This statistic presents the most popular first choice for performing searches as stated by adults in the United States as of April 2018. According to the findings, 44 percent of respondents stated their first choice in terms of performing searches was their mobile browser, while in comparison 16 percent stated their first choice being their search engine app or voice search.

Between June 2022 and March 2023, the traffic volume for the keyword "AI" has tripled, going from around 7.9 million monthly searches to more than 30.4 million during the last month of the measured period. General interest in artificial intelligence (AI) has exploded in markets like the United States by the end of 2022. Likewise, interest for the application programming interfaces (API's) and plugins of artificial intelligence solutions, especially those of ChatGPT, has also seen a major increase since the release of the tool in November of 2022.

The artificial intelligence market

Valued at around 142.3 billion U.S. dollars in 2022, the artificial intelligence market is one the most promising tech segments for the rest of the decade, with more than five billion U.S. dollars invested in startups - the most notable being the Californian company OpenAI and its flagship application ChatGPT. Disruptive as it is, the adoption of AI has already sparked an alert for several industries, likely to affect job markets and thus raising concerns about cybercrime and other online misdeeds.

The future of online search?

Of most industries, the impact of the new tool developed by OpenAI may be felt by the online search market like a global earthquake. With chatbots providing search results in a dialogue format, the trend of AI-powered search engines unleashed by ChatGPT threw giant companies like Google and Microsoft into a race with startups and other competitors to present the best candidate for this disruptive (and experimental) online solution.

These data are detections of reptiles in 2005 from visual encounter searches of an area constrained to a 100-m radius circle around each of 15 fixed sample points at Spears and Didion Ranches, Placer County, California. Surveys were done three times in April and May by trained observers who searched the entire 100-m radius (3.1 ha) circular plot looking on and under rocks, logs, and debris for reptiles and amphibians. Fifty-eight detections were made of five species of reptiles and one species of amphibian.

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 Movie: Netflix data was reported at 48.000 Score in 14 May 2025. This records an increase from the previous number of 46.000 Score for 13 May 2025. Google Search Trends: Online Movie: Netflix data is updated daily, averaging 40.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 100.000 Score in 16 Nov 2024 and a record low of 0.000 Score in 23 Mar 2023. Google Search Trends: Online Movie: Netflix data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Jamaica – Table JM.Google.GT: Google Search Trends: by Categories.

As of December 2024, global Google searches using the query "Wiki" stood at 87 percentage points, so far the lowest interest rate despite a relatively stable margin throughout the analyzed period. Meanwhile, searches for the full Wikipedia query were slightly more popular during the analyzed period.

Dataset Card for "instructional_code-search-net-java"

  Dataset Summary

This is an instructional dataset for Java. The dataset contains two different kind of tasks:

Given a piece of code generate a description of what it does. Given a description generate a piece of code that fulfils the description.

  Languages

The dataset is in English.

  Data Splits

There are no splits.

  Dataset Creation

May of 2023

  Curation Rationale

This dataset… See the full description on the dataset page: https://huggingface.co/datasets/Nan-Do/instructional_code-search-net-java.

The Saint Paul Police Department is committed to transparency. Each year, traffic stop data is released to the public. It explains who is stopped, where the stops are occurring and why officers are making the stops.2023 Data: At a glanceOfficers made 22,468 traffic stops20,964 traffic stops were made for moving violations637 traffic stops were made for equipment violations862 investigative traffic stops were made5 traffic stops were the result of 911 callsTraffic stops: An important public safety tool Most traffic stops occur in areas of the city that have the highest number of 911 calls receivedMost traffic stops occur in neighborhoods experiencing the highest levels of violent crimeOfficers are most likely to issue citations for behavior that leads to crashes, injuries and deathTraffic stops help officers take illegally possessed guns off the streets—in 2023, 116 firearms were recovered during traffic stopsPrevious data was removed from the city site to provide accuracy and consistency. The new data complies with MN data practices statutes and provides transparency to the public.Note: We have identified an issue with the time-related data in our datasets. The times are displayed correctly as Central time when viewing the data in the City’s open information portal. Upon downloading or exporting the data, any date/time columns are converted to Coordinated Universal Time (UTC). This results in the times getting converted to of either 5 hours (during Daylight savings time) or 6 hours (for Standard time) ahead of our Central time.To correct this issue, determine if it is Standard time or Daylight Savings time. Central Daylight Time (CDT) runs from the second Sunday in March to the first Sunday in November. Central Standard Time (CST) is the remainder of the year. If it is CDT, subtract 5 hours from UTC time and if it is CST, then subtract 6 hours. This issue comes from the ESRI platform and is unable to be modified at this time.

Developed as part of the BioText project at the University of California, Berkeley, the BioText Search Engine is a freely available Web-based application that provides biologists with new ways to access the scientific literature. The system indexes all open access articles available at PubMed Central. New articles are indexed daily. The current collection consists of more than 300 journals, 40,000 articles, 100,000 figures, and 60,000 tables. The Full Text & Abstract view searches the full text of articles (in addition to title, author, and abstract information) and returns full-text excerpts that match users' queries. Three selection boxes at the top (ABSTRACTS, FULL-TEXT EXCERPTS and FIGURES allow users to choose what the view displays. The BioText Search Engine allows users to search in tables. When the table view is selected, BioText searches in article titles, table captions, and table contents. The Grid View allows users to search over captions. It returns figures and truncated captions in a grid arrangement.

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. 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 100.000 Score in 30 Nov 2023 and a record low of 0.000 Score in 14 May 2025. 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 Greenland – Table GL.Google.GT: Google Search Trends: by Categories.

The beauty industry's online presence continues to grow, with La Roche Posay emerging as the most searched beauty brand on Google worldwide in 2024. The French skincare brand garnered approximately 33.6 million searches, closely followed by Cerave with 33.4 million and Sol De Janeiro with 32 million. This trend highlights the increasing consumer interest in skincare products and the power of digital visibility for beauty brands. E-commerce giants lead beauty sales As consumers increasingly turn to online platforms for their beauty purchases, major e-commerce players have solidified their positions in the market. Sephora.com led the pack in 2024, generating over 3.4 billion U.S. dollars in e-commerce net sales. Ulta.com secured the second position with approximately 2.1 billion U.S. dollars, while bathandbodyworks.com rounded out the top three with an estimated 1.4 billion U.S. dollars in sales. This dominance of established beauty retailers in the e-commerce space underscores the competitive nature of the online beauty market. Global growth in beauty e-commerce The beauty and cosmetics industry is experiencing significant growth in online traffic across various countries. In 2024, Pakistan led with a remarkable 38.1 percent year-over-year traffic growth in the online beauty and cosmetics sector. Mexico followed with 29.5 percent growth, while Australia and Turkey saw increases of 26.6 and 25.7 percent, respectively.

1. Repeatability is the cornerstone of science and it is particularly important for systematic reviews. However, little is known on how researchers’ choice of database and search platform influence the repeatability of systematic reviews. Here, we aim to unveil how the computer environment and the location where the search was initiated from influence hit results.

2. We present a comparative analysis of time-synchronized searches at different institutional locations in the world, and evaluate the consistency of hits obtained within each of the search terms using different search platforms.

3. We revealed a large variation among search platforms and showed that PubMed and Scopus returned consistent results to identical search strings from different locations. Google Scholar and Web of Science’s Core Collection varied substantially both in the number of returned hits and in the list of individual articles depending on the search location and computing environment. Inconsistency in Web of Science results has most likely emerged from the different licensing packages at different institutions.

4. To maintain scientific integrity and consistency, especially in systematic reviews, action is needed from both the scientific community and scientific search platforms to increase search consistency. Researchers are encouraged to report the search location and the databases used for systematic reviews, and database providers should make search algorithms transparent and revise access rules to titles behind paywalls. Additional options for increasing the repeatability and transparency of systematic reviews are storing both search metadata and hit results in open repositories and using Application Programming Interfaces (APIs) to retrieve standardized, machine-readable search metadata.

Methods Three major scientific search platforms, PubMed, Scopus, and Web of Science, and Google Scholar, were used in this study. We generated keyword expressions (search strings) with two complexity levels using keywords that focused on an ecological topic and ran standardized searches from various institutions in the world (see below), all within a limited timeframe.

Simple search strings contained only one main keyphrase, without using logical (Boolean) operators, whereas complex ones contained both inclusion and exclusion criteria for additional, related, keywords and key phrases (i.e. two-word expressions within quotation marks). In complex search strings Boolean operators were also used. The simple keyword was “ecosystem services” while the complex one was “ecosystem service” AND “promoting” AND “crop” NOT “livestock”. Search language was set to English in every case, and only titles, abstracts and keywords were searched. Since there is no option in Google Scholar to limit the search to titles, keywords, and abstracts, we used the default search in this case. Since different search platforms use slightly different expressions for the same query, exact search term formats were generated for each search.

Searches were conducted on one or two machines at each of the 12 institutions in Australia, Canada, China, Denmark, Germany, Hungary, UK, and the USA (Supplementary material 2), using three commonly used browsers (Mozilla Firefox, Internet Explorer, and Google Chrome). Searches were run manually (i.e. no APIs were used) according to strict protocols, which allowed standardization of search date, exact search term for every run, and the data recording procedure. Not all platforms were queried from every location: Google products are not available in China, and Scopus was not available at some institutions (Supplementary material 2). The original version of the protocol is provided in Supplementary material 3. The first run was conducted at 11:00 Australian Eastern Standard Time (01:00 GMT) on 13 April 2018 and the last search run at 18:16, Eastern Daylight Time (22:16 GMT, 13 April 2018). After each search run, the number of hits was recorded and the bibliographic data of the first 20 articles were extracted and saved in a file format that the website offered (.csv, .txt). Once search combinations were completed, the browsers’ cache was emptied, to make sure the testers’ previous searches did not influence the results, and the process was repeated. At four locations (Flakkebjerg, Denmark; Fuzhou, China; St. Catharines, Canada; Orange, Australia) the searches were also repeated on two different computers. This resulted in 228, 132, 228, and 144 search runs for Web of Science, Scopus, PubMed, and Google Scholar, respectively.

Results were collected from each contributor, bibliographic information was automatically extracted from the identically structured saved files using a loop in the R statistical software (R Core Team, 2012), and stored in a standardized MySQL database, allowing unique publications to be distinguished. If unique identifiers for individual articles were missing, authors, titles, or the combination of these were searched for, and uniqueness was double-checked across the entire dataset. Saved data files with non-standard structures were dealt with manually. All data cleaning and manipulations were done by R.

Enterprise Search Market Report is Segmented by Component (Solution, Services), Type (Conversational Search, Multimedia Search, Multilingual Search), Enterprise Size (Small and Medium Sized Enterprises, and Large Enterprises), Deployment Mode (On-Premise, and Cloud), Industry Vertical (BFSI, Healthcare, Government, Retail and E-Commerce, Travel and Hospitality, Media and Entertainment, Others), and Geography (North America, Europe, Asia Pacific, Middle East & Africa, and Latin America). The Market Sizes and Forecasts Regarding Value (USD) for all the Above Segments are Provided.

Dataset Card for "code-search-net-go"

  Dataset Summary

This dataset is the Go portion of the CodeSarchNet annotated with a summary column.The code-search-net dataset includes open source functions that include comments found at GitHub.The summary is a short description of what the function does.

  Languages

The dataset's comments are in English and the functions are coded in Go

  Data Splits

Train, test, validation labels are included in the dataset as… See the full description on the dataset page: https://huggingface.co/datasets/Nan-Do/code-search-net-go.

Data files contain summary data for participants in Experiments 1 and 2 reported in a submitted manuscript entitled, "Concurrent evaluation of independently cued features during perceptual decisions and saccadic targeting in visual search."gDataExp1.xlsx: Proportion of correct responses. The experiment used a 2 (search type: single- vs. dual-target) x 2 (target-present vs. -absent) x 3 (set size: 1, 2 & 4) factorial design. Columns represent cells in the factorial design and headings contain the following notation:"sT" and "dT" = single- and dual-target searches"1", "2" and "4" are set sizes"H" and "CR" are hits and correct rejections. gDataExp2.xlsx.Proportion of 1st fixations and saccadic latencies to targets and distractors during overt search. The experiment used a 2 (search type: single- vs. dual-target) x 3 (object: cued-target, similar distractor & dissimilar distractor) factorial design. Columns represent cells in the factorial design and headings contain the following notation:"T1" = cued-target"D1" = similar distractor"D2" = dissimilar distractor

Decomposition of the explained variance of subjective well-being variables.

Use this search engine to generate custom tables of orbital and/or physical parameters for all asteroids and comets (or a specified sub-set) in our small-body database. If this is your first time here, you may find it helpful to read our tutorial. Otherwise, simply follow the steps in each section: 'Search Constraints', 'Output Fields', and finally 'Format Options'. If you want details for a single object, use the Small Body Browser instead.

Minute-by-minute updated keyword database from Google, featuring 231 trending search terms

This dataset is about book subjects, has 2 rows. and is filtered where the books is Search engines. It features 10 columns including book subject, number of authors, number of books, earliest publication date, and latest publication date. The preview is ordered by number of books (descending).

Google Search Trends: Travel & Accommodations: Lufthansa 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: Lufthansa 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 63.000 Score in 18 Apr 2024 and a record low of 0.000 Score in 14 May 2025. Google Search Trends: Travel & Accommodations: Lufthansa data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Saint Lucia – Table LC.Google.GT: Google Search Trends: by Categories.