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.

Internet Usage: Search Engine Market Share: Mobile: Start Page data was reported at 0.000 % in 04 Sep 2024. This stayed constant from the previous number of 0.000 % for 03 Sep 2024. Internet Usage: Search Engine Market Share: Mobile: Start Page data is updated daily, averaging 0.000 % from Feb 2024 (Median) to 04 Sep 2024, with 199 observations. The data reached an all-time high of 8.330 % in 28 May 2024 and a record low of 0.000 % in 04 Sep 2024. Internet Usage: Search Engine Market Share: Mobile: Start Page data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Kiribati – Table KI.SC.IU: Internet Usage: Search Engine Market Share.

Search engines, which collect, organize and display knowledge of the internet, are the backbone of the information age and have helped popularize the ad-supported attention economy that prevails throughout the internet. From 2019 to 2024, spending on internet advertising has maintained strong momentum as consumer demand for internet access continued to surge, driven by the adoption of LTE, 5G and unlimited mobile data plans. Despite COVID-19 depressing total advertising expenditure, digital advertising continued to grow as consumers practically lived online while stay-at-home orders were in place. As a result, search engine revenue from advertising is slated to mount at a CAGR of 10.4% to $287.5 billion, including an anticipated hike of 8.4% in 2024, with profit at 18.7%. The search engine industry is fundamentally differentiated from the rest of the economy by its advertising sales framework, market aggregation and high interconnection with other industries. While search is a consumer product, search revenue comes from a platform's desirability to advertisers, not users. Search platforms must balance providing the best search experience while integrating as many advertisements as possible. This difficult balance is challenging to achieve because advertising dollars tend to scale best on the leading search platform, increasing aggregation forces for search providers. The market leaders in search, Google and Microsoft, have met this balance by using advertising revenue to grow a suite of services designed to collect extensive behavior information on and off the search website. This data then targets ads to hyper-specific markets, funding the search business model. As the number of hours spent on the internet continues to mount, search engine revenue is poised to climb at a CAGR of 7.1% to $404.9 billion through the end of 2029. Advertisers will rely increasingly on search engine marketing due to its cost-effectiveness and efficiency advantages over traditional media. With proper analytics software installed, marketers can track which terms, advertisements and websites are the most effective, enabling incremental real-time tweaks and improvements in advertising campaigns. Artificial intelligence has promised to change the purpose of search from navigation to finding answers, which will change the structure of the internet, just as search engine providers have done many times before.

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.

Egypt Internet Usage: Search Engine Market Share: Desktop: Start Page data was reported at 0.000 % in 18 Apr 2024. This stayed constant from the previous number of 0.000 % for 17 Apr 2024. Egypt Internet Usage: Search Engine Market Share: Desktop: Start Page data is updated daily, averaging 0.015 % from Dec 2023 (Median) to 18 Apr 2024, with 38 observations. The data reached an all-time high of 0.100 % in 07 Apr 2024 and a record low of 0.000 % in 18 Apr 2024. Egypt Internet Usage: Search Engine Market Share: Desktop: Start Page data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Egypt – Table EG.SC.IU: Internet Usage: Search Engine Market Share.

Brand performance data collected from AI search platforms for the query "real-time analytics tools for newsrooms".

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 2018. For a description of the data collection, processing, and output methods, please see the "methods" section below. Note that the RAMP data model changed in August, 2018 and two sets of documentation are provided to describe data collection and processing before and after the change.

Methods

RAMP Data Documentation – January 1, 2017 through August 18, 2018

Data Collection

RAMP data were 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 from January 1, 2017 through August 18, 2018 were downloaded in one dataset per participating IR. The following fields were 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.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.

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

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, data 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 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 data which records whether each URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

Processed data are then saved in a series of Elasticsearch indices. From January 1, 2017, through August 18, 2018, RAMP stored data in one index per participating IR.

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.

The data in these CSV files 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.
country: The country from which the corresponding search originated.
device: The device used for the search.
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 index 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 follow the format 2018-01_RAMP_all.csv. Using this example, the file 2018-01_RAMP_all.csv contains all data for all RAMP participating IR for the month of January, 2018.

Data Collection from August 19, 2018 Onward

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

Job Offers Web Scraping Search

Targeted Results to Find the Optimal Work Solution

By [source]

About this dataset

This dataset collects job offers from web scraping which are filtered according to specific keywords, locations and times. This data gives users rich and precise search capabilities to uncover the best working solution for them. With the information collected, users can explore options that match with their personal situation, skillset and preferences in terms of location and schedule. The columns provide detailed information around job titles, employer names, locations, time frames as well as other necessary parameters so you can make a smart choice for your next career opportunity

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For more datasets, click here.

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How to use the dataset

This dataset is a great resource for those looking to find an optimal work solution based on keywords, location and time parameters. With this information, users can quickly and easily search through job offers that best fit their needs. Here are some tips on how to use this dataset to its fullest potential:

• Start by identifying what type of job offer you want to find. The keyword column will help you narrow down your search by allowing you to search for job postings that contain the word or phrase you are looking for.

• Next, consider where the job is located – the Location column tells you where in the world each posting is from so make sure it’s somewhere that suits your needs!

• Finally, consider when the position is available – look at the Time frame column which gives an indication of when each posting was made as well as if it’s a full-time/ part-time role or even if it’s a casual/temporary position from day one so make sure it meets your requirements first before applying!

• Additionally, if details such as hours per week or further schedule information are important criteria then there is also info provided under Horari and Temps Oferta columns too! Now that all three criteria have been ticked off - key words, location and time frame - then take a look at Empresa (Company Name) and Nom_Oferta (Post Name) columns too in order to get an idea of who will be employing you should you land the gig!

  All these pieces of data put together should give any motivated individual all they need in order to seek out an optimal work solution - keep hunting good luck!

Research Ideas

• Machine learning can be used to groups job offers in order to facilitate the identification of similarities and differences between them. This could allow users to specifically target their search for a work solution.
• The data can be used to compare job offerings across different areas or types of jobs, enabling users to make better informed decisions in terms of their career options and goals.
• It may also provide an insight into the local job market, enabling companies and employers to identify where there is potential for new opportunities or possible trends that simply may have previously gone unnoticed

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: web_scraping_information_offers.csv | Column name | Description | |:-----------------|:------------------------------------| | Nom_Oferta | Name of the job offer. (String) | | Empresa | Company offering the job. (String) | | Ubicació | Location of the job offer. (String) | | Temps_Oferta | Time of the job offer. (String) | | Horari | Schedule of the job offer. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

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.

Brand performance data collected from AI search platforms for the query "how to track keyword rankings on perplexity".

This group includes the provision of infrastructure for hosting, data processing services and related activities, as well as search facilities and other portals for the Internet.

Brand performance data collected from AI search platforms for the query "best sites for wedding guest dresses".

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

Brand performance data collected from AI search platforms for the query "news SEO live blog optimization".

Brand performance data collected from AI search platforms for the query "Google Discover traffic best practices".

Explore real-time online search and web trends with Success.ai’s comprehensive data on search engines and B2B intent. Uncover actionable insights for competitive intelligence and targeted marketing. Guaranteed best price and quality.

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

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

Internet Usage: Search Engine Market Share: Mobile: Start Page data was reported at 0.000 % in 29 Apr 2025. This stayed constant from the previous number of 0.000 % for 28 Apr 2025. Internet Usage: Search Engine Market Share: Mobile: Start Page data is updated daily, averaging 0.000 % from Feb 2025 (Median) to 29 Apr 2025, with 26 observations. The data reached an all-time high of 0.110 % in 15 Feb 2025 and a record low of 0.000 % in 29 Apr 2025. Internet Usage: Search Engine Market Share: Mobile: Start Page data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Nicaragua – Table NI.SC.IU: Internet Usage: Search Engine Market Share.

Search Engine Optimization Services Market Outlook

The global market size for Search Engine Optimization (SEO) Services was valued at approximately $65 billion in 2023 and is projected to reach around $150 billion by 2032, reflecting a compound annual growth rate (CAGR) of 9.5%. This robust growth can be attributed to various factors, including the increasing emphasis on digital marketing, the rise in online content, and the growing need for businesses to improve their online visibility and search engine rankings.

One of the primary growth drivers for the SEO services market is the exponential increase in internet usage and the proliferation of digital content. As more consumers turn to the internet for information, entertainment, and shopping, businesses recognize the critical importance of appearing prominently in search engine results. Consequently, companies are increasingly investing in SEO services to enhance their online visibility, attract more visitors to their websites, and ultimately drive higher conversion rates. Furthermore, the rise of social media platforms and mobile internet usage has also underscored the need for comprehensive SEO strategies that encompass various digital channels.

Another significant factor contributing to the market's growth is the continuous evolution of search engine algorithms. Search engines like Google are constantly updating their algorithms to deliver more relevant and high-quality results to users. These updates often necessitate businesses to adapt their SEO strategies to maintain or improve their rankings. This evolving landscape creates a sustained demand for specialized SEO services that can help businesses navigate these changes effectively. Additionally, the increasing complexity of SEO, which now involves a mix of technical expertise, content creation, and analytics, has led many enterprises to seek professional SEO services rather than relying solely on in-house efforts.

The rise of e-commerce and the digital transformation of various sectors, including healthcare, finance, and education, have also bolstered the demand for SEO services. As more businesses and industries move online, the need to stand out in a crowded digital marketplace becomes even more critical. SEO services play a vital role in helping businesses achieve higher search engine rankings, reach their target audiences more effectively, and compete successfully in the digital space. Moreover, the growing importance of local SEO, driven by the increasing use of mobile search and location-based queries, has further fueled the market's expansion.

Regionally, North America remains the largest market for SEO services, driven by the high concentration of digital-savvy businesses and the advanced state of the e-commerce sector. The region is expected to maintain its dominance over the forecast period, although Asia Pacific is anticipated to exhibit the highest growth rate. The rapid digitalization in countries like China and India, coupled with the increasing penetration of the internet and smartphones, is propelling the demand for SEO services in the region. Europe, Latin America, and the Middle East & Africa are also witnessing steady growth, supported by the ongoing digital transformation across various industries.

Service Type Analysis

The SEO services market is segmented by service type, including On-Page SEO, Off-Page SEO, Technical SEO, Local SEO, Content SEO, and Others. Each of these service types addresses different aspects of SEO and together contribute to a comprehensive strategy for optimizing a website's performance.

On-Page SEO focuses on optimizing individual web pages to rank higher and earn more relevant traffic in search engines. This includes optimizing content, HTML source code, and media. It plays a crucial role as it directly impacts the visibility of the content. Factors such as meta tags, keyword density, and internal linking are critical components of On-Page SEO. As search engines become more sophisticated, On-Page SEO has evolved to include user experience elements such as page load speed and mobile friendliness.

Off-Page SEO involves activities performed outside the website to improve its ranking. This primarily includes building high-quality backlinks from authoritative websites, which act as endorsements for the website's content. Social media marketing, influencer outreach, and guest blogging are also key components of Off-Page SEO. The growing importance of backlinks in search engine algorithms has led to higher investments in Off-Page SEO services, making t