openalex-database — Открытый доступ к базе научных публикаций

--- name: openalex-database description: Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries. license: Unknown metadata: skill-author: K-Dense Inc. --- # 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): ```python from scripts.openalex_client import OpenAlexClient client = OpenAlexClient(email="your-email@example.edu") ``` ### Installation Requirements Install required package using uv: ```bash 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 ```python # 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): ```python from scripts.query_helpers import find_author_works works = find_author_works( author_name="Jennifer Doudna", client=client, limit=100 ) ``` **Manual two-step approach**: ```python # 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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): ```python client = OpenAlexClient(email="your-email@example.edu") ``` ### Use Two-Step Pattern for Entity Lookups Never filter by entity names directly - always get ID first: ```python # ✅ 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: ```python results = client.search_works(search="topic", per_page=200) ``` ### Batch Multiple IDs Use batch_lookup() for multiple IDs instead of individual requests: ```python # ✅ 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: ```python # ✅ 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: ```python results = client.search_works( search="topic", select=['id', 'title', 'publication_year', 'cited_by_count'] ) ``` ## Common Filter Patterns ### Date Ranges ```python # Single year filter_params={"publication_year": "2023"} # After year filter_params={"publication_year": ">2020"} # Range filter_params={"publication_year": "2020-2024"} ``` ### Multiple Filters (AND) ```python # All conditions must match filter_params={ "publication_year": ">2020", "is_oa": "true", "cited_by_count": ">100" } ``` ### Multiple Values (OR) ```python # Any institution matches filter_params={ "authorships.institutions.id": "I136199984|I27837315" # MIT or Harvard } ``` ### Collaboration (AND within attribute) ```python # Papers with authors from BOTH institutions filter_params={ "authorships.institutions.id": "I136199984+I27837315" # MIT AND Harvard } ``` ### Negation ```python # 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: ```python client.search_works(...) client.get_entity('authors', author_id) client.group_by('works', 'topics.id', filter_params={...}) ``` ## External IDs Use external identifiers directly: ```python # 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: 1. Ensure email is added to requests 2. Verify not exceeding 10 req/sec 3. Client automatically implements exponential backoff ### Empty Results If searches return no results: 1. Check filter syntax (see `references/api_guide.md`) 2. Use two-step pattern for entity lookups (don't filter by names) 3. Verify entity IDs are correct format ### Timeout Errors For large queries: 1. Use pagination with `per-page=200` 2. Use `select=` to limit returned fields 3. 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