Discover the Walmart Products Free Dataset, featuring 2,000 records in CSV format. This dataset includes detailed information about various Walmart products, such as names, prices, categories, and descriptions.

It’s perfect for data analysis, e-commerce research, and machine learning projects. Download now and kickstart your insights with accurate, real-world data.

Sample data for exercises in Further Adventures in Data Cleaning.

Free historical options data, dataset files in CSV format.

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

Data organization for the figures in the document: Figure 3A LineOutWithSun_SSAzi_135to225_green_Correct_ROI5_INFO.xls Figure 3b LineOutWithSun_SSAzi_m45to45_green_Correct_ROI5_INFO.xls Figure 4 fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Correct_ROI5_INFO.xls fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Sim_Correct_ROI5_INFO.xls Figure 5a LineOut_Camera_Elevation_SqAzi_m180to0_green_Sim_Correct_ROI5_INFO.xls LineOut_Camera_Elevation_SqAzi_m180to0_green_Correct_ROI5_INFO.xls Figure 5b LineOut_Camera_Elevation_SqAzi_0to180_green_Correct_ROI5_INFO.xls LineOut_Camera_Elevation_SqAzi_0to180_green_Sim_Correct_ROI5_INFO.xls Figure 6a LineOutColor_SqAzi_m180to0_CP_20to50_Correct_ROI5_INFO.xls Figure 6b LineOutROI_SqAzi_m180to0_CP_20to50_green_Correct_INFO.xls Figure 7 fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Correct_ROI5_INFO.xls LineOut_MeshAoPDif_Camera_Elevation_SqAzi_0to180_green_Correct_ROI5_INFO.xls LineOut_MeshAoPDif_Camera_Elevation_SqAzi_m180to0_green_Correct_ROI5_INFO.xls

A computer program for accessing and visualization of thermodynamic and transport property data for chemical compounds and mixtures available at the TRC/NIST ThermoML archive https://data.nist.gov/od/id/mds2-2422. The data collection contains 2.2 million distinct property values (the whole archive can also be downloaded from that link, stored, and accessed from a local storage). The program has been compiled for Windows OS and tested under Windows 10. The operation procedures are described in the embedded Help.

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

As of May 2025, nearly ** percent of apps in the Google Play app store were freely available. The number of free apps on the Google Play Store and the Apple Store alike has been consistently higher than the number of paid apps. By comparison, free Android apps on Amazon Appstore were roughly ** percent, while paid apps accounted for a share of ** percent of the total apps available in the store. Mobile apps and consumer spending Mobile apps have become integral to our daily routine, offering convenience and entertainment. In the second quarter of 2024, the total value of the global consumer spending on mobile apps was almost ** billion U.S. dollars, highlighting the significant role that mobile apps play in the digital economy. As of the third quarter of 2023, consumers spent an average of **** U.S. dollars on mobile apps per smartphone, which underlines the high demand for these digital solutions. App stores commission rates under scrutiny As of August 2023, the standard commission rates on revenues generated from apps hosted on the Apple App Store and the Google Play Store were set at ** percent. However, between the end of 2020 and mid-2021, both Apple and Google were forced to address the criticism of their app store policies. In 2020, the European Union drafted the Digital Market Act, with the purpose of ensuring a healthy degree of competition in the tech environment. In December 2022, Apple was reported to start planning to allow sideloading and the presence of alternative app stores on its devices. In August 2021, the United States Senate presented the Open Apps Market Act to reduce tech giants‘ control over the digital app market. As regulations are expected to promote competition in the tech and mobile environment, in March 2023, Microsoft was reported to preparing to launch a new mobile gaming store, which will compete with the Apple App Store and the Google Play Store.In 2026, mobile app spending is forecasted to reach *** billion U.S. dollars and ** billion U.S. dollars on the Apple App Store and the Google Play Store, respectively. While both Google and Apple started applying some changes in their app store policies in 2021, like lowering commission fees for small publishers generating less than *** million U.S. dollars in yearly revenues, the two tech giants might face additional restrictions and limitations in all their major markets. In the case of Apple, in 2021, the company updated its App Store policies, allowing developers to offer alternative payment methods. In 2022, Apple updated its review guidelines, requiring developers to share more information about collecting and using data, including disclosing the types of collected data and how it's used.

Included here is Sanvean Technologies bit sensor data amalgamated with data from National Oilwell Varco's (NOV) BlackBox tool for Reed Hycalog bits used during drilling of Well 16B(78)-32. The dataset contains information collected at the bit while drilling including rate of penetration (ROP), top drive torque, and bit box temperature. The data was recorded at the bit box and top sub of the motor. RPM was measured by onboard gyro recording continuously in each sensor, and shock levels were also recorded on X, Y and Z axis. This data was merged with EDR in time format and saved in file sets (the zipped files) then output into CSV files. Please note: fields in the CSV files, such as the date field, may need to be formatted to display properly. There is an additional zipped folder in each dataset that is password protected. Sanvean GameChanger Viewer software must be used to view this password protected data. Information on how to use and download this free software is also included here.

About this dataset

Context

The dataset tabulates the Excel population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Excel. The dataset can be utilized to understand the population distribution of Excel by age. For example, using this dataset, we can identify the largest age group in Excel.

Key observations

The largest age group in Excel, AL was for the group of age 45 to 49 years years with a population of 74 (15.64%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Excel, AL was the 85 years and over years with a population of 2 (0.42%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Excel is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Excel 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 Excel Population by Age. You can refer the same here

I am a new developer and I would greatly appreciate your support. If you find this dataset helpful, please consider giving it an upvote!

Key Features:

Complete 1h Data: Raw 1h historical data from multiple exchanges, covering the entire trading history of BTCUSD available through their API endpoints. This dataset is updated daily to ensure up-to-date coverage.

Combined Index Dataset: A unique feature of this dataset is the combined index, which is derived by averaging all other datasets into one, please see attached notebook. This creates the longest continuous, unbroken BTCUSD dataset available on Kaggle, with no gaps and no erroneous values. It gives a much more comprehensive view of the market i.e. total volume across multiple exchanges.

Superior Performance: The combined index dataset has demonstrated superior 'mean average error' (MAE) metric performance when training machine learning models, compared to single-source datasets by a whole order of MAE magnitude.

Unbroken History: The combined dataset's continuous history is a valuable asset for researchers and traders who require accurate and uninterrupted time series data for modeling or back-testing.

https://i.imgur.com/OVOyF5A.png" alt="BTCUSD Dataset Summary">

https://i.imgur.com/6hxG2G3.png" alt="Combined Dataset Close Plot"> This plot illustrates the continuity of the dataset over time, with no gaps in data, making it ideal for time series analysis.

Included Resources:

Two Notebooks:

Dataset Usage and Diagnostics: This notebook demonstrates how to use the dataset and includes a powerful data diagnostics function, which is useful for all time series analyses.

Aggregating Multiple Data Sources: This notebook walks you through the process of combining multiple exchange datasets into a single, clean dataset. (Currently unavailable, will be added shortly)

About this dataset

Context

The dataset tabulates the United States 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 United States 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 United States was 333,287,557, a 0.38% increase year-by-year from 2021. Previously, in 2021, United States population was 332,031,554, an increase of 0.16% compared to a population of 331,511,512 in 2020. Over the last 20 plus years, between 2000 and 2022, population of United States increased by 51,125,146. In this period, the peak population was 333,287,557 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 United States is shown in this column.
• Year on Year Change: This column displays the change in United States 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 United States Population by Year. You can refer the same here

This dataset represents ambient data collected longitudinally in 189 community homes. The data are collected over 18 years, from 2007 to 2024. This is a resource for analyzing naturalistic behavior in a home and building activity recognition models that operate in the wild.

Data are collected continuously from ambient sensors while residents perform their normal routines. The data fields are date, time, sensor identifier, and message. The sensors consist of PIR (motion) sensors and magnetic door (open/close) sensors. Sensors are attached to ceilings and identified by their location in the home (e.g., Bathroom, Bedroom, DiningRoom, Bed, Bath, OfficeChair). If a home contains more than one room of a given type, the corresponding sensors are distinguished by a trailing letter to differentiate the rooms (e.g., BedroomA, BedroomB). The lens of most motion sensors are constrained to cover a 1 meter diameter area. To detect movement in a larger area, an unconstrained sensor is angled to cover an entire room or region and is indicated by Area (e.g., BedroomArea).

There is one file per home. Some of the homes also include floorplans. Additionally, data from some of the homes is labeled with activities by an external annotator. The homes in this dataset are listed below with the number of residents.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

Point-of-interest (POI) is defined as a physical entity (such as a business) in a geo location (point) which may be (of interest).

We strive to provide the most accurate, complete and up to date point of interest datasets for all countries of the world. The Australian POI Dataset is one of our worldwide POI datasets with over 98% coverage.

This is our process flow:

Our machine learning systems continuously crawl for new POI data
Our geoparsing and geocoding calculates their geo locations
Our categorization systems cleanup and standardize the datasets
Our data pipeline API publishes the datasets on our data store

POI Data is in a constant flux - especially so during times of drastic change such as the Covid-19 pandemic.

Every minute worldwide on an average day over 200 businesses will move, over 600 new businesses will open their doors and over 400 businesses will cease to exist.

In today's interconnected world, of the approximately 200 million POIs worldwide, over 94% have a public online presence. As a new POI comes into existence its information will appear very quickly in location based social networks (LBSNs), other social media, pictures, websites, blogs, press releases. Soon after that, our state-of-the-art POI Information retrieval system will pick it up.

We offer our customers perpetual data licenses for any dataset representing this ever changing information, downloaded at any given point in time. This makes our company's licensing model unique in the current Data as a Service - DaaS Industry. Our customers don't have to delete our data after the expiration of a certain "Term", regardless of whether the data was purchased as a one time snapshot, or via a recurring payment plan on our data update pipeline.

The main differentiators between us vs the competition are our flexible licensing terms and our data freshness.

The core attribute coverage for Australia is as follows:

Poi Field Data Coverage (%) poi_name 100 brand 13 poi_tel 49 formatted_address 100 main_category 94 latitude 100 longitude 100 neighborhood 3 source_url 55 email 10 opening_hours 41 building_footprint 60

The dataset may be viewed online at https://store.poidata.xyz/au and a data sample may be downloaded at https://store.poidata.xyz/datafiles/au_sample.csv

Please Note - We do not distribute election map data. For digital data, please contact the Maryland Department of Planning for more information. In addition to our online maps, we do offer our "2022 Election Map" in PDF format, which is available for free to download. Paper copies are also available for a small fee. For more information, please see "Paper Maps" below. Use this form to order free GIS files of our data for use in mapping applications. To get immediate access to GIS based information use one of our web maps!

Dataset

This dataset was created by Md Younus Ahamed

Contents

The ATOM download service provides free INSPIRE elevation data (shot date)

This .las file contains sample LiDAR point cloud data collected by National Ecological Observatory Network's Airborne Observation Platform. The .las file format is a commonly used file format to store LIDAR point cloud data.This teaching data set is used for several tutorials on the NEON website (neonscience.org). The dataset is for educational purposes, data for research purposes can be obtained from the NEON Data Portal (data.neonscience.org).

3D point cloud representing all physical features (e.g. buildings, trees and terrain) across City of Melbourne. The data has been encoded into a .las file format containing geospatial coordinates and RGB values for each point. The download is a zip file containing compressed .las files for tiles across the city area.

The geospatial data has been captured in Map Grid of Australia (MGA) Zone 55 projection and is reflected in the xyz coordinates within each .las file. Also included are RGB (Red, Green, Blue) attributes to indicate the colour of each point.

Capture Information - Capture Date: May 2018 - Capture Pixel Size: 7.5cm ground sample distance - Map Projection: MGA Zone 55 (MGA55) - Vertical Datum: Australian Height Datum (AHD) - Spatial Accuracy (XYZ): Supplied survey control used for control (Madigan Surveying) – 25 cm absolute accuracy

Limitations: Whilst every effort is made to provide the data as accurate as possible, the content may not be free from errors, omissions or defects.

Sample Data: For an interactive sample of the data please see the link below. https://cityofmelbourne.maps.arcgis.com/apps/webappviewer3d/index.html?id=b3dc1147ceda46ffb8229117a2dac56dPreview:Download:A zip file containing the .las files representing tiles of point cloud data across City of Melbourne area. Download Point Cloud Data (4GB)