research-trends-scopus 0.2.0

A comprehensive tool to retrieve research data from multiple academic databases (Scopus, OpenAlex, Semantic Scholar, CrossRef, PubMed, Google Scholar), analyze trends, and generate interactive dashboards with recommendations

Research Trends Scopus

A comprehensive Python package to retrieve research data from multiple academic databases, analyze publication trends, and generate interactive dashboards with actionable recommendations for research exploration.

🌐 Supported Data Sources

Source	API Key	Coverage	Best For
OpenAlex	Not required	250M+ works	General research (recommended)
Semantic Scholar	Optional	200M+ papers	AI/ML research, citations
CrossRef	Not required	150M+ DOIs	Metadata & DOI lookup
PubMed	Optional	36M+ papers	Biomedical/life sciences
Scopus	Required	90M+ records	Comprehensive citation data
Google Scholar	N/A (scraper)	Largest	Citation counts

🚀 Features

• Multi-Source Data Retrieval: Fetch from OpenAlex, Scopus, Semantic Scholar, CrossRef, PubMed, and Google Scholar
• Unified Interface: Single API to query multiple databases with automatic fallback and deduplication
• Trend Analysis: Analyze publication trends over time, by author, institution, and topic
• Network Analysis: Visualize collaboration networks and citation patterns
• Topic Modeling: Discover emerging research themes using NLP techniques
• Interactive Dashboard: Generate beautiful dashboards using Plotly and Dash
• Smart Recommendations: Get AI-powered suggestions for research areas to explore
• Caching: Built-in caching to minimize API calls and improve performance
• Export: Export data and visualizations in multiple formats

📦 Installation

From PyPI

pip install research-trends-scopus

With optional dependencies

# For development
pip install research-trends-scopus[dev]

# For documentation
pip install research-trends-scopus[docs]

# For Jupyter notebook support
pip install research-trends-scopus[notebook]

# Install all optional dependencies
pip install research-trends-scopus[all]

From source

git clone https://github.com/research-trends/research-trends-scopus.git
cd research-trends-scopus
pip install -e ".[all]"

⚙️ Configuration

API Keys (Optional for most sources)

Most data sources work without API keys. Only Scopus requires an API key.

For Scopus (optional):

export SCOPUS_API_KEY="your-api-key-here"

For Semantic Scholar (optional, increases rate limits):

export S2_API_KEY="your-api-key-here"

.env file:

SCOPUS_API_KEY=your-api-key-here
S2_API_KEY=your-optional-s2-key

🎯 Quick Start

Using OpenAlex (Recommended - No API Key Required)

from research_trends import OpenAlexClient, TrendAnalyzer, Dashboard

# Initialize the client (no API key needed!)
client = OpenAlexClient(email="[e-mail]")  # email for polite pool

# Search for publications
results = client.search(
    query="machine learning healthcare",
    from_year=2020,
    max_results=500
)

# Analyze trends
analyzer = TrendAnalyzer(results.publications)
trends = analyzer.analyze()

# Launch interactive dashboard
dashboard = Dashboard(analyzer)
dashboard.run(port=8050)

Multi-Source Search with UnifiedClient

from research_trends import UnifiedClient, DataSource, TrendAnalyzer

# Initialize unified client
unified = UnifiedClient()

# Search multiple sources at once
multi_results = unified.search_multiple(
    query="deep learning",
    sources=[DataSource.OPENALEX, DataSource.SEMANTIC_SCHOLAR],
    max_results=100
)

# Merge and deduplicate results
publications = multi_results.merge_deduplicated()
print(f"Found {len(publications)} unique publications from multiple sources")

# Search with automatic fallback
results = unified.search_with_fallback(
    query="quantum computing",
    sources=[DataSource.SCOPUS, DataSource.OPENALEX, DataSource.CROSSREF],
    max_results=200
)

Individual Clients

from research_trends import (
    OpenAlexClient,      # 250M+ works, free
    SemanticScholarClient,  # 200M+ papers, free
    CrossRefClient,      # 150M+ DOIs, free
    PubMedClient,        # 36M+ biomedical, free
    ScopusClient,        # 90M+ records, requires API key
    GoogleScholarClient  # Largest, web scraper
)

# Semantic Scholar (great for AI/ML research)
s2 = SemanticScholarClient()
papers = s2.search("transformer architecture", max_results=100)

# CrossRef (great for DOI lookups)
crossref = CrossRefClient()
paper = crossref.get_work_by_doi("10.1038/nature12373")

# PubMed (great for biomedical research)
pubmed = PubMedClient()
results = pubmed.search("CRISPR gene editing", max_results=50)

Using Scopus (Requires API Key)

from research_trends import ScopusClient, TrendAnalyzer, Dashboard

# Initialize the client
client = ScopusClient()  # Uses SCOPUS_API_KEY from environment

# Search for publications
publications = client.search(
    query="machine learning healthcare",
    start_year=2020,
    end_year=2025,
    max_results=1000
)

# Analyze trends
analyzer = TrendAnalyzer(publications)
trends = analyzer.analyze()
recommendations = analyzer.get_recommendations()

# Launch interactive dashboard
dashboard = Dashboard(analyzer)
dashboard.run(port=8050)

Command Line Interface

# Search and analyze
research-trends search "artificial intelligence" --years 2020-2025 --output results.json

# Generate dashboard
research-trends dashboard results.json --port 8050

# Get recommendations
research-trends recommend results.json --top 10

📊 Dashboard Features

The interactive dashboard includes:

• Publication Timeline: Track publication volume over time
• Author Analysis: Identify top authors and their productivity
• Institution Rankings: Compare research output by institution
• Geographic Distribution: World map of research activity
• Keyword Trends: Track emerging keywords and topics
• Citation Analysis: Analyze citation patterns and impact
• Collaboration Network: Interactive network visualization
• Topic Clusters: Discover research themes and clusters

📈 Analysis Capabilities

Trend Analysis

• Publication count trends
• Citation trends
• Author productivity trends
• Keyword emergence patterns

Network Analysis

• Co-authorship networks
• Citation networks
• Institutional collaboration networks

Topic Modeling

• Keyword extraction
• Topic clustering
• Emerging topic detection

Recommendations

• Underexplored research areas
• Potential collaboration opportunities
• High-impact journals for publication
• Trending research directions

📚 Documentation

Full documentation is available at https://research-trends-scopus.readthedocs.io

🧪 Examples

Check out the examples directory for:

• Jupyter notebooks with step-by-step tutorials
• Sample analyses for different research domains
• Dashboard customization examples

🤝 Contributing

Contributions are welcome! Please read our Contributing Guide for details on:

• Code of conduct
• Development setup
• Submitting pull requests

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• OpenAlex - Free, open catalog of scholarly papers
• Semantic Scholar - AI-powered research tool
• CrossRef - DOI registration agency
• PubMed/NCBI - Biomedical literature database
• Elsevier for the Scopus API
• The open-source Python community

📬 Contact

• Issues: GitHub Issues
• Email: [e-mail]