Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents the median household income across different racial categories in Country Club. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.
Key observations
Based on our analysis of the distribution of Country Club population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 87.01% of the total residents in Country Club. Notably, the median household income for White households is $81,932. Interestingly, despite the White population being the most populous, it is worth noting that Two or More Races households actually reports the highest median household income, with a median income of $145,089. This reveals that, while Whites may be the most numerous in Country Club, Two or More Races households experience greater economic prosperity in terms of median household income.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Racial categories include:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Country Club median household income by race. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Country Club Hills. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.
Key observations: Insights from 2021
Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Country Club Hills, the median income for all workers aged 15 years and older, regardless of work hours, was $26,873 for males and $24,771 for females.
Based on these incomes, we observe a gender gap percentage of approximately 8%, indicating a significant disparity between the median incomes of males and females in Country Club Hills. Women, regardless of work hours, still earn 92 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.
- Full-time workers, aged 15 years and older: In Country Club Hills, among full-time, year-round workers aged 15 years and older, males earned a median income of $43,899, while females earned $33,346, leading to a 24% gender pay gap among full-time workers. This illustrates that women earn 76 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.Remarkably, across all roles, including non-full-time employment, women displayed a lower gender pay gap percentage. This indicates that Country Club Hills offers better opportunities for women in non-full-time positions.
https://i.neilsberg.com/ch/country-club-hills-mo-income-by-gender.jpeg" alt="Country Club Hills, MO gender based income disparity">
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.
Gender classifications include:
Employment type classifications include:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Country Club Hills median household income by gender. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
BackgroundOne of the greatest obstacles facing efforts to address quality of care in low and middle income countries is the absence of relevant and reliable data. This article proposes a methodology for creating a single “Quality Index” (QI) representing quality of maternal and neonatal health care based upon data collected as part of the Demographic and Health Survey (DHS) program.MethodsUsing the 2012 Indonesian Demographic and Health Survey dataset, indicators of quality of care were identified based on the recommended guidelines outlined in the WHO Integrated Management of Pregnancy and Childbirth. Two sets of indicators were created; one set only including indicators available in the standard DHS questionnaire and the other including all indicators identified in the Indonesian dataset. For each indicator set composite indices were created using Principal Components Analysis and a modified form of Equal Weighting. These indices were tested for internal coherence and robustness, as well as their comparability with each other. Finally a single QI was chosen to explore the variation in index scores across a number of known equity markers in Indonesia including wealth, urban rural status and geographical region.ResultsThe process of creating quality indexes from standard DHS data was proven to be feasible, and initial results from Indonesia indicate particular disparities in the quality of care received by the poor as well as those living in outlying regions.ConclusionsThe QI represents an important step forward in efforts to understand, measure and improve quality of MNCH care in developing countries.
The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. The purpose is to provide an open access, well documented global data set of roads between settlements using a consistent data model (UNSDI-T v.2) which is, to the extent possible, topologically integrated.Dataset SummaryThe Global Roads Open Access Data Set, Version 1 (gROADSv1) was developed under the auspices of the CODATA Global Roads Data Development Task Group. The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. All country road networks have been joined topologically at the borders, and many countries have been edited for internal topology. Source data for each country are provided in the documentation, and users are encouraged to refer to the readme file for use constraints that apply to a small number of countries. Because the data are compiled from multiple sources, the date range for road network representations ranges from the 1980s to 2010 depending on the country (most countries have no confirmed date), and spatial accuracy varies. The baseline global data set was compiled by the Information Technology Outreach Services (ITOS) of the University of Georgia. Updated data for 27 countries and 6 smaller geographic entities were assembled by Columbia University's Center for International Earth Science Information Network (CIESIN), with a focus largely on developing countries with the poorest data coverage.Documentation for the Global Roads Open Access Data Set, Version 1 (gROADSv1)Recommended CitationCenter for International Earth Science Information Network - CIESIN - Columbia University, and Information Technology Outreach Services - ITOS - University of Georgia. 2013. Global Roads Open Access Data Set, Version 1 (gROADSv1). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4VD6WCT. Accessed DAY MONTH YEAR.
Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.
Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.
Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".
Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
License information was derived automatically
For sale are domain names that were registered between Feb 24, 2018 and Feb 28, 2018 by registrants in United States. Domains which obfuscate registrant, administrative, and other WHO IS contact details have been omitted from this dataset. The following information is availble for download in this dataset: - Domain name, Created Date, Updated Date, Expiration Date, Registrar Name- Registrant Company, Name, Address, City, State/Province/Other, Postal Code, Country, Email, Phone #, Fax #- Administrative Company, Name, Address, City, State/Province/Other, Postal Code, Country, Email, Phone #, Fax #- Technical Company, Name, Address, City, State/Province/Other, Postal Code, Country, Email, Phone #, Fax #- Billing Company, Name, Address, City, State/Province/Other, Postal Code, Country, Email, Phone #, Fax #- NameServer1, NameServer2, NameServer3, NameServer4, - DomainStatus1, DomainStatus2, DomainStatus3, DomainStatus4 Still unsure about purchasing this dataset? View and Download a free sample dataset of global domain name registrations - https://www.dataandsons.com/categories/lead_generation/domain-names-registered-between-feb-12-2018-to-feb-18-2018. Are you interested in a more targeted domain name registration dataset? Select the "Ask Seller a Question" link, send me a message, and I'll get back to you as soon as I can.
Lead Generation
USA,United-States,newly-registered-domain-names,recently-registered-domain-names,who-is-data
84127
$10.00
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: ODA Recipients - America data was reported at -0.205 USD mn in 2023. This records a decrease from the previous number of 0.641 USD mn for 2022. Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: ODA Recipients - America data is updated yearly, averaging 0.088 USD mn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 13.142 USD mn in 2013 and a record low of -4.344 USD mn in 2012. Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: ODA Recipients - America data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: USD: by Region and Country: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.; Countries from AMERICA recipients of Offical Development Assistance (ODA), 30 countries: Chile, Mexico , Antigua and Barbuda, Cuba, Dominica, Dominican Republic, Grenada, Haiti, Jamaica, Montserrat, Saint Lucia, Saint Vincent and the Grenadines, Belize, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, Panama, Argentina, Bolivia, Brazil, Colombia, Ecuador, Guyana, Paraguay, Peru, Suriname, Uruguay, Venezuela
A cross-national data set of 21 variables was assembled for 212 countries from three sources (Barro and Lee 1994; Gordon 2005; CIA World Fact Book 2005). Our data set includes several proxy measures for national wealth, cultural diversity, social instability (both at national and international levels), and demography. Separate diversity measures were calculated for three different cultural domains, namely language, religion and ethnic groups . In addition, wealth variables (per capita GDP, and GINI, the coefficient of income inequality) were assembled, along with indicators of societal functioning drawn from the literature (especially Barro and Lee 1994), including indices of political rights (PRIGHTSB), revolutions and coups d'états (REVCOUP), and political instability (PINSTAB). Measures of international conflict were extracted from the social science literature, and the following were used: the proportion of the time between 1960-85 the country was involved in an external war (WARTIME), the number of international disputes in which the country was involved (TOTINTDISP), and an index of total military expenditure (TOTMILITEXP). Possible confounding variables such as population size (POPSIZE) and the number of international borders (NBINTBORDERS) were also included.
https://dataverse.harvard.edu/api/datasets/:persistentId/versions/2.0/customlicense?persistentId=doi:10.7910/DVN/HJHGYAhttps://dataverse.harvard.edu/api/datasets/:persistentId/versions/2.0/customlicense?persistentId=doi:10.7910/DVN/HJHGYA
The present dataset contains all the killings by domestic terrorist groups in Western European countries during the period 1965-2005. The unit of observation is the victim, not the attack. We include all victims in Europe as long as there is information about the political orientation of the killing and/or information about the group responsible for the killing. We only consider to be victims of terrorist violence those deaths that are a direct consequence of a terrorist attack. For instance, we exclude people who die of a heart attack that could be related to terrorist at tacks. Terrorists who die manipulating their own explosives are also excluded, because they are not considered victims (no one kills them). However, terrorists who are killed by members of their own organization or by rival organizations are included. The operational criterion that it is used to distinguish terrorist killings from other killings is the following: terrorist violence is that carried out by underground groups with political motivations. This excludes killings by underground g roups without political motivations (e.g. the mafia, narco groups) and killings by organizations that liberate territory from a state’s control and become guerrilla insurgencies, as they have different dynamics of violence to that of underground groups
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
https://dataverse.no/api/datasets/:persistentId/versions/2.1/customlicense?persistentId=doi:10.18710/NMKI2Bhttps://dataverse.no/api/datasets/:persistentId/versions/2.1/customlicense?persistentId=doi:10.18710/NMKI2B
The dataset is a cross-sectional dataset covering social and public health data pertaining to the Covid-19 outbreak in all 356 Norwegian municipalities. The dataset was compiled from public register data and media sources. Data on Covid-19 cases and related fatalities is current as of ultimo July 2020. Data on other variables is from 2018, 2019 or 2020, depending on data availability. The dataset is based on the revised municipal and county structure, as per January 1st, 2020. Standardized unique unit identifiers (kommunenummer) are included, enabling merging with other data. The dataset was assembled concurrently with a similar one on the country level, as part of the project «Ressurs for studentaktiv læring i undervisning i statistisk og romlig analyse for samfunnsfag» at the Department of Social Sciences and the Norwegian College for Fishery Science, UiT. Dette er et tverrsnittsdatasett med forskjellig samfunns- og folkehelsedata relatert til det pågående Covid-19-utbruddet i Norges 356 kommuner. Datasettet er satt sammen med data fra offentlige registre og kilder, samt norsk presse. Data om Covid-19-tilfeller og Covid-relaterte dødsfall er à jour per ultimo juli 2020. Data på andre variabler er fra 2018, 2019 og 2020, avhengig av hvilke data som var tilgjengelige. Datasettet er basert på den norske kommunestrukturen per 1. januar 2020. Standardiserte ID-variabler (kommunenummer) er inkludert for å muliggjøre sammenslåing med andre data. Datasettet ble satt sammen parallellt med et tilsvarende på landnivå, som en del av prosjektet «Ressurs for studentaktiv læring i undervisning i statistisk og romlig analyse for samfunnsfag» ved Institutt for samfunnsvitenskap og Norges fiskerihøgskole, UiT.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Country Club Heights by race. It includes the population of Country Club Heights across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Country Club Heights across relevant racial categories.
Key observations
The percent distribution of Country Club Heights population by race (across all racial categories recognized by the U.S. Census Bureau): 89.90% are white and 10.10% are multiracial.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Racial categories include:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Country Club Heights Population by Race & Ethnicity. You can refer the same here
The Swedish income panel was originally set up in the beginning of the 90s to make studies of how immigrants assimilate in the Swedish labour market possible. It consists of large samples of foreign-born and Swedish-born persons. Income information from registers is added for nearly 40 years. In addition income information relating to spouses is also available as well as for a subset of mothers and fathers. This makes it possible to construct measures of household income based on a relatively narrow definition. However, starting in 1998 there is also more information making it possible to include children over 18 and their incomes in the family. By matching with some different additional registers information has been added for people who have been unemployed or involved in labour market programmes during the 90s, on causes of deaths for people who have deceased since 1978 and on recent arrived immigrants from various origins. It has turned out that the data-base is quite useful for analysing research-questions other than originally motivating construction of the panel. The panel has been used for cross country comparisons of immigrants in the labour market and to analyse income mobility for different breakdowns of the population, and analyses the development in cohort income. There have been analyses of social assistance receipt among immigrants as well as studies of intergeneration mobility of income, the labour market situation of young immigrants and the second generation of immigrants. On-going work includes evaluation of labour market training programmes and studies of early retirement among immigrants. Planned work includes studies of the economic transition from child to adulthood during the 80s and 90s as well as studies of how frequent immigrant children are subject to measures under the Social Service Act and the Care of Youth Persons Act. The potentials of the Swedish Income Panel can be understood if one compares it with better known income-panels in other countries. For example SWIP covers more years and has a larger sample than the German Socio-Economic Panel (GSOEP). On the other hand, the fact that information is obtained from registers only makes this Swedish panel less rich in variables. There are striking parallels between the Gothenburg Income Panel and the labour market panel at the Centre for Labour Market and Social Research in Aarhus for the Danish population.
The Totalization Data Exchange (TDEX) process is an exchange between SSA and its foreign country partners to identify deaths of beneficiaries residing abroad. The process provides for the sharing of death data and restricts the disclosure of Personally Identifiable Information (PII). When TDEX was implemented in 2009 (TDEX II), it was comprised of two separate, distinct processes: one process provided death information to the Totalization (TOT) partners, the other process received death information from partner countries for use within SSA beginning in 2011. Enhancements were made to create a finder file process in which SSA sends information to our foreign partners concerning beneficiaries residing in their country. The foreign partners respond with death information for US beneficiaries who have died in their country.
This dataset includes the survey data that were commissioned and collected by Countryside Council for Wales. AccConID=21 AccConstrDescription=This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials. AccConstrDisplay=This dataset is licensed under a Creative Commons Attribution 4.0 International License. AccConstrEN=Attribution (CC BY) AccessConstraint=Attribution (CC BY) Acronym=None added_date=2005-12-19 15:12:12.287000 BrackishFlag=0 CDate=2005-12-21 cdm_data_type=Other CheckedFlag=0 Citation=Countryside Council for Wales. Marine Nature Conservation Review (MNCR) and associated benthic marine data held and managed by CCW. Countryside Council for Wales, Gwynedd, UK. Comments=None ContactEmail=None Conventions=COARDS, CF-1.6, ACDD-1.3 CurrencyDate=None DasID=657 DasOrigin=Research DasType=Data DasTypeID=1 DateLastModified={'date': '2025-03-14 01:35:29.314259', 'timezone_type': 1, 'timezone': '+01:00'} DescrCompFlag=0 DescrTransFlag=0 Easternmost_Easting=-3.01 EmbargoDate=None EngAbstract=This dataset includes the survey data that were commissioned and collected by Countryside Council for Wales. EngDescr=Note these data complement other datasets held by JNCC and the other country agencies that used the same methodology. However, this dataset comprises those data that were paid for by CCW directly. The data contributed to the MNCR programme which was initiated to provide a comprehensive baseline of information on marine habitats and their associated species around the coast of Britain which would aid coastal zone and sea-use management and to contribute to the identification of areas of marine natural heritage importance. The focus of MNCR work was on benthic habitats (often referred to as 'biotopes') in intertidal and inshore (typically within 3nm) subtidal areas.
Methods of data capture The majority of data were collected using methods described in the MNCR Rational and Methods report (Hiscock 1996). Broadly, this encompassed surveying a range of sites within a geographical area to sample and describe the variety of habitats present (sampling habitats in different substrata, depths, wave exposures, current regimes, salinity regimes and so on). Each habitat was sampled using semi-quantitative recording techniques (SACFOR abundance scales) for recording epibiota on rocky habitats.
Geographical Coverage This dataset relates to Wales. Note however that the dataset "Marine Nature Conservation Review (MNCR) and associated benthic marine data held and managed by JNCC" also includes equivalent and complementary data from Wales.
Temporal Coverage The data were collected post 1993.
Data Quality The data have been extensively checked during the course of the creation of the various reports produced by the MNCR programme. This included the MNCR Area Summary report series which describes the marine habitats in particular regions around the UK and the creation of the national marine habitat (biotope) classification system. Through this work the vast majority of the anomalies and errors in the data should have been identified but some may still remain. FreshFlag=0 geospatial_lat_max=53.38 geospatial_lat_min=51.34 geospatial_lat_units=degrees_north geospatial_lon_max=-3.01 geospatial_lon_min=-5.34 geospatial_lon_units=degrees_east infoUrl=None InputNotes=None institution=MBA, NRW License=https://creativecommons.org/licenses/by/4.0/ Lineage=None MarineFlag=1 modified_sync=2021-02-05 00:00:00 Northernmost_Northing=53.38 OrigAbstract=None OrigDescr=None OrigDescrLang=None OrigDescrLangNL=None OrigLangCode=None OrigLangCodeExtended=None OrigLangID=None OrigTitle=None OrigTitleLang=None OrigTitleLangCode=None OrigTitleLangID=None OrigTitleLangNL=None Progress=Completed PublicFlag=1 ReleaseDate=Dec 19 2005 12:00AM ReleaseDate0=2005-12-19 RevisionDate=None SizeReference=Number of records 13,897 sourceUrl=(local files) Southernmost_Northing=51.34 standard_name_vocabulary=CF Standard Name Table v70 StandardTitle=Marine Nature Conservation Review (MNCR) and associated benthic marine data held and managed by CCW StatusID=1 subsetVariables=ScientificName,BasisOfRecord,YearCollected,MonthCollected,DayCollected,aphia_id TerrestrialFlag=0 time_coverage_end=2001-09-27T01:00:00Z time_coverage_start=1993-09-06T01:00:00Z UDate=2011-01-31 VersionDate=Apr 22 2005 12:00AM VersionDay=22 VersionMonth=4 VersionName=tst VersionYear=2005 VlizCoreFlag=1 Westernmost_Easting=-5.34
https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-catalogue/licences/insitu-gridded-observations-global-and-regional/insitu-gridded-observations-global-and-regional_15437b363f02bf5e6f41fc2995e3d19a590eb4daff5a7ce67d1ef6c269d81d68.pdfhttps://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-catalogue/licences/insitu-gridded-observations-global-and-regional/insitu-gridded-observations-global-and-regional_15437b363f02bf5e6f41fc2995e3d19a590eb4daff5a7ce67d1ef6c269d81d68.pdf
This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Data from a questionnaire survey conducted from 2022-08-25 to 2022-11-15 and exploring the use of machine translation by Ukrainian refugees in the Czech Republic. The presented spreadsheet contains minimally processed data exported from the two questionnaires that were created in Google Forms in the Ukrainian and the Russian language. The links to these questionnaires were distributed by three methods: direct email to particular refugees whose contact details the authors obtained while volunteering; through a non-profit organisation helping refugees (Vesna women’s education institution) and on social networks by posting links to the survey in groups associating the Ukrainian community across Czech regions and towns. Since we asked potential respondents to spread the questionnaire further, we could not prevent it from reaching Ukrainians who had arrived in Czechia previously, or received temporary protection in other countries. Due to this fact, the textual answers to the question 1.5 "Which country are you in right now?" were replaced in the dataset by numbers (1 for the Czech Republic, 2 for other countries) in order for us to be able to separate the data of respondents not located in the Czech Republic, which were irrelevant for our survey. Also, in this version of the dataset, the textual answers to the question 1.6 "How many months have you been to this country?" were replaced by numbers, so that we could separate the data of respondents who arrived in the Czech Republic in February 2022 or later from the other data (0 for those staying in Czechia before February 2022, 1 for those staying in Czechia since February 2022 or later, 2 for those staying in other countries).
The CYI Survey invites employees to voluntarily disclose how they self-identify based on questions related to Indigenous identity, Black identity, gender, race/ethnicity, sexual orientation and if they identify as a person with a disability. The data displays the diversity within the workforce at the City of Toronto. The goal of the survey is to track progress towards realizing the City's Motto "Diversity Our Strength", and to continuously monitor and socialize diversity data across the City, in order to help inform decision-making and address gaps in representation across all levels at the City. About the Datasets The following datasets were collected through the City's CYI Workforce survey between 2013 and 2024. The data has been reported in aggregate formats that do not allow for the identification of individual employees. First Nations, Inuit, and Metis Data The City is working with an external working group of First Nations, Inuit, and Métis (FNIM) advisors to develop a framework for the collection and use of FNIM data. While this framework is in development, Indigenous data from CYI surveys conducted in 2022, 2023, and 2024 will not be made available until Ownership, Control, Access, and Possession (OCAP) and United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) principles have been applied. However, Indigenous data from 2018, 2019, 2020 and 2021 is still available. For questions related to the implications or considerations of the framework’s development, please contact dataequity@toronto.ca
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
It has never been easier to solve any database related problem using any sequel language and the following gives an opportunity for you guys to understand how I was able to figure out some of the interline relationships between databases using Panoply.io tool.
I was able to insert coronavirus dataset and create a submittable, reusable result. I hope it helps you work in Data Warehouse environment.
The following is list of SQL commands performed on dataset attached below with the final output as stored in Exports Folder QUERY 1 SELECT "Province/State" As "Region", Deaths, Recovered, Confirmed FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Deaths>0 Description: How will we estimate where Coronavirus has infiltrated, but there is effective recovery amongst patients? We can view those places by having Recovery twice more than the Death Toll.
Query 2 SELECT country, sum(confirmed) as "Confirmed Count", sum(Recovered) as "Recovered Count", sum(Deaths) as "Death Toll" FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Confirmed>0 GROUP BY country
Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries
Query 3 SELECT country as "Countries where Coronavirus has reached" FROM "public"."coronavirus_updated" WHERE confirmed>0 GROUP BY country Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries.
Query 4 SELECT country, sum(suspected) as "Suspected Cases under potential CoronaVirus outbreak" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 AND confirmed=0 GROUP BY country ORDER BY sum(suspected) DESC
Description: Coronavirus is spreading at alarming rate. In order to know which countries are newly getting the virus is important because in these countries if timely measures are taken, it could prevent any causalities. Here is a list of suspected cases with no virus resulted deaths.
Query 5 SELECT country, sum(suspected) as "Coronavirus uncontrolled spread count and human life loss", 100*sum(suspected)/(SELECT sum((suspected)) FROM "public"."coronavirus_updated") as "Global suspected Exposure of Coronavirus in percentage" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 GROUP BY country ORDER BY sum(suspected) DESC Description: Coronavirus is getting stronger in particular countries, but how will we measure that? We can measure it by knowing the percentage of suspected patients amongst countries which still doesn’t have any Coronavirus related deaths. The following is a list.
Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.
Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.
Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents the median household income across different racial categories in Country Club. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.
Key observations
Based on our analysis of the distribution of Country Club population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 87.01% of the total residents in Country Club. Notably, the median household income for White households is $81,932. Interestingly, despite the White population being the most populous, it is worth noting that Two or More Races households actually reports the highest median household income, with a median income of $145,089. This reveals that, while Whites may be the most numerous in Country Club, Two or More Races households experience greater economic prosperity in terms of median household income.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Racial categories include:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Country Club median household income by race. You can refer the same here