OpenWeb Ninja's Google Images Data (Google SERP Data) API provides real-time image search capabilities for images sourced from all public sources on the web.

The API enables you to search and access more than 100 billion images from across the web including advanced filtering capabilities as supported by Google Advanced Image Search. The API provides Google Images Data (Google SERP Data) including details such as image URL, title, size information, thumbnail, source information, and more data points. The API supports advanced filtering and options such as file type, image color, usage rights, creation time, and more. In addition, any Advanced Google Search operators can be used with the API.

OpenWeb Ninja's Google Images Data & Google SERP Data API common use cases:

• Creative Media Production: Enhance digital content with a vast array of real-time images, ensuring engaging and brand-aligned visuals for blogs, social media, and advertising.

• AI Model Enhancement: Train and refine AI models with diverse, annotated images, improving object recognition and image classification accuracy.

• Trend Analysis: Identify emerging market trends and consumer preferences through real-time visual data, enabling proactive business decisions.

• Innovative Product Design: Inspire product innovation by exploring current design trends and competitor products, ensuring market-relevant offerings.

• Advanced Search Optimization: Improve search engines and applications with enriched image datasets, providing users with accurate, relevant, and visually appealing search results.

OpenWeb Ninja's Annotated Imagery Data & Google SERP Data Stats & Capabilities:

• 100B+ Images: Access an extensive database of over 100 billion images.

• Images Data from all Public Sources (Google SERP Data): Benefit from a comprehensive aggregation of image data from various public websites, ensuring a wide range of sources and perspectives.

• Extensive Search and Filtering Capabilities: Utilize advanced search operators and filters to refine image searches by file type, color, usage rights, creation time, and more, making it easy to find exactly what you need.

• Rich Data Points: Each image comes with more than 10 data points, including URL, title (annotation), size information, thumbnail, and source information, providing a detailed context for each image.

Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

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Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

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Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

There has been an increased emphasis on plant-based foods and diets. Although mobile technology has the potential to be a convenient and innovative tool to help consumers adhere to dietary guidelines, little is known about the content and quality of free, popular mobile health (mHealth) plant-based diet apps. The objective of the study was to assess the content and quality of free, popular mHealth apps supporting plant-based diets for Canadians. Free mHealth apps with high user ratings, a high number of user ratings, available on both Apple App and GooglePlay stores, and primarily marketed to help users follow plant-based diet were included. Using pre-defined search terms, Apple App and GooglePlay App stores were searched on December 22, 2020; the top 100 returns for each search term were screened for eligibility. Included apps were downloaded and assessed for quality by three dietitians/nutrition research assistants using the Mobile App Rating Scale (MARS) and the App Quality Evaluation (AQEL) scale. Of the 998 apps screened, 16 apps (mean user ratings±SEM: 4.6±0.1) met the eligibility criteria, comprising 10 recipe managers and meal planners, 2 food scanners, 2 community builders, 1 restaurant identifier, and 1 sustainability assessor. All included apps targeted the general population and focused on changing behaviors using education (15 apps), skills training (9 apps), and/or goal setting (4 apps). Although MARS (scale: 1–5) revealed overall adequate app quality scores (3.8±0.1), domain-specific assessments revealed high functionality (4.0±0.1) and aesthetic (4.0±0.2), but low credibility scores (2.4±0.1). The AQEL (scale: 0–10) revealed overall low score in support of knowledge acquisition (4.5±0.4) and adequate scores in other nutrition-focused domains (6.1–7.6). Despite a variety of free plant-based apps available with different focuses to help Canadians follow plant-based diets, our findings suggest a need for increased credibility and additional resources to complement the low support of knowledge acquisition among currently available plant-based apps. This research received no specific grant from any funding agency.

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

App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

This is the Debsources Dataset: source code and related metadata spanning two decades of Free and Open Source Software (FOSS) history, seen through the lens of the Debian distribution. The dataset spans more than 3 billion lines of source code as well as metadata about them such as: size metrics (lines of code, disk usage), developer-defined symbols (ctags), file-level checksums (SHA1, SHA256, TLSH), file media types (MIME), release information (which version of which package containing which source code files has been released when), and license informa- tion (GPL, BSD, etc). The Debsources Dataset comes as a set of tarballs containing deduplicated unique source code files organized by their SHA1 checksums (the source code), plus a portable PostgreSQL database dump (the metadata). The Debsources Dataset is described in full in the paper The Debsources Dataset: Two Decades of Free and Open Source Software, published on the Empirical Software Engineering journal with DOI 10.1007/s10664-016-9461-5 . A preprint of the paper is available at https://upsilon.cc/~zack/research/publications/debsources-ese-2016.pdf .

The SWITCH-ON data catalogue provides metadata and links to water-relevant open datasets, to easily inspect and download data from many various data providers.

This web-based search tool enables you to: Construct a search query - on relevant (combinations of) metadata characteristics like: keyword, free text, geospatial extent - to look for required Open Datasets. Display the search results and inspect metadata of the datasets found, preview and/or download them. Post-filter the resources found based on metadata characteristics.

This is a connection to Philadelphia's open data portal - OpenDataPhilly.org - built by Azavea, a Philadelphia-based geospatial software firm. OpenDataPhilly is based on the idea that providing free and easy access to data information encourages better and more transparent government and a more engaged and knowledgeable citizenry.

OpenDataPhilly is a catalog of open data about the Philadelphia region. It includes more than 300 data sets, applications and APIs from many organizations in the region, including from City government. A full list of datasets shared by Philadelphia’s municipal government can be found here: https://www.opendataphilly.org/organization/city-of-philadelphia

The website enables users to search for and locate data sets based on keyword and category searches. For each data set, application, or API, the website includes accompanying information about the origins, update frequency, and other specifics of the data. The record for each data source also includes links for downloading the data or accessing the application or API.

What do you think of OpenDataPhilly? Let us know your ideas, suggestions, questions, or how you’ve used data in useful and inspiring ways at info@opendataphilly.org.

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If you want to know when City government releases new datasets, follow @PHLInnovation on twitter.

Global Text Analytics Market size valued at US$ 10.02 Billion in 2023, set to reach US$ 46.61 Billion by 2032 at a CAGR of about 18.62% from 2024 to 2032.

Scanned Company Files Query - You can now request these same well files, well logs, and well data as a free download through the File Request System ( https://www.data.bsee.gov/Other/FileRequestSystem/Default.aspx ). The Disc Media Store will be removed at some point in the future.

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The property management software market is expected to grow at a CAGR of 5% during the forecast period. Awareness of property management software, drivers.2, and drivers.3 are some of the significant factors fueling property management software market growth.

Awareness of property management software

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The property management software market analysis report provides a comprehensive analysis of the market with information such as the potential to grow by $ 489.02 mn during 2020-2024, and the market’s growth momentum will accelerate at a CAGR of 5%.

With a detailed analysis of the vendors, this report helps established and new market players to have a keen understanding of their competitors and plan their strategies accordingly. To gain more insights on vendor strategies request a sample of the report.

This dataset provides comprehensive local business and point of interest (POI) data from Google Maps in real-time. It includes detailed business information such as addresses, websites, phone numbers, emails, ratings, reviews, business hours, and over 40 additional data points. Perfect for applications requiring local business data (b2b lead generation, b2b marketing), store locators, and business directories. The dataset is delivered in a JSON format via REST API.

This repository contains the data and code necessary to replicate all figures and tables in the working paper: "Does the disclosure of gun ownership affect crime? Evidence from New York" by Daniel Tannenbaum
There are four folders in this repository:(1) Build: contains all the .do files required to produce the analysis datasets, using the raw data (i.e. datasets in the RawData folder).(2) Analysis: contains all the .do files required to produce all the figures and tables in the paper, using the analysis datasets (i.e. datasets in the AnalysisData folder).(3) RawData: contains all the raw datasets used to produce the AnalysisData datasets. The only raw dataset used in the paper that is excluded from this folder is the proprietary housing assessor and sales transaction data from DataQuick, owned by Corelogic. If I receive approval to include this raw data in this repository I will do so in future versions of this repository.(4) AnalysisData: contains all the analysis datasets that are created using the Build and are used to produce the tables and figures in the paper.

Running the file Master_analysis.do in the Analysis folder will produce, in one script, all the tables and figures in the paper.

Snapshot img

Note Taking App Market Size 2024-2028

The note taking app market size is forecast to increase by USD 9.74 billion, at a CAGR of 17% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing digitization and internet penetration. The integration of Artificial Intelligence (AI) and automation in note taking apps is revolutionizing the way users capture and organize information. This trend is expected to continue as technology advances, offering new opportunities for innovation and user convenience. However, the market faces challenges related to data privacy concerns. With the growing use of note taking apps, the sensitive information they store becomes a potential target for cyber threats.
Addressing these concerns through robust security measures and transparent data handling practices is essential for companies seeking to build trust and maintain user loyalty. Effective navigation of these challenges will be crucial for businesses looking to capitalize on the market's potential and stay competitive in the evolving digital landscape.

What will be the Size of the Note Taking App Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The note-taking app market continues to evolve, with dynamic market activities unfolding across various sectors. Backup and restore, cloud synchronization, and waterfall methodology are integral components of these applications, ensuring seamless data management. Handwriting recognition and user analytics offer enhanced functionality, while advertising revenue and in-app purchases generate monetization opportunities. Data security, compliance regulations, and performance optimization address growing concerns, ensuring user trust and retention. Version control, audio recording, and cost optimization are essential for efficient note-taking, while organization features, user experience (UX), and desktop app development cater to diverse user needs. Subscription models, search functionality, and collaboration tools enable effective teamwork, and product roadmaps facilitate prioritization and feature development.

How is this Note Taking App Industry segmented?

The note taking app industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Private users
  Commercial users


Type

  Window system
  Android system
  IOS system


Platform

  Mobile
  Desktop
  web-Based


End-User

  Student
  Professional
  Casual User


Geography

  North America

    US


  Europe

    Germany


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Application Insights

The private users segment is estimated to witness significant growth during the forecast period.

Note taking apps have gained popularity in both business and personal sectors, with the Private Users segment primarily consisting of individuals utilizing these tools for organizing thoughts, managing tasks, capturing ideas, journaling, and studying. Notable apps catering to this demographic include Microsoft OneNote, Evernote, Google Keep, and Apple Notes. These platforms offer features such as cloud synchronization, multimedia support, handwriting recognition, and cross-device accessibility. The growth of this segment can be attributed to the increasing prevalence of smartphones and tablets, particularly among students and knowledge workers. Many apps provide free versions with fundamental features, making them an attractive option for budget-conscious users.

Additionally, educational tools integration is a common feature for student users. Agile development methodologies, like Scrum, facilitate frequent updates and beta testing, ensuring continuous improvement. API integrations enable seamless data exchange with other applications, while tagging systems and search functionality enhance productivity. Subscription models offer advanced features, and collaboration tools foster teamwork. User interface design prioritizes user experience (UX), ensuring ease of use. Backup and restore, data encryption, and data security ensure data protection. Compliance regulations, performance optimization, and retention rate are crucial considerations for businesses. Version control, audio recording, cost optimization, organization features, and user feedback further enhance functionality.

Desktop app development and web app development cater to diverse user preferences. Software testing, security features, customer service, and data analytics ensure app reliability and user satisfaction. Mobile app development and agile development methodologies ensure app accessibility and adaptabili

The college planning app market, currently valued at $148 million in 2025, is experiencing robust growth, projected to expand significantly over the forecast period (2025-2033). A compound annual growth rate (CAGR) of 8.2% indicates a considerable increase in market size driven by several key factors. The rising number of college-bound students globally, coupled with the increasing adoption of smartphones and mobile technology, fuels the demand for user-friendly and efficient college planning applications. Furthermore, the incorporation of advanced features such as personalized college recommendations, financial aid calculators, and scholarship search tools enhances the appeal and utility of these apps. The market is segmented by application (public and private colleges) and operating system (iOS and Android), with iOS likely holding a larger market share due to its historical dominance in the higher education sector and its association with a more affluent user base. Competitive intensity is high, with established players like Microsoft To Do and Google Calendar competing alongside specialized college planning apps such as iStudiez Pro and MyStudyLife. Geographic distribution reveals strong potential in North America and Europe, given higher education spending and technological penetration. However, emerging markets in Asia-Pacific also present opportunities for growth, driven by expanding access to higher education and rising internet and smartphone usage. The sustained growth of the market is anticipated to be fueled by continuous technological advancements, such as improved AI-driven features for personalized recommendations and sophisticated data analytics for better decision-making. However, challenges remain, including concerns regarding data privacy and security, the need for continuous app updates to accommodate evolving college application processes, and the competition from free or partially free alternatives. Strategies for continued success will involve focusing on creating superior user experiences, incorporating innovative features, and expanding into new geographic markets through targeted marketing campaigns. Moreover, strategic partnerships with educational institutions and organizations can further enhance the visibility and credibility of these apps, leading to increased adoption and market share. The market's future trajectory depends on its ability to innovate and effectively address both user needs and market challenges.

Patent Search Software Market size was valued at USD 907.61 Million in 2023 and is projected to reach USD 2,282.62 Million by 2031, growing at a CAGR of 12.32% from 2024 to 2031. Global Patent Search Software Market Overview The Patent Search Software Market is experiencing significant growth, fueled by the ever-increasing volume of patent filings globally. Companies are constantly innovating, and effective patent search has become critical for navigating the complex intellectual property landscape. This software empowers businesses to make informed decisions regarding research and development (R&D), competitive analysis, freedom-to-operate assessments, and patent portfolio management. The market is driven by several key factors. Firstly, the rising number of patent applications worldwide necessitates robust search tools. Patent offices are witnessing a surge in filings, particularly in technology-driven sectors like healthcare and artificial intelligence.

This vast amount of data makes manual searching impractical, and patent search software offers a time-saving and efficient solution. Secondly, the growing emphasis on intellectual property (IP) protection is propelling market growth. Companies recognize the value of their inventions and are increasingly seeking patents to safeguard their competitive edge. Patent search software empowers them to identify potential conflicts with existing patents, ensuring they avoid infringement risks and optimize their patenting strategies. Advancements in artificial intelligence (AI) and machine learning (ML) are adding new dimensions to patent search capabilities. These technologies enable software to analyze vast datasets of patent information with greater accuracy and efficiency.

They can identify relevant patents based on keywords, classifications, and even semantic relationships, leading to more comprehensive and insightful searches. This trend is expected to further accelerate market growth as businesses seek advanced tools to gain a deeper understanding of the competitive landscape. The Patent Search Software Market is poised for continued expansion. The increasing adoption of cloud-based solutions, offering scalability and accessibility, is another factor contributing to market growth. Additionally, the growing focus on international patent filing necessitates software that can navigate the complexities of multiple patent offices and legal jurisdictions. As companies strive for global market reach, the demand for robust and versatile patent search tools is expected to remain high.

Our clickstream data offers unparalleled access to a vast array of global datasets, capturing user interactions across websites, apps, and digital platforms worldwide. With coverage spanning multiple industries and geographies, our data provides detailed insights into consumer behavior, online trends, and digital engagement patterns.

Whether you're analyzing traffic flows, identifying audience interests, or tracking competitive performance, our clickstream datasets deliver the scale and granularity needed to inform strategic decisions. Updated regularly to ensure accuracy and relevance, this robust resource empowers businesses to uncover actionable insights and stay ahead in a dynamic digital landscape.

The dataset describes the results of an online survey conducted by May 2012, on the databases Google Academical, PubMed, Scopus and Web of Science, screening for laboratory management systems. The search identified 158 online systems. The data extracted include the name of the software, the URL, the availability (paid, limited-free or full free) and a brief description of their function in lab management.