Set of data (cualitative and cuantitative parameters) and screenshots of Web pages from all countries in the world

PredictLeads Key Customers Data provides essential business intelligence by analyzing company relationships, uncovering vendor partnerships, client connections, and strategic affiliations through advanced web scraping and logo recognition. This dataset captures business interactions directly from company websites, offering valuable insights into market positioning, competitive landscapes, and growth opportunities.

Use Cases:

✅ Account Profiling – Gain a 360-degree customer view by mapping company relationships and partnerships. ✅ Competitive Intelligence – Track vendor-client connections and business affiliations to identify key industry players. ✅ B2B Lead Targeting – Prioritize leads based on their business relationships, improving sales and marketing efficiency. ✅ CRM Data Enrichment – Enhance company records with detailed key customer data, ensuring data accuracy. ✅ Market Research – Identify emerging trends and industry networks to optimize strategic planning.

Key API Attributes:

• id (string, UUID) – Unique identifier for the company connection.
• category (string) – Type of relationship (e.g., vendor, client, partner).
• source_category (string) – Where the connection was detected (e.g., partner page, case study).
• source_url (string, URL) – Website where the relationship was found.
• individual_source_url (string, URL) – Specific page confirming the connection.
• context (string) – Extracted description of the business relationship (e.g., "Company X - partners with Company Y to enhance payment processing").
• first_seen_at (ISO 8601 date-time) – Date the connection was first detected.
• last_seen_at (ISO 8601 date-time) – Most recent confirmation of the relationship.
• company1 & company2 (objects) – Details of the two connected companies, including:
• - domain (string) – Company website domain.
• - company_name (string) – Official company name.
• - ticker (string, nullable) – Stock ticker, if available.

📌 PredictLeads Key Customers Data is an indispensable tool for B2B sales, marketing, and market intelligence teams, providing actionable relationship insights to drive targeted outreach, competitor tracking, and strategic decision-making.

PredictLeads Docs: https://docs.predictleads.com/v3/guide/connections_dataset

A complete list of live websites using the Data technology, compiled through global website indexing conducted by WebTechSurvey.

As of September 2024, 75 percent of the 100 most visited websites in the United States shared personal data with advertising 3rd parties, even when users opted out. Moreover, 70 percent of them drop advertising 3rd party cookies even when users opt out.

Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly

Product provided by Wappalyzer. Instant access to website technology stacks.

Lookup API Perform near-instant technology lookups with the Lookup API. Results are fetched from our comprehensive database of millions of websites. If we haven't seen a domain before, we'll index it immediately and report back within minutes.

Web Designer Express is a reputable Miami-based company that has been in business for 20 years. With a team of experienced web designers and developers, they offer a wide range of services, including web design, e-commerce development, web development, and more. Their portfolio showcases over 10,000 websites designed, with a focus on creating custom, unique solutions for each client. With a presence in Miami, Florida, they cater to businesses and individuals seeking to establish a strong online presence. As a company, Web Designer Express is dedicated to building long-lasting relationships with their clients, providing personalized service, and exceeding expectations.

This Website Statistics dataset has four resources showing usage of the Lincolnshire Open Data website. Web analytics terms used in each resource are defined in their accompanying Metadata file.

• Website Usage Statistics: This document shows a statistical summary of usage of the Lincolnshire Open Data site for the latest calendar year.

• Website Statistics Summary: This dataset shows a website statistics summary for the Lincolnshire Open Data site for the latest calendar year.

• Webpage Statistics: This dataset shows statistics for individual Webpages on the Lincolnshire Open Data site by calendar year.

• Dataset Statistics: This dataset shows cumulative totals for Datasets on the Lincolnshire Open Data site that have also been published on the national Open Data site Data.Gov.UK - see the Source link.

  Note: Website and Webpage statistics (the first three resources above) show only UK users, and exclude API calls (automated requests for datasets). The Dataset Statistics are confined to users with javascript enabled, which excludes web crawlers and API calls.

These Website Statistics resources are updated annually in January by the Lincolnshire County Council Business Intelligence team. For any enquiries about the information contact opendata@lincolnshire.gov.uk.

During a 2024 survey, ** percent of responding consumers from the United States said they were fine with a website or app that they trusted or valued using their personal data to send them relevant advertising. The share stood at ** percent for Generation Z respondents.

List of State of Oklahoma city government websites.

This book is the result of teaching the laboratory component of an introductory course in Database Systems in the Department of Computer Science & Software Engineering, Concordia University, Montreal.. The intent of this part of the course was to have the students create a practical web-based application wherein the database forms the dynamic component of a real life application using a web browser as the user interface.

It was decided to use all open source software, namely, Apache web server, PHP, JavaScript and HTML, and also the open source database which started as MySQL and has since migrated to MariaDB.

The examples given in this book have been run successfully both using MySQL on a Windows platform and MariaDB on a Linux platform without any changes. However, the code may need to be updated as the underlying software systems evolve with time, as functions are deprecated and replaced by others. Hence the user is responsible for making any required changes to any code given in this book.

The readers are also warned of the changing privacy and data usage policy of most web sites. They should be aware that most web sites collect and mine user’s data for private profit.

The authors wish to acknowledge the contribution of many students in the introductory database course over the years whose needs and the involvement of one of the authors in the early days of the web prompted the start of this project in the late part of the 20th century. This was the era of dot com bubble

China Internet Service: Number of Website data was reported at 4.460 Unit mn in Dec 2024. This records an increase from the previous number of 3.910 Unit mn for Jun 2024. China Internet Service: Number of Website data is updated semiannually, averaging 2.939 Unit mn from Dec 2000 (Median) to Dec 2024, with 49 observations. The data reached an all-time high of 5.440 Unit mn in Jun 2018 and a record low of 0.243 Unit mn in Jun 2001. China Internet Service: Number of Website data remains active status in CEIC and is reported by China Internet Network Information Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICE: Internet: Number of Domain and Website.

This dataset is about book subjects. It has 5 rows and is filtered where the books is Database-driven web sites. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The Congressional Candidate Websites study uses congressional candidate Web site data from 2002 to 2006 to understand campaign behavior. The content analysis data includes information on major party House and Senate candidates, their districts/states, and aspects of their campaign Web sites including their use of technology and political variables such as endorsements, issue positions, image promotion, and negative commentary.

The majority of the Swedes who took part in a survey conducted on 2019, stated they were concerned that their online information was not kept secure by websites (** percent). ** percent of the respondents disagreed with that statement.

About

Data from the State of California. From website:

Access raw State data files, databases, geographic data, and other data sources. Raw State data files can be reused by citizens and organizations for their own web applications and mashups.

Openness

Open. Effectively in the public domain. Terms of use page says:

In general, information presented on this web site, unless otherwise indicated, is considered in the public domain. It may be distributed or copied as permitted by law. However, the State does make use of copyrighted data (e.g., photographs) which may require additional permissions prior to your use. In order to use any information on this web site not owned or created by the State, you must seek permission directly from the owning (or holding) sources. The State shall have the unlimited right to use for any purpose, free of any charge, all information submitted via this site except those submissions made under separate legal contract. The State shall be free to use, for any purpose, any ideas, concepts, or techniques contained in information provided through this site.

A complete list of live websites using the Corona Virus Data technology, compiled through global website indexing conducted by WebTechSurvey.

A complete list of live websites using the TREASURE DATA technology, compiled through global website indexing conducted by WebTechSurvey.

This data about nola.gov provides a window into how people are interacting with the the City of New Orleans online. The data comes from a unified Google Analytics account for New Orleans. We do not track individuals and we anonymize the IP addresses of all visitors.

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. Using advanced web scraping technology, our dataset offers real-time access to job trends, salaries, and skills demand, making it a valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence.

Key Features:

✅214M+ Job Postings Tracked – Data sourced from 92 Million company websites worldwide. ✅7,1M+ Active Job Openings – Updated in real-time to reflect hiring demand. ✅Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes:

• id (string, UUID) – Unique identifier for the job posting.
• type (string, constant: "job_opening") – Object type.
• title (string) – Job title.
• description (string) – Full job description, extracted from the job listing.
• url (string, URL) – Direct link to the job posting.
• first_seen_at – Timestamp when the job was first detected.
• last_seen_at – Timestamp when the job was last detected.
• last_processed_at – Timestamp when the job data was last processed.

Job Metadata:

• contract_types (array of strings) – Type of employment (e.g., "full time", "part time", "contract").
• categories (array of strings) – Job categories (e.g., "engineering", "marketing").
• seniority (string) – Seniority level of the job (e.g., "manager", "non_manager").
• status (string) – Job status (e.g., "open", "closed").
• language (string) – Language of the job posting.
• location (string) – Full location details as listed in the job description.
• Location Data (location_data) (array of objects)
• city (string, nullable) – City where the job is located.
• state (string, nullable) – State or region of the job location.
• zip_code (string, nullable) – Postal/ZIP code.
• country (string, nullable) – Country where the job is located.
• region (string, nullable) – Broader geographical region.
• continent (string, nullable) – Continent name.
• fuzzy_match (boolean) – Indicates whether the location was inferred.

Salary Data (salary_data)

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Currency of the salary (e.g., "USD", "EUR").
• salary_low_usd (float, nullable) – Converted minimum salary in USD.
• salary_high_usd (float, nullable) – Converted maximum salary in USD.
• salary_time_unit (string, nullable) – Time unit for the salary (e.g., "year", "month", "hour").

Occupational Data (onet_data) (object, nullable)

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (e.g., "Computer and Mathematical").
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (e.g., "Python", "JavaScript").

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

PredictLeads Dataset: https://docs.predictleads.com/v3/guide/job_openings_dataset