The fields description may be found here: https://docs.dataforseo.com/v3/databases/backlink_summary/?bash

DataForSEO Backlink Summary Database encompasses millions of domains enriched with backlink data and other related metrics. You will get a comprehensive overview of a domain’s backlink profile, including the number of inbound links, referring domains and referring pages, new & lost backlinks and referring domains, domain rank, backlink spam score, and more.

This database is available in both JSON and CSV formats.

High quality backlinks to youtube.com. The retrieval process was completed on 21-Nov-2014.

Note that the bots in varocarbas.com (Project 1 - Stage 2) are collecting a maximum of 1000 high-quality backlinks (e.g., "site.com/backlink" rather than "site.com/this/that/backlink") for each domain.

This dataset provides comprehensive insights and actionable strategies for performing effective backlink gap analysis to uncover hidden link building opportunities that your competitors are missing. The dataset covers in-depth analysis of the tools, techniques, and processes required to systematically identify high-authority linking prospects, including detailed case studies and best practices for leveraging this competitive intelligence to strengthen your website's backlink profile and improve search engine rankings.

High quality backlinks to statcounter.com. The retrieval process was completed on 13-Dec-2014.

Note that the bots in varocarbas.com (Project 1 - Stage 2) are collecting a maximum of 1000 high-quality backlinks (e.g., "site.com/backlink" rather than "site.com/this/that/backlink") for each domain.

The Backlink Checker Tool market has emerged as a crucial component of the digital marketing landscape, essential for businesses aiming to enhance their online presence and improve search engine rankings. These tools enable users to analyze their backlink profiles, uncovering valuable insights into the quality and q

This dataset provides insights and data related to the measurement and analysis of the true revenue impact of link building campaigns. It covers various attribution models, data-driven strategies, and industry best practices for accurately quantifying the return on investment from backlink acquisition efforts.

🌍 Ultimate Geographic Data Collection | Cities & Zip Codes

📌 Overview

Welcome to the Ultimate Geographic Data Collection, a comprehensive dataset providing valuable geographic insights. This dataset includes U.S. Zip Codes, U.S. Cities, and World Cities data, making it an essential resource for developers, data analysts, and researchers. Whether you're building location-based applications, conducting geographic analysis, or working on machine learning projects, this dataset offers an extensive and curated collection of location-based information.

📊 What's Inside?

• U.S. Zip Codes Database (Free Version) 🏙️

  • Includes ZIP codes, state associations, and geographic coordinates.
  • 🔗 Usage Condition: Requires a visible backlink to SimpleMaps US Zip Code Database.

• U.S. Cities Database (Free Version) 🌆

  • Includes city names, state information, latitude, longitude, and population data.
  • 🔗 Usage Condition: Requires a visible backlink to SimpleMaps US Cities Database.

• Basic World Cities Database 🗺️

  • Provides global city data licensed under Creative Commons Attribution 4.0.
  • 📜 Learn more: CC BY 4.0 License.

• Comprehensive & Pro World Cities Database (Density Data) 🌎

  • Population density estimates sourced from CIESIN - Columbia University.
  • 🔗 Licensed under Creative Commons Attribution 4.0 with no additional restrictions.

⚖️ License & Usage Terms

• ✅ You CAN:

  • Use this dataset in private and public-facing applications.
  • Create copies and backups for your projects.
  • Transfer the license (with provider approval via email).

• 🚫 You CANNOT:

  • Redistribute the dataset publicly without written permission.
  • Use it in a way that violates any laws.
  • Bypass the backlink requirement (for free U.S. Zip Code & Cities Databases).

🛠️ How to Use

1. Download the dataset 📥.
2. Ensure compliance with licensing terms.
3. Use it in your projects for analysis, visualization, or machine learning.
4. Provide attribution (if applicable) for free datasets.

⚠️ Disclaimer

• This dataset is provided "AS IS", without any warranties.
• The provider is not liable for any issues arising from usage.
• Users are responsible for ensuring legal compliance in their jurisdiction.

🔥 Get Started!

Enhance your geographic projects with this powerful dataset today! 🚀

📩 For any inquiries, licensing requests, or attribution clarifications, contact the dataset provider.

Link Building Software Market Outlook

According to our latest research, the global link building software market size reached USD 1.32 billion in 2024, reflecting robust adoption across industries driven by the increasing importance of digital presence and SEO optimization. The market is expected to grow at a CAGR of 15.1% from 2025 to 2033, with forecasts indicating a market value of USD 4.29 billion by 2033. This impressive growth trajectory is propelled by the rising demand for scalable, automated solutions to enhance online visibility and streamline backlink management in an increasingly competitive digital landscape.

One of the primary growth factors in the link building software market is the ever-increasing complexity of search engine algorithms, which has made manual link building less effective and more time-consuming. Businesses, both large and small, are now recognizing the necessity of sophisticated link building solutions to ensure their websites achieve higher rankings on search engine results pages (SERPs). The proliferation of e-commerce, digital marketing agencies, and content-driven enterprises has further amplified the need for advanced software that can automate the identification, acquisition, and management of high-quality backlinks. This trend is particularly pronounced among SEO agencies and in-house marketing teams that are under constant pressure to deliver measurable improvements in organic search performance.

Another significant driver is the integration of artificial intelligence and machine learning capabilities within link building software platforms. These technologies enable more accurate identification of authoritative domains, predictive analytics for link acquisition strategies, and real-time monitoring of backlink profiles. As organizations strive for data-driven decision-making, the adoption of AI-powered tools has become a key differentiator in the market. Furthermore, the growing awareness of the long-term benefits of sustainable link building—such as improved domain authority, brand credibility, and organic traffic—has encouraged businesses across diverse sectors to invest in comprehensive link building solutions, further fueling market expansion.

The shift towards cloud-based deployment models is also significantly contributing to market growth. Cloud-based link building software offers unparalleled scalability, accessibility, and cost-efficiency, making it particularly attractive to small and medium enterprises (SMEs) and geographically dispersed teams. The ability to collaborate in real-time, integrate with other marketing platforms, and access advanced analytics from any location has accelerated the adoption of cloud solutions. Additionally, the increasing focus on data privacy and regulatory compliance has prompted vendors to enhance security features, making cloud-based offerings even more appealing to enterprises with stringent data governance requirements.

From a regional perspective, North America currently leads the link building software market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the high concentration of digital marketing agencies, rapid technological advancements, and a mature e-commerce ecosystem. Meanwhile, Asia Pacific is expected to exhibit the fastest growth rate during the forecast period, driven by the digital transformation of businesses, rising internet penetration, and the emergence of a vibrant startup ecosystem. Latin America and the Middle East & Africa are also witnessing increased adoption, albeit at a relatively nascent stage, as organizations in these regions recognize the critical role of link building in enhancing online competitiveness.

As the digital landscape continues to evolve, the demand for comprehensive SEO Software has surged. This type of software plays a crucial role in enhancing a website's visibility on search engines, thereby driving organic traffic and improving overall online presence. SEO Software encompasses a wide range of functionalities, including keyword research, site audits, and performance tracking, which are essential for businesses aiming to stay competitive. The integration of advanced analytics and reporting tools within SEO Software allows organizations to make data-driven decisions, optimiz

This dataset provides detailed information on optimizing link velocity to scale backlink acquisition without triggering penalties. It covers strategies for sustainable backlink growth, avoiding common mistakes, and maintaining a healthy link profile. The data includes specific performance metrics, industry benchmarks, and best practices for link building that can help SEO professionals and website owners improve their off-page optimization efforts.

High quality backlinks to twitter.com. The retrieval process was completed on 06-Nov-2014.

Note that the bots in varocarbas.com (Project 1 - Stage 2) are collecting a maximum of 1000 high-quality backlinks (e.g., "site.com/backlink" rather than "site.com/this/that/backlink") for each domain.

This dataset provides comprehensive insights and strategies for link reclamation, including techniques for recovering lost backlinks and identifying unlinked brand mentions. The content covers effective methods for finding, evaluating, and reclaiming valuable backlinks that have been lost over time, as well as strategies for turning unlinked brand mentions into powerful referrals. The data includes real-world case studies, industry best practices, and actionable steps to help businesses and SEO professionals improve their overall link profile and online visibility.

Set of CSV files including the backlinks to the top-linked domains collected from varocarbas.com (Project 1 - Stage 2).

The bots are collecting a maximum of 1000 high-quality backlinks (e.g., "site.com/backlink" rather than "site.com/this/that/backlink") for each domain.

Abstract

Motivation: creating challenging dataset for testing Named-Entity

Linking. The Namesakes dataset consists of three closely related datasets: Entities, News and Backlinks. Entities were collected as Wikipedia text chunks corresponding to highly ambiguous entity names. The News were collected as random news text chunks, containing mentions that either belong to the Entities dataset or can be easily confused with them. Backlinks were obtained from Wikipedia dump data with intention to have mentions linked to the entities of the Entity dataset. The Entities and News are human-labeled, resolving the mentions of the entities.Methods

Entities were collected as Wikipedia 

text chunks corresponding to highly ambiguous entity names: the most popular people names, the most popular locations, and organizations with name ambiguity. In each Entities text chunk, the named entities with the name similar to the chunk Wikipedia page name are labeled. For labeling, these entities were suggested to human annotators (odetta.ai) to tag as "Same" (same as the page entity) or "Other". The labeling was done by 6 experienced annotators that passed through a preliminary trial task. The only accepted tags are the tags assigned in agreement by not less than 5 annotators, and then passed through reconciliation with an experienced reconciliator.

The News were collected as random news text chunks, containing mentions which either belong to the Entities dataset or can be easily confused with them. In each News text chunk one mention was selected for labeling, and 3-10 Wikipedia pages from Entities were suggested as the labels for an annotator to choose from. The labeling was done by 3 experienced annotators (odetta.ai), after the annotators passed a preliminary trial task. The results were reconciled by an experienced reconciliator. All the labeling was done using Lighttag (lighttag.io).

Backlinks were obtained from Wikipedia dump data (dumps.wikimedia.org/enwiki/20210701) with intention to have mentions linked to the entities of the Entity dataset. The backlinks were filtered to leave only mentions in a good quality text; each text was cut 1000 characters after the last mention.

Usage NotesEntities:

File: Namesakes_entities.jsonl The Entities dataset consists of 4148 Wikipedia text chunks containing human-tagged mentions of entities. Each mention is tagged either as "Same" (meaning that the mention is of this Wikipedia page entity), or "Other" (meaning that the mention is of some other entity, just having the same or similar name). The Entities dataset is a jsonl list, each item is a dictionary with the following keys and values: Key: ‘pagename’: page name of the Wikipedia page. Key ‘pageid’: page id of the Wikipedia page. Key ‘title’: title of the Wikipedia page. Key ‘url’: URL of the Wikipedia page. Key ‘text’: The text chunk from the Wikipedia page. Key ‘entities’: list of the mentions in the page text, each entity is represented by a dictionary with the keys: Key 'text': the mention as a string from the page text. Key ‘start’: start character position of the entity in the text. Key ‘end’: end (one-past-last) character position of the entity in the text. Key ‘tag’: annotation tag given as a string - either ‘Same’ or ‘Other’.

News: File: Namesakes_news.jsonl The News dataset consists of 1000 news text chunks, each one with a single annotated entity mention. The annotation either points to the corresponding entity from the Entities dataset (if the mention is of that entity), or indicates that the mentioned entity does not belong to the Entities dataset. The News dataset is a jsonl list, each item is a dictionary with the following keys and values: Key ‘id_text’: Id of the sample. Key ‘text’: The text chunk. Key ‘urls’: List of URLs of wikipedia entities suggested to labelers for identification of the entity mentioned in the text. Key ‘entity’: a dictionary describing the annotated entity mention in the text: Key 'text': the mention as a string found by an NER model in the text. Key ‘start’: start character position of the mention in the text. Key ‘end’: end (one-past-last) character position of the mention in the text. Key 'tag': This key exists only if the mentioned entity is annotated as belonging to the Entities dataset - if so, the value is a dictionary identifying the Wikipedia page assigned by annotators to the mentioned entity: Key ‘pageid’: Wikipedia page id. Key ‘pagetitle’: page title. Key 'url': page URL.

Backlinks dataset: The Backlinks dataset consists of two parts: dictionary Entity-to-Backlinks and Backlinks documents. The dictionary points to backlinks for each entity of the Entity dataset (if any backlinks exist for the entity). The Backlinks documents are the backlinks Wikipedia text chunks with identified mentions of the entities from the Entities dataset.

Each mention is identified by surrounded double square brackets, e.g. "Muir built a small cabin along [[Yosemite Creek]].". However, if the mention differs from the exact entity name, the double square brackets wrap both the exact name and, separated by '|', the mention string to the right, for example: "Muir also spent time with photographer [[Carleton E. Watkins | Carleton Watkins]] and studied his photographs of Yosemite.".

The Entity-to-Backlinks is a jsonl with 1527 items. File: Namesakes_backlinks_entities.jsonl Each item is a tuple: Entity name. Entity Wikipedia page id. Backlinks ids: a list of pageids of backlink documents.

The Backlinks documents is a jsonl with 26903 items. File: Namesakes_backlinks_texts.jsonl Each item is a dictionary: Key ‘pageid’: Id of the Wikipedia page. Key ‘title’: Title of the Wikipedia page. Key 'content': Text chunk from the Wikipedia page, with all mentions in the double brackets; the text is cut 1000 characters after the last mention, the cut is denoted as '...[CUT]'. Key 'mentions': List of the mentions from the text, for convenience. Each mention is a tuple: Entity name. Entity Wikipedia page id. Sorted list of all character indexes at which the mention occurrences start in the text.

Linkwheel.pro is a company that specializes in providing high-quality backlinks to websites through its link wheel building services. The company's link building strategy is based on creating multiple blogs or microsites for a particular niche and linking them together in tiers. These blogs are created on high PR websites such as WordPress.com, Hubpages.com, and Blogger.com, and contain unique, high-quality content that is targeted to the niche. The links are built manually and are designed to look natural, with a mix of dofollow and nofollow links and a variety of anchor texts.

The company's link building services are designed to provide a natural link profile for a website, which is essential for sustainable search engine rankings. The services are also fully manual, with no use of automated software or bots, and are designed to mimic the way links are built naturally on the internet. The company offers a range of link building packages, including link wheels, edu and gov links, and blog backlinks, and provides ongoing support and monitoring of the links to ensure that they are safe and effective. With its focus on high-quality content and natural link building, Linkwheel.pro is a great option for website owners who want to improve their search engine rankings and drive more targeted traffic to their site.

High quality backlinks to baidu.com. The retrieval process was completed on 16-Dec-2014.

Note that the bots in varocarbas.com (Project 1 - Stage 2) are collecting a maximum of 1000 high-quality backlinks (e.g., "site.com/backlink" rather than "site.com/this/that/backlink") for each domain.

Discover the booming SEO reporting tools market! This analysis reveals key trends, growth projections (CAGR), leading players (SEMrush, Ahrefs, Moz), and regional market share from 2019-2033. Learn how AI and data-driven SEO are shaping this dynamic industry.

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Search Engine Optimization (SEO) Software Market Size 2025-2029

The search engine optimization (seo) software market size is forecast to increase by USD 40.05 billion, at a CAGR of 21.3% between 2024 and 2029.

The SEO Software Market is experiencing significant growth, driven by the increasing penetration of the Internet worldwide. The global digital transformation has led to an escalating demand for SEO solutions to optimize online presence and visibility. An additional key driver is the advent of advanced Artificial Intelligence (AI) technologies, which are revolutionizing SEO by enhancing user experience and delivering more accurate and personalized search results. However, this market is not without challenges. Data privacy concerns among end-users pose a significant obstacle, as companies must ensure they comply with stringent regulations, such as GDPR and CCPA, while maintaining effective SEO strategies.
Balancing user privacy with search engine optimization requirements is a delicate challenge that demands innovative solutions and strategic planning. Companies seeking to capitalize on market opportunities and navigate these challenges effectively must stay informed of the latest trends and best practices in SEO and data privacy regulations.

What will be the Size of the Search Engine Optimization (SEO) Software Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The SEO software market continues to evolve, with new tools and techniques emerging to help businesses optimize their online presence. On-page optimization techniques, such as keyword difficulty scores and content strategy tools, remain essential for improving website performance. Local SEO optimization, website crawlability issues, and indexation monitoring tools are crucial for businesses targeting local markets and ensuring their websites are easily accessible to search engines. Content optimization features, data visualization tools, and image optimization techniques enable businesses to create engaging and optimized content for their audiences. AI-powered SEO tools, structured data validation, and SERP feature analysis offer insights into search engine behavior and user intent, providing valuable data for optimization strategies.

Backlink analysis software, website speed optimization, link building strategies, and video SEO strategies are essential for building a strong online presence and increasing visibility. Technical SEO capabilities, site audit functionalities, content promotion features, competitor SEO analysis, mobile SEO performance, conversion rate optimization, semantic keyword analysis, internal linking strategy, schema markup implementation, and keyword research tools are all critical components of a comprehensive SEO strategy. According to recent industry reports, the SEO software market is expected to grow by over 15% annually, reflecting the increasing importance of digital presence for businesses across sectors. For instance, a large e-commerce company reported a 20% increase in organic traffic after implementing a comprehensive SEO strategy using a combination of these tools and techniques.

How is this Search Engine Optimization (SEO) Software Industry segmented?

The search engine optimization (seo) software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  Cloud-based
  On-premises
  Hybrid


Product Type

  Desktop user
  Mobile user


Application

  Social media marketing
  Email marketing
  Content marketing


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period.

The cloud-based SEO software segment in the global market is witnessing significant growth due to the increasing preference for accessible, collaborative, and scalable solutions among professionals and teams. Cloud-based tools, such as Ahrefs, offer users the flexibility to access advanced SEO functionalities from any location with internet connectivity. This enables real-time collaboration, allowing team members to work together seamlessly on SEO projects, regardless of their physical proximity. The user experience of cloud-based SEO software is marked by its browser-based interfaces, ensuring a consistent and responsive experience across various devices. On-page optimization techniques, keyword difficulty scores, and local SEO optimization are essential features integrated into these tools.

Content str

This dataset provides a detailed analysis of how joining local trade organizations and business associations can significantly boost a business's local SEO performance through high-quality directory listings, natural backlink opportunities, increased local citations, and community authority signals. The data covers the specific SEO benefits, recommended membership strategies, and expected timelines for seeing measurable improvements in local search rankings and online visibility.