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.

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 .

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

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.

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.

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

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.

withpi/search-query-optimizer-companies-case-data-v01-formatted dataset hosted on Hugging Face and contributed by the HF Datasets community

I. Hall

This interface joins the Kepler Target Catalog (KTC) with other tables to allow users to access the Kepler data archive. Observed Kepler targets are included with their associated data set names. Since most of the Kepler light curve data is still proprietary, public data can be found by searching for release dates earlier than todays date.

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.

This is version v3.3.0.2022f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data. The quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. The data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format "station_code"_HadISD_HadOBS_19310101-20230101_v3.3.1.2022f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height. To keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS. For more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/ References: When using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the "citable as" reference) : Dunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note. Dunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016. Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012 Smith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1 For a homogeneity assessment of HadISD please see this following reference Dunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. "Pairwise homogeneity assessment of HadISD." Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014.

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"

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

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.

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.

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.

This technical report aims to provide detailed information on the results of Stage I of the methodology used to find references that are potentially relevant to the topic “Classification of Key Competencies for Construction Project Management.” In Stage I, potentially relevant references were searched using the American Society of Civil Engineers (ASCE) Library. In the ASCE Library, “advanced search” was used to find applicable references via specific search terms, topics and publication dates. For topics, the term “construction” was used. The option “title” was checked to specify where to look for search terms. The search terms used included competencies, competence, skill, capability, knowledge, project manager, project management, construction management, and engineering management. For more representative results, the search was restricted to references inclusively published from 1988 to 2019. When more than one chapter of a book was found, instead of counting all the chapters found, the book was counted as one single reference. In such cases, the book title might exclude all the search terms used. If the same reference was found under different search terms, it was numbered only one time when counting the total number of references initially found. This process resulted in 2,102 references retrieved from the ASCE Library (Table 1 to Table 16). In the following Tables, “Selected: Yes” indicates that the initially-retrieved reference was ultimately selected for content analysis, and “Selected: No” means that the reference was not selected for content analysis.

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.