Blog/Empowering Academic Research with Graph-Based Scraping & Open Data

Empowering Academic Research with Graph-Based Scraping & Open Data

Learn how to use ScrapeGraphAI to empower academic research with graph-based scraping and open data.

Tutorials•8 min read min read•Marco VinciguerraBy Marco Vinciguerra
Empowering Academic Research with Graph-Based Scraping & Open Data

#Empowering Academic Research with Graph-Based Scraping & Open Data

Introduction

Open data and reproducible science are essential pillars of modern academic research. They are widely used in the research community. Yet, even the datasets which are labeled "open" often hide in fragmented formats —academic portals, departmental pages, PDFs—making them difficult to harvest and integrate, which is a major issue while doing research and collecting data for academics. Traditional APIs l(OpenAlex, CORE, OpenCitations) cover a broad scope, but aren't comprehensive and lack some data that is important to and very useful too. This blog post demonstrates how to combine graph-based scraping with open data platforms using ScrapeGraphAI to streamline, enrich, and visualize academic pipelines and improve your academic research and data collection.

1. Open‑Source Academic Platforms At a Glance

OpenAlex hosts metadata on 209 M scholarly works, including authors, citations, institutions, and topics—accessible via a modern REST API.

OpenCitations and the Initiative for Open Citations (I4OC) promote open citation data (DOI-to-DOI), with OpenCitations covering upwards of 13 M links.

CORE aggregates over 125 M open-access papers by providing an API and data dumps.

These platforms are powerful and have a lot of data useful for research —but they leave gaps in domain-specific, PDF-heavy, or institution-specific data which can be an impediment to your research endeavours and data collection.

2. Why Graph-Based Scraping Still Matters

While open APIs are invaluable and may provide you with a a lot of important data, researchers often still need following data which is hard to find because there is no central dataset surrounding it: Institution-level data — e.g., scratch faculty updates, conference pages, PDF syllabi.

Visual/PDF ingestion — tables, graphs, or charts embedded mid-PDF.

Contextual enrichment — metadata gaps like abstract, keywords, or cross-citations.

ScrapeGraphAI's graph-based pipelines can crawl websites like a mini spider, intelligently extract embedded assets, and integrate results with open metadata sources. Which will help you get the above crucial data that you missed and aid as well as boost your research and data collection by many folds seamlessly.

What is ScrapeGraphAI?

ScrapeGraphAI is an API for extracting data from the web with the use of AI. ScrapeGraphAI uses graph based scraping . This service will fit in your data pipeline perfectly because of the easy to use apis that we provide which are fast and accurate. And it's all AI powered and integrates easily with any no code platform like n8n, bubble or make.We offer fast, production-grade APIs , Python & JS SDKs, auto-recovery,Agent-friendly integration (LangGraph, LangChain, etc.) and free tier + robust support 3. Build a Research Pipeline with ScrapeGraphAI a. Crawl Institutional Pages python from scrapegraph_py import Client client = Client(api_key="YOUR_KEY") response = client.smartscraper( website_url="https://example.edu/faculty", user_prompt="Extract names, titles, profile URLs of faculty" ) print(response["result"]) client.close()

This builds the first node in your graph: mapping departmental structures and metadata. You can use any websites url that you want and change the prompt based on your requirements you can also leverage the custom schema by using pydantic models and passing them inside the smartscraper call like this from pydantic import BaseModel, Field class Faculty(BaseModel): name: str = Field(description="Faculty name") title: str = Field(description="Title of the faculty") url: str = Field(description="Url of page") latestwork: str = Field(description="Latest work of the faculty") client = Client(api_key="YOUR_KEY") response = client.smartscraper( website_url="https://example.edu/faculty", user_prompt="Extract names, titles, profile URLs of faculty", output_schema=Faculty ) print(response["result"]) client.close()

b. Extract Tables & PDFs python response = client.smartscraper( website_url="https://example.edu/papers/2025_report.pdf", user_prompt="Extract table titles and content as CSV" )

ScrapeGraphAI can extract the data inside these pds and give you the correct results

c. Augment with Open Metadata

import requests for work in scraped_list: doi = extract_doi(work) openalex = requests.get(f"https://api.openalex.org/works/doi:{doi}") work.update(openalex.json())

Link your dataset with rich metadata—citations, topics, affiliations—from OpenAlex and OpenCitations.

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4. Quality, Ethics & Legality

Respect robots.txt and site TOS for scraping permissions.

Data privacy: anonymize individuals if sensitive.

Licensing: Open platforms like CORE and OpenCitations use permissive open data (CC0+).

Attribution: log source URLs and note academic attributions.

5. Case Study: Building a Co‑Authorship Network

Crawl conference site for publication list and PDFs.

Extract tables listing title, authors, affiliations.

Add metadata from OpenAlex (e.g., timestamp, abstract).

Connect authors by edges to visualize co-authorship over time.

The result: a knowledge graph that traces collaboration patterns—ready for network analytics or impact studies.

6. Tips & Best Practices

Graph visualization: use tools like Neo4j or pyvis to visualize scraping-node relationships.

Pipeline monitoring: validate schema positions after each node extraction.

Rate-limiting: keep fetch rates friendly; respect robots.txt to avoid scraping overload.

LLM QA-based quality checks: chain scraping results into prompts like: "Does every entry have title, author list, and DOI?"

Conclusion & What's Next

Graph-based scraping via ScrapeGraphAI bridges unstructured academic web data with authoritative open metadata. It empowers researchers to assemble rich datasets, validate them, and visualize findings—fueling reproducible, insightful research workflows. Try it yourself! https://dashboard.scrapegraphai.com/

##FAQ:

1. What is graph-based scraping, and how does it differ from traditional web scraping?

Graph-based scraping, as used by ScrapeGraphAI, leverages AI to intelligently navigate and extract data from websites by modeling relationships between elements (e.g., links, tables, or PDFs) as a graph. Unlike traditional scraping, which relies on rigid rules or manual parsing, graph-based scraping adapts to complex or dynamic website structures, making it ideal for academic data like faculty pages or PDF reports.

2. How can ScrapeGraphAI integrate with open data platforms like OpenAlex or CORE?

ScrapeGraphAI can extract unstructured data (e.g., faculty lists, publication tables) from institutional websites or PDFs, which can then be enriched with metadata from open data APIs like OpenAlex (for citations, topics) or CORE (for full-text access). For example, a scraped DOI can be used to fetch additional metadata via OpenAlex's REST API, as shown in the blog's code examples.

Scraping is legal if you respect the website's Terms of Service (TOS) and robots.txt file, which outline permissible crawling behavior. Ethically, ensure data privacy by anonymizing personal information and adhere to open data licensing (e.g., CC0 for OpenCitations). Always provide proper attribution to data sources, as highlighted in the blog's quality and ethics section.

4. What types of academic data can ScrapeGraphAI extract?

ScrapeGraphAI can extract a variety of data, including: Faculty names, titles, and profile URLs from institutional pages. Tables, charts, or text from PDFs (e.g., conference reports or syllabi). Publication lists, author affiliations, or DOIs from conference or departmental websites.

5. Do I need coding expertise to use ScrapeGraphAI?

While the blog provides Python code examples, ScrapeGraphAI integrates with no-code platforms like n8n, Bubble, or Make, making it accessible to non-coders. For developers, ScrapeGraphAI offers Python and JavaScript SDKs for advanced customization.

6. How can I ensure the quality of scraped data?

Use the blog's best practices: Validate schemas after each extraction node. Perform LLM-based quality checks (e.g., "Does every entry have a title, author, and DOI?"). Monitor pipelines to ensure consistent data formatting and completeness.

7. Can ScrapeGraphAI handle PDFs with complex layouts?

Yes, ScrapeGraphAI can intelligently parse PDF layouts to extract tables, text, or embedded assets without manual configuration, as demonstrated in the blog's PDF extraction example.

8. What are some practical applications of this pipeline?

The blog's case study illustrates building a co-authorship network by combining scraped publication data with OpenAlex metadata. Other applications include: Mapping institutional research output. Analyzing citation patterns across conferences. Creating datasets for bibliometric studies or impact analysis.

9. How do I visualize the results of my scraping pipeline?

Use graph visualization tools like Neo4j or pyvis to map relationships (e.g., co-authorship networks or citation graphs), as suggested in the blog's tips section. These tools can render scraped data into interactive knowledge graphs.

10. Where can I learn more or get started with ScrapeGraphAI?

Start with the Python Starter Guide mentioned in the blog. You can also explore ScrapeGraphAI’s documentation, join the community to share workflows, or contact the team for collaboration on open-science initiatives.

Want to learn more about ScrapeGraphAI's capabilities? Explore these guides:

These resources will help you understand different approaches to web scraping and choose the right method for your needs.