OpenAlex Database
Overview
OpenAlex is a comprehensive open catalog of 240M+ scholarly works, authors, institutions, topics, sources, publishers, and funders. This skill provides tools and workflows for querying the OpenAlex API to search literature, analyze research output, track citations, and conduct bibliometric studies.
Quick Start
Basic Setup
Always initialize the client with an email address to access the polite pool (10x rate limit boost):
from scripts.openalex_client import OpenAlexClient
client = OpenAlexClient(email="[email protected]")
Installation Requirements
Install required package using uv:
uv pip install requests
No API key required - OpenAlex is completely open.
Core Capabilities
1. Search for Papers
Use for: Finding papers by title, abstract, or topic
# Simple search
results = client.search_works(
search="machine learning",
per_page=100
)
# Search with filters
results = client.search_works(
search="CRISPR gene editing",
filter_params={
"publication_year": ">2020",
"is_oa": "true"
},
sort="cited_by_count:desc"
)
2. Find Works by Author
Use for: Getting all publications by a specific researcher
Use the two-step pattern (entity name → ID → works):
from scripts.query_helpers import find_author_works
works = find_author_works(
author_name="Jennifer Doudna",
client=client,
limit=100
)
Manual two-step approach:
# Step 1: Get author ID
author_response = client._make_request(
'/authors',
params={'search': 'Jennifer Doudna', 'per-page': 1}
)
author_id = author_response['results'][0]['id'].split('/')[-1]
# Step 2: Get works
works = client.search_works(
filter_params={"authorships.author.id": author_id}
)
3. Find Works from Institution
Use for: Analyzing research output from universities or organizations
from scripts.query_helpers import find_institution_works
works = find_institution_works(
institution_name="Stanford University",
client=client,
limit=200
)
4. Highly Cited Papers
Use for: Finding influential papers in a field
from scripts.query_helpers import find_highly_cited_recent_papers
papers = find_highly_cited_recent_papers(
topic="quantum computing",
years=">2020",
client=client,
limit=100
)
5. Open Access Papers
Use for: Finding freely available research
from scripts.query_helpers import get_open_access_papers
papers = get_open_access_papers(
search_term="climate change",
client=client,
oa_status="any", # or "gold", "green", "hybrid", "bronze"
limit=200
)
6. Publication Trends Analysis
Use for: Tracking research output over time
from scripts.query_helpers import get_publication_trends
trends = get_publication_trends(
search_term="artificial intelligence",
filter_params={"is_oa": "true"},
client=client
)
# Sort and display
for trend in sorted(trends, key=lambda x: x['key'])[-10:]:
print(f"{trend['key']}: {trend['count']} publications")
7. Research Output Analysis
Use for: Comprehensive analysis of author or institution research
from scripts.query_helpers import analyze_research_output
analysis = analyze_research_output(
entity_type='institution', # or 'author'
entity_name='MIT',
client=client,
years='>2020'
)
print(f"Total works: {analysis['total_works']}")
print(f"Open access: {analysis['open_access_percentage']}%")
print(f"Top topics: {analysis['top_topics'][:5]}")
8. Batch Lookups
Use for: Getting information for multiple DOIs, ORCIDs, or IDs efficiently
dois = [
"https://doi.org/10.1038/s41586-021-03819-2",
"https://doi.org/10.1126/science.abc1234",
# ... up to 50 DOIs
]
works = client.batch_lookup(
entity_type='works',
ids=dois,
id_field='doi'
)
9. Random Sampling
Use for: Getting representative samples for analysis
# Small sample
works = client.sample_works(
sample_size=100,
seed=42, # For reproducibility
filter_params={"publication_year": "2023"}
)
# Large sample (>10k) - automatically handles multiple requests
works = client.sample_works(
sample_size=25000,
seed=42,
filter_params={"is_oa": "true"}
)
10. Citation Analysis
Use for: Finding papers that cite a specific work
# Get the work
work = client.get_entity('works', 'https://doi.org/10.1038/s41586-021-03819-2')
# Get citing papers using cited_by_api_url
import requests
citing_response = requests.get(
work['cited_by_api_url'],
params={'mailto': client.email, 'per-page': 200}
)
citing_works = citing_response.json()['results']
11. Topic and Subject Analysis
Use for: Understanding research focus areas
# Get top topics for an institution
topics = client.group_by(
entity_type='works',
group_field='topics.id',
filter_params={
"authorships.institutions.id": "I136199984", # MIT
"publication_year": ">2020"
}
)
for topic in topics[:10]:
print(f"{topic['key_display_name']}: {topic['count']} works")
12. Large-Scale Data Extraction
Use for: Downloading large datasets for analysis
# Paginate through all results
all_papers = client.paginate_all(
endpoint='/works',
params={
'search': 'synthetic biology',
'filter': 'publication_year:2020-2024'
},
max_results=10000
)
# Export to CSV
import csv
with open('papers.csv', 'w', newline='', encoding='utf-8') as f:
writer = csv.writer(f)
writer.writerow(['Title', 'Year', 'Citations', 'DOI', 'OA Status'])
for paper in all_papers:
writer.writerow([
paper.get('title', 'N/A'),
paper.get('publication_year', 'N/A'),
paper.get('cited_by_count', 0),
paper.get('doi', 'N/A'),
paper.get('open_access', {}).get('oa_status', 'closed')
])
Critical Best Practices
Always Use Email for Polite Pool
Add email to get 10x rate limit (1 req/sec → 10 req/sec):
client = OpenAlexClient(email="[email protected]")
Use Two-Step Pattern for Entity Lookups
Never filter by entity names directly - always get ID first:
# ✅ Correct
# 1. Search for entity → get ID
# 2. Filter by ID
# ❌ Wrong
# filter=author_name:Einstein # This doesn't work!
Use Maximum Page Size
Always use per-page=200 for efficient data retrieval:
results = client.search_works(search="topic", per_page=200)
Batch Multiple IDs
Use batch_lookup() for multiple IDs instead of individual requests:
# ✅ Correct - 1 request for 50 DOIs
works = client.batch_lookup('works', doi_list, 'doi')
# ❌ Wrong - 50 separate requests
for doi in doi_list:
work = client.get_entity('works', doi)
Use Sample Parameter for Random Data
Use sample_works() with seed for reproducible random sampling:
# ✅ Correct
works = client.sample_works(sample_size=100, seed=42)
# ❌ Wrong - random page numbers bias results
# Using random page numbers doesn't give true random sample
Select Only Needed Fields
Reduce response size by selecting specific fields:
results = client.search_works(
search="topic",
select=['id', 'title', 'publication_year', 'cited_by_count']
)
Common Filter Patterns
Date Ranges
# Single year
filter_params={"publication_year": "2023"}
# After year
filter_params={"publication_year": ">2020"}
# Range
filter_params={"publication_year": "2020-2024"}
Multiple Filters (AND)
# All conditions must match
filter_params={
"publication_year": ">2020",
"is_oa": "true",
"cited_by_count": ">100"
}
Multiple Values (OR)
# Any institution matches
filter_params={
"authorships.institutions.id": "I136199984|I27837315" # MIT or Harvard
}
Collaboration (AND within attribute)
# Papers with authors from BOTH institutions
filter_params={
"authorships.institutions.id": "I136199984+I27837315" # MIT AND Harvard
}
Negation
# Exclude type
filter_params={
"type": "!paratext"
}
Entity Types
OpenAlex provides these entity types:
- works - Scholarly documents (articles, books, datasets)
- authors - Researchers with disambiguated identities
- institutions - Universities and research organizations
- sources - Journals, repositories, conferences
- topics - Subject classifications
- publishers - Publishing organizations
- funders - Funding agencies
Access any entity type using consistent patterns:
client.search_works(...)
client.get_entity('authors', author_id)
client.group_by('works', 'topics.id', filter_params={...})
External IDs
Use external identifiers directly:
# DOI for works
work = client.get_entity('works', 'https://doi.org/10.7717/peerj.4375')
# ORCID for authors
author = client.get_entity('authors', 'https://orcid.org/0000-0003-1613-5981')
# ROR for institutions
institution = client.get_entity('institutions', 'https://ror.org/02y3ad647')
# ISSN for sources
source = client.get_entity('sources', 'issn:0028-0836')
Reference Documentation
Detailed API Reference
See references/api_guide.md for:
- Complete filter syntax
- All available endpoints
- Response structures
- Error handling
- Performance optimization
- Rate limiting details
Common Query Examples
See references/common_queries.md for:
- Complete working examples
- Real-world use cases
- Complex query patterns
- Data export workflows
- Multi-step analysis procedures
Scripts
openalex_client.py
Main API client with:
- Automatic rate limiting
- Exponential backoff retry logic
- Pagination support
- Batch operations
- Error handling
Use for direct API access with full control.
query_helpers.py
High-level helper functions for common operations:
find_author_works() - Get papers by authorfind_institution_works() - Get papers from institutionfind_highly_cited_recent_papers() - Get influential papersget_open_access_papers() - Find OA publicationsget_publication_trends() - Analyze trends over timeanalyze_research_output() - Comprehensive analysis
Use for common research queries with simplified interfaces.
Troubleshooting
Rate Limiting
If encountering 403 errors:
- Ensure email is added to requests
- Verify not exceeding 10 req/sec
- Client automatically implements exponential backoff
Empty Results
If searches return no results:
- Check filter syntax (see
references/api_guide.md)
- Use two-step pattern for entity lookups (don't filter by names)
- Verify entity IDs are correct format
Timeout Errors
For large queries:
- Use pagination with
per-page=200
- Use
select= to limit returned fields
- Break into smaller queries if needed
Rate Limits
- Default: 1 request/second, 100k requests/day
- Polite pool (with email): 10 requests/second, 100k requests/day
Always use polite pool for production workflows by providing email to client.
Notes
- No authentication required
- All data is open and free
- Rate limits apply globally, not per IP
- Use LitLLM with OpenRouter if LLM-based analysis is needed (don't use Perplexity API directly)
- Client handles pagination, retries, and rate limiting automatically
