About this dataset

Context

The dataset tabulates the Queen Anne household income by gender. The dataset can be utilized to understand the gender-based income distribution of Queen Anne income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Queen Anne, MD annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Queen Anne, MD annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Queen Anne income distribution by gender. You can refer the same here

Music is part of our lives.

Many of us can't stop listening to music and spend a considerable amount of time trying to decide what to listen to next or what is worse, looking for that song whose title we have forgotten! How do we go about finding that song we want to listen to, but have forgotten?

We can try to remember a fragment of the lyrics and simply use a text-based search engine. What if we don't recall the lyrics or they are in a language we don't even speak? Well, we can ask for help: we hum to the song and hope that someone will recognize it - no matter how poorly we do it. Think about it. It is amazing that we can recognize a song when we listen to it. But isn't it even more amazing that we can recognize it when someone else is humming or whistling to it? Wouldn't it be great to have an audio-based search engine that did this for us?

This would truly be extreme music recognition.

The MLEnd Hums and Whistles dataset will give you an opportunity to explore the non-trivial problem of recognizing music from extreme interpretations, in our case, hums and whistles produced by people like you and me. This dataset comes with additional demographic information about our participants, so that you can explore how people with different backgrounds interpret music. A small version of this dataset can be found here.

The MLEnd datasets have been created by students at the School of Electronic Engineering and Computer Science, Queen Mary University of London. Other datasets include the MLEnd Spoken Numerals dataset, also available on Kaggle. Do not hesitate to reach out if you want to know more about how we did it.

Enjoy!

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within De Queen. The dataset can be utilized to gain insights into gender-based income distribution within the De Queen population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within De Queen, among individuals aged 15 years and older with income, there were 1,949 men and 1,694 women in the workforce. Among them, 1,222 men were engaged in full-time, year-round employment, while 520 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 8.27% fell within the income range of under $24,999, while 10.77% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 4.66% of men in full-time roles earned incomes exceeding $100,000, while 4.81% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 De Queen median household income by race. You can refer the same here

Background Glaucoma is a worldwide leading cause of irreversible sight loss. Worldwide, an estimated 60 million people have glaucoma. Glaucoma is a condition of increased intraocular pressure in the eye. Because it may be asymptomatic until a relatively late stage, diagnosis is frequently delayed. There are four general categories of glaucoma: primary open-angle and angle-closure, and secondary open and angle-closure glaucoma.

The UHB glaucoma dataset is a longitudinal dataset consisting of routinely collected clinical metadata from patients receiving treatment for glaucoma at UHB, from 2007 to the present.

This dataset encompasses all patients at UHB who have received a diagnosis of primary or secondary glaucoma or ocular hypertension. Clinical metadata includes demographic information, visual acuities, central corneal thickness, intraocular pressure, optic nerve head findings, and mean deviation of the Humphrey visual fields.

This dataset is continuously updating, however, as of 1st October 2021, it consisted of 5065 people This is a large single centre database from patients with glaucoma and covers more than a decade of follow-up for these patients.

Geography The Queen Elizabeth Hospital is one of the largest single-site hospitals in the United Kingdom, with 1,215 inpatient beds. Queen Elizabeth Hospital is part of one of the largest teaching trusts in England (University Hospitals Birmingham). Set within the West Midlands and it has a catchment population of circa 5.9million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.

Data source: Ophthalmology department at Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.

-- Dataset documentation --

1- Introduction

The present dataset was developed in the context of our work in [1] that focus on the automatic recognition of beehive sounds. The problem is posed as the classification of sound segments in two classes: Bee and noBee. The novelty of the explored approach and the need for annotated data, dictated the construction of such dataset.

2- Description

2.1- Audio recordings:

The annotated dataset was developed based on a selected set of recordings acquired in the context of two different projects: the Open Source Beehive (OSBH) project [2] and the NU-Hive project [3]. Both projects main goal is to develop a beehive monitoring system capable of identifying and predict certain events and states of the hive that are of interest to the beekeeper. Among many different variables that can be measured and that help the recognition of different states of the hive, the analysis and use of the sound the bees produce is a big focus for both projects.

The recordings from the OSBH project were acquired through a citizen science initiative which asked people from the general public to record the sound from their beehives together with the registering of the hive state at the moment. Because of the amateur and collaborative nature of this project, the recordings from the OSBH project present great diversity due to the very different conditions in which the signals were acquired: different recording devices used, different environments where the hives were placed, and even different position for the microphones inside the hive. This variety of settings makes this dataset a very interesting tool to help evaluate and challenge the methods developed.

The NU-Hive project is a comprehensive effort of data acquisition, concerning not only sound, but a vast amount of variables that will allow the study of bees behaviors and other unknown aspects. The selected recordings are taken from 2 hives and labeled regarding two states: queen bee is present, and queen bee not present. Contrary to the OSBH project recordings, the recordings from the NU-Hive project are from a much more controlled and homogeneous environment. Here the occurring external sounds are mainly traffic, car honks and birds.

The annotated dataset:

For each selected recording, time segments are labeled as Bee or noBee depending on the perceived source of the sound signal being from bees or external to the hive.

The whole annotated dataset consists of 78 recordings of varying lengths which make up for a total duration of approximately 12 hours of which 25% is annotated as noBee events.

About 60% of the recordings are from the NU-Hive dataset and represent 2 hives, the remaining are recordings from the OSBH dataset and 6 different hives. The recorded hives are from 3 main locations: North America, Australia and Europe.

2- Annotation procedure¶

The annotation procedure consists in hearing the selected recordings and marking the beginning and the end of every sound that could not be recognized as a beehive sound. The recognition of external sounds is based primarily on the perceived heard sounds, but a visual aid is also used by visualizing the log-mel frequency spectrum of the signal. All the above are functionalities offered by the Sonic Visualiser software, which was used by two volunteers that are neither bee-specialists nor specially trained in sound annotation tasks.

By marking these pairs of moments corresponding to the beginning and end of external sound periods, we are able to get the whole recording labeled into Bee and noBee intervals. Thus in the resulting Bee intervals only pure beehive sounds, (no external sounds) should be perceived for the entirety of the segment. The noBee intervals refer to periods where an external sound can be perceived (superimposed to the bee sounds).

File Structure:

Each audio file is coupled with its corresponding annotation file, identified by the same name and extension .lab.
For convenience, all the annotations are collected in a single master label file named beeAnnotations.mlf

The .lab files consist of :

• First row identifies the audio file to which the annotations refer to.
• Each line after that describes an interval with starting time point, end time point and label. The time points are expressed in seconds.

Below is an example of such an annotation file:

Hive3_20_07_2017_QueenBee_H3_audio_15_30_00
0 78.45 bee
78.46 78.95 nobee
78.96 103.92 bee
103.93 112.48 nobee
112.49 152.48 bee
.

This dataset is licensed under a Creative Commons Attribution 4.0 International License.
When using this dataset, please cite [1]:

[1] I. Nolasco and E. Benetos, “To bee or not to bee: Investigating machine learning approaches to beehive sound recognition,” in Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2018, submitted.

[2] “Open Source Beehives Project,” https://www.osbeehives.com/.

[3] S. Cecchi, A. Terenzi, S. Orcioni, P. Riolo, S. Ruschioni, and N. Isidoro, “A preliminary study of sounds emitted by honey bees in a beehive,” in Audio Engineering Society Convention 144, 2018.

About this dataset

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 King and Queen County. 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 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In King and Queen County, the median income for all workers aged 15 years and older, regardless of work hours, was $48,646 for males and $29,663 for females.

These income figures highlight a substantial gender-based income gap in King and Queen County. Women, regardless of work hours, earn 61 cents for each dollar earned by men. This significant gender pay gap, approximately 39%, underscores concerning gender-based income inequality in the county of King and Queen County.

- Full-time workers, aged 15 years and older: In King and Queen County, among full-time, year-round workers aged 15 years and older, males earned a median income of $65,216, while females earned $50,458, leading to a 23% gender pay gap among full-time workers. This illustrates that women earn 77 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.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in King and Queen County.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 King and Queen County median household income by race. You can refer the same here

Abstract copyright UK Data Service and data collection copyright owner.

One of the key expectations citizens have of a political system is that the government of the day is able to set out its priorities for the year ahead as a clear statement of intentions or promises, upon which it can be judged by the media, experts and citizens themselves. In Britain, the annual statement of legislative intent is the institution of the Queen’s (or King’s) speech, which is made to Parliament each year at the beginning of the session or shortly after a General Election. This occasion is highly ceremonial, but the speech, which is written by No 10, is a serious list of legislative intentions, with little general or procedural content, and which is closely followed by the media as their guide to the year ahead. But there has been very little academic work seeking to report the content of these speeches over time. The project aimed to understand the nature of the setting of executive priorities in the UK, by examining Queen’s or King’s speeches since 1940, and to also use these to understand the origins and consequences of the policy priorities. The objectives of the research were, first, to report the content of the speeches and how they change over time; second, to explain why the content changes, such as whether it is different according to the party in power; third, to find out whether the policy priorities of government match the policy content of party manifestoes and public opinion; and fourth, to find out whether the policy priorities were reflected in the budget priorities of government departments. The methods of the project was a content analysis of the Queen’s and King’s speeches from 1940-2005.


Main Topics:

The King’s / Queen’s Speeches (1940-2005) were coded using the US Policy Agendas Project Topics Codebook written by Frank Baumgartner and Bryan Jones, revised by Adler and Wilkerson. The coders blind coded each sentence of the Queen’s / King's Speeches 1940-2005 between March and December 2006. The basic coding system for the King’s / Queen’s Speeches dataset was as follows:
1. The yearly Queen’s Speech is identified by its year (variable name “year” in the coded dataset)
2. Each sentence of a speech is given a number indicating its sequential order in the speech from 1 (variable name “number” in the coded dataset)
3. The exact sentence copied from the Queen’s Speech (variable name “sentence” in the coded dataset)
4. Word count of a sentence (variable name “words” in the coded dataset)
5. The final agreed major topic for the given sentence (variable name “finemajor” in the coded dataset)
6. Distinguishing between policy and non-policy statements (variable name “policy” in the coded dataset)
7. Giving information about whether different coders allocated same or different code to the same statement (variable name “intercoder” in the coded dataset)

Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.

Some college or associate's degree Health Insurance Coverage Statistics for 2022. This is part of a larger dataset covering consumer health insurance coverage rates in De Queen, Arkansas by age, education, race, gender, work experience and more.

Context

The Paradise Papers is a cache of some 13GB of data that contains 13.4 million confidential records of offshore investment by 120,000 people and companies in 19 tax jurisdictions (Tax Heavens - an awesome video to understand this); that was published by the International Consortium of Investigative Journalists (ICIJ) on November 5, 2017. Here is a brief video about the leak. The people include Queen Elizabeth II, the President of Columbia (Juan Manuel Santos), Former Prime Minister of Pakistan (Shaukat Aziz), U.S Secretary of Commerce (Wilbur Ross) and many more. According to an estimate by the Boston Consulting Group, the amount of money involved is around $10 trillion. The leak contains many famous companies, including Facebook, Apple, Uber, Nike, Walmart, Allianz, Siemens, McDonald’s and Yahoo.

It also contains a lot of U. S President Donald Trump allies including Rax Tillerson, Wilbur Ross, Koch Brothers, Paul Singer, Sheldon Adelson, Stephen Schwarzman, Thomas Barrack and Steve Wynn etc. The complete list of Politicians involve is avaiable here.

The Panama Papers in the cache of 38GB of data from the national corporate registry of Bahamas. It contains world’s top politicians and influential persons as head and director of offshore companies registered in Bahamas.

Offshore Leaks details 13,000 offshore accounts in a report.

I am calling all data scientists to help me stop the corruption and reveal the patterns and linkages invisible for the untrained eye.

Content

The data is the effort of more than 100 journalists from 60+ countries

The original data is available under creative common license and can be downloaded from this link.

I will keep updating the datasets with more leaks and data as it’s available

Acknowledgements

International Consortium of Investigative Journalists (ICIJ)

Paradise Papers Update

Paradise Papers data has been uploaded as released by ICIJ on Nov 21, 2017. You can find Paradise Papers zip file and six extracted files in CSV format, all starting with a prefix of Paradise. Happy Coding!

Inspiration

Some ideas worth exploring:

1. How many companies and individuals are there in all of the leaks data

2. How many countries involved

3. Total money involved

4. What is the biggest best tax heaven

5. Can we compare the corruption with human development index and make an argument that would correlate corruption with bad conditions in that country

6. Who are the biggest cheaters and where they live

7. What role Fortune 500 companies play in this game

I need your help to make this world corruption free in the age of NLP and Big Data

Social insects provide promising new avenues for aging research. Within a colony, individuals that share the same genetic background can differ in lifespan by up to two orders of magnitude. Reproducing queens (and in termites also kings) can live for more than 20 years, extraordinary lifespans for insects. We studied aging in a termite species, Cryptotermes secundus, which lives in less socially complex societies with a few hundred colony members. Reproductives develop from workers which are totipotent immatures. Comparing transcriptomes of young and old individuals, we found evidence for aging in reproductives that was especially associated with DNA and protein damage and the activity of transposable elements. By contrast, workers seemed to be better protected against aging. Thus our results differed from those obtained for social insects that live in more complex societies. Yet, they are in agreement with lifespan estimates for the study species. Our data are also in line with expectations from evolutionary theory. For individuals that are able to reproduce, it predicts that aging should only start after reaching maturity. As C. secundus workers are immatures with full reproductive options we expect them to invest into anti-aging processes. Our study illustrates that the degree of aging can differ between social insects and that it may be associated with caste-specific opportunities for reproduction.

All methods for the collection and analysis of the data are fully described in Majidifar et al (Functional Ecology, 2024).

This is raw data from a cross secional study of 510 people living with diabetes attending the Queen Elizabeth Central Hospital diabetes clinic. Ethical approval for the study was granted by the College of Medicine Research and Ethics Committee (Ref: P.08/17/229). The data were collected between November 2017 and May 2018 using an interviewer administered questionnaire that solicited data on participants demographic and clinical clinical characteristics, five social cognitive theory factors (self-efficacy, outcome expectations, knowledge, social support and barriers to self-management) and self-management (diet, exercise, foot care, medication, self-monitoring of blood glucose and smoking). The data were entered into a Microsoft Access database ten exported into Stata version 14.0 for cleaning and analysis.

About this dataset

Context

The dataset tabulates the Queen City household income by gender. The dataset can be utilized to understand the gender-based income distribution of Queen City income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Queen City, TX annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Queen City, TX annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Queen City income distribution by gender. You can refer the same here

When animals reproduce in social groups, the potential for conflict and cooperation is shaped by the number of reproductive individuals (breeders), their relatedness to one another, and division of reproduction among them. These features comprise species’ “breeding systems.†Despite their importance, breeding systems are poorly characterized in most social animals, and detailed accounts for single species are rare. Here, we fully characterize the breeding systems in invasive populations of the fire ant Solenopsis invicta, a species in which a large genetic element (supergene) determines whether a colony has a single queen (monogyne social form) or multiple queens (polygyne form). Colonies of the monogyne form are simple families, and the breeding system is correspondingly straightforward. The breeding system of the polygyne form is complex, with many features still uncharacterized. We conducted a large longitudinal experiment tracking parentage, relatedness, and supergene genotype in se..., , # Data from Reproductive Competition in Multiple-queen Fire Ant Colonies: Insights from Analyses of Breeding Systems

Dataset DOI: 10.5061/dryad.vhhmgqp5g

Description of the data and file structure

This repository contains datasets associated with the manuscript:

Hale Walker S, Lacy KD, Ross KG, Zeng H. (2025). Reproductive Competition in Multiple-queen Fire Ant Colonies: Insights from Analyses of Breeding Systems. Molecular Ecology.

The study investigates the breeding systems of Solenopsis invicta (fire ants), focusing on the polygyne social form. The data include information on queen longevity, reproductive output, parentage, and genetic structure, providing a detailed characterization of reproductive skew and supergene effects in multiple-queen colonies.

File Versions

Each raw data file has an Excel version containing annotations, colors, and notes to aid understanding.

For analytical purposes, most files are also provided in...,

The file contains the microsatellite genotypes of the individuals used in the paper by Doums et al. Animal Behaviour (2017). Each line corresponds to an individual with its identity, enclosure number (enclosure 7 is missing), nest number, nest origin (QR = from queenright colony , QL = from queenless colony), its caste (male, foreignmale, diploidmale, worker, gyne, queen), its age (found at the pupal stage, as callow or adult), and its genotypes. For each microsatellite locus, the two alleles are given in two separated column labelled by the locus name and the allele number (a or b). Missing data or the second allele of haploid genotypes is indicated by NA.

Abstract copyright UK Data Service and data collection copyright owner.

The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online.

The Great Britain Historical GIS Project has also produced digitised boundary data, which can be obtained from the UK Data Service Census Support service. Further information is available at census.ukdataservice.ac.uk

Main Topics:

The Great Britain Historical Database is a large database of British nineteenth and twentieth-century statistics. Where practical the referencing of spatial units has been integrated, data for different dates have been assembled into single tables.

The Great Britain Historical Database currently contains :

• Statistics from the 1861 Census and the Registrar General's reports, 1851-1861
  
• Employment statistics from the census, 1841-1931
  
• Demographic statistics from the census, 1841-1931
  
• Mortality statistics from the Registrar General's reports, 1861-1920
  
• Marriage statistics from the Registrar General's reports, 1841-1870
  
• Trade union statistics for the Amalgamated Society of Engineers (ASE), 1851-1918
  
• Trade union statistics for the Amalgamated Society of Carpenters and Joiners (ASCJ), 1863-1912
  
• Official poor law statistics, 1859-1915 and 1919-1939
  
• Wage statistics, 1845-1906
  
• Hours of work statistics, 1900-1913
  
• Small debt statistics from county courts, 1847-1913 and 1938
  

There are six tables in this part of the Great Britain Historical Database :

Tu_pc holds monthly local unemployment statistics for engineers (January 1902-December 1914), shipbuilders (January 1902-December 1914), printers (February 1902-December 1914) and carpenters and joiners (May 1901-October 1905). For details of the districts used please see the documentation.

Div23_38 holds annual data taken from the official tabulations of the Ministry of Labour's Local Unemployment Index from 1923 to 1938 for eight divisions.

Lui holds quarterly data taken from the official tabulations of the Ministry of Labour's Local Unemployment Index from January 1927 to July 1939.

Lui_gaz provides locational information for the exchange areas listed in lui.

Un_1928 holds information on registered unemployment at labour exchanges for 30th January 1928.

Un_1933 holds information on registered unemployment at labour exchanges for 23rd January 1933.

Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.

About this dataset

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 Queen Anne. 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 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Queen Anne, the median income for all workers aged 15 years and older, regardless of work hours, was $43,250 for males and $40,357 for females.

Based on these incomes, we observe a gender gap percentage of approximately 7%, indicating a significant disparity between the median incomes of males and females in Queen Anne. Women, regardless of work hours, still earn 93 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Queen Anne, among full-time, year-round workers aged 15 years and older, males earned a median income of $72,000, while females earned $65,250, resulting in a 9% gender pay gap among full-time workers. This illustrates that women earn 91 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the town of Queen Anne.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Queen Anne, showcasing a consistent income pattern irrespective of employment status.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Queen Anne median household income by race. You can refer the same here

The data contains the genetic identity of parents (maternal and paternal identities and assignment probabilities) identified from DNA extracted from tail tips analysed using the MASTERBAYES program, for individual banded mongooses in a wild population on the Mweya Peninsula, Queen Elizabeth National Park, Uganda between 2000-2019. A nine generation deep genetic pedigree was constructed from which maternity and paternity assignments were calculated. This data was used to calculate lifetime reproductive success for individuals in the population who were exposed to conflict with rival groups to determine the fitness costs and benefits of intergroup conflict. In addition the type of microsatellite panel used to genotype the DNA samples is recorded. Full details about this dataset can be found at https://doi.org/10.5285/f397e842-b411-4256-b507-a4aa4647b914

This dataset is the survey dataset from a booster survey undertaken among BROOK NI service users on sexual grooming and sexual risks experienced. The questions in this survey are repeated from the 2010 (sexual grooming and risks) and 2011 (sexual activity and experiences) Young Life and Times (YLT) survey of 16 years olds undertaken in Northern Ireland.

This Knowledge Exchange project is a joint activity between ARK – a joint initiative by Queen’s University Belfast and the University of Ulster - and Brook Northern Ireland (Brook NI). It is based on data collected in the 2010 and 2011 Young Life and Times (YLT) survey of 16-year olds which is undertaken annually in Northern Ireland. The YLT surveys had asked questions about sexual risks faced by young people and their sexual experiences. The main objective of this project is to facilitate sexual capacity and confidence building among young people in Northern Ireland who are at the start of their sexual careers, i.e. who have not been sexually active or have only been sexually active for a short period of time. This will be done by: (1) collecting a boaster sample for the YLT surveys, to inform; (2)participatory group work sessions with up to 100 young people; (3) the development of an educational resource and young people-led publicity campaign about sexual safety. The project is undertaken in a participatory manner. 12 peer educators ar trained who will help with the group discussions and with the design of the education resource and campaign.

About this dataset

Context

The dataset tabulates the Queen City household income by gender. The dataset can be utilized to understand the gender-based income distribution of Queen City income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Queen City, MO annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Queen City, MO annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Queen City income distribution by gender. You can refer the same here