A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

This dataset is about artists. It has 1 row and is filtered where the artworks is Family Group - Earth Red and Yellow. It features 9 columns including birth date, death date, country, and gender.

Philippines Number of Families: National Capital Region (NCR) data was reported at 3,019,000.000 Unit in 2015. This records an increase from the previous number of 2,917,000.000 Unit for 2012. Philippines Number of Families: National Capital Region (NCR) data is updated yearly, averaging 2,240,837.500 Unit from Dec 1988 (Median) to 2015, with 10 observations. The data reached an all-time high of 3,019,000.000 Unit in 2015 and a record low of 1,435,436.000 Unit in 1988. Philippines Number of Families: National Capital Region (NCR) data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H014: Family Income and Expenditure Survey: No of Families: By Income Class and Main Source of Income.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Blue Earth, MN, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Blue Earth did not include 3, 6, or 7-person households. Across the different household sizes in Blue Earth the mean income is $82,072, and the standard deviation is $50,457. The coefficient of variation (CV) is 61.48%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $24,432. It then further increased to $72,826 for 5-person households, the largest household size for which the bureau reported a median household income.

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Blue Earth median household income. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in Globe, AZ, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Globe did not include 6, or 7-person households. Across the different household sizes in Globe the mean income is $68,554, and the standard deviation is $29,234. The coefficient of variation (CV) is 42.64%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $27,069. It then further increased to $97,625 for 5-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/globe-az-median-household-income-by-household-size.jpeg" alt="Globe, AZ median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Globe median household income. You can refer the same here

This dataset presents the percentage of women of reproductive age (typically 15–49 years) who want to avoid or delay pregnancy but are not using any method of contraception. Unmet need for family planning is a key indicator of gaps in access to sexual and reproductive health services and is linked to higher rates of unintended pregnancy and maternal health risks. Reducing unmet need is essential to advancing reproductive rights, gender equality, and informed choice. This indicator helps identify inequities and guide investment in voluntary, rights-based family planning services. Data are sourced from the UNFPA World Population Dashboard.UNFPA world population dashboard: https://www.unfpa.org/data/world-population-dashboardThis is one of many datasets featured on the Midwives’ Data Hub, a digital platform designed to strengthen midwifery and advocate for better maternal and newborn health services.

THIS DATASET WAS LAST UPDATED AT 8:11 PM EASTERN ON JULY 30

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

The Comparative Political Economy Database (CPEDB) began at the Centre for Learning, Social Economy and Work (CLSEW) at the Ontario Institute for Studies in Education at the University of Toronto (OISE/UT) as part of the Changing Workplaces in a Knowledge Economy (CWKE) project. This data base was initially conceived and developed by Dr. Wally Seccombe (independent scholar) and Dr. D.W. Livingstone (Professor Emeritus at the University of Toronto). Seccombe has conducted internationally recognized historical research on evolving family structures of the labouring classes (A Millennium of Family Change: Feudalism to Capitalism in Northwestern Europe and Weathering the Storm: Working Class Families from the Industrial Revolution to the Fertility Decline). Livingstone has conducted decades of empirical research on class and labour relations. A major part of this research has used the Canadian Class Structure survey done at the Institute of Political Economy (IPE) at Carleton University in 1982 as a template for Canadian national surveys in 1998, 2004, 2010 and 2016, culminating in Tipping Point for Advanced Capitalism: Class, Class Consciousness and Activism in the ‘Knowledge Economy’ (https://fernwoodpublishing.ca/book/tipping-point-for-advanced-capitalism) and a publicly accessible data base including all five of these Canadian surveys (https://borealisdata.ca/dataverse/CanadaWorkLearningSurveys1998-2016). Seccombe and Livingstone have collaborated on a number of research studies that recognize the need to take account of expanded modes of production and reproduction. Both Seccombe and Livingstone are Research Associates of CLSEW at OISE/UT. The CPEDB Main File (an SPSS data file) covers the following areas (in order): demography, family/household, class/labour, government, electoral democracy, inequality (economic, political & gender), health, environment, internet, macro-economic and financial variables. In its present form, it contains annual data on 725 variables from 12 countries (alphabetically listed): Canada, Denmark, France, Germany, Greece, Italy, Japan, Norway, Spain, Sweden, United Kingdom and United States. A few of the variables date back to 1928, and the majority date from 1960 to 1990. Where these years are not covered in the source, a minority of variables begin with more recent years. All the variables end at the most recent available year (1999 to 2022). In the next version developed in 2025, the most recent years (2023 and 2024) will be added whenever they are present in the sources’ datasets. For researchers who are not using SPSS, refer to the Chart files for overviews, summaries and information on the dataset. For a current list of the variable names and their labels in the CPEDB data base, see the excel file: Outline of SPSS file Main CPEDB, Nov 6, 2023. At the end of each variable label in this file and the SPSS datafile, you will find the source of that variable in a bracket. If I have combined two variables from a given source, the bracket will begin with WS and then register the variables combined. In the 14 variables David created at the beginning of the Class Labour section, you will find DWL in these brackets with his description as to how it was derived. The CPEDB’s variables have been derived from many databases; the main ones are OECD (their Statistics and Family Databases), World Bank, ILO, IMF, WHO, WIID (World Income Inequality Database), OWID (Our World in Data), Parlgov (Parliaments and Governments Database), and V-Dem (Varieties of Democracy). The Institute for Political Economy at Carleton University is currently the main site for continuing refinement of the CPEDB. IPE Director Justin Paulson and other members are involved along with Seccombe and Livingstone in further development and safe storage of this updated database both at the IPE at Carleton and the UT dataverse. All those who explore the CPEDB are invited to share their perceptions of the entire database, or any of its sections, with Seccombe generally (wseccombe@sympatico.ca) and Livingstone for class/labour issues (davidlivingstone@utoronto.ca). They welcome any suggestions for additional variables together with their data sources. A new version CPEDB will be created in the spring of 2025 and installed as soon as the revision is completed. This revised version is intended to be a valuable resource for researchers in all of the included countries as well as Canada.

NoSQL Database Market Outlook 2032

The global NoSQL database market size was USD 5.9 Billion in 2023 and is likely to reach USD 36.6 Billion by 2032, expanding at a CAGR of 30% during 2024–2032. The market growth is attributed to the rising adoption of NoSQL databases by industries to manage large amounts of data efficiently.



Increasing adoption of digital solutions by businesses is augmenting the NoSQL database industry. Businesses continue using the unique capabilities that NoSQL databases bring to their data management strategies. The NoSQL solutions work without any predefined schemas, thus, offering more flexibility to businesses that need to handle and manage ever-evolving data types and formats.

The factors behind the accelerating growth of the NoSQL database market include the omnipresence of internet-related activities, a surge in big data, and others. NoSQL database solutions present exceptional scalability and offer superior performance while managing extensive datasets. Moreover, the shift from conventional SQL databases to NoSQL databases to handle big-data and real-time web application data augmented the market.

Impact of Artificial Intelligence (AI) on the NoSQL Database Market

Artificial Intelligence (AI) has a significant impact on the NoSQL databases market by creating a surge in data volume and variety. AI technologies, including machine learning and deep learning, generate and process vast amounts of data, necessitating efficient data management solutions. The integration of AI with NoSQL databases further enhances data analysis capabilities and enables businesses to acquire valuable insights and make informed decisions. Therefore, the rise of AI technologies is propelling the market.

Non-Relational Databases, commonly referred to as NoSQL databases, have gained significant traction in recent years due to their ability to handle diverse data types and structures. Unlike traditional relational databases, non-relational databases do not rely on a fixed schema, which allows for greater flexibility and scalability. This adaptability is particularly beneficial for businesses dealing with large volumes of unstructured data, such as social media content, customer reviews, and multimedia files. As organizations continue to embrace digital transformation, the demand for non-relational databases is expected to rise, further driving the growth of the NoSQL database market.

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About this dataset

Context

The dataset presents median household incomes for various household sizes in Black Earth Town, Wisconsin, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Black Earth town did not include 5, 6, or 7-person households. Across the different household sizes in Black Earth town the mean income is $129,146, and the standard deviation is $42,288. The coefficient of variation (CV) is 32.74%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $85,122. It then further increased to $185,106 for 4-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/black-earth-town-wi-median-household-income-by-household-size.jpeg" alt="Black Earth Town, Wisconsin median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Black Earth town median household income. You can refer the same here

This dataset is about books. It has 1 row and is filtered where the book is How to play the 200 best-ever card games : a fantastic compendium of the greatest card games from around the world, including the history, rules, and winning strategies for each game, with more than 400 colour images : everything from fun games and simple ways to get started for beginners and family players, to professional tips and expert guidance for advanced play in serious games of chance. It features 7 columns including author, publication date, language, and book publisher.

Household surveys:

Subnational information from different available household surveys of farmers and smallholders in developing and emerging countries.

Household data provides an overview on farmer households livelihoods, decisions, constraints, among other dimensions. One of the main purposes of this suite of data is to provide farmer information disaggregated at different subnational levels, as well as georeferenced information, when available.

The surveys available in this section provide information divided in ten main dimensions: Production, Consumption, Income, Capital, Inputs, Access to markets, Labor, Technology adoption, Infrastructure, and Social.

Currently available is the Data Portrait of Small Family Farms, more will be added.

Data Portrait:

The Data Portrait of Small Family Farms is a project developed by FAO with the objective to set the ground for a standardized definition of smallholders across countries as well as provide consistent measures of inputs, production, sociodemographic characteristics of smallholder farmers across the world. It generates an image on how small family farmers in developing and emerging countries live their lives, putting in numbers the constraints they face, and the choices they make so that policies can be informed by evidence to meet the challenge of agricultural development.

The Data Portrait of Small Family Farms makes use of household surveys developed by national statistical offices in conjunction with the World Bank as part of its Living Standards Measurement Study (LSMS).

The Data Portrait of Small Family Farms collected data for 19 countries across the world, and for some of them data was reported for more than one round, resulting in a total of 29 surveys. The following table shows the sources of the data. Country and year available information is also presented. Find the link to the table here

This dataset is the aggregation of the following datasets;

1. Household Survey (Data Portrait - Admin 1 - GADM - 1998-2012)
2. Household Survey (Data Portrait - Admin 2 - GADM - 1998-2013)
3. Household Survey (Data Portrait - Admin 3 - GADM - 1992-2013)
4. Household Survey (Data Portrait - Admin 1 - GAUL - 1992-2012)
5. Household Survey (Data Portrait - Admin 2 - GAUL - 1992-2012)

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Children/households receiving child/family cash benefit (%)





  Dataset Description

This dataset provides country-level data for the indicator "1.3.1 Children/households receiving child/family cash benefit (%)" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable Development Goals (SDGs). The data is… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/children-households-receiving-child-family-cash-benefit-for-african-countries.

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

The GBIF Backbone Taxonomy is a single, synthetic management classification with the goal of covering all names GBIF is dealing with. It's the taxonomic backbone that allows GBIF to integrate name based information from different resources, no matter if these are occurrence datasets, species pages, names from nomenclators or external sources like EOL, Genbank or IUCN. This backbone allows taxonomic search, browse and reporting operations across all those resources in a consistent way and to provide means to crosswalk names from one source to another.

It is updated regulary through an automated process in which the Catalogue of Life acts as a starting point also providing the complete higher classification above families. Additional scientific names only found in other authoritative nomenclatural and taxonomic datasets are then merged into the tree, thus extending the original catalogue and broadening the backbones name coverage. The GBIF Backbone taxonomy also includes identifiers for Operational Taxonomic Units (OTUs) drawn from the barcoding resources iBOL and UNITE.

International Barcode of Life project (iBOL), Barcode Index Numbers (BINs). BINs are connected to a taxon name and its classification by taking into account all names applied to the BIN and picking names with at least 80% consensus. If there is no consensus of name at the species level, the selection process is repeated moving up the major Linnaean ranks until consensus is achieved.

UNITE - Unified system for the DNA based fungal species, Species Hypotheses (SHs). SHs are connected to a taxon name and its classification based on the determination of the RefS (reference sequence) if present or the RepS (representative sequence). In the latter case, if there is no match in the UNITE taxonomy, the lowest rank with 100% consensus within the SH will be used.

The GBIF Backbone Taxonomy is available for download at https://hosted-datasets.gbif.org/datasets/backbone/ in different formats together with an archive of all previous versions.

The following 105 sources have been used to assemble the GBIF backbone with number of names given in brackets:

• Catalogue of Life Checklist - 4766428 names
• International Barcode of Life project (iBOL) Barcode Index Numbers (BINs) - 635951 names
• UNITE - Unified system for the DNA based fungal species linked to the classification - 611208 names
• The Paleobiology Database - 212054 names
• World Register of Marine Species - 188857 names
• The Interim Register of Marine and Nonmarine Genera - 183894 names
• The World Checklist of Vascular Plants (WCVP) - 131891 names
• GBIF Backbone Taxonomy - 114350 names
• TAXREF - 109374 names
• The Leipzig catalogue of vascular plants - 75380 names
• ZooBank - 73549 names
• Integrated Taxonomic Information System (ITIS) - 68377 names
• Plazi.org taxonomic treatments database - 61346 names
• Genome Taxonomy Database r207 - 60545 names
• International Plant Names Index - 52329 names
• Fauna Europaea - 45077 names
• The National Checklist of Taiwan (Catalogue of Life in Taiwan, TaiCoL) - 36193 names
• Dyntaxa. Svensk taxonomisk databas - 35892 names
• The Plant List with literature - 32692 names
• United Kingdom Species Inventory (UKSI) - 29643 names
• Artsnavnebasen - 29208 names
• The IUCN Red List of Threatened Species - 21221 names
• Afromoths, online database of Afrotropical moth species (Lepidoptera) - 13961 names
• Brazilian Flora 2020 project - Projeto Flora do Brasil 2020 - 13829 names
• Prokaryotic Nomenclature Up-to-Date (PNU) - 10079 names
• Checklist Dutch Species Register - Nederlands Soortenregister - 8814 names
• ICTV Master Species List (MSL) - 7852 names
• Cockroach Species File - 6020 names
• GRIN Taxonomy - 5882 names
• Taxon list of fungi and fungal-like organisms from Germany compiled by the DGfM - 4570 names
• Catalogue of Afrotropical Bees - 3623 names
• Catalogue of Tenebrionidae (Coleoptera) of North America - 3327 names
• Checklist of Beetles (Coleoptera) of Canada and Alaska. Second Edition. - 3312 names
• Systema Dipterorum - 2850 names
• Catalogue of the Pterophoroidea of the World - 2807 names
• The Clements Checklist - 2675 names
• Taxon list of Hymenoptera from Germany compiled in the context of the GBOL project - 2496 names
• IOC World Bird List, v13.2 - 2366 names
• Official Lists and Indexes of Names in Zoology - 2310 names
• National checklist of all species occurring in Denmark - 1922 names
• Myriatrix - 1876 names
• Database of Vascular Plants of Canada (VASCAN) - 1822 names
• Taxon list of vascular plants from Bavaria, Germany compiled in the context of the BFL project - 1771 names
• Orthoptera Species File - 1742 names
• A list of the terrestrial fungi, flora and fauna of Madeira and Selvagens archipelagos - 1602 names
• Aphid Species File - 1565 names
• World Spider Catalog - 1561 names
• Taxon list of Jurassic Pisces of the Tethys Palaeo-Environment compiled at the SNSB-JME - 1270 names
• Backbone Family Classification Patch - 1143 names
• GBIF Algae Classification - 1100 names
• International Cichorieae Network (ICN): Cichorieae Portal - 975 names
• Psocodea Species File - 803 names
• New Zealand Marine Macroalgae Species Checklist - 787 names
• Annotated checklist of endemic species from the Western Balkans - 754 names
• Taxon list of animals with German names (worldwide) compiled at the SMNS - 503 names
• Catalogue of the Alucitoidea of the World - 472 names
• Lygaeoidea Species File - 462 names
• Catálogo de Plantas y Líquenes de Colombia - 422 names
• GBIF Backbone Patch - 317 names
• Phasmida Species File - 259 names
• Cortinariaceae fetched from the Index Fungorum API - 234 names
• Coreoidea Species File - 233 names
• GTDB supplement - 139 names
• Mantodea Species File - 119 names
• Endemic species in Taiwan - 93 names
• Taxon list of Araneae from Germany compiled in the context of the GBOL project - 88 names
• Species of Hominidae - 78 names
• Taxon list of Sternorrhyncha from Germany compiled in the context of the GBOL project - 77 names
• Taxon list of mosses from Germany compiled in the context of the GBOL project - 75 names
• Mammal Species of the World - 73 names
• Plecoptera Species File - 71 names
• Species Fungorum Plus - 64 names
• Catalogue of the type specimens of Cosmopterigidae (Lepidoptera: Gelechioidea) from research collections of the Zoological Institute, Russian Academy of Sciences - 47 names
• Species named after famous people - 41 names
• Dermaptera Species File - 36 names
• Taxon list of Trichoptera from Germany compiled in the context of the GBOL project - 34 names
• True Fruit Flies (Diptera, Tephritidae) of the Afrotropical Region - 33 names
• Range and Regularities in the Distribution of Earthworms of the Earthworms of the USSR Fauna. Perel, 1979 - 32 names
• Taxon list of Diplura from Germany compiled in the context of the GBOL project - 30 names
• Lista de referencia de especies de aves de Colombia - 2022 - 24 names
• Taxon list of Auchenorrhyncha from Germany compiled in the context of the GBOL project - 20 names
• Catalogue of the type specimens of Polycestinae (Coleoptera: Buprestidae) from research collections of the Zoological Institute, Russian Academy of Sciences - 19 names
• Taxon list of Thysanoptera from Germany compiled in the context of the GBOL project - 19 names
• Lista de especies de vertebrados registrados en jurisdicción del Departamento del Huila - 18 names
• Taxon list of Microcoryphia (Archaeognatha) from Germany compiled in the context of the GBOL project - 15 names
• Catalogue of the type specimens of Bufonidae and Megophryidae (Amphibia: Anura) from research collections of the Zoological Institute, Russian Academy of Sciences - 12 names
• Grylloblattodea Species File - 11 names
• Coleorrhyncha Species File - 9 names
• Taxon list of liverworts from Germany compiled in the context of the GBOL project - 9 names
• Embioptera Species File - 7 names
• Taxon list of Pisces and Cyclostoma from Germany compiled in the context of the GBOL project - 6 names
• Taxon list of Pteridophyta from Germany compiled in the context of the GBOL project - 6 names
• Taxon list of Siphonaptera from Germany compiled in the context of the GBOL project - 5 names
• The Earthworms of the Fauna of Russia. Perel, 1997 - 5 names
• Taxon list of Zygentoma from Germany compiled in the context of the GBOL project - 4 names
• Asiloid Flies: new taxa of Diptera: Apioceridae, Asilidae, and Mydidae - 3 names
• Taxon list of Protura from Germany compiled in the context of the GBOL project - 3 names
• Taxon list of hornworts from Germany compiled in the context of the GBOL project - 2 names
• Chrysididae Species File - 1 names
• Taxon list of Dermaptera from Germany compiled in the context of the GBOL project - 1 names
• Taxon list of Diplopoda from Germany in the context of the GBOL project - 1 names
• Taxon list of Orthoptera (Grashoppers) from Germany compiled at the SNSB - 1 names
• Taxon list of Pscoptera from Germany compiled in the context of the GBOL project - 1 names
• Taxon list of Pseudoscorpiones from Germany compiled in the context of the GBOL project - 1 names
• Taxon list of Raphidioptera from Germany compiled in the context of the GBOL project - 1 names

Iran IR: Contributing Family Workers: Modeled ILO Estimate: Female: % of Female Employment data was reported at 17.288 % in 2017. This records a decrease from the previous number of 19.773 % for 2016. Iran IR: Contributing Family Workers: Modeled ILO Estimate: Female: % of Female Employment data is updated yearly, averaging 26.715 % from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 35.254 % in 2005 and a record low of 17.288 % in 2017. Iran IR: Contributing Family Workers: Modeled ILO Estimate: Female: % of Female Employment data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank.WDI: Employment and Unemployment. Contributing family workers are those workers who hold 'self-employment jobs' as own-account workers in a market-oriented establishment operated by a related person living in the same household.; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

This report, which is part of a four-volume series, provides a cross-national comparison of the social security systems in 48 countries in Africa. It summarizes the five main social insurance programs in those countries: old-age, disability, and survivors; sickness and maternity; work injury; unemployment; and family allowances. The other regional volumes in the series focus on the social security systems of countries in Europe, Asia and the Pacific, and the Americas. Together, the reports provide important information for researchers and policymakers who are reviewing different ways of approaching social security challenges and adapting the systems to the evolving needs of individuals, households, and families.

Analysis of ‘🏠 Construction Price Indexes’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/construction-price-indexese on 13 February 2022.

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

About this dataset

The Construction Price Indexes provide price indexes for single-family houses sold and for single-family houses under construction. The houses sold index incorporates the value of the land and is available quarterly at the national level and annually by region. The indexes for houses under construction are available monthly at the national level. The indexes are based on data funded by HUD and collected in the Survey of Construction (SOC).

Source: https://catalog.data.gov/dataset/construction-price-indexes

This dataset was created by Finance and contains around 100 samples along with Unnamed: 17, Price Indexes Of New Single Family Houses Sold Including Lot Value, technical information and other features such as: - Unnamed: 9 - Unnamed: 1 - and more.

How to use this dataset

• Analyze Unnamed: 14 in relation to Unnamed: 16
• Study the influence of Unnamed: 7 on Unnamed: 5
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

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