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 .

The goal of this research is to examine direct answers in Google web search engine. Dataset was collected using Senuto (https://www.senuto.com/). Senuto is as an online tool, that extracts data on websites visibility from Google search engine.

Dataset contains the following elements:

keyword,

number of monthly searches,

featured domain,

featured main domain,

featured position,

featured type,

featured url,

content,

content length.

Dataset with visibility structure has 743 798 keywords that were resulting in SERPs with direct answer.

Search for a business by name. You can obtain business information and then proceed to purchase a certificate of good standing or other documents. The purpose of this search is simply to determine whether a company/entity exists and to provide basic information on the company/entity.

500k User Session Collection This collection is distributed for NON-COMMERCIAL RESEARCH USE ONLY. Any application of this collection for commercial purposes is STRICTLY PROHIBITED. #### Brief description: This collection consists of ~20M web queries collected from ~650k users over three months. The data is sorted by anonymous user ID and sequentially arranged. The goal of this collection is to provide real query log data that is based on real users. It could be used for personalization, query reformulation or other types of search research. The data set includes AnonID, Query, QueryTime, ItemRank, ClickURL. AnonID - an anonymous user ID number. Query - the query issued by the user, case shifted with most punctuation removed. QueryTime - the time at which the query was submitted for search. ItemRank - if the user clicked on a search result, the rank of the item on which they clicked is listed. ClickURL - if the user clicked on a search result, the domain portion of the URL i

Data from the Interactive Social Book Search Track Series 2014-2016

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.

Description from the SaRNet: A Dataset for Deep Learning Assisted Search and Rescue with Satellite Imagery GitHub Repository * The "Note" was added by the Roboflow team.

Satellite Imagery for Search And Rescue Dataset - ArXiv

This is a single class dataset consisting of tiles of satellite imagery labeled with potential 'targets'. Labelers were instructed to draw boxes around anything they suspect may a paraglider wing, missing in a remote area of Nevada. Volunteers were shown examples of similar objects already in the environment for comparison. The missing wing, as it was found after 3 weeks, is shown below.

https://michaeltpublic.s3.amazonaws.com/images/anomaly_small.jpg" alt="anomaly">

The dataset contains the following:

The data is in the COCO format, and is directly compatible with faster r-cnn as implemented in Facebook's Detectron2.

Getting hold of the Data

Download the data here: sarnet.zip

Or follow these steps

# download the dataset
wget https://michaeltpublic.s3.amazonaws.com/sarnet.zip

# extract the files
unzip sarnet.zip

***Note* with Roboflow, you can download the data here** (original, raw images, with annotations): https://universe.roboflow.com/roboflow-public/sarnet-search-and-rescue/ (download v1, original_raw-images) * Download the dataset in COCO JSON format, or another format of choice, and import them to Roboflow after unzipping the folder to get started on your project.

Getting started

Get started with a Faster R-CNN model pretrained on SaRNet: SaRNet_Demo.ipynb

Source Code for Paper

Source code for the paper is located here: SaRNet_train_test.ipynb

Cite this dataset

@misc{thoreau2021sarnet,
   title={SaRNet: A Dataset for Deep Learning Assisted Search and Rescue with Satellite Imagery}, 
   author={Michael Thoreau and Frazer Wilson},
   year={2021},
   eprint={2107.12469},
   archivePrefix={arXiv},
   primaryClass={eess.IV}
}

Acknowledgment

The source data was generously provided by Planet Labs, Airbus Defence and Space, and Maxar Technologies.

A subset of data collected when individuals are interviewed by NOPD Officers (including individuals stopped for questioning and complainants).Disclaimer: The New Orleans Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information. The New Orleans Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The New Orleans Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of New Orleans or New Orleans Police Department web page. The user specifically acknowledges that the New Orleans Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. Any use of the information for commercial purposes is strictly prohibited. The unauthorized use of the words "New Orleans Police Department," "NOPD," or any colorable imitation of these words or the unauthorized use of the New Orleans Police Department logo is unlawful. This web page does not, in any way, authorize such use.

This represents the top 10 searches that visitors have conducted on via Google Search. The data represents the most recent one-month period. *Note: On July 1, 2023, standard Universal Analytics properties will stop processing data.

Minute-by-minute updated keyword database from Google, featuring 304 trending search terms

I. Hall

This dataset includes the detailed values and scripts used to study behavioral aspects of users searching online for Art and Culture by analyzing quantitative data collected by the Art Boulevard search engine using machine learning techniques. This dataset is part of the core methodology, results and discussion sections of the research paper entitled "Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques"

Lesotho Google Search Trends: Computer & Electronics: Apple data was reported at 9.000 Score in 15 May 2025. This records a decrease from the previous number of 12.000 Score for 14 May 2025. Lesotho Google Search Trends: Computer & Electronics: Apple 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 28 Sep 2023 and a record low of 0.000 Score in 03 May 2025. Lesotho 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 Lesotho – Table LS.Google.GT: Google Search Trends: by Categories.

We designed a human study to collect fixation data during visual search. We opted for a task that involved searching for a single image (the target) within a synthesised collage of images (the search set). Each of the collages are the random permutation of a finite set of images. To explore the impact of the similarity in appearance between target and search set on both fixation behaviour and automatic inference, we have created three different search tasks covering a range of similarities. In prior work, colour was found to be a particularly important cue for guiding search to targets and target-similar objects. Therefore we have selected for the first task 78 coloured O'Reilly book covers to compose the collages. These covers show a woodcut of an animal at the top and the title of the book in a characteristic font underneath. Given that overall cover appearance was very similar, this task allows us to analyse fixation behaviour when colour is the most discriminative feature. For the second task we use a set of 84 book covers from Amazon. In contrast to the first task, appearance of these covers is more diverse. This makes it possible to analyse fixation behaviour when both structure and colour information could be used by participants to find the target. Finally, for the third task, we use a set of 78 mugshots from a public database of suspects. In contrast to the other tasks, we transformed the mugshots to grey-scale so that they did not contain any colour information. In this case, allows abalysis of fixation behaviour when colour information was not available at all. We found faces to be particularly interesting given the relevance of searching for faces in many practical applications. 18 participants (9 males), age 18-30 Gaze data recorded with a stationary Tobii TX300 eye tracker More information about the dataset can be found in the README file.

Brand performance data collected from AI search platforms for the query "combine facebook and google ads data".

Earthdata Search is a web application developed by NASA EOSDIS to enable data discovery, search, comparison, visualization, and access across EOSDIS' Earth Science data holdings.

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.

Data related to the Master Thesis on The Impact of Biased Search Results on User Engagement in Web Search. The dataset consists of search results, search result annotations, interaction logs of the final study and the survey responses for the pilot and final study.

The transition from analog to digital archives and the recent explosion of online content offers researchers novel ways of engaging with data. The crucial question for ensuring a balance between the supply and demand-side of data, is whether this trend connects to existing scholarly practices and to the average search skills of researchers. To gain insight into this process a survey was conducted among nearly three hundred (N= 288) humanities scholars in the Netherlands and Belgium with the aim of finding answers to the following questions: 1) To what extent are digital databases and archives used? 2) What are the preferences in search functionalities 3) Are there differences in search strategies between novices and experts of information retrieval? Our results show that while scholars actively engage in research online they mainly search for text and images. General search systems such as Google and JSTOR are predominant, while large-scale collections such as Europeana are rarely consulted. Searching with keywords is the dominant search strategy and advanced search options are rarely used. When comparing novice and more experienced searchers, the first tend to have a more narrow selection of search engines, and mostly use keywords. Our overall findings indicate that Google is the key player among available search engines. This dominant use illustrates the paradoxical attitude of scholars toward Google: while transparency of provenance and selection are deemed key academic requirements, the workings of the Google algorithm remain unclear. We conclude that Google introduces a black box into digital scholarly practices, indicating scholars will become increasingly dependent on such black boxed algorithms. This calls for a reconsideration of the academic principles of provenance and context.

This dataset contains the digitized treatments in Plazi based on the original journal article Gardzińska, Joanna, Patoleta, Barbara (2013): A new species and records of Diolenius Thorell, 1870 (Araneae: Salticidae) from New Guinea. Zootaxa 3664: 63-68, URL: http://www.mapress.com/zootaxa/2013/f/zt03664p068.pdf