Search Engine Market Report is Segmented by Search Type (Crawler-Based Engines, Meta-Search Engines and More), Platform (Desktop, Mobile and More), by Application (Personal, Commercial and More), Revenue Model (Advertising-Based, Subscription and More), End-Use Industry (BFSI, Travel & Hospitality and More) and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Relevancy ranking is an important component of making a data repository's search system responsive to data seekers’ needs. The Research Data Alliance (RDA) Data Discovery Paradigms Interest Group (https://www.rd-alliance.org/groups/data-discovery-paradigms-ig) is a collaborative activity within our data community which aims to improve data searchability. This survey is intended to gather information about the current practices and lessons learnt by data repositories in implementing relevancy ranking in search systems. We expect that analysis of the survey results will:

* Help data repositories choose appropriate technologies when implementing or improving their search functionality;
* Provide a means for sharing experiences in improving relevancy ranking;
* Capture the aspirations, successes and challenges encountered from research data repository managers;
* Help the Data Discovery Paradigms Interest group align future activities on data search improvement with the interests of data search service providers.

For the above the purpose, we designed a survey instrument to answer the following topics (the numbers in brackets indicate the number of questions asked per topic):

* What are characteristics of each repositories (5)?
* What are system configurations (e.g., ranking model, index methods, query methods) (7)?
* Evaluation methods and benchmark (10)
  ** What has been evaluated?
  ** What evaluation methods have been applied?
  ** How was the evaluation collection built?
  ** What is approximate performance range of search systems with certain configuration?
* What methods have been used to boost searchability to web search engines (e.g., Google, Bing) (2)
* What other technologies or system configurations have been employed (5)?
* Wish list for future activities for the RDA relevance task force (2)?

This collection consists of survey instrument, survey responses and survey report.

The Search Engine industry is highly concentrated, with three companies controlling almost the entire industry; the largest company, Alphabet Inc., has a market share greater than 96%. Search engines provide web portals that generate and maintain extensive databases of internet addresses. Industry companies generate most, if not all, of their revenue from advertising. Technological growth has resulted in more households being connected to the Internet and a boom in e-commerce has made the industry increasingly innovative. A climb in the proportion of households with internet access has supported revenue growth, while expanding technological integration with daily life has boosted demand for web search. A greater proportion of transactions being carried out online has driven innovation in targeted digital advertising, with declines in rival advertising formats like print media and television expanding the focus on digital marketing as a core strategy. Industry revenue is expected to jump at a compound annual rate of 3.8%, to reach £5.4 billion over the five years through 2025-26. Revenue is forecast to climb by 3.5% in 2025-26. Industry profit has remained high and expanded alongside a surge in search and display advertising, with total UK digital ad spend. The rise of the mobile advertising market and the proliferation of mobile devices mean there are plenty of opportunities for search engines, which are expected to capitalise on these trends further moving forward. While continued growth in localised digital marketing and rising overall UK marketing budgets are set to propel industry revenues, Google faces mounting regulatory scrutiny. The Digital Markets, Competition and Consumers Act 2024, with the impending Strategic Market Status designation for Google, is poised to shake up the landscape by curtailing Google’s market power and fostering greater transparency. Search engines will need to innovate to fend off rising competition from social media platforms, which are attracting advertisers through advanced targeting capabilities. Although niche, privacy-centric search engines could capture incremental market share as consumer privacy concerns intensify, the industry’s overwhelming concentration, with Google’s unmatched user base and ad inventory, means transformative change will likely be incremental. Nonetheless, technological advancements that incorporate user data are anticipated to make it easier to tailor advertisements and develop new ways of using consumer data. Industry revenue is forecast to jump at a compound annual rate of 5.9% over the five years through 2030-31, to reach £7.2 billion.

Search engines, which collect, organize and display knowledge of the internet, remain central to the digital economy but are entering a period of rapid transformation driven by AI and shifting user behavior. Over the past five years, internet advertising spending maintained strong momentum, propelled by growing mobile internet access and consumer screen time. Consequently, industry revenue is expected to climb at a CAGR of 9.4% to $316.8 billion, including an anticipated rise of 7.7% in 2025, with profit at 18.6%. The industry stands apart from most in the tech sector, because of its platform-based revenue model, aggregation dynamics and deep integration with the broader digital ecosystem. While user engagement fuels relevance, it is advertiser demand that sustains revenue, requiring a careful balance between utility and monetization. This landscape has been reshaped by the rise of generative AI. Conversational tools and AI-generated summaries are reducing user interaction with traditional search results, challenging established SEO practices and disrupting referral-based traffic flows. Meanwhile, search engines are reconfiguring their ad models to prioritize quality and contextual relevance, moving away from legacy monetization strategies. These trends signal a broader shift in how search platforms operate, less as navigational tools and more as integrated, AI-driven environments. As digital behavior fragments and users seek information across apps like Amazon, TikTok and ChatGPT, industry revenue is still projected to climb at a CAGR of 7.3% to $449.9 billion through 2030. Advertisers are expected to continue investing in search, drawn by the format’s performance insights and optimization capabilities. However, AI is redefining search from a navigational tool into a task-oriented solution engine, where users expect conversational, multimodal and predictive answers instead of traditional results pages. To stay relevant, incumbent platforms must evolve into embedded AI utilities that power experiences across devices and enterprise workflows.

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.

As of March 2025, Google represented 79.1 percent of the global online search engine market on desktop devices. Despite being much ahead of its competitors, this represents the lowest share ever recorded by the search engine in these devices for over two decades. Meanwhile, its long-time competitor Bing accounted for 12.21 percent, as tools like Yahoo and Yandex held shares of over 2.9 percent each. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of 2.02 trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly 348.16 billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than 63 percent of internet users in Russia used Yandex, whereas Google users represented little over 33 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 21 percent of users in Mexico said they used Yahoo.

We introduce a unique approach to privacy research by creating a virtual persona that mimics human web-searching behaviors. The persona's activities, categorized into 'morning', 'afternoon', and 'evening', were automated using the Selenium WebDriver, enabling the persona to conduct searches as a real user would. The resulting dataset comprises 1,537 records, each representing a unique search query. Each record contains the first two pages of a query result, including the query keywords and a list of the first 2 pages of the query result. The study offers a fresh perspective on the study of privacy and personalization in online environments. The potential for reusing this dataset is significant, as it can be applied to studies on privacy, data collection, and search engine personalization, and it can be used to develop and test algorithms and models that aim to protect user privacy.

In recent years, social media has taken on a growing significance as a search channel for products and brands, particularly among younger consumers, surpassing search engines. According to a survey conducted in 2022, ** percent of Gen Z consumers stated that they used social media to gather additional information before making a purchase, whereas ** percent of respondents relied on search engines for the same purpose.

[Abstract] Software development is a knowledge-intensive activity. Official documentation for developers may not be sufficient for all developer needs. Searching for information on the Internet is a usual practice, but finding really useful information may be challenging, because the best solutions are not always among the first ranked pages. So, developers have to read and discard irrelevant pages, that is, pages that do not have code examples or that have content with little focus on the desired solution. This work aims at proposing an approach to mine relevant solutions for programming tasks from search engine results that remove irrelevant pages. The approach works as follows: a query related to the programming task is prepared, and given as an input to a search engine. The returned pages pass through an automatic filter to select relevant pages. We evaluated the top-20 pages returned by the Google search engine, for 10 different queries, and observed that only 31\% of the evaluated pages are relevant to developers. Then, we proposed and evaluated three different approaches to mine the relevant pages returned by the search engine. Google’s search engine has been used as a baseline, and our results have shown that Google’s search engine returns a reasonable number of irrelevant pages for developers, and we could find an effective approach to remove irrelevant pages, suggesting that developers could benefit from a customized web search filter for development content. [Contents of Research Data.rar file] The Research Data.rar file has a folder called Research Data that contains 3 folders internally, with the names: “01 – Source Code”, “02 - Data” and “03 – Preprocessing rules”. The folder “01 – Source Code” contains the JAVA source code of the implementations of the proposed approaches. The folder “02 - Data” contains the data of the evaluations carried out in the work, which are in the folders “01 - Evaluation results of pages returned by Google” and “02 - Results of approaches comparisons”. The folder “01 - Evaluation results of pages returned by Google” has the evaluations carried out on the first 20 pages returned by Google, following the criteria defined in the work, for the 10 queries considered in the evaluation. The folder “02 - Results of approaches comparisons” contains the results of the evaluation of the proposed approaches, for the 10 queries considered in the evaluation. In this evaluation, the number of pages given as input for the approaches was increased from 3 to 20 pages, for each number of pages a folder was generated with the results. In addition to the results of the Precision, Recall and F-Measure metrics that are in the file named Results Approaches.txt, other files were generated for analysis. For example, the Instances_without_outliers.txt file shows which pages were filtered out after applying the outlier page removal filter. The Selected Pages Approach 4.txt file, on the other hand, shows which pages were filtered after applying the filters of the GORCUO approach. The folder “03 - Preprocessing rules” has a file called Rules.java. In this file, there is the commented JAVA source code, from the implementation of the rules created in the pre-processing stage of the proposed approach.

Our research data compares the effects of generative AI, such as ChatGPT, with traditional search engines across various search tasks and examines their combined use.

According to Cognitive Market Research, the global Enterprise Search Engine market size will be USD 4358.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 9.70% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 1743.28 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.9% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 1307.46 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 1002.39 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.7% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 217.91 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.1% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 87.16 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.4% from 2024 to 2031.
The Solution category is the fastest growing segment of the Enterprise Search Engine industry

Market Dynamics of Enterprise Search Engine Market

Key Drivers for Enterprise Search Engine Market

Increasing Data Volume to Boost Market Growth

The increasing volume of data generated by organizations is a primary driver of the Enterprise Search Engine Market. As businesses accumulate vast amounts of structured and unstructured data from various sources—such as emails, documents, social media, and databases—the need for efficient retrieval and management becomes critical. Enterprise search engines enable organizations to sift through this data quickly, providing employees with timely access to information that can enhance decision-making and productivity. Additionally, the proliferation of big data technologies and cloud storage solutions contributes to data growth, necessitating robust search capabilities to ensure that valuable insights are not lost. This demand for streamlined access to comprehensive information continues to fuel the expansion of the enterprise search engine market. For instance, Google launched local search functionalities that were previewed earlier this year. These features enable users to explore their environment using their smartphone camera. Additionally, Google has added an option to search for restaurants by specific dishes and introduced new search capabilities within the Live View feature of Google Maps.

Increasing Demand for Data-Driven Decision-Making to Drive Market Growth

The rising demand for data-driven decision-making is significantly driving the Enterprise Search Engine Market. Organizations increasingly recognize the value of leveraging data analytics to inform strategic decisions, enhance operational efficiency, and improve customer experiences. As businesses strive to become more agile and responsive to market changes, they require quick access to relevant data across various departments and sources. Enterprise search engines facilitate this by enabling employees to efficiently retrieve and analyze critical information, thus supporting informed decision-making processes. Moreover, the integration of advanced analytics and artificial intelligence into enterprise search solutions further empowers organizations to derive actionable insights from their data. This trend towards a data-centric approach in business operations continues to propel the growth of the enterprise search engine market.

Restraint Factor for the Enterprise Search Engine Market

High Implementation Costs will Limit Market Growth

High implementation costs are a significant restraint on the growth of the Enterprise Search Engine Market. Deploying enterprise search solutions often involves substantial initial investments in software, hardware, and integration services. Organizations must consider expenses related to customizing the search engine to fit their unique data architectures and user needs. Additionally, ongoing maintenance, updates, and training for staff can contribute to overall costs, making it challenging for smaller businesses or those with limited budgets to adopt these systems. This financial barrier can hinder organizations from fully realizing the benefits of enterprise search engines, leading to under...

Search Engine Market size was valued at USD 167 Billion in 2024 and is projected to reach USD 531.2 Billion by 2031, growing at a CAGR of 11.1% during the forecast period 2024-2031.

Global Search Engine Market Drivers

The market drivers for the Search Engine Market can be influenced by various factors. These may include:

Growth in Internet Penetration: Increase in internet accessibility worldwide, with more individuals and businesses going online.

Rising Mobile Device Usage: Surge in smartphone and tablet usage, leading to more searches conducted via mobile devices.

E-commerce Expansion: Growth in online shopping boosts search engine usage as consumers look for products and services online.

Technological Advancements: Innovations in artificial intelligence (AI), machine learning, and natural language processing enhance search engine functionalities.

Marketing and Advertising Needs: Increased demand for digital marketing and search engine optimization (SEO) as companies seek to improve online visibility.

Big Data Analytics: Use of big data to refine search algorithms and provide more personalized search results.

Voice Search and Virtual Assistants: Rising popularity of voice-activated searches through devices like Amazon Echo and Google Home.

Local Search Optimization: Growth in localized searches as businesses focus on targeting specific geographic areas.

Content Digitalization: Increasing volumes of digital content available on the internet, making search engines critical tools for information retrieval.

Improvement in User Experience: Enhanced user interfaces and faster search results improve user satisfaction and drive more frequent usage.

This description is part of the blog post "Systematic Literature Review of teaching Open Science" https://sozmethode.hypotheses.org/839

According to my opinion, we do not pay enough attention to teaching Open Science in higher education. Therefore, I designed a seminar to teach students the practices of Open Science by doing qualitative research.About this seminar, I wrote the article ”Teaching Open Science and qualitative methods“. For the article ”Teaching Open Science and qualitative methods“, I started to review the literature on ”Teaching Open Science“. The result of my literature review is that certain aspects of Open Science are used for teaching. However, Open Science with all its aspects (Open Access, Open Data, Open Methodology, Open Science Evaluation and Open Science Tools) is not an issue in publications about teaching.

Based on this insight, I have started a systematic literature review. I realized quickly that I need help to analyse and interpret the articles and to evaluate my preliminary findings. Especially different disciplinary cultures of teaching different aspects of Open Science are challenging, as I myself, as a social scientist, do not have enough insight to be able to interpret the results correctly. Therefore, I would like to invite you to participate in this research project!

I am now looking for people who would like to join a collaborative process to further explore and write the systematic literature review on “Teaching Open Science“. Because I want to turn this project into a Massive Open Online Paper (MOOP). According to the 10 rules of Tennant et al (2019) on MOOPs, it is crucial to find a core group that is enthusiastic about the topic. Therefore, I am looking for people who are interested in creating the structure of the paper and writing the paper together with me. I am also looking for people who want to search for and review literature or evaluate the literature I have already found. Together with the interested persons I would then define, the rules for the project (cf. Tennant et al. 2019). So if you are interested to contribute to the further search for articles and / or to enhance the interpretation and writing of results, please get in touch. For everyone interested to contribute, the list of articles collected so far is freely accessible at Zotero: https://www.zotero.org/groups/2359061/teaching_open_science. The figure shown below provides a first overview of my ongoing work. I created the figure with the free software yEd and uploaded the file to zenodo, so everyone can download and work with it:

To make transparent what I have done so far, I will first introduce what a systematic literature review is. Secondly, I describe the decisions I made to start with the systematic literature review. Third, I present the preliminary results.

Systematic literature review – an Introduction

Systematic literature reviews “are a method of mapping out areas of uncertainty, and identifying where little or no relevant research has been done.” (Petticrew/Roberts 2008: 2). Fink defines the systematic literature review as a “systemic, explicit, and reproducible method for identifying, evaluating, and synthesizing the existing body of completed and recorded work produced by researchers, scholars, and practitioners.” (Fink 2019: 6). The aim of a systematic literature reviews is to surpass the subjectivity of a researchers’ search for literature. However, there can never be an objective selection of articles. This is because the researcher has for example already made a preselection by deciding about search strings, for example “Teaching Open Science”. In this respect, transparency is the core criteria for a high-quality review.

In order to achieve high quality and transparency, Fink (2019: 6-7) proposes the following seven steps:

1. Selecting a research question.
2. Selecting the bibliographic database.
3. Choosing the search terms.
4. Applying practical screening criteria.
5. Applying methodological screening criteria.
6. Doing the review.
7. Synthesizing the results.

I have adapted these steps for the “Teaching Open Science” systematic literature review. In the following, I will present the decisions I have made.

Systematic literature review – decisions I made

1. Research question: I am interested in the following research questions: How is Open Science taught in higher education? Is Open Science taught in its full range with all aspects like Open Access, Open Data, Open Methodology, Open Science Evaluation and Open Science Tools? Which aspects are taught? Are there disciplinary differences as to which aspects are taught and, if so, why are there such differences?
2. Databases: I started my search at the Directory of Open Science (DOAJ). “DOAJ is a community-curated online directory that indexes and provides access to high quality, open access, peer-reviewed journals.” (https://doaj.org/) Secondly, I used the Bielefeld Academic Search Engine (base). Base is operated by Bielefeld University Library and “one of the world’s most voluminous search engines especially for academic web resources” (base-search.net). Both platforms are non-commercial and focus on Open Access publications and thus differ from the commercial publication databases, such as Web of Science and Scopus. For this project, I deliberately decided against commercial providers and the restriction of search in indexed journals. Thus, because my explicit aim was to find articles that are open in the context of Open Science.
3. Search terms: To identify articles about teaching Open Science I used the following search strings: “teaching open science” OR teaching “open science” OR teach „open science“. The topic search looked for the search strings in title, abstract and keywords of articles. Since these are very narrow search terms, I decided to broaden the method. I searched in the reference lists of all articles that appear from this search for further relevant literature. Using Google Scholar I checked which other authors cited the articles in the sample. If the so checked articles met my methodological criteria, I included them in the sample and looked through the reference lists and citations at Google Scholar. This process has not yet been completed.
4. Practical screening criteria: I have included English and German articles in the sample, as I speak these languages (articles in other languages are very welcome, if there are people who can interpret them!). In the sample only journal articles, articles in edited volumes, working papers and conference papers from proceedings were included. I checked whether the journals were predatory journals – such articles were not included. I did not include blogposts, books or articles from newspapers. I only included articles that fulltexts are accessible via my institution (University of Kassel). As a result, recently published articles at Elsevier could not be included because of the special situation in Germany regarding the Project DEAL (https://www.projekt-deal.de/about-deal/). For articles that are not freely accessible, I have checked whether there is an accessible version in a repository or whether preprint is available. If this was not the case, the article was not included. I started the analysis in May 2019.
5. Methodological criteria: The method described above to check the reference lists has the problem of subjectivity. Therefore, I hope that other people will be interested in this project and evaluate my decisions. I have used the following criteria as the basis for my decisions: First, the articles must focus on teaching. For example, this means that articles must describe how a course was designed and carried out. Second, at least one aspect of Open Science has to be addressed. The aspects can be very diverse (FOSS, repositories, wiki, data management, etc.) but have to comply with the principles of openness. This means, for example, I included an article when it deals with the use of FOSS in class and addresses the aspects of openness of FOSS. I did not include articles when the authors describe the use of a particular free and open source software for teaching but did not address the principles of openness or re-use.
6. Doing the review: Due to the methodical approach of going through the reference lists, it is possible to create a map of how the articles relate to each other. This results in thematic clusters and connections between clusters. The starting point for the map were four articles (Cook et al. 2018; Marsden, Thompson, and Plonsky 2017; Petras et al. 2015; Toelch and Ostwald 2018) that I found using the databases and criteria described above. I used yEd to generate the network. „yEd is a powerful desktop application that can be used to quickly and effectively generate high-quality diagrams.” (https://www.yworks.com/products/yed) In the network, arrows show, which articles are cited in an article and which articles are cited by others as well. In addition, I made an initial rough classification of the content using colours. This classification is based on the contents mentioned in the articles’ title and abstract. This rough content classification requires a more exact, i.e., content-based subdivision and

In the past five years, the web portal industry in Germany has seen dynamic growth, driven by high internet penetration and the increased use of mobile devices. Demand for digital services has remained robust across all sectors, with advertising revenue, premium models and commission business establishing themselves as key revenue pillars. At the same time, competition from international technology groups, increasing regulatory requirements and growing data protection awareness are intensifying the pressure to innovate. Web portals are increasingly investing in mobile applications, personalisation and a differentiated range of services in order to maintain user intensity and user loyalty despite increasing saturation and growing digital detox trends. Industry revenue increased by an average of 9.6% per year between 2020 and 2025 and is expected to reach 14 billion euros in the current year.In 2025, industry turnover is expected to increase by 3.9%. The industry is currently characterised by a greater awareness of data protection and user trust. New studies show that many users are sceptical about web portals with inadequate data protection measures and are switching. At the same time, content and community-orientated portals are gaining massive visibility, while traditional e-commerce and technology portals are coming under pressure. Increasing mobile use and the trend towards digital self-regulation functions are influencing development priorities. To ensure their competitiveness, providers are increasingly focussing on transparent data protection solutions, innovative content and cross-service platform strategies.In the next five years, turnover in the sector is expected to increase by an average of 3.2% per year to 16.5 billion euros. The web portal industry is undergoing a phase of profound change, which is primarily characterised by stricter data protection regulations, higher technological requirements and new tax regulations. In particular, the complex compliance with data protection regulations is hampering innovation and making the development of data-based business models more difficult. In addition, the minimum tax law deprives international providers of an important locational advantage and thus changes the competitive landscape. In response, companies are driving forward automation and the use of artificial intelligence in order to fulfil regulatory requirements more efficiently. At the same time, there is a strategic focus on the integration and diversification of digital services. The bundling of email, cloud, calendar and other services increases user loyalty and advertising revenue, but at the same time increases the pressure to consolidate and makes it more difficult for smaller providers to participate in the market.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to

establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data

Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered.
Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review:

Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection.
Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation.

See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

Welcome to APISCRAPY, where our comprehensive SERP Data solution reshapes your digital insights. SERP, or Search Engine Results Page, data is the pivotal information generated when users query search engines such as Google, Bing, Yahoo, Baidu, and more. Understanding SERP Data is paramount for effective digital marketing and SEO strategies.

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Top Browsers Supported:

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This statistic displays the results of a survey on the apps and search engines tracking online users in Italy in 2016, broken down by type. During the survey period, it was found that seven in ten respondents thought that search engines could clearly understand the lifestyle and interests of its users.

Quantum-Enhanced Neural Search Engine Market Outlook

According to our latest research, the global Quantum-Enhanced Neural Search Engine market size reached USD 1.47 billion in 2024, reflecting a transformative shift in the way enterprises harness artificial intelligence for information retrieval. The market is poised to expand at a robust CAGR of 27.9% from 2025 to 2033, with the forecasted market value expected to reach USD 13.73 billion by 2033. This rapid growth is primarily driven by the increasing demand for high-speed and contextually accurate search capabilities across sectors such as healthcare, finance, and e-commerce, all underpinned by advancements in quantum computing and neural network architectures.

One of the primary growth factors for the Quantum-Enhanced Neural Search Engine market is the exponential rise in unstructured data generation across industries. As organizations accumulate vast troves of text, images, audio, and video data, traditional search engines struggle to deliver timely, relevant results. Quantum-enhanced neural search engines, leveraging the computational prowess of quantum processors and the adaptive learning capabilities of deep neural networks, enable organizations to extract deep insights from complex datasets in real time. The ability to process multi-modal data and understand semantic context is particularly valuable for mission-critical applications in healthcare diagnostics, fraud detection in finance, and personalized recommendations in e-commerce. This paradigm shift is spurring significant investments in research and development, further accelerating market growth.

Another significant driver is the increasing integration of quantum computing with artificial intelligence frameworks. Quantum-enhanced search engines utilize quantum algorithms, such as Grover’s and amplitude amplification, to dramatically reduce search times and improve accuracy in large-scale databases. This synergy is attracting attention from technology giants and startups alike, as enterprises seek to gain a competitive edge through superior information retrieval. Additionally, the proliferation of cloud-based quantum computing platforms is democratizing access to quantum-enhanced neural search capabilities, enabling even small and medium enterprises to leverage cutting-edge search solutions without the need for substantial capital investment in quantum hardware. This democratization is expected to expand the addressable market and foster innovation across diverse sectors.

The evolution of regulatory frameworks and data privacy standards is also shaping the trajectory of the Quantum-Enhanced Neural Search Engine market. With heightened concerns around data security, especially in sectors like healthcare and banking, quantum-enhanced solutions offer advanced encryption and secure search capabilities that comply with stringent regulations such as GDPR and HIPAA. This regulatory alignment is encouraging adoption among risk-averse enterprises, particularly in regions with mature legal frameworks. Furthermore, ongoing collaborations between academia, industry consortia, and government agencies are fostering the development of interoperable standards and best practices, reducing barriers to market entry and stimulating long-term growth.

From a regional perspective, North America continues to dominate the Quantum-Enhanced Neural Search Engine market, accounting for the largest revenue share in 2024. This dominance is attributed to the presence of leading quantum computing firms, robust digital infrastructure, and a strong culture of innovation. However, Asia Pacific is emerging as the fastest-growing region, fueled by aggressive investments in quantum research by countries such as China, Japan, and South Korea. Europe, with its focus on data privacy and digital sovereignty, is also experiencing steady growth, while Latin America and the Middle East & Africa are gradually catching up through targeted government initiatives and international collaborations. This global expansion is setting the stage for a highly competitive and dynamic market landscape.

Component Analysis

The Quantum-Enhanced Neural Search Engine market is segmented by component into hardware, software, and services, each playing a pivotal role in the ecosystem’s development. The hardware segment encompasses quantum processors, memory devices, and specialized accelerators designed to handle the unique computational demands

Quantum-Enhanced Neural Search Engine Market Outlook

According to our latest research, the Quantum-Enhanced Neural Search Engine market size reached USD 1.82 billion globally in 2024, reflecting the rapid adoption of quantum computing and advanced neural network architectures in enterprise search solutions. The market is projected to grow at a robust CAGR of 28.7% from 2025 to 2033, culminating in a forecasted market size of USD 15.46 billion by the end of 2033. This remarkable trajectory is primarily driven by the demand for highly efficient, accurate, and context-aware search engines capable of processing vast and complex datasets across industries.

Several key growth factors are propelling the quantum-enhanced neural search engine market forward. The exponential increase in unstructured data, combined with the limitations of classical search algorithms, has created a significant need for more sophisticated search technologies. Quantum computing, when integrated with neural search algorithms, delivers unparalleled computational power and speed, enabling real-time semantic understanding and contextual relevance in search results. Organizations across sectors such as healthcare, finance, and e-commerce are investing heavily in these technologies to improve data-driven decision-making, enhance user experiences, and maintain a competitive edge in the digital era. The synergy between quantum computing and neural networks is unlocking new possibilities for natural language processing, image recognition, and predictive analytics, further fueling market growth.

Another significant driver is the growing adoption of artificial intelligence and machine learning across enterprise operations. As businesses transition towards digital transformation, the need for intelligent search capabilities that can extract actionable insights from massive datasets becomes increasingly critical. Quantum-enhanced neural search engines offer a transformative leap in search efficiency, delivering faster and more accurate results than traditional systems. This is particularly valuable for industries dealing with sensitive or time-critical information, such as BFSI and healthcare, where the ability to retrieve relevant data instantaneously can have a direct impact on operational efficiency and customer satisfaction. Additionally, the scalability and adaptability of these solutions make them attractive to both large enterprises and SMEs, supporting widespread market penetration.

The ongoing advancements in quantum hardware and software ecosystems are also contributing to the market’s expansion. Major technology players and startups alike are investing in the development of quantum processors, quantum-safe algorithms, and hybrid quantum-classical architectures tailored for search applications. As quantum computing becomes more accessible through cloud-based platforms, organizations of all sizes can leverage its power without the need for significant upfront infrastructure investments. This democratization of quantum technology is expected to accelerate adoption rates, drive innovation in search engine design, and lower barriers to entry for new market participants. Furthermore, collaborative efforts between academia, industry, and government agencies are fostering a vibrant ecosystem that supports research, standardization, and commercialization of quantum-enhanced neural search solutions.

From a regional perspective, North America currently leads the quantum-enhanced neural search engine market, accounting for the largest share in 2024, primarily due to its advanced technological infrastructure, significant R&D investments, and early adoption by key industry players. Europe follows closely, supported by robust governmental initiatives and a strong presence of quantum research institutions. The Asia Pacific region is witnessing the fastest growth, driven by increasing digitalization, expanding tech startups, and supportive regulatory frameworks, particularly in countries like China, Japan, and South Korea. Latin America and the Middle East & Africa are also emerging as promising markets, with growing interest in quantum technologies and AI-driven solutions to address local industry challenges. Each region presents unique opportunities and challenges, shaping the competitive landscape and influencing market dynamics over the forecast period.

These data and reports are the outcome of a project the purpose of which is to gain insight into the findability of Utrecht University’s (UU) research datasets, in order to be able to improve it. The data is on 24 search engines that allow searching for research data sets, in particular on their size, functionality, and coverage of Utrecht University data sets shared publicly on the Yoda and DataverseNL data publication platforms. The reports also deal with issues regarding findability and include recommendations to solve these issues.