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Structured datasets save time and simplify data collection for AI and research projects.
Pre-built marketplaces and APIs reduce errors and accelerate large-scale scraping.
Social media and public-domain datasets provide high-quality, ready-to-use information.
Web scraping has become a crucial aspect of data work in 2025. Companies, researchers, and developers rely on it to gather information from websites. Access to the right datasets and databases saves time and simplifies projects.
Powerful data collection tools simplify the process of extracting structured information from websites. Many projects leverage public domain datasets to train models, test algorithms, and analyze trends. Several resources stand out as particularly useful this year for efficient and accurate data collection.
Wikipedia now offers a structured dataset through Kaggle. This includes English and French content like article summaries, infoboxes, and sections of articles. References and other non-text content are removed, which makes it easier to work with. This dataset is popular for AI projects and research because it provides a large, ready-to-use collection of information without requiring manual scraping of Wikipedia.
Harvard University released a huge dataset of almost one million public-domain books. This collection encompasses a diverse range of genres, languages, and time periods. The dataset is great for AI, language research, and natural language processing. Previously, access to such a large dataset was limited mainly to major tech companies.
Also Read: Why Perplexity is Good for AI-Powered Web Scraping
Bright Data provides pre-built datasets from websites like Amazon, LinkedIn, Pinterest, and Redfin. These datasets encompass various categories, including product prices, real estate listings, and sports statistics. The marketplace charges a subscription fee starting around $250 per month for 100,000 records. It’s a quick way for businesses to get data without building a scraper from scratch.
Datarade works like a marketplace for web scraping datasets. Users can preview samples before purchasing, which helps ensure the data meets their needs. The platform offers hundreds of datasets across different fields, including e-commerce, finance, and marketing. It’s useful for anyone who needs high-quality data for analysis or AI projects.
Oxylabs has a web scraper API that combines multiple scrapers into one tool. It can pull data from marketplaces, search engines, and other websites. The API allows targeting by country, ZIP code, city, or coordinates, which is helpful for businesses that need specific, large-scale data. It makes scraping faster and more accurate.
Also Read: Why is Web Scraping Important for Data Scientists?
AI developers often use AI datasets for training machine learning models and enhancing predictive analytics. Ethical social media scraping enables the analysis of trends, sentiment, and engagement patterns without violating privacy.
Bright Data: Handles large-scale scraping for Facebook, Instagram, TikTok, X (formerly Twitter), and YouTube.
Octoparse: A visual, no-code scraper that works well for beginners.
ScrapeStorm: Uses AI to extract data across multiple social platforms.
PhantomBuster: Focuses on social automation and data collection.
Web scraping in 2025 is faster, smarter, and more accessible thanks to these datasets and tools. From Wikipedia and public-domain books to pre-built marketplaces and social media scrapers, there are options for every type of project. Using these resources can save time, reduce errors, and make data-driven work more effective.
1. What is the Wikipedia-Kaggle dataset used for in 2025?
It provides structured Wikipedia content for AI, research, and data projects without manual scraping.
2. How many books are in Harvard’s Public Domain Book Collection?
Nearly one million public-domain books across genres, languages, and time periods for AI and NLP projects.
3. What types of datasets does Bright Data Marketplace offer?
Pre-built datasets from sites like Amazon, LinkedIn, and Redfin, covering prices, listings, and sports stats.
4. How does Oxylabs Web Scraper API enhance data collection?
It combines multiple scrapers, allows location targeting, and enables faster, accurate large-scale scraping.
5. Which tools are best for social media data scraping?
Bright Data, Octoparse, ScrapeStorm, and PhantomBuster handle large-scale social platform data efficiently.
