autumn/ Winter 2003

The panel data allows the analysis of key transitions and events over this period such as labour market or geographical mobility and observe the consequent outcomes for the well-being of individuals and households in the survey. The panel data provides information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty is experienced by different types of households and individuals over the three year period. And most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within FBiH and RS at a time of social reform and rapid change.

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation; Republic

Kind of data

Sample survey data [ssd]

Sampling procedure

The Wave 3 sample consisted of 2878 households who had been interviewed at Wave 2 and a further 73 households who were interviewed at Wave 1 but were non-contact at Wave 2 were issued. A total of 2951 households (1301 in the RS and 1650 in FBiH) were issued for Wave 3. As at Wave 2, the sample could not be replaced with any other households.

Panel design

Eligibility for inclusion

The household and household membership definitions are the same standard definitions as a Wave 2. While the sample membership status and eligibility for interview are as follows: i) All members of households interviewed at Wave 2 have been designated as original sample members (OSMs). OSMs include children within households even if they are too young for interview. ii) Any new members joining a household containing at least one OSM, are eligible for inclusion and are designated as new sample members (NSMs). iii) At each wave, all OSMs and NSMs are eligible for inclusion, apart from those who move outof-scope (see discussion below). iv) All household members aged 15 or over are eligible for interview, including OSMs and NSMs.

Following rules

The panel design means that sample members who move from their previous wave address must be traced and followed to their new address for interview. In some cases the whole household will move together but in others an individual member may move away from their previous wave household and form a new split-off household of their own. All sample members, OSMs and NSMs, are followed at each wave and an interview attempted. This method has the benefit of maintaining the maximum number of respondents within the panel and being relatively straightforward to implement in the field.

Definition of 'out-of-scope'

It is important to maintain movers within the sample to maintain sample sizes and reduce attrition and also for substantive research on patterns of geographical mobility and migration. The rules for determining when a respondent is 'out-of-scope' are as follows:

i. Movers out of the country altogether i.e. outside FBiH and RS. This category of mover is clear. Sample members moving to another country outside FBiH and RS will be out-of-scope for that year of the survey and not eligible for interview.

ii. Movers between entities Respondents moving between entities are followed for interview. The personal details of the respondent are passed between the statistical institutes and a new interviewer assigned in that entity.

iii. Movers into institutions Although institutional addresses were not included in the original LSMS sample, Wave 3 individuals who have subsequently moved into some institutions are followed. The definitions for which institutions are included are found in the Supervisor Instructions.

iv. Movers into the district of Brcko are followed for interview. When coding entity Brcko is treated as the entity from which the household who moved into Brcko originated.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaire design

Approximately 90% of the questionnaire (Annex B) is based on the Wave 2 questionnaire, carrying forward core measures that are needed to measure change over time. The questionnaire was widely circulated and changes were made as a result of comments received.

Pretesting

In order to undertake a longitudinal test the Wave 2 pretest sample was used. The Control Forms and Advance letters were generated from an Access database containing details of ten households in Sarajevo and fourteen in Banja Luka. The pretest was undertaken from March 24-April 4 and resulted in 24 households (51 individuals) successfully interviewed. One mover household was successfully traced and interviewed.
In order to test the questionnaire under the hardest circumstances a briefing was not held. A list of the main questionnaire changes was given to experienced interviewers.

Issues arising from the pretest

Interviewers were asked to complete a Debriefing and Rating form. The debriefing form captured opinions on the following three issues:

1. General reaction to being re-interviewed. In some cases there was a wariness of being asked to participate again, some individuals asking “Why Me?” Interviewers did a good job of persuading people to take part, only one household refused and another asked to be removed from the sample next year. Having the same interviewer return to the same households was considered an advantage. Most respondents asked what was the benefit to them of taking part in the survey. This aspect was reemphasised in the Advance Letter, Respondent Report and training of the Wave 3 interviewers.

2. Length of the questionnaire. The average time of interview was 30 minutes. No problems were mentioned in relation to the timing, though interviewers noted that some respondents, particularly the elderly, tended to wonder off the point and that control was needed to bring them back to the questions in the questionnaire. One interviewer noted that the economic situation of many respondents seems to have got worse from the previous year and it was necessary to listen to respondents “stories” during the interview.

3. Confidentiality. No problems were mentioned in relation to confidentiality. Though interviewers mentioned it might be worth mentioning the new Statistics Law in the Advance letter. The Rating Form asked for details of specific questions that were unclear. These are described below with a description of the changes made.

• Module 3. Q29-31 have been added to capture funds received for education, scholarships etc.

• Module 4. Pretest respondents complained that the 6 questions on "Has your health limited you..." and the 16 on "in the last 7 days have you felt depressed” etc were too many. These were reduced by half (Q38-Q48). The LSMS data was examined and those questions where variability between the answers was widest were chosen.

• Module 5. The new employment questions (Q42-Q44) worked well and have been kept in the main questionnaire.

• Module 7. There were no problems reported with adding the credit questions (Q28-Q36)

• Module 9. SIG recommended that some of Questions 1-12 were relevant only to those aged over 18 so additional skips have been added. Some respondents complained the questionnaire was boring. To try and overcome

This dataset presents data on family and community available from the ABS Data by Region statistics. This release of Data by Region presents various data for 2011-2018 and Census of Population and Housing data for 2011 and 2016 and is based on the Local Government Area (LGA) 2019 boundaries. The dataset includes information in the following specified areas of family and community: Gifts/Donations Reported by Taxpayers, Method of Travel to Work, Households, Families, Count of All Children in Family, Social Marital Status & Registered Marital Status, Unpaid Work, Access to Internet at Home, Housing Suitability, Dwelling Structure, Tenure Type, Rent and Mortgage Payments, Housing Stress, Commuting to Work and Socio Economic Indexes of Areas (SEIFA).

Data by Region contains a standard set of data for each region type, depending on the availability of statistics for particular geographies. Data are sourced from a wide variety of collections, both ABS and non-ABS. When analysing these statistics, care needs to be taken as time periods, definitions, methodologies, scope and coverage can differ across collections. Where available, data have been presented as a time series - to enable users to assess changes over time. However, when looked at on a period to period basis, some series may sometimes appear volatile. When analysing the data, users are encouraged to consider the longer term behaviour of the series, where this extra information is available.

For more information please visit the Explanatory Notes.

AURIN has made the following changes to the original data:

• Spatially enabled the original data with the ABS Australian Statistical Geography Standard (ASGS) LGA 2019 dataset.

• Some data values in Data by Region have been randomly adjusted or suppressed to avoid the release of confidential details.

• Where data was not available, not available for publication, nil or rounded to zero in the original data, it has been set to null.

• Columns and rows that did not contain any values in the original data have been removed.

This publication provides the most timely picture available of people using NHS funded secondary mental health, learning disabilities and autism services in England. These are experimental statistics which are undergoing development and evaluation. This information will be of use to people needing access to information quickly for operational decision making and other purposes. More detailed information on the quality and completeness of these statistics is made available later in our Mental Health Bulletin: Annual Report publication series. • COVID-19 and the production of statistics Due to the coronavirus illness (COVID-19) disruption, it would seem that this is now starting to affect the quality and coverage of some of our statistics, such as an increase in non-submissions for some datasets. We are also starting to see some different patterns in the submitted data. For example, fewer patients are being referred to hospital and more appointments being carried out via phone/telemedicine/email. Therefore, data should be interpreted with care over the COVID-19 period. • Update on previously unpublished measures 72 hour follow-up measures (MHS78, MHS79 and MHS80) have now been released for Performance October 2020. Data for these measures for previous reporting periods will be published as soon as they are available. NHS Digital apologises for the inconvenience caused. • Early release of statistics To support the ongoing COVID-19 work, Provisional November 2020 monthly statistics were made available early and presented on our supplementary information pages. https://digital.nhs.uk/data-and-information/supplementary-information/2020/provisional-november-2020-mental-health-statistics • Changing existing measures The move to MHSDS version 4.1 from April 2020 has brought with it changes to the dataset; the construction of a number of measures have been changed as a result. Improvements in the methodology of reporting delay of discharge has also resulted in a change in the construction of the measure from Performance April 2020 onwards. From Performance August 2020 onwards, the methodology for calculating restrictive interventions (MHS76 and MHS77) in the reporting month has been updated to include all restraints that span several months. Previously the measure only includes restraints that started or ended in the month and did not include those spanning more than 2 months. This change predominately impacts segregation. From Performance September 2020 onwards, the methodology for MHS26 (Delayed transfer of care) has been updated. Previously only the first midnight of a delayed transfer of care within a hospital provider spell was excluded. The updated methodology now excludes the first midnight for delay start date where there is a gap of more than one day within the same hospital provider spell. This is to take into account valid gaps between delay reasons. Full details of these changes are available in the associated Metadata file. From Performance September 2020 onwards, a presentational change has been introduced to restrictive interventions csv files to exclude providers with no inpatients. • New measures A number of new measures have been included from Performance July 2020 onwards: - MHS81 Number of Detentions - MHS82 Number of Short Term Orders - MHS83 Number of uses of Section 136 - MHS84 Number of Community Treatment Orders Full details of these are available in the associated Metadata file.