PyEuropePMC Features

**✨ Explore what PyEuropePMC can do** - Comprehensive feature overview and workflows [🔍 Search](search/) • [📄 Full-Text](fulltext/) • [🔬 Parsing](parsing/) • [⬅️ Back to Docs](/pyEuropePMC/)

🔍 Core Features

Query the Europe PMC database with powerful search capabilities

Quick Example:

from pyeuropepmc import SearchClient

with SearchClient() as client:
    results = client.search("cancer AND therapy", pageSize=50, sort="CITED desc")

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Full-Text Retrieval

Download complete article content in multiple formats

Quick Example:

from pyeuropepmc import FullTextClient

with FullTextClient() as client:
    pdf_path = client.download_pdf_by_pmcid("PMC1234567")
    xml_content = client.download_xml_by_pmcid("PMC1234567")

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XML Parsing

Extract structured data from full-text XML documents

Quick Example:

from pyeuropepmc import FullTextXMLParser, ElementPatterns

parser = FullTextXMLParser(xml_content)

# Extract metadata
metadata = parser.extract_metadata()

# Extract tables
tables = parser.extract_tables()

# Convert to markdown
markdown = parser.to_markdown()

# Validate schema coverage
coverage = parser.validate_schema_coverage()
print(f"Coverage: {coverage['coverage_percentage']:.1f}%")

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🔧 Query Builder

Advanced fluent API for building complex search queries with type safety

Quick Example:

from pyeuropepmc import QueryBuilder

qb = QueryBuilder()
query = (qb
    .keyword("cancer", field="title")
    .and_()
    .citation_count(min_count=50)
    .and_()
    .date_range(start_year=2020)
    .build())
# Result: "(TITLE:cancer) AND (CITED:[50 TO *]) AND (PUB_YEAR:[2020 TO *])"

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📋 Systematic Review Tracking

PRISMA/Cochrane-compliant search logging and audit trails

Quick Example:

from pyeuropepmc import QueryBuilder
from pyeuropepmc.utils.search_logging import start_search

log = start_search("Cancer Review", executed_by="Researcher")
qb = QueryBuilder().keyword("cancer").and_().field("open_access", True)
qb.log_to_search(log, filters={"open_access": True}, results_returned=100)

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📊 Feature Comparison

Feature SearchClient FullTextClient FullTextXMLParser FTPDownloader QueryBuilder
Search Europe PMC ✅ - - - ✅
Build Complex Queries - - - - ✅
Type-Safe Fields - - - - ✅
Query Validation - - - - ✅
Query Translation - - - - ✅
Download PDFs - ✅ - ✅ -
Download XML - ✅ - - -
Parse XML - - ✅ - -
Extract Metadata - - ✅ - -
Extract Tables - - ✅ - -
Bulk Downloads - - - ✅ -
Systematic Review Logging - - - - ✅
Caching ✅ ✅ - - -
Progress Tracking - ✅ - ✅  

🚀 Common Workflows

Workflow 1: Advanced Query → Search → Parse

from pyeuropepmc import QueryBuilder, SearchClient, FullTextXMLParser

# Step 1: Build complex query with QueryBuilder
qb = QueryBuilder()
query = (qb
    .keyword("machine learning", field="title")
    .and_()
    .citation_count(min_count=25)
    .and_()
    .date_range(start_year=2020)
    .build())

# Step 2: Search with the query
with SearchClient() as client:
    results = client.search(query, pageSize=20, sort="CITED desc")

    # Step 3: Process results
    for paper in results['resultList']['result']:
        if paper.get('pmcid'):
            # Download and parse XML
            xml_content = client.get_fulltext_xml(paper['pmcid'])
            parser = FullTextXMLParser(xml_content)
            metadata = parser.extract_metadata()
            print(f"High-impact paper: {metadata['title']}")

Workflow 2: Systematic Review with Audit Trail

from pyeuropepmc import QueryBuilder
from pyeuropepmc.utils.search_logging import start_search

# Start systematic review
log = start_search("ML in Biology Review", executed_by="Researcher Name")

# Build comprehensive search strategy
qb = QueryBuilder()
comprehensive_query = (qb
    .keyword("machine learning")
    .and_()
    .keyword("biology")
    .and_()
    .field("open_access", True)
    .and_()
    .date_range(start_year=2019)
    .build())

# Execute and log search
with SearchClient() as client:
    results = client.search(comprehensive_query, pageSize=100)

    # Log for systematic review compliance
    qb.log_to_search(
        search_log=log,
        filters={"open_access": True, "date_range": "2019+"},
        results_returned=len(results['resultList']['result']),
        notes="Comprehensive ML in biology search"
    )

# Save review log
log.save("systematic_review_log.json")

Workflow 3: Advanced Search → Filter → Extract

from pyeuropepmc import SearchClient, FullTextXMLParser

with SearchClient() as client:
    # Advanced search with filters
    results = client.search(
        query="cancer AND (therapy OR treatment)",
        sort="CITED desc",
        pageSize=100,
        resultType="core"
    )

    # Filter for high-impact papers
    high_impact = [
        paper for paper in results['resultList']['result']
        if paper.get('citedByCount', 0) > 50 and paper.get('pmcid')
    ]

    # Extract detailed information
    for paper in high_impact:
        xml_content = client.get_fulltext_xml(paper['pmcid'])
        parser = FullTextXMLParser(xml_content)
        # Analyze...

📊 Feature Matrix

Search Features

Capability Supported Notes
Keyword search ✅ Full-text search across all fields
Boolean operators ✅ AND, OR, NOT
Field-specific ✅ Search specific fields (author, title, etc.)
Date filtering ✅ Publication date ranges
Citation sorting ✅ Sort by citation count
Pagination ✅ Handle large result sets
Multiple formats ✅ JSON, XML, Dublin Core

Full-Text Features

Capability Supported Notes
PDF download ✅ Open access articles only
XML download ✅ JATS/NLM XML format
HTML content ✅ HTML representation
Bulk FTP ✅ Efficient for large datasets
Progress tracking ✅ Real-time progress callbacks
Auto-retry ✅ Robust error handling

Parsing Features

Capability Supported Notes
Metadata extraction ✅ Title, authors, journal, dates, etc.
Table extraction ✅ Structured table data
Reference extraction ✅ Complete bibliography
Plaintext conversion ✅ Full article text
Markdown conversion ✅ Formatted markdown
Schema validation ✅ Coverage analysis
Custom patterns ✅ Flexible configuration
Multiple XML schemas ✅ JATS, NLM, custom

🎓 Learning Resources

By Feature

By Use Case

By Skill Level

💡 Best Practices

Performance

Error Handling

Rate Limiting

🔄 What’s Next?

Explore each feature in detail:

  1. Search Features - Master the search API
  2. Full-Text Retrieval - Download article content
  3. XML Parsing - Extract structured data
  4. Caching - Optimize performance

Or jump to:

📚 Related Sections

Section Why Visit?
🚀 Getting Started Installation and basics
📚 API Reference Complete method documentation
🎯 Examples Working code samples
⚙️ Advanced Power user features
**[⬆ Back to Top](#pyeuropepmc-features)** • [⬅️ Back to Main Docs](/pyEuropePMC/)