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 .

OpenWeb Ninja's Google Images Data (Google SERP Data) API provides real-time image search capabilities for images sourced from all public sources on the web.

The API enables you to search and access more than 100 billion images from across the web including advanced filtering capabilities as supported by Google Advanced Image Search. The API provides Google Images Data (Google SERP Data) including details such as image URL, title, size information, thumbnail, source information, and more data points. The API supports advanced filtering and options such as file type, image color, usage rights, creation time, and more. In addition, any Advanced Google Search operators can be used with the API.

OpenWeb Ninja's Google Images Data & Google SERP Data API common use cases:

• Creative Media Production: Enhance digital content with a vast array of real-time images, ensuring engaging and brand-aligned visuals for blogs, social media, and advertising.

• AI Model Enhancement: Train and refine AI models with diverse, annotated images, improving object recognition and image classification accuracy.

• Trend Analysis: Identify emerging market trends and consumer preferences through real-time visual data, enabling proactive business decisions.

• Innovative Product Design: Inspire product innovation by exploring current design trends and competitor products, ensuring market-relevant offerings.

• Advanced Search Optimization: Improve search engines and applications with enriched image datasets, providing users with accurate, relevant, and visually appealing search results.

OpenWeb Ninja's Annotated Imagery Data & Google SERP Data Stats & Capabilities:

• 100B+ Images: Access an extensive database of over 100 billion images.

• Images Data from all Public Sources (Google SERP Data): Benefit from a comprehensive aggregation of image data from various public websites, ensuring a wide range of sources and perspectives.

• Extensive Search and Filtering Capabilities: Utilize advanced search operators and filters to refine image searches by file type, color, usage rights, creation time, and more, making it easy to find exactly what you need.

• Rich Data Points: Each image comes with more than 10 data points, including URL, title (annotation), size information, thumbnail, and source information, providing a detailed context for each image.

The Google Trends dataset will provide critical signals that individual users and businesses alike can leverage to make better data-driven decisions. This dataset simplifies the manual interaction with the existing Google Trends UI by automating and exposing anonymized, aggregated, and indexed search data in BigQuery. This dataset includes the Top 25 stories and Top 25 Rising queries from Google Trends. It will be made available as two separate BigQuery tables, with a set of new top terms appended daily. Each set of Top 25 and Top 25 rising expires after 30 days, and will be accompanied by a rolling five-year window of historical data in 210 distinct locations in the United States. This Google dataset is hosted in Google BigQuery as part of Google Cloud's Datasets solution 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

United States Google Search Trends: Government Measures: Government Subsidy data was reported at 0.000 Score in 14 May 2025. This stayed constant from the previous number of 0.000 Score for 13 May 2025. United States Google Search Trends: Government Measures: Government Subsidy data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 0.000 Score in 14 May 2025 and a record low of 0.000 Score in 14 May 2025. United States Google Search Trends: Government Measures: Government Subsidy data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s United States – Table US.Google.GT: Google Search Trends: by Categories.

You can check the fields description in the documentation: current Full database: https://docs.dataforseo.com/v3/databases/google/full/?bash; Historical Full database: https://docs.dataforseo.com/v3/databases/google/history/full/?bash.

Full Google Database is a combination of the Advanced Google SERP Database and Google Keyword Database.

Google SERP Database offers millions of SERPs collected in 67 regions with most of Google’s advanced SERP features, including featured snippets, knowledge graphs, people also ask sections, top stories, and more.

Google Keyword Database encompasses billions of search terms enriched with related Google Ads data: search volume trends, CPC, competition, and more.

This database is available in JSON format only.

You don’t have to download fresh data dumps in JSON – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

China Google Search Trends: Online Shopping: Tmall data was reported at 8.000 Score in 14 May 2025. This stayed constant from the previous number of 8.000 Score for 13 May 2025. China Google Search Trends: Online Shopping: Tmall data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 70.000 Score in 22 Jan 2023 and a record low of 0.000 Score in 02 May 2025. China Google Search Trends: Online Shopping: Tmall data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s China – Table CN.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Online Training: Udemy data was reported at 0.000 Score in 14 May 2025. This stayed constant from the previous number of 0.000 Score for 13 May 2025. Google Search Trends: Online Training: Udemy data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 100.000 Score in 24 Dec 2024 and a record low of 0.000 Score in 14 May 2025. Google Search Trends: Online Training: Udemy data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Laos – Table LA.Google.GT: Google Search Trends: by Categories.

Oman Google Search Trends: Computer & Electronics: Apple data was reported at 13.000 Score in 14 May 2025. This records a decrease from the previous number of 15.000 Score for 13 May 2025. Oman Google Search Trends: Computer & Electronics: Apple data is updated daily, averaging 21.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 100.000 Score in 24 Sep 2024 and a record low of 0.000 Score in 30 Jul 2023. Oman Google Search Trends: Computer & Electronics: Apple data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Oman – Table OM.Google.GT: Google Search Trends: by Categories.

Version update: The originally uploaded versions of the CSV files in this dataset included an extra column, "Unnamed: 0," which is not RAMP data and was an artifact of the process used to export the data to CSV format. This column has been removed from the revised dataset. The data are otherwise the same as in the first version.

The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (http://rampanalytics.org), consisting of data from all participating repositories for the calendar year 2020. For a description of the data collection, processing, and output methods, please see the "methods" section below.

Methods Data Collection

RAMP data are downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data are downloaded in two sets per participating IR. The first set includes page level statistics about URLs pointing to IR pages and content files. The following fields are downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Following data processing describe below, on ingest into RAMP a additional field, citableContent, is added to the page level data.

The second set includes similar information, but instead of being aggregated at the page level, the data are grouped based on the country from which the user submitted the corresponding search, and the type of device used. The following fields are downloaded for combination of country and device, with one row per country/device combination:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, the page level data described above are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of page level statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the page level data which records whether each page/URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

The data aggregated by the search country of origin and device type do not include URLs. No additional processing is done on these data. Harvested data are passed directly into Elasticsearch.

Processed data are then saved in a series of Elasticsearch indices. Currently, RAMP stores data in two indices per participating IR. One index includes the page level data, the second index includes the country of origin and device type data.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository content, CCD represent click activity on IR content that may correspond to research use.

CCD information is summary data calculated on the fly within the RAMP web application. As noted above, data provided by GSC include whether and how many times a URL was clicked by users. Within RAMP, a "click" is counted as a potential download, so a CCD is calculated as the sum of clicks on pages/URLs that are determined to point to citable content (as defined above).

For any specified date range, the steps to calculate CCD are:

Filter data to only include rows where "citableContent" is set to "Yes."
Sum the value of the "clicks" field on these rows.

Output to CSV

Published RAMP data are exported from the production Elasticsearch instance and converted to CSV format. The CSV data consist of one "row" for each page or URL from a specific IR which appeared in search result pages (SERP) within Google properties as described above. Also as noted above, daily data are downloaded for each IR in two sets which cannot be combined. One dataset includes the URLs of items that appear in SERP. The second dataset is aggregated by combination of the country from which a search was conducted and the device used.

As a result, two CSV datasets are provided for each month of published data:

page-clicks:

The data in these CSV files correspond to the page-level data, and include the following fields:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.
citableContent: Whether or not the URL points to a content file (ending with pdf, csv, etc.) rather than HTML wrapper pages. Possible values are Yes or No.
index: The Elasticsearch index corresponding to page click data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the previous field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data end with “page-clicks”. For example, the file named 2020-01_RAMP_all_page-clicks.csv contains page level click data for all RAMP participating IR for the month of January, 2020.

country-device-info:

The data in these CSV files correspond to the data aggregated by country from which a search was conducted and the device used. These include the following fields:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.
index: The Elasticsearch index corresponding to country and device access information data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the previous field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data end with “country-device-info”. For example, the file named 2020-01_RAMP_all_country-device-info.csv contains country and device data for all participating IR for the month of January, 2020.

References

Google, Inc. (2021). Search Console APIs. Retrieved from https://developers.google.com/webmaster-tools/search-console-api-original.

Google Search Trends: Travel & Accommodations: Emirates data was reported at 0.000 Score in 14 May 2025. This stayed constant from the previous number of 0.000 Score for 13 May 2025. Google Search Trends: Travel & Accommodations: Emirates data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 99.000 Score in 08 Jan 2022 and a record low of 0.000 Score in 14 May 2025. Google Search Trends: Travel & Accommodations: Emirates data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Laos – Table LA.Google.GT: Google Search Trends: by Categories.

Kyrgyzstan Google Search Trends: Jobs Searching: Indeed data was reported at 0.000 Score in 14 May 2025. This stayed constant from the previous number of 0.000 Score for 13 May 2025. Kyrgyzstan Google Search Trends: Jobs Searching: Indeed data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 75.000 Score in 02 Aug 2022 and a record low of 0.000 Score in 14 May 2025. Kyrgyzstan Google Search Trends: Jobs Searching: Indeed data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Kyrgyzstan – Table KG.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Online Games: Call of Duty data was reported at 0.000 Score in 15 May 2025. This stayed constant from the previous number of 0.000 Score for 14 May 2025. Google Search Trends: Online Games: Call of Duty data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 15 May 2025, with 1262 observations. The data reached an all-time high of 83.000 Score in 28 Jun 2022 and a record low of 0.000 Score in 15 May 2025. Google Search Trends: Online Games: Call of Duty data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Costa Rica – Table CR.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Travel & Accommodations: Booking.com data was reported at 2.000 Score in 14 May 2025. This stayed constant from the previous number of 2.000 Score for 13 May 2025. Google Search Trends: Travel & Accommodations: Booking.com data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 19.000 Score in 21 Apr 2023 and a record low of 0.000 Score in 02 May 2025. Google Search Trends: Travel & Accommodations: Booking.com data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s China – Table CN.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Computer & Electronics: Apple data was reported at 25.000 Score in 15 May 2025. This stayed constant from the previous number of 25.000 Score for 14 May 2025. Google Search Trends: Computer & Electronics: Apple data is updated daily, averaging 27.000 Score from Dec 2021 (Median) to 15 May 2025, with 1262 observations. The data reached an all-time high of 100.000 Score in 10 Sep 2024 and a record low of 0.000 Score in 08 Apr 2023. Google Search Trends: Computer & Electronics: Apple data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Taiwan – Table TW.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Online Shopping: Wayfair data was reported at 0.000 Score in 14 May 2025. This stayed constant from the previous number of 0.000 Score for 13 May 2025. Google Search Trends: Online Shopping: Wayfair data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 9.000 Score in 09 Jan 2022 and a record low of 0.000 Score in 14 May 2025. Google Search Trends: Online Shopping: Wayfair data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Laos – Table LA.Google.GT: Google Search Trends: by Categories.

Moldova Google Search Trends: Online Training: Udemy data was reported at 4.000 Score in 15 May 2025. This records a decrease from the previous number of 7.000 Score for 14 May 2025. Moldova Google Search Trends: Online Training: Udemy data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 15 May 2025, with 1262 observations. The data reached an all-time high of 100.000 Score in 15 Oct 2022 and a record low of 0.000 Score in 06 May 2025. Moldova Google Search Trends: Online Training: Udemy data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Moldova – Table MD.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Travel & Accommodations: American Airlines data was reported at 1.000 Score in 14 May 2025. This stayed constant from the previous number of 1.000 Score for 13 May 2025. Google Search Trends: Travel & Accommodations: American Airlines data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 19.000 Score in 02 Nov 2022 and a record low of 0.000 Score in 03 May 2025. Google Search Trends: Travel & Accommodations: American Airlines data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s China – Table CN.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Online Training: Coursera data was reported at 0.000 Score in 14 May 2025. This stayed constant from the previous number of 0.000 Score for 13 May 2025. Google Search Trends: Online Training: Coursera data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 100.000 Score in 06 Feb 2022 and a record low of 0.000 Score in 14 May 2025. Google Search Trends: Online Training: Coursera data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Iceland – Table IS.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Economic Measures: Mortgage Loan data was reported at 10.000 Score in 14 May 2025. This records a decrease from the previous number of 12.000 Score for 13 May 2025. Google Search Trends: Economic Measures: Mortgage Loan data is updated daily, averaging 10.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 47.000 Score in 21 Apr 2023 and a record low of 0.000 Score in 14 Feb 2023. Google Search Trends: Economic Measures: Mortgage Loan data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Spain – Table ES.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Travel & Accommodations: American Airlines data was reported at 2.000 Score in 14 May 2025. This records a decrease from the previous number of 3.000 Score for 13 May 2025. Google Search Trends: Travel & Accommodations: American Airlines data is updated daily, averaging 1.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 50.000 Score in 30 Jan 2025 and a record low of 0.000 Score in 02 May 2025. Google Search Trends: Travel & Accommodations: American Airlines data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Taiwan – Table TW.Google.GT: Google Search Trends: by Categories.