6 datasets found
  1. COVID-19 Search Trends symptoms dataset

    • console.cloud.google.com
    Updated Dec 17, 2019
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    https://console.cloud.google.com/marketplace/browse?filter=partner:BigQuery%20Public%20Datasets%20Program&inv=1&invt=Ab2UXQ (2019). COVID-19 Search Trends symptoms dataset [Dataset]. https://console.cloud.google.com/marketplace/product/bigquery-public-datasets/covid19-search-trends
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    Dataset updated
    Dec 17, 2019
    Dataset provided by
    BigQueryhttps://cloud.google.com/bigquery
    Googlehttp://google.com/
    Description

    The COVID-19 Search Trends symptoms dataset shows aggregated, anonymized trends in Google searches for a broad set of health symptoms, signs, and conditions. The dataset provides a daily or weekly time series for each region showing the relative volume of searches for each symptom. This dataset is intended to help researchers to better understand the impact of COVID-19. It shouldn't be used for medical diagnostic, prognostic, or treatment purposes. It also isn't intended to be used for guidance on personal travel plans. To learn more about the dataset, how we generate it and preserve privacy, read the data documentation . To visualize the data, try exploring these interactive charts and map of symptom search trends . As of Dec. 15, 2020, the dataset was expanded to include trends for Australia, Ireland, New Zealand, Singapore, and the United Kingdom. This expanded data is available in new tables that provide data at country and two subregional levels. We will not be updating existing state/county tables going forward. All bytes processed in queries against this dataset will be zeroed out, making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 15, queries over these datasets will revert to the normal billing rate. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

  2. m

    Data for "Firearm-related Internet Searches as a Predictor of Suicides by...

    • data.mendeley.com
    Updated Apr 4, 2020
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    Joo-Young Lee (2020). Data for "Firearm-related Internet Searches as a Predictor of Suicides by Shooting in the United States: Cross-correlation Analyses of Monthly Google Search Volumes and Method-specific Suicide Rates" [Dataset]. http://doi.org/10.17632/96ts2pf2st.1
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    Dataset updated
    Apr 4, 2020
    Authors
    Joo-Young Lee
    License

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

    Description

    This dataset includes the raw data from Google Trends, averaged data, the construct of (S)ARIMA models, and cross-correlation coefficients. Three sets of data are due to sensitivity analyses performed in 3 different time spans. The monthly rates of suicide by 3 differents means in the USA are also included.

    The study elucidated 3 Google search terms whose search volume trends precede trends in means-specific suicide rate in the United States.

  3. d

    US Consumer Marketing Data - 269M+ Consumer Records - 95% Email and Direct...

    • datarade.ai
    Updated Jun 1, 2022
    + more versions
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    Giant Partners (2022). US Consumer Marketing Data - 269M+ Consumer Records - 95% Email and Direct Dials Accuracy [Dataset]. https://datarade.ai/data-products/consumer-business-data-postal-phone-email-demographics-giant-partners
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    Dataset updated
    Jun 1, 2022
    Dataset authored and provided by
    Giant Partners
    Area covered
    United States of America
    Description

    Premium B2C Consumer Database - 269+ Million US Records

    Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

    Core Database Statistics

    Consumer Records: Over 269 million

    Email Addresses: Over 160 million (verified and deliverable)

    Phone Numbers: Over 76 million (mobile and landline)

    Mailing Addresses: Over 116,000,000 (NCOA processed)

    Geographic Coverage: Complete US (all 50 states)

    Compliance Status: CCPA compliant with consent management

    Targeting Categories Available

    Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

    Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

    Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

    Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

    Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

    Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

    Multi-Channel Campaign Applications

    Deploy across all major marketing channels:

    Email marketing and automation

    Social media advertising

    Search and display advertising (Google, YouTube)

    Direct mail and print campaigns

    Telemarketing and SMS campaigns

    Programmatic advertising platforms

    Data Quality & Sources

    Our consumer data aggregates from multiple verified sources:

    Public records and government databases

    Opt-in subscription services and registrations

    Purchase transaction data from retail partners

    Survey participation and research studies

    Online behavioral data (privacy compliant)

    Technical Delivery Options

    File Formats: CSV, Excel, JSON, XML formats available

    Delivery Methods: Secure FTP, API integration, direct download

    Processing: Real-time NCOA, email validation, phone verification

    Custom Selections: 1,000+ selectable demographic and behavioral attributes

    Minimum Orders: Flexible based on targeting complexity

    Unique Value Propositions

    Dual Spouse Targeting: Reach both household decision-makers for maximum impact

    Cross-Platform Integration: Seamless deployment to major ad platforms

    Real-Time Updates: Monthly data refreshes ensure maximum accuracy

    Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

    Compliance Management: Built-in opt-out and suppression list management

    Ideal Customer Profiles

    E-commerce retailers seeking customer acquisition

    Financial services companies targeting specific demographics

    Healthcare organizations with compliant marketing needs

    Automotive dealers and service providers

    Home improvement and real estate professionals

    Insurance companies and agents

    Subscription services and SaaS providers

    Performance Optimization Features

    Lookalike Modeling: Create audiences similar to your best customers

    Predictive Scoring: Identify high-value prospects using AI algorithms

    Campaign Attribution: Track performance across multiple touchpoints

    A/B Testing Support: Split audiences for campaign optimization

    Suppression Management: Automatic opt-out and DNC compliance

    Pricing & Volume Options

    Flexible pricing structures accommodate businesses of all sizes:

    Pay-per-record for small campaigns

    Volume discounts for large deployments

    Subscription models for ongoing campaigns

    Custom enterprise pricing for high-volume users

    Data Compliance & Privacy

    VIA.tools maintains industry-leading compliance standards:

    CCPA (California Consumer Privacy Act) compliant

    CAN-SPAM Act adherence for email marketing

    TCPA compliance for phone and SMS campaigns

    Regular privacy audits and data governance reviews

    Transparent opt-out and data deletion processes

    Getting Started

    Our data specialists work with you to:

    1. Define your target audience criteria

    2. Recommend optimal data selections

    3. Provide sample data for testing

    4. Configure delivery methods and formats

    5. Implement ongoing campaign optimization

    Why We Lead the Industry

    With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

    Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

  4. d

    Data from: Adaptive nowcasting of influenza outbreaks using Google searches

    • datadryad.org
    • data.niaid.nih.gov
    zip
    Updated Sep 23, 2015
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    Tobias Preis; Helen Susannah Moat (2015). Adaptive nowcasting of influenza outbreaks using Google searches [Dataset]. http://doi.org/10.5061/dryad.r06h2
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    zipAvailable download formats
    Dataset updated
    Sep 23, 2015
    Dataset provided by
    Dryad
    Authors
    Tobias Preis; Helen Susannah Moat
    Time period covered
    Sep 22, 2014
    Description

    Unweighted Percentages of Weekly Outpatient Visits for ILI and Google Flu Trends dataWe retrieved the weekly unweighted percentages of patient visits due to influenza-like illness (ILI), reported through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet), from http://www.cdc.gov/flu/weekly/ on 10th December 2013. Here, ILI is defined as fever with a temperature of 100°F or greater, accompanied by a cough or a sore throat. Note that the data recorded for a given week can be updated in subsequent weeks, if the CDC have reason to believe that an updated figure would be more accurate. Here, we focus our analysis on the latest data available on the date of retrieval.

    We obtained the weekly time series of query volume for searches relating to ILI symptoms from Google Flu Trends (http://www.google.org/flutrends) on 18th December 2013. This time series is restricted to searches made in the United States, and has been shown by Ginsberg et al. to be correlated with the perc...

  5. f

    Table_1_Public interest in different types of masks and its relationship...

    • figshare.com
    xlsx
    Updated Jun 8, 2023
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    Andy Wai Kan Yeung; Emil D. Parvanov; Jarosław Olav Horbańczuk; Maria Kletecka-Pulker; Oliver Kimberger; Harald Willschke; Atanas G. Atanasov (2023). Table_1_Public interest in different types of masks and its relationship with pandemic and policy measures during the COVID-19 pandemic: a study using Google Trends data.XLSX [Dataset]. http://doi.org/10.3389/fpubh.2023.1010674.s002
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 8, 2023
    Dataset provided by
    Frontiers
    Authors
    Andy Wai Kan Yeung; Emil D. Parvanov; Jarosław Olav Horbańczuk; Maria Kletecka-Pulker; Oliver Kimberger; Harald Willschke; Atanas G. Atanasov
    License

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

    Description

    Google Trends data have been used to investigate various themes on online information seeking. It was unclear if the population from different parts of the world shared the same amount of attention to different mask types during the COVID-19 pandemic. This study aimed to reveal which types of masks were frequently searched by the public in different countries, and evaluated if public attention to masks could be related to mandatory policy, stringency of the policy, and transmission rate of COVID-19. By referring to an open dataset hosted at the online database Our World in Data, the 10 countries with the highest total number of COVID-19 cases as of 9th of February 2022 were identified. For each of these countries, the weekly new cases per million population, reproduction rate (of COVID-19), stringency index, and face covering policy score were computed from the raw daily data. Google Trends were queried to extract the relative search volume (RSV) for different types of masks from each of these countries. Results found that Google searches for N95 masks were predominant in India, whereas surgical masks were predominant in Russia, FFP2 masks were predominant in Spain, and cloth masks were predominant in both France and United Kingdom. The United States, Brazil, Germany, and Turkey had two predominant types of mask. The online searching behavior for masks markedly varied across countries. For most of the surveyed countries, the online searching for masks peaked during the first wave of COVID-19 pandemic before the government implemented mandatory mask wearing. The search for masks positively correlated with the government response stringency index but not with the COVID-19 reproduction rate or the new cases per million.

  6. f

    Data_Sheet_1_Public interest in different types of masks and its...

    • frontiersin.figshare.com
    docx
    Updated Jun 8, 2023
    + more versions
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    Andy Wai Kan Yeung; Emil D. Parvanov; Jarosław Olav Horbańczuk; Maria Kletecka-Pulker; Oliver Kimberger; Harald Willschke; Atanas G. Atanasov (2023). Data_Sheet_1_Public interest in different types of masks and its relationship with pandemic and policy measures during the COVID-19 pandemic: a study using Google Trends data.docx [Dataset]. http://doi.org/10.3389/fpubh.2023.1010674.s001
    Explore at:
    docxAvailable download formats
    Dataset updated
    Jun 8, 2023
    Dataset provided by
    Frontiers
    Authors
    Andy Wai Kan Yeung; Emil D. Parvanov; Jarosław Olav Horbańczuk; Maria Kletecka-Pulker; Oliver Kimberger; Harald Willschke; Atanas G. Atanasov
    License

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

    Description

    Google Trends data have been used to investigate various themes on online information seeking. It was unclear if the population from different parts of the world shared the same amount of attention to different mask types during the COVID-19 pandemic. This study aimed to reveal which types of masks were frequently searched by the public in different countries, and evaluated if public attention to masks could be related to mandatory policy, stringency of the policy, and transmission rate of COVID-19. By referring to an open dataset hosted at the online database Our World in Data, the 10 countries with the highest total number of COVID-19 cases as of 9th of February 2022 were identified. For each of these countries, the weekly new cases per million population, reproduction rate (of COVID-19), stringency index, and face covering policy score were computed from the raw daily data. Google Trends were queried to extract the relative search volume (RSV) for different types of masks from each of these countries. Results found that Google searches for N95 masks were predominant in India, whereas surgical masks were predominant in Russia, FFP2 masks were predominant in Spain, and cloth masks were predominant in both France and United Kingdom. The United States, Brazil, Germany, and Turkey had two predominant types of mask. The online searching behavior for masks markedly varied across countries. For most of the surveyed countries, the online searching for masks peaked during the first wave of COVID-19 pandemic before the government implemented mandatory mask wearing. The search for masks positively correlated with the government response stringency index but not with the COVID-19 reproduction rate or the new cases per million.

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https://console.cloud.google.com/marketplace/browse?filter=partner:BigQuery%20Public%20Datasets%20Program&inv=1&invt=Ab2UXQ (2019). COVID-19 Search Trends symptoms dataset [Dataset]. https://console.cloud.google.com/marketplace/product/bigquery-public-datasets/covid19-search-trends
Organization logoOrganization logo

COVID-19 Search Trends symptoms dataset

Explore at:
56 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Dec 17, 2019
Dataset provided by
BigQueryhttps://cloud.google.com/bigquery
Googlehttp://google.com/
Description

The COVID-19 Search Trends symptoms dataset shows aggregated, anonymized trends in Google searches for a broad set of health symptoms, signs, and conditions. The dataset provides a daily or weekly time series for each region showing the relative volume of searches for each symptom. This dataset is intended to help researchers to better understand the impact of COVID-19. It shouldn't be used for medical diagnostic, prognostic, or treatment purposes. It also isn't intended to be used for guidance on personal travel plans. To learn more about the dataset, how we generate it and preserve privacy, read the data documentation . To visualize the data, try exploring these interactive charts and map of symptom search trends . As of Dec. 15, 2020, the dataset was expanded to include trends for Australia, Ireland, New Zealand, Singapore, and the United Kingdom. This expanded data is available in new tables that provide data at country and two subregional levels. We will not be updating existing state/county tables going forward. All bytes processed in queries against this dataset will be zeroed out, making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 15, queries over these datasets will revert to the normal billing rate. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

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