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="your-email@example.edu")

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" )

1. 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} )

1. 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 )

1. 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 )

1. 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 )

1. 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")

1. 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]}")

1. 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' )

1. 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"} )

1. 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']

1. 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")

1. 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="your-email@example.edu")

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 author find_institution_works() - Get papers from institution find_highly_cited_recent_papers() - Get influential papers get_open_access_papers() - Find OA publications get_publication_trends() - Analyze trends over time analyze_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