The Common Crawl corpus contains petabytes of data collected over 12 years of web crawling. The corpus contains raw web page data, metadata extracts and text extracts. Common Crawl data is stored on Amazon Web Services’ Public Data Sets and on multiple academic cloud platforms across the world.

According to Cognitive Market Research, The Global Anti crawling Techniques market size is USD XX million in 2023 and will expand at a compound annual growth rate (CAGR) of 6.00% from 2023 to 2030.

North America Anti crawling Techniques held the major market of more than 40% of the global revenue and will grow at a compound annual growth rate (CAGR) of 4.2% from 2023 to 2030.
Europe Anti crawling Techniques accounted for a share of over 30% of the global market and are projected to expand at a compound annual growth rate (CAGR) of 4.5% from 2023 to 2030.
Asia Pacific Anti crawling Techniques held the market of more than 23% of the global revenue and will grow at a compound annual growth rate (CAGR) of 8.0% from 2023 to 2030.
South American Anti crawling Techniques market of more than 5% of the global revenue and will grow at a compound annual growth rate (CAGR) of 5.4% from 2023 to 2030.
Middle East and Africa Anti crawling Techniques held the major market of more than 2% of the global revenue and will grow at a compound annual growth rate (CAGR) of 5.7% from 2023 to 2030.
The market for anti-crawling techniques has grown dramatically as a result of the increasing number of data breaches and public awareness of the need to protect sensitive data. 
Demand for bot fingerprint databases remains higher in the anti crawling techniques market.
The content protection category held the highest anti crawling techniques market revenue share in 2023.

Increasing Demand for Protection and Security of Online Data to Provide Viable Market Output

The market for anti-crawling techniques is expanding due in large part to the growing requirement for online data security and protection. Due to an increase in digital activity, organizations are processing and storing enormous volumes of sensitive data online. Organizations are being forced to invest in strong anti-crawling techniques due to the growing threat of data breaches, illegal access, and web scraping occurrences. By protecting online data from harmful activity and guaranteeing its confidentiality and integrity, these technologies advance the industry. Moreover, the significance of protecting digital assets is increased by the widespread use of the Internet for e-commerce, financial transactions, and sensitive data transfers. Anti-crawling techniques are essential for reducing the hazards connected to online scraping, which is a tactic often used by hackers to obtain important data.

Increasing Incidence of Cyber Threats to Propel Market Growth

The growing prevalence of cyber risks, such as site scraping and data harvesting, is driving growth in the market for anti-crawling techniques. Organizations that rely significantly on digital platforms run a higher risk of having illicit data extracted. In order to safeguard sensitive data and preserve the integrity of digital assets, organizations have been forced to invest in sophisticated anti-crawling techniques that strengthen online defenses. Moreover, the market's growth is a reflection of growing awareness of cybersecurity issues and the need to put effective defenses in place against changing cyber threats. Moreover, cybersecurity is constantly challenged by the spread of advanced and automated crawling programs. The ever-changing threat landscape forces enterprises to implement anti-crawling techniques, which use a variety of tools like rate limitation, IP blocking, and CAPTCHAs to prevent fraudulent scraping efforts.

Market Restraints of the Anti crawling Techniques

Increasing Demand for Ethical Web Scraping to Restrict Market Growth

The growing desire for ethical web scraping presents a unique challenge to the anti-crawling techniques market. Ethical web scraping is the process of obtaining data from websites for lawful objectives, such as market research or data analysis, but without breaching the terms of service. Furthermore, the restraint arises because anti-crawling techniques must distinguish between criminal and ethical scraping operations, finding a balance between preventing websites from misuse and permitting authorized data harvest. This dynamic calls for more complex and adaptable anti-crawling techniques to distinguish between destructive and ethical scrapping actions.

Impact of COVID-19 on the Anti Crawling Techniques Market

The demand for online material has increased as a result of the COVID-19 pandemic, which has...

An automatic pipeline based on an algorithm that identifies new resources in publications every month to assist the efficiency of NIF curators. The pipeline is also able to find the last time the resource's webpage was updated and whether the URL is still valid. This can assist the curator in knowing which resources need attention. Additionally, the pipeline identifies publications that reference existing NIF Registry resources as this is also of interest. These mentions are available through the Data Federation version of the NIF Registry, http://neuinfo.org/nif/nifgwt.html?query=nlx_144509 The RDF is based on an algorithm on how related it is to neuroscience. (hits of neuroscience related terms). Each potential resource gets assigned a score (based on how related it is to neuroscience) and the resources are then ranked and a list is generated.

*** german version see below ***

The ‘Ancillary Monitor Corpus: Common Crawl - german web’ was designed with the aim of enabling a broad-based linguistic analysis of the German-language (visible) internet over time - with the aim of achieving comparability with the DeReKo (‘German Reference Corpus’ of the Leibniz Institute for the German Language - DeReKo volume 57 billion tokens - status: DeReKo Release 2024-I). The corpus is separated by year (here year 2021) and versioned (here version 1). Version 1 comprises (all years 2013-2024) 97.45 billion tokens.

The corpus is based on the data dumps from CommonCrawl (https://commoncrawl.org/). CommonCrawl is a non-profit organisation that provides copies of the visible Internet free of charge for research purposes.

The CommonCrawl WET raw data was first filtered by TLD (top-level domain). Only pages ending in the following TLDs were taken into account: ‘.at; .bayern; .berlin; .ch; .cologne; .de; .gmbh; .hamburg; .koeln; .nrw; .ruhr; .saarland; .swiss; .tirol; .wien; .zuerich’. These are the exclusive German-language TLDs according to ICANN (https://data.iana.org/TLD/tlds-alpha-by-domain.txt) as of 1 June 2024 - TLDs with a purely corporate reference (e.g. ‘.edeka; .bmw; .ford’) were excluded. The language of the individual documents (URLs) was then estimated with the help of NTextCat (https://github.com/ivanakcheurov/ntextcat) (via the CORE14 profile of NTextCat) - only those documents/URLs for which German was the most likely language were processed further (e.g. to exclude foreign-language material such as individual subpages). The third step involved filtering for manual selectors and filtering for 1:1 duplicates (within one year).

The filtering and subsequent processing was carried out using CorpusExplorer (http://hdl.handle.net/11234/1-2634) and our own (supplementary) scripts, and the TreeTagger (http://hdl.handle.net/11372/LRT-323) was used for automatic annotation. The corpus was processed on the HELIX HPC cluster. The author would like to take this opportunity to thank the state of Baden-Württemberg and the German Research Foundation (DFG) for the possibility to use the bwHPC/HELIX HPC cluster - funding code HPC cluster: INST 35/1597-1 FUGG.

Data content: - Tokens and record boundaries - Automatic lemma and POS annotation (using TreeTagger) - Metadata: - GUID - Unique identifier of the document - YEAR - Year of capture (please use this information for data slices) - Url - Full URL - Tld - Top-Level Domain - Domain - Domain without TLD (but with sub-domains if applicable) - DomainFull - Complete domain (incl. TLD) - DomainFull - Complete domain (incl. TLD) - Datum - (System Information): Date of the CorpusExplorer (date of capture by CommonCrawl - not date of creation/modification of the document). - Hash - (System Information): SHA1 hash of the CommonCrawl - Pfad - (System Information): Path of the cluster (raw data) - is supplied by the system.

Please note that the files are saved as *.cec6.gz. These are binary files of the CorpusExplorer (see above). These files ensure efficient archiving. You can use both CorpusExplorer and the ‘CEC6-Converter’ (available for Linux, MacOS and Windows - see: https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-5705) to convert the data. The data can be exported in the following formats:

• CATMA v6
• CoNLL
• CSV
• CSV (only meta-data)
• DTA TCF-XML
• DWDS TEI-XML
• HTML
• IDS I5-XML
• IDS KorAP XML
• IMS Open Corpus Workbench
• JSON
• OPUS Corpus Collection XCES
• Plaintext
• SaltXML
• SlashA XML
• SketchEngine VERT
• SPEEDy/CODEX (JSON)
• TLV-XML
• TreeTagger
• TXM
• WebLicht
• XML

Please note that an export increases the storage space requirement extensively. The ‘CorpusExplorerConsole’ (https://github.com/notesjor/CorpusExplorer.Terminal.Console - available for Linux, MacOS and Windows) also offers a simple solution for editing and analysing. If you have any questions, please contact the author.

Legal information The data was downloaded on 01.11.2024. The use, processing and distribution is subject to §60d UrhG (german copyright law), which authorises the use for non-commercial purposes in research and teaching. LINDAT/CLARIN is responsible for long-term archiving in accordance with §69d para. 5 and ensures that only authorised persons can access the data. The data has been checked to the best of our knowledge and belief (on a random basis) - should you nevertheless find legal violations (e.g. right to be forgotten, personal rights, etc.), please write an e-mail to the author (amc_report@jan-oliver-ruediger.de) with the following information: 1) why this content is undesirable (please outline only briefly) and 2) how the content can be identified - e.g. file name, URL or domain, etc. The author will endeavour to identify the content. The author will endeavour to remove the content and re-upload the data (modified) within two weeks (new version). If

The dataset includes news articles gathered from CommonCrawl for media outlets that were selected based on their political orientation. The news articles span publication dates from 2010 to 2021.

Microformat, Microdata and RDFa data from the November 2013 Common Crawl web corpus. We found structured data within 585 million HTML pages out of the 2.24 billion pages contained in the crawl (26%). These pages originate from 1.7 million different pay-level-domains out of the 12.8 million pay-level-domains covered by the crawl (13%).

In the recent years, Transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data to be (pre-)trained and there is a lack of corpora in languages other than English. Recently, several initiatives have presented multilingual datasets obtained from automatic web crawling. However, the results in Spanish present important shortcomings, as they are either too small in comparison with other languages, or present a low quality derived from sub-optimal cleaning and deduplication. In this paper, we introduce esCorpius, a Spanish crawling corpus obtained from near 1 Pb of Common Crawl data. It is the most extensive corpus in Spanish with this level of quality in the extraction, purification and deduplication of web textual content. Our data curation process involves a novel highly parallel cleaning pipeline and encompasses a series of deduplication mechanisms that together ensure the integrity of both document and paragraph boundaries. Additionally, we maintain both the source web page URL and the WARC shard origin URL in order to complain with EU regulations. esCorpius has been released under CC BY-NC-ND 4.0 license.

This object has been created as a part of the web harvesting project of the Eötvös Loránd University Department of Digital Humanities ELTE DH. Learn more about the workflow HERE about the software used HERE.The aim of the project is to make online news articles and their metadata suitable for research purposes. The archiving workflow is designed to prevent modification or manipulation of the downloaded content. The current version of the curated content with normalized formatting in standard TEI XML format with Schema.org encoded metadata is available HERE. The detailed description of the raw content is the following:

• The portal's archived content (from 2014-09-17 to 2019-12-31) in WARC format available HERE (crawled: 2020-01-27T18:58:23 - 2020-01-27T22:58:20.024419). No further versions are expected because the crawl is created after the portal has stopped publication.

mC4 is a multilingual variant of the C4 dataset called mC4. mC4 comprises natural text in 101 languages drawn from the public Common Crawl web scrape.

The Webis MS MARCO Anchor Text 2022 dataset enriches Version 1 and 2 of the document collection of MS MARCO with anchor text extracted from six Common Crawl snapshots. The six Common Crawl snapshots cover the years 2016 to 2021 (between 1.7-3.4 billion documents each). Overall, the MS MARCO Anchor Text 2022 dataset enriches 1,703,834 documents for Version 1 and 4,821,244 documents for Version 2 with up to 1,000 anchor texts each.

At Crawlbee, we take pride in presenting our comprehensive Consumer Database, a treasure trove of essential data touchpoints that will empower your marketing endeavors.

Why Choose Crawlbee's Consumer Data:

Our database is meticulously crafted from a multitude of trusted sources, including real estate transactional data. This vast compilation ensures the utmost accuracy and relevance, making it a resource for businesses seeking to reach their target audiences effectively. Whether you're in need of Audience Data, B2C Data, or specialized insights such as US Household Data, Housing Data, or Mortgage Data, our database has it all.

Personalized Targeting:

Our data allows for highly versatile targeting across a wide spectrum of use cases. Be it refining your Audience Data for precise marketing campaigns, acquiring in-depth Consumer Data for analytical insights, or accessing specific US Household Data for residential market analysis, Crawlbee's database empowers you to achieve your goals with confidence.

Flexible Pricing:

This versatility ensures that our data fits seamlessly into your budget and operational plans, whether you're seeking B2C Data for short-term projects or a continuous stream of Mortgage Data for long-term strategies.

Exceptional Value:

When you choose Crawlbee, you're selecting a partner dedicated to your success. Our Consumer Data, Audience Data, B2C Data, US Household Data, Housing Data, and Mortgage Data are designed to help you stand out in your industry, outperform competitors, and reach your business goals with precision.

Experience the difference of data that's built on reliability, precision, and performance. Unlock the potential of your marketing campaigns and analytical endeavors with Crawlbee's comprehensive data offerings. Get started today and take a step towards unparalleled success, backed by Consumer Data, Audience Data, B2C Data, US Household Data, Housing Data, and Mortgage Data that meet your unique needs.

This object has been created as a part of the web harvesting project of the Eötvös Loránd University Department of Digital Humanities ELTE DH. Learn more about the workflow HERE about the software used HERE.The aim of the project is to make online news articles and their metadata suitable for research purposes. The archiving workflow is designed to prevent modification or manipulation of the downloaded content. The current version of the curated content with normalized formatting in standard TEI XML format with Schema.org encoded metadata is available HERE. The detailed description of the raw content is the following:

• The portal's archived content (from 2020-09-26 to 2022-02-16) in WARC format available HERE (crawled: 2022-02-14T17:38:18.806603 - 2022-02-16T22:33:59.771349).

Altosight | AI Custom Web Scraping Data

✦ Altosight provides global web scraping data services with AI-powered technology that bypasses CAPTCHAs, blocking mechanisms, and handles dynamic content.

We extract data from marketplaces like Amazon, aggregators, e-commerce, and real estate websites, ensuring comprehensive and accurate results.

✦ Our solution offers free unlimited data points across any project, with no additional setup costs.

We deliver data through flexible methods such as API, CSV, JSON, and FTP, all at no extra charge.

― Key Use Cases ―

➤ Price Monitoring & Repricing Solutions

🔹 Automatic repricing, AI-driven repricing, and custom repricing rules 🔹 Receive price suggestions via API or CSV to stay competitive 🔹 Track competitors in real-time or at scheduled intervals

➤ E-commerce Optimization

🔹 Extract product prices, reviews, ratings, images, and trends 🔹 Identify trending products and enhance your e-commerce strategy 🔹 Build dropshipping tools or marketplace optimization platforms with our data

➤ Product Assortment Analysis

🔹 Extract the entire product catalog from competitor websites 🔹 Analyze product assortment to refine your own offerings and identify gaps 🔹 Understand competitor strategies and optimize your product lineup

➤ Marketplaces & Aggregators

🔹 Crawl entire product categories and track best-sellers 🔹 Monitor position changes across categories 🔹 Identify which eRetailers sell specific brands and which SKUs for better market analysis

➤ Business Website Data

🔹 Extract detailed company profiles, including financial statements, key personnel, industry reports, and market trends, enabling in-depth competitor and market analysis

🔹 Collect customer reviews and ratings from business websites to analyze brand sentiment and product performance, helping businesses refine their strategies

➤ Domain Name Data

🔹 Access comprehensive data, including domain registration details, ownership information, expiration dates, and contact information. Ideal for market research, brand monitoring, lead generation, and cybersecurity efforts

➤ Real Estate Data

🔹 Access property listings, prices, and availability 🔹 Analyze trends and opportunities for investment or sales strategies

― Data Collection & Quality ―

► Publicly Sourced Data: Altosight collects web scraping data from publicly available websites, online platforms, and industry-specific aggregators

► AI-Powered Scraping: Our technology handles dynamic content, JavaScript-heavy sites, and pagination, ensuring complete data extraction

► High Data Quality: We clean and structure unstructured data, ensuring it is reliable, accurate, and delivered in formats such as API, CSV, JSON, and more

► Industry Coverage: We serve industries including e-commerce, real estate, travel, finance, and more. Our solution supports use cases like market research, competitive analysis, and business intelligence

► Bulk Data Extraction: We support large-scale data extraction from multiple websites, allowing you to gather millions of data points across industries in a single project

► Scalable Infrastructure: Our platform is built to scale with your needs, allowing seamless extraction for projects of any size, from small pilot projects to ongoing, large-scale data extraction

― Why Choose Altosight? ―

✔ Unlimited Data Points: Altosight offers unlimited free attributes, meaning you can extract as many data points from a page as you need without extra charges

✔ Proprietary Anti-Blocking Technology: Altosight utilizes proprietary techniques to bypass blocking mechanisms, including CAPTCHAs, Cloudflare, and other obstacles. This ensures uninterrupted access to data, no matter how complex the target websites are

✔ Flexible Across Industries: Our crawlers easily adapt across industries, including e-commerce, real estate, finance, and more. We offer customized data solutions tailored to specific needs

✔ GDPR & CCPA Compliance: Your data is handled securely and ethically, ensuring compliance with GDPR, CCPA and other regulations

✔ No Setup or Infrastructure Costs: Start scraping without worrying about additional costs. We provide a hassle-free experience with fast project deployment

✔ Free Data Delivery Methods: Receive your data via API, CSV, JSON, or FTP at no extra charge. We ensure seamless integration with your systems

✔ Fast Support: Our team is always available via phone and email, resolving over 90% of support tickets within the same day

― Custom Projects & Real-Time Data ―

✦ Tailored Solutions: Every business has unique needs, which is why Altosight offers custom data projects. Contact us for a feasibility analysis, and we’ll design a solution that fits your goals

✦ Real-Time Data: Whether you need real-time data delivery or scheduled updates, we provide the flexibility to receive data when you need it. Track price changes, monitor product trends, or gather...

This object contains only a fraction of the available content for the portal. For further information on the content and for other fractions see: Kuruc.info.
Please fill in the following form before requesting access to this dataset:ACCES FORM

Microformat, Microdata and RDFa data from the December 2014 Common Crawl web corpus. We found structured data within 620 million HTML pages out of the 2.01 billion pages contained in the crawl (30%). These pages originate from 2.72 million different pay-level-domains out of the 15.68 million pay-level-domains covered by the crawl (17%). Altogether, the extracted data sets consist of 20.48 billion RDF quads.

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.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

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.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

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.

This object contains only a fraction of the available content for the portal. For further information on the content and for other fractions see: Abcúg.

Market Overview: The global live crawling service market is experiencing significant growth, fueled by the increasing adoption of data analytics and the need for real-time data insights. With a market size of USD XXX million in 2025 and a CAGR of XX%, it is projected to reach a value of USD million by 2033. The market is driven by the proliferation of digital technologies, the growing demand for personalization in various industries, and the need to improve decision-making capabilities. Key Trends and Segments: Two primary segments drive the live crawling service market: Type (web data crawling, PDF data crawling, others) and Application (SMEs, large enterprises). Key trends include the rise of artificial intelligence (AI) and machine learning (ML), which enhance data extraction accuracy and efficiency. Moreover, the adoption of cloud-based crawling services is increasing due to their scalability, cost-effectiveness, and ease of implementation. Regionally, North America dominates the market, followed by Europe and Asia-Pacific. Emerging economies in Asia-Pacific and the Middle East and Africa are expected to witness significant growth due to rapid digitalization and the expanding adoption of data analytics solutions.

This object contains is the most comprehensive curated version available at the date of publication. For further information on the content and for other fractions see: Természet Világa.
Please fill in the following form before requesting access to this dataset:ACCES FORM

This object has been created as a part of the web harvesting project of the Eötvös Loránd University Department of Digital Humanities ELTE DH. Learn more about the workflow HERE about the software used HERE.The aim of the project is to make online news articles and their metadata suitable for research purposes. The archiving workflow is designed to prevent modification or manipulation of the downloaded content. The current version of the curated content with normalized formatting in standard TEI XML format with Schema.org encoded metadata is available HERE. The detailed description of the raw content is the following:

• The portal's archived content (from 2013-03-28 to 2021-01-30) in WARC format available HERE (crawled: 2021-01-30 13:01:33.164201 - 2021-01-31 19:47:27.191660).
• The portal's archived content (from 2021-01-31 to 2021-05-24) in WARC format available HERE (crawled: 2021-05-25 08:47:51.634068 - 2021-05-25 17:04:43.254877).