Uzbekistan Google Search Trends: Travel & Accommodations: Airbnb data was reported at 3.000 Score in 14 May 2025. This stayed constant from the previous number of 3.000 Score for 13 May 2025. Uzbekistan Google Search Trends: Travel & Accommodations: Airbnb 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 23.000 Score in 22 Jul 2023 and a record low of 0.000 Score in 02 May 2025. Uzbekistan Google Search Trends: Travel & Accommodations: Airbnb data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Uzbekistan – Table UZ.Google.GT: Google Search Trends: by Categories.

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

Google Trends data on searches for "pollen" for DMA regions near National Allergy Bureau pollen counting stations from 2012-2017, downloaded in 10x replicates, from Jan-Jun and Apr-Dec of each year. Search data for the term "ragweed" is included as a comparator in pollen searches (no file suffix), and can also be found as a separate search term (in files with the suffix "ragweed.csv")

Google Search Trends: Online Classroom: Google Classroom data was reported at 40.000 Score in 14 May 2025. This records a decrease from the previous number of 47.000 Score for 13 May 2025. Google Search Trends: Online Classroom: Google Classroom data is updated daily, averaging 32.000 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 Mar 2023 and a record low of 0.000 Score in 02 May 2025. Google Search Trends: Online Classroom: Google Classroom data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Trinidad and Tobago – Table TT.Google.GT: Google Search Trends: by Categories.

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 .

Google Trends allows you to study the trends and patterns of search queries on Google. Google Trends represents the absolute number of searches relative to the total number of searches in a defined period of interest. The number retrieved ranges from 0 to 100, where 100 is the highest relative search term for a given search query in the period of interest. 2The data was collecte rom January 2018 to January 2022.

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.

Summary of EM-DAT and GT information connected to flood, for the period 2004-2023. Focus on Europe.

Reliability of individual and averaged time series for selected Google Trends search terms.

2020

Global Google Trends Internet Search Data 2022 to 2024 [RSVs] for fasting, diet, nutrition, liver, GLP-1 RAs

This Excel dataset includes all for publication used worldwide Google Trends data from 2004 to 2021 on the topic of climate change, downloaded on 09/01/2021 in Berlin, Germany, after filtering for the periods and subjects of interest. [Accessed at: https://trends.google.com/]

The datasets include all analyzed Google Trends data on climate change and COVID-19 data for publication.

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.

Global Google Trends Internet Search Data as accessed on September 30 2024 [RSVs] for Intermittent Fasting (World) , Fasting (World), Diet (World), Nutrition (World)

The dataset contains:

1) The list of tweets corresponding to the keywords "heat wave india" and "heatwave india" between 2010 and 2017.

2) The daily count of the same tweets

3) The monthly Google Trends data corresponding to the keywords "heat wave", "heatwave", "heat wave india", and "heatwave india" limited to the searches from India in the period 2010-2017

The Twitter data has been obtained wth the Python package Get-Old-Tweets (https://github.com/Jefferson-Henrique/GetOldTweets-python); the Google Trends data are obtained from the Google Trends webpage (https://trends.google.com/trends/?geo=US).

Data used for research project: Shedding light on dark figures: Steps towards a methodology for estimating actual numbers of COVID-19 infections in Germany based on Google Trends

Data obtained from Google Trends (trends.google.de) and Robert Koch Institute (https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/nCoV.html).

This dataset includes the data utilized in the article "From Global Health to Global Warming: Tracing Climate Change Interest During the First Two Years of COVID-19 using Google Trends Data from the United States". For reference, please cite the corresponding publication in Environments at https://doi.org/10.3390/environments10120221

As response rates continue to decline, the need to learn more about the survey participation process remains an important task for survey researchers. Search engine data may be one possible source for learning about what information some potential respondents are looking up about a survey when they are making a participation decision. In the present study, we explored the potential of search engine data for learning about survey participation and how it can inform survey design decisions. We drew on freely available Google Trends (GT) data to learn about the use of Google Search with respect to our case study: participation in the Family Research and Demographic Analysis (FReDA) panel survey. Our results showed that some potential respondents were using Google Search to gather information on the FReDA survey. We also showed that the additional data obtained via GT can help survey researchers to discover topics of interest to respondents and geographically stratified search patterns. Moreover, we introduced different approaches for obtaining data via GT, discussed the challenges that come with these data, and closed with practical recommendations on how survey researchers might utilize GT data to learn about survey participation.

Abstract (our paper) The frequency of a web search keyword generally reflects the degree of public interest in a particular subject matter. Search logs are therefore useful resources for trend analysis. However, access to search logs is typically restricted to search engine providers. In this paper, we investigate whether search frequency can be estimated from a different resource such as Wikipedia page views of open data. We found frequently searched keywords to have remarkably high correlations with Wikipedia page views. This suggests that Wikipedia page views can be an effective tool for determining popular global web search trends. Data personal-name.txt.gz: The first column is the Wikipedia article id, the second column is the search keyword, the third column is the Wikipedia article title, and the fourth column is the total of page views from 2008 to 2014. personal-name_data_google-trends.txt.gz, personal-name_data_wikipedia.txt.gz: The first column is the period to be collected, the second column is the source (Google or Wikipedia), the third column is the Wikipedia article id, the fourth column is the search keyword, the fifth column is the date, and the sixth column is the value of search trend or page view. Publication This data set was created for our study. If you make use of this data set, please cite: Mitsuo Yoshida, Yuki Arase, Takaaki Tsunoda, Mikio Yamamoto. Wikipedia Page View Reflects Web Search Trend. Proceedings of the 2015 ACM Web Science Conference (WebSci '15). no.65, pp.1-2, 2015. http://dx.doi.org/10.1145/2786451.2786495 http://arxiv.org/abs/1509.02218 (author-created version) Note The raw data of Wikipedia page views is available in the following page. http://dumps.wikimedia.org/other/pagecounts-raw/ {"references": ["Mitsuo Yoshida, Yuki Arase, Takaaki Tsunoda, Mikio Yamamoto. Wikipedia Page View Reflects Web Search Trend. Proceedings of the 2015 ACM Web Science Conference (WebSci '15). no.65, pp.1-2, 2015.", "Mitsuo Yoshida, Yuki Arase, Takaaki Tsunoda, Mikio Yamamoto. Wikipedia Page View Analysis for Search Trend Prediction. Proceedings of the Annual Conference of Japanese Society for Artificial Intelligence (in Japanese). vol.29, no.2I1-1, pp.1-4, 2015."]}