With Semantic Scholar—part of the world’s leading academic search and citation analysis platform—booming, are you wasting countless hours manually collecting academic data such as paper titles, author info, citation counts, journal details, abstracts, keywords, and impact factors? Faced with its massive repository of scholarly works, spanning millions of papers, paginated results and multi-dimensional citation metrics, efficiently acquiring structured academic data to track research trends for scholars or support literature reviews for students has become a common challenge for researchers, university students, academic editors, and industry R&D teams. Say goodbye to tedious manual copy-and-paste and page-by-page recording of scholarly info. BrowserAct will revolutionize the way you access Semantic Scholar’s academic data.

What is BrowserAct Semanticscholar Scraper ?

BrowserAct is a powerful automated data extraction tool that lets you easily scrape required data from any web page without programming knowledge. It can efficiently capture key Semantic Scholar academic data, including paper titles, author info, citation counts, journal details, abstracts, keywords, and impact factors. What can it do for you?

• Semantic Scholar Academic Scraping: Our Semantic Scholar crawler intelligently extracts core scholarly data. This includes paper titles (e.g., “AI-Driven Drug Discovery,” “Climate Change Modeling”), authors (e.g., “Michael Lee, Sarah Chen”), citation counts (e.g., 500+, 1k+), journals (e.g., “Nature,” “IEEE Transactions”), abstracts, keywords, and impact factors. It covers all critical info to track academic research trends.
• AI-Powered Field Suggestions: Using AI to identify Semantic Scholar page structures (paper search results, article detail pages), it quickly suggests key fields like "paper title, author, citation count, journal, abstract". No manual positioning—direct structured data for analysis.
• Ideal Users: Suitable for researchers, university students, academic editors, and industry R&D teams. It provides structured Semantic Scholar data to drive decisions—like tracking research trends and supporting literature reviews—or meet needs such as evaluating paper influence, identifying collaboration partners and optimizing R&D direction.

Features and Workflow Capabilities

• Input Parameters for Effective Conecte Imóvel Scraping. Detailed explanation of required input parameters, presented in a table for clarity:

How to Use BrowserAct as a CoinMarketCap Scraper

Step 1: Create Workflow and Set Input Parameters

• Click the "Workflow" button in the left sidebar, then "Create" to name your workflow (e.g., "Financial Data Automation").
• Define customizable inputs for flexibility:

SemanticScholar_Link
Keyword

Step 2: Add Navigation and Search Actions 📍

• Click the "+" icon to add actions. Start with "Visit Page" and enter "Visit /url" to direct the workflow to the specified URL, such as https://www.semanticscholar.org/. BrowserAct's AI will automatically understand the page structure, powering your Forbes web scraper without hassle.

Step 3: Add "Extract Data" Action 📊

• Click "+" and select "Extract Data." In the description box, specify what to extract and set limits, such as:

Extract name/title and add it to "Name" - add Authors to "Authors" - add Abstract to "Abstract" - Add link to the item to "URL"

• The AI will interpret your request and precisely scrape Rightmove houses list—no CSS selectors, no XPath, no coding required. This makes BrowserAct a seamless job scraper for scraping jobs from the internet.

Step 4: Add Output, Publish, and Run 📈

• Click "+" and select "Finish: Output Data." Choose CSV as the output format and enable "Output as a file" for easy downloading.

• Click "Publish" to save and finalize your Forbes scraper.

• Navigate to the "Run" section. Adjust parameters if Forbes (or use defaults), then click "Start" to execute the scrape.

Step 5: Download the Results

• Before downloading, you can preview the scraped results to see if they meet your expectations.