100+ datasets found
  1. f

    Data_Sheet_1_Raw Data Visualization for Common Factorial Designs Using SPSS:...

    • frontiersin.figshare.com
    zip
    Updated Jun 2, 2023
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    Florian Loffing (2023). Data_Sheet_1_Raw Data Visualization for Common Factorial Designs Using SPSS: A Syntax Collection and Tutorial.ZIP [Dataset]. http://doi.org/10.3389/fpsyg.2022.808469.s001
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    Frontiers
    Authors
    Florian Loffing
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

  2. c

    Walmart products free dataset

    • crawlfeeds.com
    csv, zip
    Updated Apr 27, 2025
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    Crawl Feeds (2025). Walmart products free dataset [Dataset]. https://crawlfeeds.com/datasets/walmart-products-free-dataset
    Explore at:
    zip, csvAvailable download formats
    Dataset updated
    Apr 27, 2025
    Dataset authored and provided by
    Crawl Feeds
    License

    https://crawlfeeds.com/privacy_policyhttps://crawlfeeds.com/privacy_policy

    Description

    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.

  3. d

    NFL Data (Historic Data Available) - Sports Data, National Football League...

    • datarade.ai
    Updated Sep 26, 2024
    + more versions
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    APISCRAPY (2024). NFL Data (Historic Data Available) - Sports Data, National Football League Datasets. Free Trial Available [Dataset]. https://datarade.ai/data-products/nfl-data-historic-data-available-sports-data-national-fo-apiscrapy
    Explore at:
    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset updated
    Sep 26, 2024
    Dataset authored and provided by
    APISCRAPY
    Area covered
    Iceland, China, Poland, Bosnia and Herzegovina, Lithuania, Ireland, Malta, Italy, Portugal, Norway
    Description

    Our NFL Data product offers extensive access to historic and current National Football League statistics and results, available in multiple formats. Whether you're a sports analyst, data scientist, fantasy football enthusiast, or a developer building sports-related apps, this dataset provides everything you need to dive deep into NFL performance insights.

    Key Benefits:

    Comprehensive Coverage: Includes historic and real-time data on NFL stats, game results, team performance, player metrics, and more.

    Multiple Formats: Datasets are available in various formats (CSV, JSON, XML) for easy integration into your tools and applications.

    User-Friendly Access: Whether you are an advanced analyst or a beginner, you can easily access and manipulate data to suit your needs.

    Free Trial: Explore the full range of data with our free trial before committing, ensuring the product meets your expectations.

    Customizable: Filter and download only the data you need, tailored to specific seasons, teams, or players.

    API Access: Developers can integrate real-time NFL data into their apps with API support, allowing seamless updates and user engagement.

    Use Cases:

    Fantasy Football Players: Use the data to analyze player performance, helping to draft winning teams and make better game-day decisions.

    Sports Analysts: Dive deep into historical and current NFL stats for research, articles, and game predictions.

    Developers: Build custom sports apps and dashboards by integrating NFL data directly through API access.

    Betting & Prediction Models: Use data to create accurate predictions for NFL games, helping sportsbooks and bettors alike.

    Media Outlets: Enhance game previews, post-game analysis, and highlight reels with accurate, detailed NFL stats.

    Our NFL Data product ensures you have the most reliable, up-to-date information to drive your projects, whether it's enhancing user experiences, creating predictive models, or simply enjoying in-depth football analysis.

  4. f

    Summary descriptive statistics of TIMSS dataset.

    • plos.figshare.com
    xls
    Updated Feb 2, 2024
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    Jonathan Fries; Sandra Oberleiter; Jakob Pietschnig (2024). Summary descriptive statistics of TIMSS dataset. [Dataset]. http://doi.org/10.1371/journal.pone.0297033.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Feb 2, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Jonathan Fries; Sandra Oberleiter; Jakob Pietschnig
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.

  5. About COVID-19 Public Datasets

    • console.cloud.google.com
    Updated Jun 19, 2022
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    https://console.cloud.google.com/marketplace/browse?filter=partner:BigQuery%20Public%20Datasets%20Program&inv=1&invt=Ab2YUw (2022). About COVID-19 Public Datasets [Dataset]. https://console.cloud.google.com/marketplace/product/bigquery-public-datasets/covid19-public-data-program
    Explore at:
    Dataset updated
    Jun 19, 2022
    Dataset provided by
    Googlehttp://google.com/
    BigQueryhttps://cloud.google.com/bigquery
    Description

    In an effort to help combat COVID-19, we created a COVID-19 Public Datasets program to make data more accessible to researchers, data scientists and analysts. The program will host a repository of public datasets that relate to the COVID-19 crisis and make them free to access and analyze. These include datasets from the New York Times, European Centre for Disease Prevention and Control, Google, Global Health Data from the World Bank, and OpenStreetMap. Free hosting and queries of COVID datasets As with all data in the Google Cloud Public Datasets Program , Google pays for storage of datasets in the program. BigQuery also provides free queries over certain COVID-related datasets to support the response to COVID-19. Queries on COVID datasets will not count against the BigQuery sandbox free tier , where you can query up to 1TB free each month. Limitations and duration Queries of COVID data are free. If, during your analysis, you join COVID datasets with non-COVID datasets, the bytes processed in the non-COVID datasets will be counted against the free tier, then charged accordingly, to prevent abuse. Queries of COVID datasets will remain free until Sept 15, 2021. The contents of these datasets are provided to the public strictly for educational and research purposes only. We are not onboarding or managing PHI or PII data as part of the COVID-19 Public Dataset Program. Google has practices & policies in place to ensure that data is handled in accordance with widely recognized patient privacy and data security policies. See the list of all datasets included in the program

  6. f

    Additional file 3 of Computing and graphing probability values of pearson...

    • springernature.figshare.com
    bin
    Updated Feb 23, 2024
    + more versions
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    Qing Yang; Xinming An; Wei Pan (2024). Additional file 3 of Computing and graphing probability values of pearson distributions: a SAS/IML macro [Dataset]. http://doi.org/10.6084/m9.figshare.11423325.v1
    Explore at:
    binAvailable download formats
    Dataset updated
    Feb 23, 2024
    Dataset provided by
    figshare
    Authors
    Qing Yang; Xinming An; Wei Pan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Sample dataset 2. The dataset dataIV.sas7bdat was taken from [1].

  7. g

    Youth statistics: Satisfaction with free time for young people aged 15 to 29...

    • gimi9.com
    Updated Oct 27, 2017
    + more versions
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    (2017). Youth statistics: Satisfaction with free time for young people aged 15 to 29 | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_0d90f94f588336e6ba0de4b45dc5f97a3f4c5673/
    Explore at:
    Dataset updated
    Oct 27, 2017
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The Basque Youth Observatory is an instrument of the Basque Government that allows to have a global and permanent vision of the situation and evolution of the youth world that allows to evaluate the impact of the actions carried out in the CAPV by the different administrations in the field of youth.The Basque Youth Observatory regularly publishes more than 100 statistical indicators that can be consulted in euskadi.eus, along with other research and reports. Statistics are provided in various formats (csv, excel).

  8. Free Mobile average LTE data use in France 2015-2019

    • statista.com
    Updated Jul 8, 2025
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    Statista (2025). Free Mobile average LTE data use in France 2015-2019 [Dataset]. https://www.statista.com/statistics/1187378/free-mobile-lte-data-use/
    Explore at:
    Dataset updated
    Jul 8, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    France
    Description

    The average LTE data use of Free Mobile has been increasing from 2015 to 2019 in France. In the second quarter of 2019, it was around ** gigabytes per month per subscriber.

  9. Big Data as a Service (BDaaS) Market Analysis North...

    • technavio.com
    Updated Dec 20, 2023
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    Technavio (2023). Big Data as a Service (BDaaS) Market Analysis North America,APAC,Europe,South America,Middle East and Africa - US,Canada,China,Germany,UK - Size and Forecast 2024-2028 [Dataset]. https://www.technavio.com/report/big-data-as-a-service-market-industry-analysis
    Explore at:
    Dataset updated
    Dec 20, 2023
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    Germany, Canada, China, United Kingdom, United States, Global
    Description

    Snapshot img

    Big Data as a Service Market Size 2024-2028

    The big data as a service market size is forecast to increase by USD 41.20 billion at a CAGR of 28.45% between 2023 and 2028.

    The market is experiencing significant growth due to the increasing volume of data and the rising demand for advanced data insights. Machine learning algorithms and artificial intelligence are driving product quality and innovation in this sector. Hybrid cloud solutions are gaining popularity, offering the benefits of both private and public cloud platforms for optimal data storage and scalability. Industry standards for data privacy and security are increasingly important, as large amounts of data pose unique risks. The BDaaS market is expected to continue its expansion, providing valuable data insights to businesses across various industries.
    

    What will be the Big Data as a Service Market Size During the Forecast Period?

    Request Free Sample

    Big Data as a Service (BDaaS) has emerged as a game-changer in the business world, enabling organizations to harness the power of big data without the need for extensive infrastructure and expertise. This service model offers various components such as data management, analytics, and visualization tools, enabling businesses to derive valuable insights from their data. BDaaS encompasses several key components that drive market growth. These include Business Intelligence (BI), Data Science, Data Quality, and Data Security. BI provides organizations with the ability to analyze data and gain insights to make informed decisions.
    
    
    
    Data Science, on the other hand, focuses on extracting meaningful patterns and trends from large datasets using advanced algorithms. Data Quality is a critical component of BDaaS, ensuring that the data being analyzed is accurate, complete, and consistent. Data Security is another essential aspect, safeguarding sensitive data from cybersecurity threats and data breaches. Moreover, BDaaS offers various data pipelines, enabling seamless data integration and data lifecycle management. Network Analysis, Real-time Analytics, and Predictive Analytics are other essential components, providing businesses with actionable insights in real-time and enabling them to anticipate future trends. Data Mining, Machine Learning Algorithms, and Data Visualization Tools are other essential components of BDaaS.
    

    How is this market segmented and which is the largest segment?

    The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

    Type
    
      Data analytics-as-a-Service
      Hadoop-as-a-service
      Data-as-a-service
    
    
    Deployment
    
      Public cloud
      Hybrid cloud
      Private cloud
    
    
    Geography
    
      North America
    
        Canada
        US
    
    
      APAC
    
        China
    
    
      Europe
    
        Germany
        UK
    
    
      South America
    
    
    
      Middle East and Africa
    

    By Type Insights

    The data analytics-as-a-service segment is estimated to witness significant growth during the forecast period.
    

    Big Data as a Service (BDaaS) is a significant market segment, highlighted by the availability of Hadoop-as-a-Service solutions. These offerings enable businesses to access essential datasets on-demand without the burden of expensive infrastructure. DAaaS solutions facilitate real-time data analysis, empowering organizations to make informed decisions. The DAaaS landscape is expanding rapidly as companies acknowledge its value in enhancing internal data. Integrating DAaaS with big data systems amplifies analytics capabilities, creating a vibrant market landscape. Organizations can leverage diverse datasets to gain a competitive edge, driving the growth of the global BDaaS market. In the context of digital transformation, cloud computing, IoT, and 5G technologies, BDaaS solutions offer optimal resource utilization.

    However, regulatory scrutiny poses challenges, necessitating stringent data security measures. Retail and other industries stand to benefit significantly from BDaaS, particularly with distributed computing solutions. DAaaS adoption is a strategic investment for businesses seeking to capitalize on the power of external data for valuable insights.

    Get a glance at the market report of share of various segments Request Free Sample

    The Data analytics-as-a-Service segment was valued at USD 2.59 billion in 2018 and showed a gradual increase during the forecast period.

    Regional Analysis

    North America is estimated to contribute 35% to the growth of the global market during the forecast period.
    

    Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

    For more insights on the market share of various regions Request Free Sample

    Big Data as a Service Market analysis, North America is experiencing signif

  10. f

    Data from: SPEED Stat: a free, intuitive, and minimalist spreadsheet program...

    • figshare.com
    • scielo.figshare.com
    xls
    Updated Mar 26, 2021
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    André Mundstock Xavier de Carvalho; Felipe Queiroz Mendes; Fabrícia Queiroz Mendes; Laene de Fátima Tavares (2021). SPEED Stat: a free, intuitive, and minimalist spreadsheet program for statistical analyses of experiments [Dataset]. http://doi.org/10.6084/m9.figshare.14328730.v1
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Mar 26, 2021
    Dataset provided by
    SciELO journals
    Authors
    André Mundstock Xavier de Carvalho; Felipe Queiroz Mendes; Fabrícia Queiroz Mendes; Laene de Fátima Tavares
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Abstract SPEED Stat is a new spreadsheet program for univariate statistical analyses, focused on the dominant profile of agricultural experimentation. The program can perform analysis of variance; tests for normality, homoscedasticity, additivity, outliers; complex contrasts; multiple comparison tests; Scott-Knott's grouping analysis; regression analysis; and others. It has available at speedstatsoftware.wordpress.com.

  11. Data from: Substance-Free Transitional Housing and Community Corrections in...

    • catalog.data.gov
    • search.gesis.org
    • +2more
    Updated Mar 12, 2025
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    National Institute of Justice (2025). Substance-Free Transitional Housing and Community Corrections in Washington County, Oregon, 2005-2008 [Dataset]. https://catalog.data.gov/dataset/substance-free-transitional-housing-and-community-corrections-in-washington-county-or-2005-02602
    Explore at:
    Dataset updated
    Mar 12, 2025
    Dataset provided by
    National Institute of Justicehttp://nij.ojp.gov/
    Area covered
    Washington County, Oregon
    Description

    The study investigated self-sufficiency, community adjustment, substance use, and criminal recidivism outcomes for substance abusing offenders served through the Washington County (Oregon) Community Corrections Department (WCCC) to document the value-added of providing substance-free transitional housing services. The study addressed the value-added of Oxford House and other transitional housing services to the combination of services offenders receive, and documented the relative costs and benefits of substance-free transitional housing services. Individuals were eligible for the study if they entered Oxford Houses, entered some other form of substance-free transitional housing, or could benefit from, but did not enter, any form of substance-free transitional housing. A total of 356 supervisees were eligible for the study; 301 agreed to participate in baseline interviews, and 238 participated in 12-month follow-up interviews. The study included both interview data collection and administrative records data collection. The research team also collected Housing Data (Part 2) from the housing section of the interviews and Treatment Data (Part 3) from a statewide treatment database.

  12. Data from: FEASST: Free Energy and Advanced Sampling Simulation Toolkit

    • catalog.data.gov
    • datadiscoverystudio.org
    • +2more
    Updated Jul 29, 2022
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    National Institute of Standards and Technology (2022). FEASST: Free Energy and Advanced Sampling Simulation Toolkit [Dataset]. https://catalog.data.gov/dataset/feasst-free-energy-and-advanced-sampling-simulation-toolkit-7f08c
    Explore at:
    Dataset updated
    Jul 29, 2022
    Dataset provided by
    National Institute of Standards and Technologyhttp://www.nist.gov/
    Description

    The Free Energy and Advanced Sampling Simulation Toolkit (FEASST) is a free, open-source, modular program to conduct molecular and particle-based simulations with flat-histogram Monte Carlo and molecular dynamics methods. It is a software written in C++ and python which is made publicly available to aid in reproducibility. It is also provided as a service to the scientific community in which there are few , if any, Monte Carlo programs that support flat histogram methods and advanced sampling algorithms. This software is expected to be updated frequently with new methods.

  13. C

    Raw Data for ConfLab: A Data Collection Concept, Dataset, and Benchmark for...

    • data.4tu.nl
    Updated Jun 7, 2022
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    Chirag Raman; Jose Vargas Quiros; Stephanie Tan; Ashraful Islam; Ekin Gedik; Hayley Hung (2022). Raw Data for ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild [Dataset]. http://doi.org/10.4121/20017748.v2
    Explore at:
    Dataset updated
    Jun 7, 2022
    Dataset provided by
    4TU.ResearchData
    Authors
    Chirag Raman; Jose Vargas Quiros; Stephanie Tan; Ashraful Islam; Ekin Gedik; Hayley Hung
    License

    https://data.4tu.nl/info/fileadmin/user_upload/Documenten/4TU.ResearchData_Restricted_Data_2022.pdfhttps://data.4tu.nl/info/fileadmin/user_upload/Documenten/4TU.ResearchData_Restricted_Data_2022.pdf

    Description

    This file contains raw data for cameras and wearables of the ConfLab dataset.


    ./cameras

    contains the overhead video recordings for 9 cameras (cam2-10) in MP4 files.

    These cameras cover the whole interaction floor, with camera 2 capturing the

    bottom of the scene layout, and camera 10 capturing top of the scene layout.

    Note that cam5 ran out of battery before the other cameras and thus the recordings

    are cut short. However, cam4 and 6 contain significant overlap with cam 5, to

    reconstruct any information needed.


    Note that the annotations are made and provided in 2 minute segments.

    The annotated portions of the video include the last 3min38sec of x2xxx.MP4

    video files, and the first 12 min of x3xxx.MP4 files for cameras (2,4,6,8,10),

    with "x" being the placeholder character in the mp4 file names. If one wishes

    to separate the video into 2 min segments as we did, the "video-splitting.sh"

    script is provided.


    ./camera-calibration contains the camera instrinsic files obtained from

    https://github.com/idiap/multicamera-calibration. Camera extrinsic parameters can

    be calculated using the existing intrinsic parameters and the instructions in the

    multicamera-calibration repo. The coordinates in the image are provided by the

    crosses marked on the floor, which are visible in the video recordings.

    The crosses are 1m apart (=100cm).


    ./wearables

    subdirectory includes the IMU, proximity and audio data from each

    participant at the Conflab event (48 in total). In the directory numbered

    by participant ID, the following data are included:

    1. raw audio file

    2. proximity (bluetooth) pings (RSSI) file (raw and csv) and a visualization

    3. Tri-axial accelerometer data (raw and csv) and a visualization

    4. Tri-axial gyroscope data (raw and csv) and a visualization

    5. Tri-axial magnetometer data (raw and csv) and a visualization

    6. Game rotation vector (raw and csv), recorded in quaternions.


    All files are timestamped.

    The sampling frequencies are:

    - audio: 1250 Hz

    - rest: around 50Hz. However, the sample rate is not fixed

    and instead the timestamps should be used.


    For rotation, the game rotation vector's output frequency is limited by the

    actual sampling frequency of the magnetometer. For more information, please refer to

    https://invensense.tdk.com/wp-content/uploads/2016/06/DS-000189-ICM-20948-v1.3.pdf


    Audio files in this folder are in raw binary form. The following can be used to convert

    them to WAV files (1250Hz):


    ffmpeg -f s16le -ar 1250 -ac 1 -i /path/to/audio/file


    Synchronization of cameras and werables data

    Raw videos contain timecode information which matches the timestamps of the data in

    the "wearables" folder. The starting timecode of a video can be read as:

    ffprobe -hide_banner -show_streams -i /path/to/video


    ./audio

    ./sync: contains wav files per each subject

    ./sync_files: auxiliary csv files used to sync the audio. Can be used to improve the synchronization.

    The code used for syncing the audio can be found here:

    https://github.com/TUDelft-SPC-Lab/conflab/tree/master/preprocessing/audio

  14. c

    Free or Reduced-price Meal Eligibility - Datasets - CTData.org

    • data.ctdata.org
    Updated Mar 16, 2016
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    (2016). Free or Reduced-price Meal Eligibility - Datasets - CTData.org [Dataset]. http://data.ctdata.org/dataset/free-or-reduced-price-meal-eligibility
    Explore at:
    Dataset updated
    Mar 16, 2016
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Eligibility indicates students from families whose total income is at or below 185 percent of the poverty level. Household income below 130 percent of the poverty level qualifies students for free meals. Household income between 130 and 185 percent of the poverty level qualifies students for reduced-price meals. Connecticut State Department of Education collects data for grades PreK through 12 on a school year basis. CTdata.org carries annual school year data for grades K through 3.

  15. i

    Grant Giving Statistics for Chicago Free School

    • instrumentl.com
    Updated Jul 4, 2021
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    (2021). Grant Giving Statistics for Chicago Free School [Dataset]. https://www.instrumentl.com/990-report/chicago-free-school
    Explore at:
    Dataset updated
    Jul 4, 2021
    Area covered
    Chicago
    Variables measured
    Total Assets, Total Giving
    Description

    Financial overview and grant giving statistics of Chicago Free School

  16. B

    Data Cleaning Sample

    • borealisdata.ca
    • dataone.org
    Updated Jul 13, 2023
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    Rong Luo (2023). Data Cleaning Sample [Dataset]. http://doi.org/10.5683/SP3/ZCN177
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 13, 2023
    Dataset provided by
    Borealis
    Authors
    Rong Luo
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Sample data for exercises in Further Adventures in Data Cleaning.

  17. Global Hands-Free Barcode Scanner Market Risk Analysis 2025-2032

    • statsndata.org
    excel, pdf
    Updated Jun 2025
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    Stats N Data (2025). Global Hands-Free Barcode Scanner Market Risk Analysis 2025-2032 [Dataset]. https://www.statsndata.org/report/hands-free-barcode-scanner-market-54514
    Explore at:
    excel, pdfAvailable download formats
    Dataset updated
    Jun 2025
    Dataset authored and provided by
    Stats N Data
    License

    https://www.statsndata.org/how-to-orderhttps://www.statsndata.org/how-to-order

    Area covered
    Global
    Description

    The Hands-Free Barcode Scanner market has emerged as a crucial component of inventory management and point-of-sale systems across various industries, such as retail, logistics, and healthcare. These scanners enhance operational efficiency by allowing users to scan barcodes without needing to hold the device, thus st

  18. d

    Altosight | AI Custom Web Scraping Data | 100% Global | Free Unlimited Data...

    • datarade.ai
    .json, .csv, .xls
    Updated Sep 7, 2024
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    Altosight (2024). Altosight | AI Custom Web Scraping Data | 100% Global | Free Unlimited Data Points | Bypassing All CAPTCHAs & Blocking Mechanisms | GDPR Compliant [Dataset]. https://datarade.ai/data-products/altosight-ai-custom-web-scraping-data-100-global-free-altosight
    Explore at:
    .json, .csv, .xlsAvailable download formats
    Dataset updated
    Sep 7, 2024
    Dataset authored and provided by
    Altosight
    Area covered
    Chile, Czech Republic, Tajikistan, Wallis and Futuna, Svalbard and Jan Mayen, Singapore, Côte d'Ivoire, Greenland, Guatemala, Paraguay
    Description

    Altosight | AI Custom Web Scraping Data

    ✦ Altosight provides global web scraping data services with AI-powered technology that bypasses CAPTCHAs, blocking mechanisms, and handles dynamic content.

    We extract data from marketplaces like Amazon, aggregators, e-commerce, and real estate websites, ensuring comprehensive and accurate results.

    ✦ Our solution offers free unlimited data points across any project, with no additional setup costs.

    We deliver data through flexible methods such as API, CSV, JSON, and FTP, all at no extra charge.

    ― Key Use Cases ―

    ➤ Price Monitoring & Repricing Solutions

    🔹 Automatic repricing, AI-driven repricing, and custom repricing rules 🔹 Receive price suggestions via API or CSV to stay competitive 🔹 Track competitors in real-time or at scheduled intervals

    ➤ E-commerce Optimization

    🔹 Extract product prices, reviews, ratings, images, and trends 🔹 Identify trending products and enhance your e-commerce strategy 🔹 Build dropshipping tools or marketplace optimization platforms with our data

    ➤ Product Assortment Analysis

    🔹 Extract the entire product catalog from competitor websites 🔹 Analyze product assortment to refine your own offerings and identify gaps 🔹 Understand competitor strategies and optimize your product lineup

    ➤ Marketplaces & Aggregators

    🔹 Crawl entire product categories and track best-sellers 🔹 Monitor position changes across categories 🔹 Identify which eRetailers sell specific brands and which SKUs for better market analysis

    ➤ Business Website Data

    🔹 Extract detailed company profiles, including financial statements, key personnel, industry reports, and market trends, enabling in-depth competitor and market analysis

    🔹 Collect customer reviews and ratings from business websites to analyze brand sentiment and product performance, helping businesses refine their strategies

    ➤ Domain Name Data

    🔹 Access comprehensive data, including domain registration details, ownership information, expiration dates, and contact information. Ideal for market research, brand monitoring, lead generation, and cybersecurity efforts

    ➤ Real Estate Data

    🔹 Access property listings, prices, and availability 🔹 Analyze trends and opportunities for investment or sales strategies

    ― Data Collection & Quality ―

    ► Publicly Sourced Data: Altosight collects web scraping data from publicly available websites, online platforms, and industry-specific aggregators

    ► AI-Powered Scraping: Our technology handles dynamic content, JavaScript-heavy sites, and pagination, ensuring complete data extraction

    ► High Data Quality: We clean and structure unstructured data, ensuring it is reliable, accurate, and delivered in formats such as API, CSV, JSON, and more

    ► Industry Coverage: We serve industries including e-commerce, real estate, travel, finance, and more. Our solution supports use cases like market research, competitive analysis, and business intelligence

    ► Bulk Data Extraction: We support large-scale data extraction from multiple websites, allowing you to gather millions of data points across industries in a single project

    ► Scalable Infrastructure: Our platform is built to scale with your needs, allowing seamless extraction for projects of any size, from small pilot projects to ongoing, large-scale data extraction

    ― Why Choose Altosight? ―

    ✔ Unlimited Data Points: Altosight offers unlimited free attributes, meaning you can extract as many data points from a page as you need without extra charges

    ✔ Proprietary Anti-Blocking Technology: Altosight utilizes proprietary techniques to bypass blocking mechanisms, including CAPTCHAs, Cloudflare, and other obstacles. This ensures uninterrupted access to data, no matter how complex the target websites are

    ✔ Flexible Across Industries: Our crawlers easily adapt across industries, including e-commerce, real estate, finance, and more. We offer customized data solutions tailored to specific needs

    ✔ GDPR & CCPA Compliance: Your data is handled securely and ethically, ensuring compliance with GDPR, CCPA and other regulations

    ✔ No Setup or Infrastructure Costs: Start scraping without worrying about additional costs. We provide a hassle-free experience with fast project deployment

    ✔ Free Data Delivery Methods: Receive your data via API, CSV, JSON, or FTP at no extra charge. We ensure seamless integration with your systems

    ✔ Fast Support: Our team is always available via phone and email, resolving over 90% of support tickets within the same day

    ― Custom Projects & Real-Time Data ―

    ✦ Tailored Solutions: Every business has unique needs, which is why Altosight offers custom data projects. Contact us for a feasibility analysis, and we’ll design a solution that fits your goals

    ✦ Real-Time Data: Whether you need real-time data delivery or scheduled updates, we provide the flexibility to receive data when you need it. Track price changes, monitor product trends, or gather...

  19. S

    Global Grain-Free Pet Food Market Economic and Social Impact 2025-2032

    • statsndata.org
    excel, pdf
    Updated May 2025
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    Stats N Data (2025). Global Grain-Free Pet Food Market Economic and Social Impact 2025-2032 [Dataset]. https://www.statsndata.org/report/grain-free-pet-food-market-378850
    Explore at:
    excel, pdfAvailable download formats
    Dataset updated
    May 2025
    Dataset authored and provided by
    Stats N Data
    License

    https://www.statsndata.org/how-to-orderhttps://www.statsndata.org/how-to-order

    Area covered
    Global
    Description

    The Grain-Free Pet Food market has witnessed significant growth in recent years, reflecting a shift in consumer attitudes towards pet nutrition. The demand for grain-free options has surged as pet owners become more aware of the potential benefits these diets can offer, particularly for pets with specific dietary se

  20. d

    ICA250 - Individuals aged 16 years and over who use free apps and issues...

    • datasalsa.com
    csv, json-stat, px +1
    Updated Jan 4, 2025
    + more versions
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    Central Statistics Office (2025). ICA250 - Individuals aged 16 years and over who use free apps and issues encountered when deleting/closing free apps [Dataset]. https://datasalsa.com/dataset/?catalogue=data.gov.ie&name=ica250--years-and-over-who-use-free-apps-and-issues-encountered-when-deletingclosing-free-apps-6ea8
    Explore at:
    json-stat, csv, px, xlsxAvailable download formats
    Dataset updated
    Jan 4, 2025
    Dataset authored and provided by
    Central Statistics Office
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jul 8, 2025
    Description

    ICA250 - Individuals aged 16 years and over who use free apps and issues encountered when deleting/closing free apps. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Individuals aged 16 years and over who use free apps and issues encountered when deleting/closing free apps...

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Florian Loffing (2023). Data_Sheet_1_Raw Data Visualization for Common Factorial Designs Using SPSS: A Syntax Collection and Tutorial.ZIP [Dataset]. http://doi.org/10.3389/fpsyg.2022.808469.s001

Data_Sheet_1_Raw Data Visualization for Common Factorial Designs Using SPSS: A Syntax Collection and Tutorial.ZIP

Related Article
Explore at:
zipAvailable download formats
Dataset updated
Jun 2, 2023
Dataset provided by
Frontiers
Authors
Florian Loffing
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

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