About Dataset

The dataset you provided, titled "Report Card Enrollment 2023-24 School Year," appears to be a comprehensive collection of information regarding student enrollment and demographics within educational institutions for the specified academic year. Here are some observations about the dataset:

1. Granularity: The dataset seems to be quite granular, providing detailed information not only about overall student enrollment but also about various demographic categories such as gender, race/ethnicity, English language learners, students with disabilities, and socioeconomic status.

2. Demographic Diversity: It captures the diversity of the student population by including counts for various racial/ethnic groups, as well as categories such as gender X, indicating a recognition of diverse gender identities.

3. Socioeconomic Indicators: The dataset includes indicators of socioeconomic status such as students in foster care, homeless students, and those from low-income families, which can provide insights into equity and access issues within the educational system.

4. Special Education and Gifted Programs: It tracks the enrollment of students with disabilities and those identified as highly capable, which are important metrics for evaluating the inclusivity and effectiveness of special education and gifted programs.

5. Geographical Context: The dataset includes information about the county, educational service district, and school district, providing a geographical context for the enrollment data.

6. Temporal Aspect: The "DataAsOf" column indicates that the data has a temporal aspect, suggesting that it may be periodically updated to reflect changes in enrollment and demographics throughout the academic year.

**columns : ** SchoolYear: Indicates the academic year for which the data is reported, in this case, it's 2023-24.

OrganizationLevel: Specifies the level of educational organization, which could be school, district, or any other relevant level within the educational system.

County: Refers to the county where the educational organization is located.

ESDName: Stands for Educational Service District Name, which represents the intermediate level of educational administration in some states.

ESDOrganizationID: ID assigned to the Educational Service District.

DistrictCode: Code assigned to the district within the educational system.

DistrictName: Name of the school district.

DistrictOrganizationId: ID assigned to the district organization.

SchoolCode: Code assigned to the school within the district.

SchoolName: Name of the school.

SchoolOrganizationID: ID assigned to the school organization.

CurrentSchoolType: Indicates the current type of the school, such as elementary, middle, or high school.

GradeLevel: Specifies the grade level(s) served by the school.

All Students: Total number of enrolled students in the school.

Female: Number of female students enrolled.

Gender X: Number of students who identify as gender X, indicating a non-binary or non-conforming gender identity.

Male: Number of male students enrolled.

American Indian/ Alaskan Native: Number of students identifying as American Indian or Alaskan Native.

Asian: Number of students identifying as Asian.

Black/ African American: Number of students identifying as Black or African American.

Hispanic/ Latino of any race(s): Number of students identifying as Hispanic or Latino of any race.

Native Hawaiian/ Other Pacific Islander: Number of students identifying as Native Hawaiian or other Pacific Islander.

Two or More Races: Number of students identifying as belonging to two or more races.

White: Number of students identifying as White.

English Language Learners: Number of students who are learning English as a second language.

Foster Care: Number of students in foster care.

Highly Capable: Number of students identified as highly capable or gifted.

Homeless: Number of students experiencing homelessness.

Low-Income: Number of students from low-income families.

Migrant: Number of students from migrant families.

Military Parent: Number of students with parents serving in the military.

Mobile: Number of students who frequently change residences.

Section 504: Number of students covered under Section 504 of the Rehabilitation Act, which provides accommodations for students with disabilities.

Students with Disabilities: Number of students with disabilities.

Non-English Language Learners: Number of students who are not learning English as a second language.

Non-Foster Care: Number of students who are not in foster care.

Non-Highly Capable: Number of students who are not identified as highly capable or gifted.

Non-Homeless: Number of students wh...

Explore demographic data on the Massachusetts executive branch workforce. Track our progress toward our goals to reflect the diversity of the people we serve, and to stand out as an employer of choice.

Context

Workforce diversity is an increasingly salient issue, but it can be difficult to easily check how a specific company is performing. This dataset was created by Fortune to show what was discoverable by someone considering employment with one of the Fortune 500 firms and curious about their commitment to diversity and inclusion could find.

Content

This dataset contains the name of each firm, its rank in the 2017 Fortune 500, a link to its diversity and inclusion page or equal opportunity statement, and whether the company releases full, partial, or no data about the gender, race, and ethnicity of its employees. Additional detail is included where it was available. As there are over 200 fields in this dataset; please consult the data dictionary for details about specific features.

Acknowledgements

This dataset was assembled by Fortune.com data reporter Grace Donnelly. The details of her data preparation process can be found here.

Inspiration

Are the companies that release the most information more or less diverse than their peers? Are there any particular industries that stand out?

Diversity in Tech Statistics: In today's tech-driven world, discussions about diversity in the technology sector have gained significant traction. Recent statistics shed light on the disparities and opportunities within this industry. According to data from various sources, including reports from leading tech companies and diversity advocacy groups, the lack of diversity remains a prominent issue. For example, studies reveal that only 25% of computing jobs in the United States are held by women, while Black and Hispanic individuals make up just 9% of the tech workforce combined. Additionally, research indicates that LGBTQ+ individuals are underrepresented in tech, with only 2.3% of tech workers identifying as LGBTQ+. Despite these challenges, there are promising signs of progress. Companies are increasingly recognizing the importance of diversity and inclusion initiatives, with some allocating significant resources to address these issues. For instance, tech giants like Google and Microsoft have committed millions of USD to diversity programs aimed at recruiting and retaining underrepresented talent. As discussions surrounding diversity in tech continue to evolve, understanding the statistical landscape is crucial in fostering meaningful change and creating a more inclusive industry for all. Editor’s Choice In 2021, 7.9% of the US labor force was employed in technology. Women hold only 26.7% of tech employment, while men hold 73.3% of these positions. White Americans hold 62.5% of the positions in the US tech sector. Asian Americans account for 20% of jobs, Latinx Americans 8%, and Black Americans 7%. 83.3% of tech executives in the US are white. Black Americans comprised 14% of the population in 2019 but held only 7% of tech employment. For the same position, at the same business, and with the same experience, women in tech are typically paid 3% less than men. The high-tech sector employs more men (64% against 52%), Asian Americans (14% compared to 5.8%), and white people (68.5% versus 63.5%) compared to other industries. The tech industry is urged to prioritize inclusion when hiring, mentoring, and retaining employees to bridge the digital skills gap. Black professionals only account for 4% of all tech workers despite being 13% of the US workforce. Hispanic professionals hold just 8% of all STEM jobs despite being 17% of the national workforce. Only 22% of workers in tech are ethnic minorities. Gender diversity in tech is low, with just 26% of jobs in computer-related sectors occupied by women. Companies with diverse teams have higher profitability, with those in the top quartile for gender diversity being 25% more likely to have above-average profitability. Every month, the tech industry adds about 9,600 jobs to the U.S. economy. Between May 2009 and May 2015, over 800,000 net STEM jobs were added to the U.S. economy. STEM jobs are expected to grow by another 8.9% between 2015 and 2024. The percentage of black and Hispanic employees at major tech companies is very low, making up just one to three percent of the tech workforce. Tech hiring relies heavily on poaching and incentives, creating an unsustainable ecosystem ripe for disruption. Recruiters have a significant role in disrupting the hiring process to support diversity and inclusion. You May Also Like To Read Outsourcing Statistics Digital Transformation Statistics Internet of Things Statistics Computer Vision Statistics

According to the 2021 Census, London was the most ethnically diverse region in England and Wales – 63.2% of residents identified with an ethnic minority group.

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 Pleasantville. The dataset can be utilized to gain insights into gender-based income distribution within the Pleasantville population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Pleasantville, among individuals aged 15 years and older with income, there were 2,640 men and 2,439 women in the workforce. Among them, 1,867 men were engaged in full-time, year-round employment, while 1,164 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 2.46% fell within the income range of under $24,999, while 1.20% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 52.97% of men in full-time roles earned incomes exceeding $100,000, while 41.49% 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)

https://i.neilsberg.com/ch/pleasantville-ny-income-distribution-by-gender-and-employment-type.jpeg" alt="Pleasantville, NY gender and employment-based income distribution analysis (Ages 15+)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Pleasantville median household income by gender. You can refer the same here

Diversity Index of Every US County using the Simpson Diversity Index: D = 1 - ∑(n/N)^2 (where n = number of people of a given race and N is the total number of people of all races, to get the probability of randomly selecting two people and getting two people of different races (ecological entropy))

As of July 2024, the largest age group among the United States population were adults aged 30 to 34 years old. There were 11.9 million males and some 12.1 million females in this age cohort. The total population of the country was estimated to be 340.1 million Which U.S. state has the largest population? The United States is the third most populous country in the world. It is preceded by China and India, and followed by Indonesia in terms of national population. The gender distribution in the U.S. has remained consistent for many years, with the number of females narrowly outnumbering males. In terms of where the residents are located, California was the state with the largest population. The U.S. population by race and ethnicity The United States poses an ethnically diverse population. In 2023, the number of Black or African American individuals was estimated to be 45.76 million, which represented an increase of over four million since the 2010 census. The number of Asian residents has increased at a similar rate during the same time period and the Hispanic population in the U.S. has also continued to grow.

A number of characteristics of individuals are protected under the 2010 Equality Act, in order to limit the discrimination and disadvantage of groups with one or several shared characteristics. This table brings together a range of sources to present estimates of London's population by gender, age, ethnicity, religion, disability status, country of birth and sexual identity. It also shows population breakdowns for subgroups in each of these categories by broad age group and ethnicity. The socio-economic position of individuals is not a protected characteristic, but is nonetheless an important factor affecting outcomes. The table therefore also includes social class. The latest update showcases London's population as in 2023.

Context

There has been a lot of discussion of the ways in which the workforce for Silicon Valley tech companies differs from that of the United States as a whole. In particular, a lot of evidence suggests that tech workers (who tend to be more highly paid than workers in many other professions) are more likely to be white and male. This dataset will allow you to investigate the demographics for 23 Silicon Valley tech companies for yourself.

Updates!

NEW June 2018: The spreadsheet Distributions_data_2016.csv contains workforce distributions by job category and race for 177 of the largest tech companies headquartered in Silicon Valley.

Each figure in the dataset represents the percentage of each job category that is made up of employees with a given race/gender combination, and are based on each company's EEO-1 report.

This dataset was created through a unique collaboration with the Center for Employment Equity and Reveal. The equity center provided Reveal with anonymized data for 177 large companies, and Reveal identified companies that have publicly released their data in this anonymized dataset. The equity center and Reveal analyzed the data independently.

For more information on the data, read our post here.

The spreadsheet Reveal_EEO1_for_2016.csv has been updated to include EEO-1s from companies PayPal, NetApp and Sanmina for 2016. The race and job categories have been modified to ensure consistency across all the datasets.

NEW April 2018: The spreadsheet Tech_sector_diversity_demographics_2016.csv contains aggregated diversity data for 177 large Silicon Valley tech companies. We calculated averages for the largest race and gender groups across job categories. For information on the aggregated data, read our post here.

This repository also contains EEO-1 reports filed by Silicon Valley tech companies. Please read our complete methodology for details on this data.

The data was compiled by Reveal from The Center for Investigative Reporting.

Contents

This database contains EEO-1 reports filed by Silicon Valley tech companies. It was compiled by Reveal from The Center for Investigative Reporting.

There are six columns in this dataset:

• company: Company name
• year: For now, 2016 only
• race: Possible values: "American_Indian_Alaskan_Native", "Asian", "Black_or_African_American", "Latino", "Native_Hawaiian_or_Pacific_Islander", "Two_or_more_races", "White", "Overall_totals"
• gender: Possible values: "male", "female". Non-binary gender is not counted in EEO-1 reports.
• job_category: Possible values: "Administrative support", "Craft workers", "Executive/Senior officials & Mgrs", "First/Mid officials & Mgrs", "laborers and helpers", "operatives", "Professionals", "Sales workers", "Service workers", "Technicians", "Previous_totals", "Totals"
• count: Mostly integer values, but contains "na" for a no-data variable.

Acknowledgements:

The EEO-1 database is licensed under the Open Database License (ODbL) by Reveal from The Center for Investigative Reporting.

You are free to copy, distribute, transmit and adapt the spreadsheet, so long as you:

• credit Reveal (including this link if it’s distributed online);
• inform Reveal that you are using the data in your work by emailing Sinduja Rangarajan at srangarajan@revealnews.org; and
• offer any new work under the same license.

Inspiration:

• How does each company’s workforce compare to the United States population as a whole? You can find county level diversity information here.
• Which company is the most diverse? Least diverse?

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 Person County. The dataset can be utilized to gain insights into gender-based income distribution within the Person County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Person County, among individuals aged 15 years and older with income, there were 14,382 men and 15,474 women in the workforce. Among them, 7,076 men were engaged in full-time, year-round employment, while 6,619 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 9.23% fell within the income range of under $24,999, while 13.81% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 18.85% of men in full-time roles earned incomes exceeding $100,000, while 18.45% 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 Person County median household income by race. You can refer the same here

Description:

Introducing the Synthetic Demographic Dataset, a large-scale, simulated dataset encompassing 5,000,000 rows. This dataset is a fictional yet intricate assembly of individual profiles, each characterized by various demographic and lifestyle attributes such as name, gender, country, age, income, education level, occupation, and more. It is designed to illustrate the potential of the data generation script, available in the 'Code' section.

Purpose and Use:

The dataset's primary purpose is to display the versatility and depth of the data generation script. It exemplifies how diverse demographic data can be synthesized. This dataset is ideal for understanding the structure and potential of synthetic data but is not intended for predictive modeling or statistical analysis due to the lack of a target variable or real-world correlation.

Key Note:

This dataset does not correlate with any real-world data or target values. It is artificially generated for demonstration purposes only and should not be employed for machine learning models or statistical analyses intending to derive real-world insights or predictions.

Data Format and Attributes:

Each of the 5,000,000 rows represents an individual with attributes including:

• Name
• Gender
• Country
• Age
• Income
• Education Level
• Occupation
• Marital Status
• Number of Children
• Location Type (Urban/Suburban/Rural)
• Health Index
• Exercise Frequency
• Diet Quality Score
• Credit Score
• Car Ownership Status

Dataset Size:

5,000,000 Rows

Code Availability:

Access the code used for generating this dataset in the 'Code' section. It offers insight into synthetic data generation techniques, valuable for educational and demonstration purposes.

Detecting recent demographic changes is a crucial component of species conservation and management, as many natural populations face declines due to anthropogenic habitat alteration and climate change. Genetic methods allow researchers to detect changes in effective population size (Ne) from sampling at a single timepoint. However, in species with long lifespans, there is a lag between the start of a decline in a population and the resulting decrease in genetic diversity. This lag slows the rate at which diversity is lost, and therefore makes it difficult to detect recent declines using genetic data. However, the genomes of old individuals can provide a window into the past, and can be compared to those of younger individuals, a contrast that may help reveal recent demographic declines. To test whether comparing the genomes of young and old individuals can help infer recent demographic bottlenecks, we use forward-time, individual-based simulations with varying mean individual lifespans and extents of generational overlap. We find that age information can be used to aid in the detection of demographic declines when the decline has been severe. When average lifespan is long, comparing young and old individuals from a single timepoint has greater power to detect a recent (within the last 50 years) bottleneck event than comparing individuals sampled at different points in time. Our results demonstrate how longevity and generational overlap can be both a hindrance and a boon to detecting recent demographic declines from population genomic data. Methods All data for this publication were generated via evolutionary simulations in SLiM. Here, we archive all scripts necesarily to generate, analyze, and visualize the results presented in Clark et al. 2024. First, we performed simulations in SLiM using a perennial and annual model for a variety of average lifespans (for the perennial model), and bottleneck severities. The output of these simulations is (1) a .tree file contain the geneological history of the population, from which we will extract information about genetic diversity, (2) individual-based metadata for all individuls alive during the simulation sampling time: the generation number, individual pedigree id and the individual's age, (3) Census population size information about the population at each generation in the sampling period. Second, we used tskit, msprime, and pyslim to load and process .tree files as tree sequences. We then loop through focal sampling points in the tree sequence, and sampling individuals to perform age and temporal comparisons. Genetic diversity data from the sampled bins is exported as .txt files. Finally, genetic diversity data is loaded in R, permutation tests are performed to test for significant differences in genetic diversity between bins, and figures are created.

US Presidential Campaign Logos

Color Diversity and Demographics

By Amber Thomas [source]

About this dataset

We conducted extensive research on popular election campaigns from 1968-2020 as compiled on Wikipedia's entry for each year. From this initial list, we excluded 32 candidates whose images could not be found--leaving us with a total of 271 primary and general party candidates across 14 electoral cycles during that period. In our search for campaign logo images, we prioritized official signs used at rallies, podiums, yards, posters, and bumper stickers with required Federal Election Commission disclaimers--resorting to using buttons only when absolutely necessary . We acknowledge that due to advances in technology, the printing process has significantly impacted the design aesthetics for modern logos compared to those made decades ago.

Using Chrome DevTools or Adobe Photoshop software programs; hexadecimal color values were retreived for each logo clipped from sources such as candidate websites or obtained through additional research efforts. To recognize RWB logos--those using only three colors of red white blue (RWB) --we also surveyed designs including accent tones paired with RWB palettes , two-color schemes (Red/Blue; Red/White; Blue/White), and multiple shades derived from a combination of any 3 primary or secondary RBW hues respectively.

In addition to visual elements associated with picture datasets , candidate demographics such as race , gender are indicated here as binary categories indicating whether a particular demographic is identifiable under one particular label ie either male / female or White / non White individuals . Candidates who fit into both these dual criteria are classfied under majority categories identified under binary labels ie ' whiteMale '. For greater census accuracy candidates classified simply as minority categorizations are merged sounding various Other labels including males belonging outidese racial definitions regardless if identifyingthemselves belonging within -- inclusion of them details belongs hereinunder :

name: The name of the candidate    (String);
party: The political party of thhe candiatate (String);              
white : Binary value indicating if thee candidiate is White     (Boolean);        male: Binary value indocating ffffthueee ccandidate is maille      (Boolean );           whitaeMaile :: Binary alula indicatig

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be useful for understanding trends in campaign symbolism and visual rhetoric surrounding US presidential elections over time. This data could be used to evaluate how diversity amongst candidates is reflected in their campaign visuals by looking at changes in color usage or exploring differences between Democratic and Republican campaigns.

The data can also be visualized to create charts or maps that display possible trends or themes across different elections. This can help users more easily identify patterns between campaign logs for research purposes or simply make for an interesting comparison tool to explore different aspects of certain elections through visuals rather than text alone.

Using this data is easy! Start by familiarizing yourself with all the columns included; you will find information regarding RWB & non-RWB percentages, hexadecimal value breakdowns of each logo's colors & general candidate demographic information such as gender & race. Select desired columns to focus on and decide which analysis method works best; graphical representational options including line graphs, scatter graphs & pie charts are great ways to visually explore how various factors affect color usage both within an election cycle & across multiple cycles over time! Finally you can use these insights gleaned from your analysis to generate interesting questions regarding campaign symbolism design's relationship/influence on voting population demographics/politics!

Research Ideas

• Create an interactive map to show the color trends of presidential logos over the years.
• Use a machine learning algorithm to analyze how the logo colors correlate with primary and general elections.
• Analyze how diversity and inclusion in presidential campaigns has changed by comparing RWB versus non-RWB percentages for each year or election cycle

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

...

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 Lowell. The dataset can be utilized to gain insights into gender-based income distribution within the Lowell population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Lowell, among individuals aged 15 years and older with income, there were 42,026 men and 40,484 women in the workforce. Among them, 21,882 men were engaged in full-time, year-round employment, while 19,032 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 6.34% fell within the income range of under $24,999, while 7.16% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 22.95% of men in full-time roles earned incomes exceeding $100,000, while 13.30% 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 Lowell median household income by race. You can refer the same here

Our species is characterized by a great degree of cultural variation, both within and between populations. Understanding how group-level patterns of culture emerge from individual-level behaviour is a long-standing question in the biological and social sciences. We develop a simulation model capturing demographic and cultural dynamics relevant to human cultural evolution, focusing on the interface between population-level patterns and individual-level processes. The model tracks the distribution of variants of cultural traits across individuals in a population over time, conditioned on different pathways for the transmission of information between individuals. From these data, we obtain theoretical expectations for a range of statistics commonly used to capture population-level characteristics (e.g. the degree of cultural diversity). Consistent with previous theoretical work, our results show that the patterns observed at the level of groups are rooted in the interplay between the trans...

Hair samples were collected in discrete areas during radio-collar studies in Vermont under the auspices of University of Vermont IACUC protocol #17-035 (n=106), New Hampshire (n=34), and Maine (n=57). Hair and tissue samples were opportunistically collected from animals that were harvested, died in vehicle collisions, or translocated throughout Vermont (n = 105), Quebec (n = 198), Massachusetts (n = 5), and New York (n = 24). Of the 317 previously identified autosomal moose SNPs, 136 loci were utilized to develop a MALDI-TOF MS genotyping assay. After filtering problematic loci and individuals, genotypes from 112 of 136 SNPs (82%) were obtained for 507 individuals and all loci met Hardy-Weinberg expectations in the nine geographic regions samples.

BackgroundThe COVID-19 pandemic has significantly impacted global health, with diverse factors influencing the risk of death among reported cases. This study mainly analyzes the main characteristics that have contributed to the increase or decrease in the risk of death among Severe Acute Respiratory Syndrome (SARS) cases classified as COVID-19 reported in southeast Brazil from 2020 to 2023.MethodsThis cohort study utilized COVID-19 notification data from the Sistema de Vigilância Epidemiológica (SIVEP) information system in the southeast region of Brazil from 2020 to 2023. Data included demographics, comorbidities, vaccination status, residence area, and survival outcomes. Classical Cox, Cox mixed effects, Prentice, Williams & Peterson (PWP), and PWP fragility models were used to assess the risk of dying over time.ResultsAcross 987,534 cases, 956,961 hospitalizations, and 330,343 deaths were recorded over the period. Mortality peaked in 2021. The elderly, males, black individuals, lower-educated, and urban residents faced elevated risks. Vaccination reduced death risk by around 20% and 13% in 2021 and 2022, respectively. Hospitalized individuals had lower death risks, while comorbidities increased risks by 20–26%.ConclusionThe study identified demographic and comorbidity factors influencing COVID-19 mortality. Rio de Janeiro exhibited the highest risk, while São Paulo had the lowest. Vaccination significantly reduces death risk. Findings contribute to understanding regional mortality variations and guide public health policies, emphasizing the importance of targeted interventions for vulnerable groups.

University Student Enrollment Data

Demographics and Academic Details of University Student Enrollments

By Gove [source]

About this dataset

This dataset represents a comprehensive collection of valuable and relevant information regarding student registration across a multitude of universities. It provides an in-depth insight into various aspects of this subject matter, making it an indispensable tool for any research related to university student registrations.

The information contained within this particular dataset offers extensive details about each individual student. This rich, individual data includes demographic particulars such as their age, gender and nationality - details which could yield interesting points of analysis when correlated against other factors within the data.

Additionally, this dataset maintains academic records for each registered student, providing detailed descriptions like course of study and year of enrollment. This formative data aids in understanding students' registration patterns over the years or tracking their academic progression throughout their tenure at university.

Moreover, the dataset is also expected to contain vital statistics tied to individual universities where these students are enrolled. Such expected details include each institution's location which can provide geo-political or socio-economic insights pertaining to university selection trends amongst students.

Further enriching the body of knowledge available within this repository is potential data related to specific course offerings by these universities – a feature useful for assessing popular disciplines or identifying shifts in educational trends based on subject popularity.

Another significant set of information which might be found inside this repository pertains to faculty specifics including number and qualifications alongside overall ranking standings – these can serve as additional metrics in gauging perceived quality or reputation among the registered student bodies with respect to selecting universities for further studies.

In sum, whether you’re interested in mapping out educational trends over time; analyzing demographic profiles against choice courses; studying correlations between nationality and select colleges; or looking into institutional rankings’ sway over enrollments – this amalgamation holds invaluable keys that unlock numerous possibilities through exploration via different combinations making it versatile enough for diverse investigatory needs while offering deep analytical potentials for those willing explore its depths

How to use the dataset

• Student Demographic Analysis: You can use this dataset to understand the demographic distribution of students across universities. This involves analyzing information related to age, nationality, and gender among others. For example, you might want to find out which university has the highest number of international students or what is the gender ratio in a specific course of study.

• Analysis on Courses & Faculties: Data from this dataset can be used for insightful exploration into various courses and faculties offered by different universities. You may want to investigate questions like What is the most popular course?or Which university has a larger faculty for science stream?.

• University Comparison: The data allows for comparison between different universities based on their student population, diversity, departments/faculties and courses being offered etc.. In doing so, you could discern trends or patterns linked with university ranking and location that may play role in student enrollment decisions.

• Tracking Enrollment Trends: By examining factors such as year of enrollment and course selections over time, it becomes possible to track trends within each school's student body population or wider academic field at large scale over multiple years; potentially even predicting future movements.

• The dataset also provides excellent resources for machine learning applications such as predictive models for student academic performance or building recommender systems capable off suggesting best suited unversities or courses based on individual characterstics.

• This data set can also aid administrative decision making processes around things like budget allocation (based on number of students per faculty), policy changes related with improving diversity within campus etc., providing valuable quantitative backing towards making such important decisions.

Remember that while using this dataset correctly respecting privacy norms is paramount given sensitive nature involved with personal details included here; always adhere...

In 2024, more than ** percent of people employed in the motion picture and video industries in the United States identified as white. About one out of ten employees identified as Black or African American. That same year, almost ********** of employees in the U.S. film industry were male.