Empowering Academic Research with Graph-Based Scraping & Open Data

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"])

# Scrape conference proceedings and paper listings
conference_response = client.smartscraper(
    website_url="https://conference-site.com/proceedings",
    user_prompt="Extract paper titles, authors, abstracts, and DOIs"
)

# Extract structured data from PDF documents
pdf_response = client.smartscraper(
    website_url="https://arxiv.org/pdf/2301.00001.pdf",
    user_prompt="Extract title, authors, abstract, and key findings"
)

import requests
from scrapegraph_py import Client
 
def enrich_paper_data(doi):
    # Get basic metadata from OpenAlex
    openalex_url = f"https://api.openalex.org/works/doi:{doi}"
    metadata = requests.get(openalex_url).json()
    
    # Use ScrapeGraphAI to get additional details
    client = Client(api_key="YOUR_KEY")
    
    if metadata.get('pdf_url'):
        enhanced_data = client.smartscraper(
            website_url=metadata['pdf_url'],
            user_prompt="Extract methodology, results, and conclusions"
        )
        
        return {
            'openalex_data': metadata,
            'enhanced_content': enhanced_data
        }
    
    return metadata

def automated_literature_review(topic, max_papers=50):
    """Automatically collect and analyze papers on a topic"""
    
    # Search OpenAlex for relevant papers
    search_url = f"https://api.openalex.org/works?search={topic}&per-page={max_papers}"
    papers = requests.get(search_url).json()['results']
    
    enriched_papers = []
    
    for paper in papers:
        if paper.get('open_access', {}).get('oa_url'):
            # Use ScrapeGraphAI to extract key insights
            analysis = client.smartscraper(
                website_url=paper['open_access']['oa_url'],
                user_prompt=f"Analyze this paper's contribution to {topic} research"
            )
            
            enriched_papers.append({
                'title': paper['title'],
                'authors': paper['authorships'],
                'citations': paper['cited_by_count'],
                'analysis': analysis
            })
    
    return enriched_papers

def build_citation_network(seed_doi):
    """Build a citation network from a seed paper"""
    
    # Get citations from OpenCitations
    citations_url = f"https://opencitations.net/index/coci/api/v1/citations/{seed_doi}"
    citations = requests.get(citations_url).json()
    
    network_data = []
    
    for citation in citations:
        # Enhance with scraped content
        if citation.get('cited_url'):
            content = client.smartscraper(
                website_url=citation['cited_url'],
                user_prompt="Extract main research question and key findings"
            )
            
            network_data.append({
                'citing_paper': citation['citing'],
                'cited_paper': citation['cited'],
                'content_summary': content
            })
    
    return network_data

class ResearchDataFusion:
    def __init__(self, api_key):
        self.client = Client(api_key=api_key)
    
    def fuse_sources(self, research_topic):
        """Combine multiple academic data sources"""
        
        # OpenAlex for metadata
        openalex_data = self.get_openalex_papers(research_topic)
        
        # CORE for full-text access
        core_data = self.get_core_papers(research_topic)
        
        # ScrapeGraphAI for institutional pages
        institutional_data = self.scrape_institutions(research_topic)
        
        # Combine and deduplicate
        return self.merge_datasets([openalex_data, core_data, institutional_data])
    
    def scrape_institutions(self, topic):
        """Scrape relevant university departments"""
        universities = [
            "https://cs.stanford.edu/research",
            "https://www.csail.mit.edu/research",
            "https://www.cs.cmu.edu/research"
        ]
        
        institutional_research = []
        
        for uni_url in universities:
            data = self.client.smartscraper(
                website_url=uni_url,
                user_prompt=f"Find research projects related to {topic}"
            )
            institutional_research.append(data)
        
        return institutional_research

def verify_research_claims(paper_url, claims_to_verify):
    """Verify specific claims against scraped evidence"""
    
    verification_results = []
    
    for claim in claims_to_verify:
        # Search for supporting evidence
        evidence = client.smartscraper(
            website_url=paper_url,
            user_prompt=f"Find evidence that supports or refutes: {claim}"
        )
        
        verification_results.append({
            'claim': claim,
            'evidence': evidence,
            'confidence': assess_evidence_strength(evidence)
        })
    
    return verification_results

import matplotlib.pyplot as plt
import pandas as pd
 
def analyze_research_trends(scraped_data):
    """Analyze trends from scraped research data"""
    
    # Convert to DataFrame for analysis
    df = pd.DataFrame(scraped_data)
    
    # Extract publication years and topics
    trends = df.groupby(['year', 'topic']).size().reset_index()
    
    # Create visualization
    plt.figure(figsize=(12, 8))
    for topic in trends['topic'].unique():
        topic_data = trends[trends['topic'] == topic]
        plt.plot(topic_data['year'], topic_data[0], label=topic)
    
    plt.xlabel('Year')
    plt.ylabel('Number of Papers')
    plt.title('Research Trends Over Time')
    plt.legend()
    plt.show()
    
    return trends

import networkx as nx
 
def map_collaboration_networks(scraped_authors):
    """Create collaboration networks from scraped author data"""
    
    G = nx.Graph()
    
    for paper in scraped_authors:
        authors = paper['authors']
        
        # Add nodes for each author
        for author in authors:
            G.add_node(author['name'], 
                      affiliation=author.get('affiliation'),
                      papers=author.get('paper_count', 0))
        
        # Add edges for collaborations
        for i in range(len(authors)):
            for j in range(i+1, len(authors)):
                if G.has_edge(authors[i]['name'], authors[j]['name']):
                    G[authors[i]['name']][authors[j]['name']]['weight'] += 1
                else:
                    G.add_edge(authors[i]['name'], authors[j]['name'], weight=1)
    
    return G

class EthicalResearchScraper:
    def __init__(self, api_key):
        self.client = Client(api_key=api_key)
        self.request_delays = {}
    
    def respectful_scrape(self, url, delay=2):
        """Implement respectful scraping practices"""
        
        import time
        from urllib.parse import urlparse
        
        domain = urlparse(url).netloc
        
        # Implement per-domain delays
        if domain in self.request_delays:
            time.sleep(delay)
        
        self.request_delays[domain] = time.time()
        
        # Check robots.txt compliance
        if self.check_robots_txt(url):
            return self.client.smartscraper(
                website_url=url,
                user_prompt="Extract academic content respectfully"
            )
        else:
            print(f"Robots.txt disallows scraping {url}")
            return None
    
    def check_robots_txt(self, url):
        """Check robots.txt compliance"""
        # Implementation for robots.txt checking
        return True  # Simplified for example

def validate_academic_data(scraped_data):
    """Validate scraped academic data quality"""
    
    quality_metrics = {
        'completeness': 0,
        'accuracy': 0,
        'consistency': 0
    }
    
    # Check for required fields
    required_fields = ['title', 'authors', 'abstract']
    complete_records = sum(1 for record in scraped_data 
                          if all(field in record for field in required_fields))
    
    quality_metrics['completeness'] = complete_records / len(scraped_data)
    
    # Additional validation checks...
    
    return quality_metrics

def ai_research_discovery(research_interests):
    """Use AI to discover relevant research automatically"""
    
    discovery_prompt = f"""
    Based on these research interests: {research_interests}
    Suggest:
    1. Emerging research areas to explore
    2. Key papers to read
    3. Potential collaboration opportunities
    4. Funding opportunities
    """
    
    suggestions = client.smartscraper(
        website_url="https://academic-discovery-engine.com",
        user_prompt=discovery_prompt
    )
    
    return suggestions