Google Scholar is an indispensable tool for academics, researchers, and faculty members who need to access relevant information for their research. This search engine is a real lifesaver with features like academic literature, forward citations, and auto-generated Bib TeX.
There are instances where you may need to extract a large amount of data from Google Scholar, but certain restrictions may hinder it. In such cases, web scraping can be a helpful solution to gather a bulk of scholarly articles and academic resources from this search engine.
If you're interested in scraping Google Scholar data in a more convenient manner, then follow this blog guide which will provide you with a step-by-step process for web scraping.
It is Possible to Scrape Google Scholar?
Despite the initial complexity, you can extract academic literature data smoothly. Scraping data from Google Scholar is possible with the help of a dependable Google Scholar scraper.
Using a Google Scholar scraper, you can gather vast amounts of data, including long research papers, and create a database of backward and forward citations, academic resources, and academics, social networking websites such as ResearchGate.
Can Google Scholar Offer API Access?
Accessing Google Scholar's data through API for web scraping is impossible as the robot.txt file forbids it. Scraping of most pages is not allowed and is only accessible by Google Scholar's bots. However, if you try to access certain information, you may be prompted to clear a CAPTCHA to proceed.
Web Scraping Google Scholar and University Databases For Emails
One approach to extract data from Google Scholar is to scrape the database for PDF links and download the PDFs to a local directory.
- Another possibility is to extract emails from within the PDFs.
- Furthermore, it is feasible to sort emails into different lists based on country, such as .edu.cn emails in a China list and .edu.vn emails in a Vietnam list.
- Additionally, we can enter university websites and automatically extract postgraduate student email lists.
- Any programming language or script, such as Python, can be used for this task.
How to Extract Google Scholar With No Coding?
Scraping data from Google Scholar can be challenging, as it often requires knowledge of complex coding languages. However, with Actowiz Solutions, you can easily extract Google Scholar data into Excel without coding. Actowiz Solutions allows you to automatically scrape web pages and apply advanced functions such as pagination, loops, and Ajax timeouts. In addition, it provides preset templates for scraping Google Scholar articles, making it easy to extract large amounts of data quickly and efficiently.
Steps to Follow While Scraping Academic Sources from Google Scholar
Firstly, create a free account and install Actowiz Solutions Scraper on your device. Once done, follow the simple instructions in the Google Scholar Search Results Scraping user guide.
1. Enter a page link needed to extract from Google Scholar
To scrape data from Google Scholar using Actowiz Solutions, start by copying the page URL you want to target on Google Scholar. Then, paste the URL into the Actowiz Solutions home screen search bar. Click the Start button and the targeted URL will be scraped automatically.
2. Customize workflow for more data
Once the auto-detection process is complete, Actowiz Solutions will generate a workflow. You can modify the workflow using the Tip panel to extract more data. The preview section will display the data that will be scrapped.
3. Scrape data from Google Scholar search result pages
To initiate the scraping process, simply click the "Run" button and allow some time for Actowiz Solutions to complete the scraping process. Once the process is finished, you can easily download the extracted data in CSV/Excel format or directly save it to your preferred database.
Scraping Google Scholar with Python
In today's era, it is necessary to have programming language knowledge to scrape data from Google Scholar. Although we have discussed an effortless method earlier, learning how to extract Google Scholar data using Python is crucial. This can be achieved through a few simple steps.
1: Firstly, prepare virtual environment as well as install libraries for CSS selectors for extracting data from related attributes and tags.
2: Add SelectorGadget Extensions for taking data from various CSS selectors. After that, use a particular Python codes to scrape Google Scholar searches results.
3: Use Actowiz API for that, as it could scrape title, publication information, snippet, link to article, link to associated articles, link to various article versions, and links at a bottom; RefWorks, EndNote, BibTeX, RefMan, etc.
4: Despite that, Actowiz API can scrape Google Scholar Profile data, including author’s name, affiliation(s), link, interests, email, and Public access.
5: Then another crucial data is Google Scholar cite results and for that, a provisional list is made for storing citation data. Utilize these command lines to repeat organic results and pass result id with search query:
6: Then another crucial data is Google Scholar cite results and for that, a provisional list is made for storing citation data. Utilize these command lines to repeat organic results and pass result id with search query:
7: Some special commands you could use according to your need, either add or delete a column from chosen data.
For students and researchers, Google Scholar is a popular platform for accessing scholarly articles and academic resources, including citations. Web scraping on Google Scholar can enhance the academic journey. With the help of Python coding, Google Scholar data can be scrapped. Actowiz Solutions can extract a large amount of data from web pages to local devices without requiring extensive programming knowledge.
For more information, contact Actowiz Solutions now! You can also call us for all your mobile app scraping or web scraping service requirements.
