About this dataset

Context

This list ranks the 40 cities in the Blue Earth County, MN by Non-Hispanic Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Non-Hispanic Other Race Population: This column displays the rank of cities in the Blue Earth County, MN by their Non-Hispanic Some Other Race (SOR) population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Non-Hispanic Other Race Population: The Non-Hispanic Other Race population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Non-Hispanic Other Race. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Blue Earth County Non-Hispanic Other Race Population: This tells us how much of the entire Blue Earth County, MN Non-Hispanic Other Race population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

About this dataset

Context

The dataset presents the median household income across different racial categories in Black Earth. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Black Earth population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 93.91% of the total residents in Black Earth. Notably, the median household income for White households is $72,054. Interestingly, despite the White population being the most populous, it is worth noting that Two or More Races households actually reports the highest median household income, with a median income of $131,250. This reveals that, while Whites may be the most numerous in Black Earth, Two or More Races households experience greater economic prosperity in terms of median household income.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Black Earth.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

The dataset presents the median household income across different racial categories in White Earth township. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of White Earth township population by race & ethnicity, the population is predominantly American Indian and Alaska Native. This particular racial category constitutes the majority, accounting for 46% of the total residents in White Earth township. Notably, the median household income for American Indian and Alaska Native households is $17,375. Interestingly, despite the American Indian and Alaska Native population being the most populous, it is worth noting that Two or More Races households actually reports the highest median household income, with a median income of $88,036. This reveals that, while American Indian and Alaska Natives may be the most numerous in White Earth township, Two or More Races households experience greater economic prosperity in terms of median household income.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in White Earth township.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 White Earth township median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the median household income across different racial categories in Globe. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Globe population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 58.09% of the total residents in Globe. Notably, the median household income for White households is $65,261. Interestingly, despite the White population being the most populous, it is worth noting that Some Other Race households actually reports the highest median household income, with a median income of $98,333. This reveals that, while Whites may be the most numerous in Globe, Some Other Race households experience greater economic prosperity in terms of median household income.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Globe.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

The dataset presents the median household income across different racial categories in Black Earth town. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Black Earth town population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 95.17% of the total residents in Black Earth town. Notably, the median household income for White households is $137,500. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $137,500.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Black Earth town.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

The dataset presents the median household income across different racial categories in Blue Earth County. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Blue Earth County population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 86.76% of the total residents in Blue Earth County. Notably, the median household income for White households is $75,999. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $75,999.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Blue Earth County.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

The dataset presents the median household income across different racial categories in Lincoln township. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Lincoln township population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 92.69% of the total residents in Lincoln township. Notably, the median household income for White households is $84,157. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $84,157.

https://i.neilsberg.com/ch/lincoln-township-blue-earth-county-mn-median-household-income-by-race.jpeg" alt="Lincoln township median household income diversity across racial categories">

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Lincoln township.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

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 Lincoln township median household income by race. You can refer the same here

This dataset is about book subjects. It has 1 row and is filtered where the books is That near-death thing : inside the most dangerous race in the world. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This Dataset shows the Alexa Top 100 International Websites, and provides metrics on the volume of traffic that these sites were able to handle. The Alexa top 100 lists the 100 most visited websites in the world and measures various statistical information. I have looked up the Headquarters, either through alexa, or a Whois Lookup to get street address with i was then able to geocode. I was only able to successfully geocode 85 of the top 100 sites throughout the world. Source of Data was Alexa.com, Source URL: http://www.alexa.com/site/ds/top_sites?ts_mode=global&lang=none Data was from October 12, 2007. Alexa is updated daily so to get more up to date information visit their site directly. they don't have maps though.

The 2007 World Bank Group Entrepreneurship Survey measures entrepreneurial activity in 84 developing and industrial countries over the period 2003-2005. The database includes cross-country, time-series data on the number of total and newly registered businesses, collected directly from Registrar of Companies around the world. In its second year, this survey incorporates improvements in methodology, and expanded participation from countries covered, allowing for greater cross-border compatibility of data compared with the 2006 survey. This joint effort by the IFC SME Department and the World Bank Developing Research Group is the most comprehensive dataset on cross-country firm entry data available today. This database The World Bank Group Entrepreneurship Dataaset presents data collected primarily from country business registries using the first annual World Bank Group Questionnaire on Entrepreneurship (alternative sources were tax authorities, finance ministries, and national statistics offices). For more information on the author of the database, Leora Klapper, visit: http://go.worldbank.org/DK5AHCQSO0. This data was access at the preceeding link, on October 11, 2007. Please visit the link for more information in regards to this dataset.

This dataset is a boundary file obtained from the US Census Tiger Shape file library which can be found online. I downloaded the File for California then Used ESRI ArcMap to cut out the San Francisco MSA from Californai. Census demographic data was joined to the boundaries. This file includes attributes on Race and Populations and other demographic data.

This dataset shows the locations of the 100 companies that had the greatest revenue increase from 2005 to 2006 that are among the Fortune Global 500 for 2006. The list comes from cnnmoney.com whose Fortune section does analysis on the top corporations throughout the world

Oscar-Winning Directors Analysis

Examining Gender, Race and Director Trends from 1930-2019

By Priyanka Dobhal [source]

About this dataset

This dataset collects information on the Academy Award for Best Director winners from 1930 to 2019, and provides insight into the gender and racial disparity of filmmaking over time. It includes the winner's name, their respective award year, race, gender, nominated/winning film title, and the filmmakers' names. By looking at this data it is possible to identify emerging trends in cinema- such as who is dominating in terms of awards recognition- and consider how much progress has been made when it comes to equal opportunity within Hollywood. Examining Oscar winning directors over time can tell us a lot about its impact on systemic issues in our society as diversity increases among winners. To deepen our understanding of this award’s significance it is necessary to consider all factors included; from awarded directors’ gender to what kind of films are being supported by these awards annually. So come explore with us! Let's take an analysis deep dive into almost nine decades worth of cinematic history - starting from 1930 - and see who won big at the Academy Awards…

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset is the perfect resource for anyone looking to conduct an analysis of the Academy Award for Best Director winners from 1930 to 2019. It contains information on the year, gender, race, director(s), film and nomination/winner of each winner.

By using this dataset, you can gain insight into trends in Oscar winning directors over time. For example, you can compare the number of nominees between different years or examine differences in representation of gender and race among directors who have won Oscars over time. Additionally, you can use this data to explore the films that have received an Oscar for best director – which films were most successful from a narrative perspective? Or analyze which films used unique filming techniques or visual designs? Finally, this dataset also makes it possible to conduct more targeted analyses by identifying patterns across multiple aspects such as furthering social issues that are depicted in film through positive filmmaking - such as LGBTQ representation.

To start exploring with this dataset:

2) Open your favorite spreadsheet program ('Microsoft Excel', 'Libre Office', etc.)

3) Load csv file with' File —> Open' command

4) Review column headers and values contained within each row

5) Start creating charts and graphs (pie charts barplots etc.) that show trends over time according to your needs

6) Take notes while analyzing datasets

7) Publish your findings online if desired

The possibilities are endless! If you’d like additional guidance or tips on how to effectively use this data set please subscribe our newsletter at oscarwinningdirectorsanalysisgmail.com

Research Ideas

• Analyzing gender and racial disparity in the Academy award for Best Director across different years.
• Investigating if the age of directors has an effect on what film they create and how successful it is at winning an Oscar for Best Director.
• Crafting a recommendation system to recommend movies based on a director's previous Oscar-winning work or even pair users with film recommendations that have similar director/genre preference in order to discover new titles they may enjoy watching

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: Oscar Winners - Director.csv | Column name | Description | |:----------------------|:--------------------------------------------------------------| | Year | The year in which the award was given. (Integer) | | Gender | The gender of the director. (String) | | Race | The race of the director. (String) | | Director(s) | The name of the director(s). (String) | | Film | The title of the film that won the award. (String) | | Nomination/Winner | Whether the director was nominated or won the award. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Priyanka Dobhal.

Overview

The World of Warcraft Avatar History Dataset is a collection of records that detail information about player characters in the game over time. It includes information about their character level, race, class, location, and social guild. The Kaggle version of this dataset includes only the information from 2008 (and the dataset in general only includes information from the 'Horde' faction of players in the game from a single game server).

• Full Dataset Source and Information: http://mmnet.iis.sinica.edu.tw/dl/wowah/
• Code used to clean the data: https://github.com/myles-oneill/WoWAH-parser

Ideas for Using the Dataset

From the perspective of game system designers, players' behavior is one of the most important factors they must consider when designing game systems. To gain a fundamental understanding of the game play behavior of online gamers, exploring users' game play time provides a good starting point. This is because the concept of game play time is applicable to all genres of games and it enables us to model the system workload as well as the impact of system and network QoS on users' behavior. It can even help us predict players' loyalty to specific games.

Open Questions

• Understand user gameplay behavior (game sessions, movement, leveling)
• Understand user interactions (guilds)
• Predict players unsubscribing from the game based on activity
• What are the most popular zones in WoW, what level players tend to inhabit each?

Wrath of the Lich King

An expansion to World of Warcraft, "Wrath of the Lich King" (Wotlk) was released on November 13, 2008. It introduced new zones for players to go to, a new character class (the death knight), and a new level cap of 80 (up from 70 previously). This event intersects nicely with the dataset and is probably interesting to investigate.

Map

This dataset doesn't include a shapefile (if you know of one that exists, let me know!) to show where the zones the dataset talks about are. Here is a list of zones an information from this version of the game, including their recommended levels: http://wowwiki.wikia.com/wiki/Zones_by_level_(original) .

Update (Version 3): dmi3kno has generously put together some supplementary zone information files which have now been included in this dataset. Some notes about the files:

Note that some zone names contain Chinese characters. Unicode names are preserved as a key to the original dataset. What this addition will allow is to understand properties of the zones a bit better - their relative location to each other, competititive properties, type of gameplay and, hopefully, their contribution to character leveling. Location coordinates contain some redundant (and possibly duplicate) records as they are collected from different sources. Working with uncleaned location coordinate data will allow users to demonstrate their data wrangling skills (both working with strings and spatial data).

This dataset is a point based representation of major port and harbors. The dataset layer is comprised of 4792 derivative vector framework library features derived based on 1:3 000 000 data originally from RWDBII. The layer provides nominal analytical/mapping at 1:3 000 000. Data processing complete globally. This data was collected from: http://www.fao.org/geonetwork/srv/en/metadata.show?id=29042&currTab=simple Access Date: October 15, 2007.

This dataset has been migrated from our Geocommons platform, and lacks a description from the original posting user. This is not a Fortiusone provided dataset. Please keep this in mind, and make of the dataset what you will. Thank you for visiting Finder!

This dataset was created from the CDC's National Vital Statistics Reports Volume 56, Number 6. The dataset includes all data available from this report by state level and includes births by race and Hispanic origin, births to unmarried women, rates of cesarean delivery, and twin and multiple birth rates. The data are final for 2005. No value is represented by a -1. "Descriptive tabulations of data reported on the birth certificates of the 4.1 million births that occurred in 2005 are presented. Denominators for population-based rates are postcensal estimates derived from the U.S. 2000 census".

This dataset shows the locations of the largest corporations in the world by the city where their headquarters is located. The rankings are based on the amount of total revenue that the corporation earned during the year 2005. These 500 corporations are known as being a part of the Fortune 500 group which is an annual poll of the top corporations in the world. The poll is conducted by cnnmoney.com.

This dataset explores Percentage of eighth-grade public school students and average scores in NAEP writing by race and state, USA, 2007 Notes: Not available. The state/jurisdiction did not participate. # Rounds to zero. Reporting standards not met. Sample size is insufficient to permit a reliable estimate. NOTE: Black includes African American, Hispanic includes Latino, and Pacifi c Islander includes Native Hawaiian. Race categories exclude Hispanic origin. Results are not shown for students whose race/ethnicity was unclassified Detail may not sum to totals because of rounding. SOURCE: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress (NAEP), 2007 Writing Assessment.

Hate speech detection in Arabic poses a complex challenge due to the dialectal diversity across the Arab world. Most existing hate speech datasets for Arabic cover only one dialect or one hate speech category. They also lack balance across dialects, topics, and hate/non-hate classes. In this paper, we address this gap by presenting ADHAR—a comprehensive multi-dialect, multi-category hate speech corpus for Arabic. ADHAR contains 70,369 words and spans four language variants: Modern Standard Arabic (MSA), Egyptian, Levantine, Gulf and Maghrebi. It covers four key hate speech categories: nationality, religion, ethnicity, and race. A major contribution is that ADHAR is carefully curated to maintain balance across dialects, categories, and hate/non-hate classes to enable unbiased dataset evaluation. We describe the systematic data collection methodology, followed by a rigorous annotation process involving multiple annotators per dialect. Extensive qualitative and quantitative analyses demonstrate the quality and usefulness of ADHAR. Our experiments with various classical and deep learning models demonstrate that our dataset enables the development of robust hate speech classifiers for Arabic, achieving accuracy and F1-scores of up to 90% for hate speech detection and up to 92% for category detection. When trained with Arabert, we achieved an accuracy and F1-score of 94% for hate speech detection, as well as 95% for the category detection.