Understanding the dynamics of citizens' opinions, preferences, perceptions, and attitudes is pivotal in political science and essential for informed policymaking. Although highly sophisticated tools have been developed for analyzing these dynamics through surveys, outside the field of polarization, these analyses often focus on average responses, thereby missing important information embedded in other parameters of data distribution. Our study aims to fill this gap by illustrating how analyzing the evolution of both the mean and the distribution shape of responses can offer complementary insights into opinion dynamics. Specifically, we explore this through the French public's perception of defense issues, both before and after the onset of the war in Ukraine. Our findings underscore how routinely combining classical approaches with the use of existing tools for measuring distribution shapes can provide valuable perspectives for researchers and policymakers alike, by highlighting the nuanced shifts in public opinion that traditional methods might overlook.

Financial Security (Credit Union Dataset)

Geographic Distribution and Risk Factors

By Homeland Infrastructure Foundation [source]

About this dataset

This dataset contains credit union headquarters information from the National Credit Union Administration (NCUA) collected over the past two years. The data includes coordinates, addresses, names, telephone numbers, and other details for each credit union. It is a great tool for understanding the state of credit unions in the United States and their hierarchical structure.The NCUA has used this data to help measure financial health and performance across federal and state-chartered institutions in order to safeguard the savings of millions of account holders throughout the US. With this dataset you can explore regional trends within various regions while examining legal structures such as Federal or State-chartered boasting detailed analyses from underlying variables such as county FIPS codes alongside a variety useful features like latitude/longitude pairs and sourcedate values . These insights could then be applied to diverse financial products like loans, lending practices or investments furthering our understanding into how different finance divisions operate across countries. Through these resources we can take steps towards safer banking environments with sounder policies that are upto date with federal regulations while maintaining strong economic growth within our nation’s boundaries

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For more datasets, click here.

Featured Notebooks

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How to use the dataset

This dataset contains information about credit union headquarters, including address, contact information, and location. With this data you can explore how different financial variables affect the health of credit unions.

Research Ideas

• Developing mapping and heat-mapping applications to visually identify credit union saturation across the country.
• Establishing patterns in credit union success and failure based on geographical areas, types of services offered, or other factors such as telephone access.
• Targeting marketing campaigns with an emphasis on local outreach and developing a better understanding of the demographic composition, interests, or financial needs of customers in specific areas accessible to particular credit unions

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Credit_Union_Headquarters.csv.crdownload | Column name | Description | |:---------------|:--------------------------------------------------------------------------------------------------------------------------------| | X | X coordinate of the credit union headquarters. (Numeric) | | Y | Y coordinate of the credit union headquarters. (Numeric) | | CU_NUMBER | The unique number assigned to the credit union. (String) | | NAME | The name of the credit union. (String) | | ADDRESS | The street address of the credit union headquarters. (String) | | ADDRESS2 | The secondary address of the credit union headquarters (if applicable). (String) | | CITY | The city where the credit union headquarters is located. (String) | | STATE | The state where the credit union headquarters is located. (String) | |...

About this dataset

Context

The dataset tabulates the South Carolina median household income by race. The dataset can be utilized to understand the racial distribution of South Carolina 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).

• South Carolina median household income breakdown by race betwen 2012 and 2022
• Median Household Income by Racial Categories in South Carolina (2022)

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 South Carolina median household income by race. You can refer the same here

The attached files are different formats of the same Jupyter Notebook containing R code. This document is about finding distribution properties of the genetic relationship matrix (K) and its inverse (used in best linear unbiased prediction (BLUP)) influencing the genetic variance, the estimated heritability (h^2), and the variance of estimated breeding values. The main factors influencing the distribution of estimated breeding values are the phenotypic variance, estimated h^2, and K^-1. Matrices K and K^-1 underwent transformations to find the distribution properties of these two matrices affecting the estimated h^2 and the distribution of estimated breeding values. The wheat data from R package BGLR were used. The inverses of the pedigree-based K (A^-1) and the genomic-based K (G^-1) had similar averages of diagonal and similar averages of off-diagonal elements but very different h^2 estimates. Therefore, the distributions of A^-1 and G^-1 elements were studied closely.

About this dataset

Context

The dataset tabulates the New Haven household income by gender. The dataset can be utilized to understand the gender-based income distribution of New Haven 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).

• New Haven, IN annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• New Haven, IN 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 New Haven income distribution by gender. You can refer the same here

Wikipedia Movie Data

Exploring Production and Distribution Trends Across Four Decades

By Michael Tauberg [source]

About this dataset

This comprehensive dataset spans a substantial sampling of movies from the last five decades, giving insight into the financial and creative successes of Hollywood film productions. Containing various production details such as director, actors, editing team, budget, and overall gross revenue, it can be used to understand how different elements come together to make a movie successful. With information covering all aspects of movie-making – from country of origin to soundtrack composer – this collection offers an unparalleled opportunity for a data-driven dive into the world of cinematic storytelling

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The columns are important factors to analyze the data in depth – they range from general information such as year, name and language of movie to more specific info such as directors and editors of movie production teams. A good first step is to get an understanding of what kind of data exists and getting familiar with different columns.

Good luck exploring!

Research Ideas

• Analyzing the correlations between budget, gross revenue, and number of awards or nominations won by a movie. Movie-makers and studios can use this data to understand what factors have an impact on the success of a movie and make better creative decisions accordingly.
• Studying the trend of movies from different countries over time to understand how popular genres are changing over time across regions and countries; this data could be used by international film producers to identify potential opportunities for co-productions with other countries or regions.
• Identifying unique topics for films (based on writers, directors, music etc) that hadn’t been explored in previous decades - studios can use this data to find unique stories or ideas for new films that often succeed commercially due to its novelty factor with audiences

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: movies_1970_2018.csv | Column name | Description | |:-------------------|:----------------------------------------------------------| | year | Year the movie was released. (Integer) | | wiki_ref | Reference to the Wikipedia page for the movie. (String) | | wiki_query | Query used to search for the movie on Wikipedia. (String) | | producer | Name of the producer of the movie. (String) | | distributor | Name of the distributor of the movie. (String) | | name | Name of the movie. (String) | | country | Country of origin of the movie. (String) | | director | Name of the director of the movie. (String) | | cinematography | Name of the cinematographer of the movie. (String) | | editing | Name of the editor of the movie. (String) | | studio | Name of the studio that produced the movie. (String) | | budget | Budget of the movie. (Integer) | | gross | Gross box office receipts of the movie. (Integer) | | runtime | Length of the movie in minutes. (Integer) | | music | Name of the composer of the movie's soundtrack. (String) | | writer | Name of the writer of the movie. (String) | | starring | Names of the actors in the movie. (String) | | language | Language of the movie. (String) |

Acknowledgements

If you use this dataset in your research, p...

The data include three supplemental tables necessary to replicate the analyses performed in The Ideal Distribution of Farmers: Explaining the Euro-American Settlement of Utah. The data are too large to include in the published format. The table titled "Years_Occupied" contains seven columns, including the settlement name, the year, the population (linearly interpolated between census years), the Moisture Index value for the settlement, the probability of cultivation value (S), UTM Zone 12 NAD83 easting, and NAD83 northing. Settlements have a row from settlement date, through occupation, to abandonment (if abandoned before 1950). The data in this table allow for users to determine the average MI and S values of occupied habitats through time. The table titled "Settlement_Vals_UTMS" contains six columns, including settlement name, year settled, a Moisture Index value, a probability of cultivation (S) value, UTM Zone 12 NAD83 easting, and a NAD83 northing. Each row is a settlement used in our analyses. The data in this table allow users to see a complete list of settlements used, the year of settlement used, and each settlements location. The table titled "AnnualPop_AvgMISVals" contains four columns, including the year, state population, the average MI value of occupied habitats for that year, and the average S value of occupied habitats for that year. The data in this table allow users to compare population and average suitability values of occupied habitats through time.

The Mega Star Distribution Centre Dataset is a comprehensive simulation dataset designed to provide valuable insights and hands-on experience in the field of warehousing and logistics. Created by leveraging extensive knowledge in inventory management, this dataset aims to assist individuals and organizations in understanding the fundamental tasks involved in efficiently managing a large-scale distribution centre.

Overview: The Mega Star Distribution Centre is a fictitious facility, encompassing an impressive area of over 50,000 square meters and containing more than 60,000 distinct locations for storing various products. The dataset comprises four primary components, each serving a unique purpose in offering a holistic view of the distribution centre's operations:

Product List: The "Product List" contains essential information about all the products housed within the distribution centre. This dataset provides crucial details, such as product names, descriptions, unique identifiers, and other relevant attributes necessary for managing inventory effectively.

Warehouse Stocks: The "Warehouse Stocks" section of the dataset offers a snapshot of the current stock levels within the Mega Star Distribution Centre. It provides a comprehensive inventory report, listing the available quantities of each product and their respective locations within the facility. Understanding the stock levels and their distribution across various locations is essential for optimizing storage space and managing inventory replenishment efficiently.

Receiving Records: The "Receiving" records track the activities related to receiving new stock into the distribution centre over five days. This dataset captures information about incoming shipments, including the products received, their quantities, origins, and the personnel responsible for handling the receiving tasks. Analyzing this data can provide valuable insights into the efficiency of the receiving process and identify any potential bottlenecks.

Picking Records: The "Picking" records document the tasks involved in fulfilling customer orders from the distribution centre during the same five-day period. This dataset includes details on the products picked, the quantities involved, the locations from which the items were retrieved, and the personnel responsible for executing the picking tasks. Analyzing this data can help optimize the order fulfilment process, minimize picking errors, and improve overall customer satisfaction.

Benefits and Applications: The Mega Star Distribution Centre Dataset is a valuable resource for anyone seeking to gain practical experience and insights into the complex world of inventory management. It offers a safe and controlled environment for honing skills, testing strategies, and understanding the challenges faced in real-world distribution centres. Some key benefits and applications include:

Training and Education: The dataset can serve as an educational tool for students, professionals, and researchers in logistics, supply chain management, and related fields, allowing them to explore and experiment with inventory management concepts.

Process Optimization: By analysing the dataset, supply chain managers and warehouse operators can identify areas for process improvement, streamline operations, and enhance overall efficiency.

Decision-making Support: The data can aid in making informed decisions regarding inventory levels, replenishment strategies, and resource allocation, leading to better inventory control and cost savings.

Performance Evaluation: The dataset enables the evaluation of key performance indicators (KPIs) related to warehousing and logistics, facilitating a data-driven approach to assessing the distribution centre's effectiveness.

Conclusion: The Mega Star Distribution Centre Dataset offers a rich and diverse collection of data, providing an immersive experience in managing inventory in a large-scale distribution centre. Whether you are a student, a logistics professional, or a researcher, this dataset presents a unique opportunity to gain practical insights and refine your skills in inventory management. With its simulated yet realistic scenarios, the dataset aims to contribute to the continuous improvement and advancement of warehousing and logistics practices.

About this dataset

Context

The dataset tabulates the Central Point household income by gender. The dataset can be utilized to understand the gender-based income distribution of Central Point 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).

• Central Point, OR annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Central Point, OR 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 Central Point income distribution by gender. You can refer the same here

Context

Netflix in the past 5-10 years has captured a large populate of viewers. With more viewers, there most likely an increase of show variety. However, do people understand the distribution of ratings on Netflix shows?

Content

Because of the vast amount of time it would take to gather 1,000 shows one by one, the gathering method took advantage of the Netflix’s suggestion engine. The suggestion engine recommends shows similar to the selected show. As part of this data set, I took 4 videos from 4 ratings (totaling 16 unique shows), then pulled 53 suggested shows per video. The ratings include: G, PG, TV-14, TV-MA. I chose not to pull from every rating (e.g. TV-G, TV-Y, etc.).

Acknowledgements

The data set and the research article can be found at The Concept Center

Inspiration

I was watching Netflix with my wife and we asked ourselves, why are there so many R and TV-MA rating shows?

About this dataset

Context

The dataset tabulates the West Union town household income by gender. The dataset can be utilized to understand the gender-based income distribution of West Union town 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).

• West Union, New York annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• West Union, New York 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 West Union town income distribution by gender. You can refer the same here

About this dataset

Context

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

• Bay City, OR annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Bay City, OR 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 Bay City income distribution by gender. You can refer the same here

About this dataset

Context

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

• Flora, IN annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Flora, IN 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 Flora income distribution by gender. You can refer the same here

About this dataset

Context

The dataset tabulates the Lane County household income by gender. The dataset can be utilized to understand the gender-based income distribution of Lane County 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).

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

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 Lane County income distribution by gender. You can refer the same here

About this dataset

Context

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

• Union, IL annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Union, IL 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 Union income distribution by gender. You can refer the same here

About this dataset

Context

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

• Prairie City, OR annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Prairie City, OR 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 Prairie City income distribution by gender. You can refer the same here

About this dataset

Context

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

• Industry, PA annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Industry, PA 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 Industry income distribution by gender. You can refer the same here

About this dataset

Context

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

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

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 Portland income distribution by gender. You can refer the same here

About this dataset

Context

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

• Orchard City, CO annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Orchard City, CO 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 Orchard City income distribution by gender. You can refer the same here

About this dataset

Context

The dataset tabulates the Oak Creek household income by gender. The dataset can be utilized to understand the gender-based income distribution of Oak Creek 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).

• Oak Creek, CO annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Oak Creek, CO 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

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