About this dataset

Context

The dataset tabulates the population of Gratis by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Gratis across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.0% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Gratis is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Gratis total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Gratis Population by Race & Ethnicity. You can refer the same here

Looking for a free Walmart product dataset? The Walmart Products Free Dataset delivers a ready-to-use ecommerce product data CSV containing ~2,100 verified product records from Walmart.com. It includes vital details like product titles, prices, categories, brand info, availability, and descriptions — perfect for data analysis, price comparison, market research, or building machine-learning models.

Key Features

Complete Product Metadata: Each entry includes URL, title, brand, SKU, price, currency, description, availability, delivery method, average rating, total ratings, image links, unique ID, and timestamp.

CSV Format, Ready to Use: Download instantly - no need for scraping, cleaning or formatting.

Good for E-commerce Research & ML: Ideal for product cataloging, price tracking, demand forecasting, recommendation systems, or data-driven projects.

Free & Easy Access: Priced at USD $0.0, making it a great starting point for developers, data analysts or students.

Who Benefits?

• Data analysts & researchers exploring e-commerce trends or product catalog data.
• Developers & data scientists building price-comparison tools, recommendation engines or ML models.
• E-commerce strategists/marketers need product metadata for competitive analysis or market research.
• Students/hobbyists needing a free dataset for learning or demo projects.

Why Use This Dataset Instead of Manual Scraping?

• Time-saving: No need to write scrapers or deal with rate limits.
• Clean, structured data: All records are verified and already formatted in CSV, saving hours of cleaning.
• Risk-free: Avoid Terms-of-Service issues or IP blocks that come with manual scraping.
  Instant access: Free and immediately downloadable.

Introducing Job Posting Datasets: Uncover labor market insights!

Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.

Job Posting Datasets Source:

1. Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.

2. Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.

3. StackShare: Access StackShare datasets to make data-driven technology decisions.

Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.

Choose your preferred dataset delivery options for convenience:

Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.

Why Choose Oxylabs Job Posting Datasets:

1. Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.

2. Time and resource savings: Focus on data analysis and your core business objectives while we efficiently handle the data extraction process cost-effectively.

3. Customized solutions: Tailor our approach to your business needs, ensuring your goals are met.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
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• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.

Description:

The "Daily Social Media Active Users" dataset provides a comprehensive and dynamic look into the digital presence and activity of global users across major social media platforms. The data was generated to simulate real-world usage patterns for 13 popular platforms, including Facebook, YouTube, WhatsApp, Instagram, WeChat, TikTok, Telegram, Snapchat, X (formerly Twitter), Pinterest, Reddit, Threads, LinkedIn, and Quora. This dataset contains 10,000 rows and includes several key fields that offer insights into user demographics, engagement, and usage habits.

Dataset Breakdown:

• Platform: The name of the social media platform where the user activity is tracked. It includes globally recognized platforms, such as Facebook, YouTube, and TikTok, that are known for their large, active user bases.

• Owner: The company or entity that owns and operates the platform. Examples include Meta for Facebook, Instagram, and WhatsApp, Google for YouTube, and ByteDance for TikTok.

• Primary Usage: This category identifies the primary function of each platform. Social media platforms differ in their primary usage, whether it's for social networking, messaging, multimedia sharing, professional networking, or more.

• Country: The geographical region where the user is located. The dataset simulates global coverage, showcasing users from diverse locations and regions. It helps in understanding how user behavior varies across different countries.

• Daily Time Spent (min): This field tracks how much time a user spends on a given platform on a daily basis, expressed in minutes. Time spent data is critical for understanding user engagement levels and the popularity of specific platforms.

• Verified Account: Indicates whether the user has a verified account. This feature mimics real-world patterns where verified users (often public figures, businesses, or influencers) have enhanced status on social media platforms.

• Date Joined: The date when the user registered or started using the platform. This data simulates user account history and can provide insights into user retention trends or platform growth over time.

Context and Use Cases:

• This synthetic dataset is designed to offer a privacy-friendly alternative for analytics, research, and machine learning purposes. Given the complexities and privacy concerns around using real user data, especially in the context of social media, this dataset offers a clean and secure way to develop, test, and fine-tune applications, models, and algorithms without the risks of handling sensitive or personal information.

Researchers, data scientists, and developers can use this dataset to:

• Model User Behavior: By analyzing patterns in daily time spent, verified status, and country of origin, users can model and predict social media engagement behavior.

• Test Analytics Tools: Social media monitoring and analytics platforms can use this dataset to simulate user activity and optimize their tools for engagement tracking, reporting, and visualization.

• Train Machine Learning Algorithms: The dataset can be used to train models for various tasks like user segmentation, recommendation systems, or churn prediction based on engagement metrics.

• Create Dashboards: This dataset can serve as the foundation for creating user-friendly dashboards that visualize user trends, platform comparisons, and engagement patterns across the globe.

• Conduct Market Research: Business intelligence teams can use the data to understand how various demographics use social media, offering valuable insights into the most engaged regions, platform preferences, and usage behaviors.

• Sources of Inspiration: This dataset is inspired by public data from industry reports, such as those from Statista, DataReportal, and other market research platforms. These sources provide insights into the global user base and usage statistics of popular social media platforms. The synthetic nature of this dataset allows for the use of realistic engagement metrics without violating any privacy concerns, making it an ideal tool for educational, analytical, and research purposes.

The structure and design of the dataset are based on real-world usage patterns and aim to represent a variety of users from different backgrounds, countries, and activity levels. This diversity makes it an ideal candidate for testing data-driven solutions and exploring social media trends.

Future Considerations:

As the social media landscape continues to evolve, this dataset can be updated or extended to include new platforms, engagement metrics, or user behaviors. Future iterations may incorporate features like post frequency, follower counts, engagement rates (likes, comments, shares), or even sentiment analysis from user-generated content.

By leveraging this dataset, analysts and data scientists can create better, more effective strategies ...

This collection contains three datasets: Courses, Instructors, and Course_Instructors. Each dataset provides comprehensive information related to skill development courses from major platforms in Bangladesh. Below are the details of each dataset and the columns they contain.

thumbnail & header image credit: textiletoday

1. Courses Dataset

The Courses dataset contains information about the individual courses offered by the platforms. This dataset includes the following columns:

• course_id: Unique identifier for each course (Primary Key).
• title: The title of the course.
• price: The price of the course.
• link: The URL link to the course.
• total_enrolled_students: The total number of students enrolled in the course.
• category: The category to which the course belongs.
• batch_no: The batch number of the course.
• launch_date: The date when the course was first launched.
• platform: The platform offering the course.
• subscription_type: The type of subscription required for the course (e.g., Free, Paid).

2. Instructors Dataset

The Instructors dataset provides information about the instructors who teach the courses. This dataset includes the following columns:

• instructor_id: Unique identifier for each instructor (Primary Key).
• name: The name of the instructor.
• img: The URL link to the instructor’s image.
• bio: A short biography of the instructor.

3. Course_Instructors Dataset

The Course_Instructors dataset establishes a many-to-many relationship between courses and instructors. It shows which instructors are teaching which courses. This dataset includes the following columns:

• course_id: Identifier for the course (Foreign Key referencing Courses table).
• instructor_id: Identifier for the instructor (Foreign Key referencing Instructors table).

Columns Details

Courses Dataset

1. course_id:

   • Type: Integer
   • Description: A unique identifier assigned to each course.

2. title:

   • Type: String
   • Description: The name or title of the course.

3. price:

   • Type: Float
   • Description: The cost of the course in the respective platform's currency.

4. link:

   • Type: String
   • Description: The direct URL to access the course page on the platform.

5. total_enrolled_students:

   • Type: Integer
   • Description: The total number of students who have enrolled in the course.

6. category:

   • Type: String
   • Description: The category or subject area of the course (e.g., Programming, Data Science).

7. batch_no:

   • Type: Integer
   • Description: The batch number indicating the course session.

8. launch_date:

   • Type: Date
   • Description: The date when the course was first launched on the platform.

9. platform:

   • Type: String
   • Description: The name of the platform offering the course.

10. subscription_type:

    • Type: String
    • Description: Indicates whether the course is free or paid.

Instructors Dataset

1. instructor_id:

   • Type: Integer
   • Description: A unique identifier assigned to each instructor.

2. name:

   • Type: String
   • Description: The full name of the instructor.

3. img:

   • Type: String
   • Description: URL to the instructor's profile image.

4. bio:

   • Type: Text
   • Description: A short biography or profile of the instructor.

Course_Instructors Dataset

1. course_id:

   • Type: Integer
   • Description: Identifier linking to the course (Foreign Key referencing Courses table).

2. instructor_id:

   • Type: Integer
   • Description: Identifier linking to the instructor (Foreign Key referencing Instructors table).

These datasets provide a rich source of information for analyzing the trends in skill development courses. You can explore various aspects such as the popularity of courses, the distribution of courses across different categories, instructor profiles, and much more. Whether you are interested in data analysis, machine learning, or trend analysis, these datasets offer a valuable resource for your projects.

Feel free to reach out if you have any questions or need further assistance with the data. Happy analyzing!

The dataset focusing on research publications related to the American horseshoe crab (Limulus polyphemus) and ecological studies over multiple decades. The dataset contains detailed information about individual research publications, including: Year and Decade of publication. Publication Title and a Link to access the document. Classification of publications based on their relation to either Ecology/Management/Conservation or Physiology/Morphology/Genetics/Evolution.

About this dataset

Context

The dataset tabulates the Tuscaloosa population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Tuscaloosa across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Tuscaloosa was 110,602, a 1.39% increase year-by-year from 2021. Previously, in 2021, Tuscaloosa population was 109,082, an increase of 4.67% compared to a population of 104,214 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Tuscaloosa increased by 31,687. In this period, the peak population was 110,602 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Tuscaloosa is shown in this column.
• Year on Year Change: This column displays the change in Tuscaloosa population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Tuscaloosa Population by Year. You can refer the same here

The AmeriCorps Office of Research and Evaluation provides grants to researchers, scholars, and dissertators at institutions of higher education, enabling them to engage in comprehensive studies on civic engagement, volunteering, and national service. Studies include a variety of populations and ranges from local to organizational, and national contexts throughout the United States. This AmeriCorps Research Grantee dataset provides comprehensive information about the grantees and their studies. For each award, we identify the: 1) study title; 2) background; 3) cohort year; 4) principal investigators and their affiliated university; 5) study location(s) associated with each grant; 6) civic engagement topic areas; and 7) the research approach. Please be aware that there may be multiple rows corresponding to a single research grantee study, reflecting the various study sites where the grantee is actively involved. Each study was thematically coded to identify their civic engagement topic areas. An individual study can be categorized into more than one group. The topic areas include: • Arts & Culture, • Community Development, • Education Across the Life Course, • Youth Development, • Environmental Stewardship, • Health & Social Wellbeing, • New Americans, • Economic Opportunity and Employment, • Social Capital, • Senior Development, and • Volunteering, Nonprofit Studies, and National Service. Additionally, the research grantees’ studies were categorized into two distinct research approaches: traditional research and participatory research. To learn more about the studies’ civic engagement topic areas and research approaches, please refer to the AmeriCorps Research Grantee Data Dictionary under Attachments. For up-to-date information surrounding the AmeriCorps Research Grantees please see: • AmeriCorps Research Grantee Activities and Insights: https://americorps.gov/grantees-sponsors/research-evaluation/grantee-profiles • Participatory Research: https://americorps.gov/sites/default/files/document/2021_07_20_ParticipatoryResearchOnePager_ORE.pdf

About this dataset

Context

The dataset tabulates the population of Yellville by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Yellville across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 53.83% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Yellville is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Yellville total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Yellville Population by Race & Ethnicity. You can refer the same here

The use of data sets are getting more relevance in surgical robotics since they can be used to recognise and automate tasks in the lab. Also, it allows to use a common data set to compare different algorithms and methods. The objective of this work is to provide a complete data set of several training tasks that surgeons perform to improve their skills. For this purpose, the Da Vinci research kit has been used to perform a different training tasks. The obtained data set includes all the information provided by the da Vinci robot together with the corresponding video from the camera. Kinematic data has been collected at 50 frames per seconds, and images at 15 frames per seconds. All the information has been carefully timestamped and provided in a readable csv format. The application used to retrieve the information from the da Vinci research kit, as well as tools to access the information are also provided.

Coups d'Ètat are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup d’État Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e., realized, unrealized, or conspiracy) the type of actor(s) who initiated the coup (i.e., military, rebels, etc.), as well as the fate of the deposed leader. Version 2.1.3 adds 19 additional coup events to the data set, corrects the date of a coup in Tunisia, and reclassifies an attempted coup in Brazil in December 2022 to a conspiracy. Version 2.1.2 added 6 additional coup events that occurred in 2022 and updated the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrected a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixed this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removed two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and added executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Version 2.0.0 improved several aspects of the previous version (v1.0.0) and incorporated additional source material to include: • Reconciling missing event data • Removing events with irreconcilable event dates • Removing events with insufficient sourcing (each event needs at least two sources) • Removing events that were inaccurately coded as coup events • Removing variables that fell below the threshold of inter-coder reliability required by the project • Removing the spreadsheet ‘CoupInventory.xls’ because of inadequate attribution and citations in the event summaries • Extending the period covered from 1945-2005 to 1945-2019 • Adding events from Powell and Thyne’s Coup Data (Powell and Thyne, 2011)
Items in this Dataset 1. Cline Center Coup d'État Codebook v.2.1.3 Codebook.pdf - This 15-page document describes the Cline Center Coup d’État Project dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d'État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. Revised February 2024 2. Coup Data v2.1.3.csv - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 1000 observations. Revised February 2024 3. Source Document v2.1.3.pdf - This 325-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify that particular event. Revised February 2024 4. README.md - This file contains useful information for the user about the dataset. It is a text file written in markdown language. Revised February 2024
Citation Guidelines 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2024. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Emilio Soto. 2024. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7

About this dataset

Context

The dataset tabulates the population of Troy by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Troy across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 59.37% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Troy is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Troy total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Troy Population by Race & Ethnicity. You can refer the same here

A database containing details of water-related researchers located in Irish academic institutions

This is a part of REDD data set.

The REDD data set is presented in high frequency (kHz) and low frequency (Hz) groups. High frequency data include current and voltage measurements, while low frequency data cover Power measurement of individual circuits within the houses. The low frequency folder contains six folders, one for each house. Each sub-folder contains a number of channels for different circuits in the house. Labels of each channel which include appliance names are given in a label.dat file in each sub-folder.

link

Paper and citation: J.Zico Kolter and Matthew J. Johnson. REDD: A public data set for energy disaggregation research. In proceedings of the SustKDD workshop on Data Mining Applications in Sustainability, 2011.

Movies Performance and Feature Statistics

Analyzing Box Office Performance, Rating and Audience Reactions

By Yashwanth Sharaff [source]

About this dataset

This dataset contains essential characteristics of a variety of movies, including basic pieces of information such as the movie's title and budget, as well as performance indicators like the movie's MPAA rating, gross revenue, release date, genre, runtime, rating count and summary. With this data set we can better understand the film industry and uncover insights on how different features and performance metrics impact one another to guarantee a movie's success. The movies dataset also helps you make informed decisions about which features are key indicators in setting up a high-grossing feature film

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

To get the most out of this data set you need to understand what each column in it represents. The ‘Title’ column gives you the title of the movie which can be used for further search or exploration on popular streaming services and websites that are dedicated to providing detailed information about movies. The ‘MPAA Rating’ lists any Motion Picture Association (MPAA) rating for a movie which consists of G (General Audiences), PG (Parental Guidance Suggested), PG-13 (Parents Strongly Cautioned), R (Under 17 Requires Accompanying Parent or Guardian) etc. The 'Budget' column give you an approximate idea about how much a particular production cost while the 'Gross' columns depicts its earnings if it was released in theaters while its successor 'Release Date' reveals when each film has been released or is going to release in future. The columns 'Genre', 'Runtime', and ‘Rating Count’ cover subje​cts such as what type of movie is it? Every genre will have an associated runtime limit along with rating count which refers to number people who have rated/reviewed a particular flick whether on IMDB or other streaming services as well as paper mediums like newspapers . Last but not least summary field states an overview of what we can expect from film so take this in account before watching anything especially if include children members in your family.

So go ahead - start exploring this interesting dataset today!

Research Ideas

• Creating a box office prediction model using budget, genre, release date and MPAA rating
• Using the summary data to create a sentiment analysis tool for movie reviews
• Building a recommendation engine for users based on their prior ratings and what other users with similar tastes have rated as highly

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: movies.csv | Column name | Description | |:-----------------|:-------------------------------------------------------------------------------| | Title | The title of the movie. (String) | | MPAA Rating | The Motion Picture Association of America (MPAA) rating of the movie. (String) | | Budget | The budget of the movie in US dollars. (Integer) | | Gross | The gross revenue of the movie in US dollars. (Integer) | | Release Date | The date the movie was released. (Date) | | Genre | The genre of the movie. (String) | | Runtime | The length of the movie in minutes. (Integer) | | Rating Count | The number of ratings the movie has received. (Integer) | | Summary | A brief summary of the movie. (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 Yashwanth Sharaff.

Dataset

This dataset was created by Aamir Arshad Sheikh

Contents

American Stories offers high-quality structured data from historical newspapers suitable for pre-training large language models to enhance the understanding of historical English and world knowledge. It can also be integrated into external databases of retrieval-augmented language models, enabling broader access to historical information, including interpretations of political events and intricate details about people's ancestors. Additionally, the structured article texts facilitate the application of transformer-based methods for popular tasks like detecting reproduced content, significantly improving accuracy compared to traditional OCR methods. American Stories serves as a substantial and valuable dataset for advancing multimodal layout analysis models and other multimodal applications.

This dataset tracks the updates made on the dataset "Research Payment Data – Detailed Dataset 2020 Reporting Year" as a repository for previous versions of the data and metadata.

TinySOL: Isolated musical notes audio dataset

A Balanced Audio Dataset for Music Information Retrieval

By [source]

About this dataset

TinySOL is a complete audio dataset of isolated musical notes from 14 instruments, collected from Ircam in Paris and released under the Creative Commons Attribution 4.0 International license. The instruments include Bass Tuba, French Horn, Trombone, Trumpet in C, Accordion, Contrabass, Violin, Viola, Violoncello - a stringed family comprising some of the most iconic sounds in classical music - as well as Bassoon, Clarinet in B-flat; Flute; Oboe; and Alto Saxophone.

Using TinySOL to understand music information retrieval can be valuable on many levels. The application possibilities range from music creation to audio search engines or even AI-driven study of classical compositions. All TinySOL files come with associated metadata that describe the pitch and dynamics of each note as well as its instrument family and performance technique (abbreviations included) - making this dataset an incredibly useful resource for any kind of MIR research or experimental project!

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How to use the dataset

This dataset provides a comprehensive collection of isolated musical notes of 14 instruments recorded at Ircam in Paris, under the Creative Commons Attribution 4.0 International license. It can be used for various music information retrieval applications, such as instrument recognition systems, automatic genre classification algorithms, and audio synthesis.

Research Ideas

• Training and Retraining AI/ML algorithms for Source Separation of Musical Instruments: By using this balanced dataset, researchers can experiment with various source separation techniques and architectures to develop AI and ML models that can robustly isolate different types of instruments from an audio track.
• Generating Isolated Instrument Samples for Synthesizers & Sound Banks: Using the isolated notes in this dataset, developers can generate a large library of samples for use in software synthesisers or hardware samplers, allowing producers to get the most accurate sound possible from their instruments.
• Automatically Tagging Different Labelled Notes in Audio Files: By leveraging the metadata included within each audio file in this dataset, machine learning algorithms could be used to automatically tag different labelled notes within any given audiofile, enabling quicker annotation workflows throughout the music industry

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: TinySOL_metadata.csv | Column name | Description | |:----------------------------|:--------------------------------------------------------------------------| | Path | The file path of the audio sample. (String) | | Fold | The fold number of the audio sample. (Integer) | | Family | The family of instrument the audio sample belongs to. (String) | | Instrument (abbr.) | The abbreviation of the instrument the audio sample belongs to. (String) | | Instrument (in full) | The full name of the instrument the audio sample belongs to. (String) | | Technique (abbr.) | The abbreviation of the technique used to play the audio sample. (String) | | Technique (in full) | The full name of the technique used to play the audio sample. (String) | | Pitch | The pitch of the audio sample. (Integer) | | Dynamics | The dynamics of the audio sample. (Integer) | | Needed digital retuning | The amount of digital retuning needed for the audio sample. (Integer) |

Acknowledgements

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

About this dataset

Context

The dataset tabulates the population of Portland by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Portland across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 51.9% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Portland is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Portland total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Portland Population by Race & Ethnicity. You can refer the same here