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Zhongwei Li
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skills/openalex-database/references/common_queries.md
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skills/openalex-database/references/common_queries.md
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# Common OpenAlex Query Examples
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This document provides practical examples for common research queries using OpenAlex.
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## Finding Papers by Author
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**User query**: "Find papers by Albert Einstein"
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**Approach**: Two-step pattern
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1. Search for author to get ID
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2. Filter works by author ID
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**Python example**:
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```python
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from scripts.openalex_client import OpenAlexClient
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from scripts.query_helpers import find_author_works
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client = OpenAlexClient(email="your-email@example.edu")
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works = find_author_works("Albert Einstein", client, limit=100)
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for work in works:
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print(f"{work['title']} ({work['publication_year']})")
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```
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## Finding Papers from an Institution
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**User query**: "What papers has MIT published in the last year?"
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**Approach**: Two-step pattern with date filter
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1. Search for institution to get ID
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2. Filter works by institution ID and year
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**Python example**:
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```python
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from scripts.query_helpers import find_institution_works
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works = find_institution_works("MIT", client, limit=200)
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# Filter for recent papers
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import datetime
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current_year = datetime.datetime.now().year
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recent_works = [w for w in works if w['publication_year'] == current_year]
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```
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## Highly Cited Papers on a Topic
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**User query**: "Find the most cited papers on CRISPR from the last 5 years"
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**Approach**: Search + filter + sort
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**Python example**:
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```python
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works = client.search_works(
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search="CRISPR",
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filter_params={
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"publication_year": ">2019"
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},
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sort="cited_by_count:desc",
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per_page=100
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)
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for work in works['results']:
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title = work['title']
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citations = work['cited_by_count']
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year = work['publication_year']
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print(f"{title} ({year}): {citations} citations")
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```
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## Open Access Papers on a Topic
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**User query**: "Find open access papers about climate change"
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**Approach**: Search + OA filter
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**Python example**:
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```python
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from scripts.query_helpers import get_open_access_papers
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papers = get_open_access_papers(
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search_term="climate change",
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client=client,
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oa_status="any", # or "gold", "green", "hybrid", "bronze"
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limit=200
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)
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for paper in papers:
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print(f"{paper['title']}")
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print(f" OA Status: {paper['open_access']['oa_status']}")
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print(f" URL: {paper['open_access']['oa_url']}")
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```
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## Publication Trends Analysis
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**User query**: "Show me publication trends for machine learning over the years"
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**Approach**: Use group_by to aggregate by year
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**Python example**:
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```python
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from scripts.query_helpers import get_publication_trends
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trends = get_publication_trends(
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search_term="machine learning",
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client=client
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)
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# Sort by year
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trends_sorted = sorted(trends, key=lambda x: x['key'])
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for trend in trends_sorted[-10:]: # Last 10 years
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year = trend['key']
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count = trend['count']
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print(f"{year}: {count} publications")
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```
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## Analyzing Research Output
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**User query**: "Analyze the research output of Stanford University from 2020-2024"
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**Approach**: Multiple aggregations for comprehensive analysis
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**Python example**:
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```python
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from scripts.query_helpers import analyze_research_output
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analysis = analyze_research_output(
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entity_type='institution',
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entity_name='Stanford University',
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client=client,
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years='2020-2024'
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)
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print(f"Institution: {analysis['entity_name']}")
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print(f"Total works: {analysis['total_works']}")
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print(f"Open access: {analysis['open_access_percentage']}%")
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print("\nTop topics:")
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for topic in analysis['top_topics'][:5]:
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print(f" - {topic['key_display_name']}: {topic['count']} works")
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```
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## Finding Papers by DOI (Batch)
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**User query**: "Get information for these 10 DOIs: ..."
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**Approach**: Batch lookup with pipe separator
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**Python example**:
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```python
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dois = [
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"https://doi.org/10.1371/journal.pone.0266781",
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"https://doi.org/10.1371/journal.pone.0267149",
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"https://doi.org/10.1038/s41586-021-03819-2",
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# ... up to 50 DOIs
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]
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works = client.batch_lookup(
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entity_type='works',
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ids=dois,
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id_field='doi'
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)
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for work in works:
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print(f"{work['title']} - {work['publication_year']}")
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```
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## Random Sample of Papers
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**User query**: "Give me 50 random papers from 2023"
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**Approach**: Use sample parameter with seed for reproducibility
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**Python example**:
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```python
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works = client.sample_works(
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sample_size=50,
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seed=42, # For reproducibility
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filter_params={
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"publication_year": "2023",
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"is_oa": "true"
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}
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)
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print(f"Got {len(works)} random papers from 2023")
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```
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## Papers from Multiple Institutions
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**User query**: "Find papers with authors from both MIT and Stanford"
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**Approach**: Use + operator for AND within same attribute
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**Python example**:
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```python
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# First, get institution IDs
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mit_response = client._make_request(
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'/institutions',
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params={'search': 'MIT', 'per-page': 1}
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)
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mit_id = mit_response['results'][0]['id'].split('/')[-1]
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stanford_response = client._make_request(
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'/institutions',
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params={'search': 'Stanford', 'per-page': 1}
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)
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stanford_id = stanford_response['results'][0]['id'].split('/')[-1]
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# Find works with authors from both institutions
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works = client.search_works(
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filter_params={
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"authorships.institutions.id": f"{mit_id}+{stanford_id}"
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},
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per_page=100
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)
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print(f"Found {works['meta']['count']} collaborative papers")
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```
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## Papers in a Specific Journal
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**User query**: "Get all papers from Nature published in 2023"
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**Approach**: Two-step - find journal ID, then filter works
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**Python example**:
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```python
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# Step 1: Find journal source ID
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source_response = client._make_request(
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'/sources',
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params={'search': 'Nature', 'per-page': 1}
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)
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source = source_response['results'][0]
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source_id = source['id'].split('/')[-1]
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print(f"Found journal: {source['display_name']} (ID: {source_id})")
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# Step 2: Get works from that source
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works = client.search_works(
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filter_params={
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"primary_location.source.id": source_id,
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"publication_year": "2023"
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},
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per_page=200
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)
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print(f"Found {works['meta']['count']} papers from Nature in 2023")
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```
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## Topic Analysis by Institution
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**User query**: "What topics does MIT research most?"
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**Approach**: Filter by institution, group by topics
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**Python example**:
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```python
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# Get MIT ID
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inst_response = client._make_request(
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'/institutions',
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params={'search': 'MIT', 'per-page': 1}
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)
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mit_id = inst_response['results'][0]['id'].split('/')[-1]
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# Group by topics
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topics = client.group_by(
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entity_type='works',
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group_field='topics.id',
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filter_params={
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"authorships.institutions.id": mit_id,
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"publication_year": ">2020"
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}
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)
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print("Top research topics at MIT (2020+):")
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for i, topic in enumerate(topics[:10], 1):
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print(f"{i}. {topic['key_display_name']}: {topic['count']} works")
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```
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## Citation Analysis
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**User query**: "Find papers that cite this specific DOI"
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**Approach**: Get work by DOI, then use cited_by_api_url
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**Python example**:
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```python
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# Get the work
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doi = "https://doi.org/10.1038/s41586-021-03819-2"
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work = client.get_entity('works', doi)
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# Get papers that cite it
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cited_by_url = work['cited_by_api_url']
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# Extract just the query part and use it
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import requests
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response = requests.get(cited_by_url, params={'mailto': client.email})
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citing_works = response.json()
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print(f"{work['title']}")
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print(f"Total citations: {work['cited_by_count']}")
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print(f"\nRecent citing papers:")
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for citing_work in citing_works['results'][:5]:
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print(f" - {citing_work['title']} ({citing_work['publication_year']})")
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```
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## Large-Scale Data Extraction
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**User query**: "Get all papers on quantum computing from the last 3 years"
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**Approach**: Paginate through all results
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**Python example**:
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```python
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all_papers = client.paginate_all(
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endpoint='/works',
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params={
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'search': 'quantum computing',
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'filter': 'publication_year:2022-2024'
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},
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max_results=10000 # Limit to prevent excessive API calls
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)
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print(f"Retrieved {len(all_papers)} papers")
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# Save to CSV
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import csv
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with open('quantum_papers.csv', 'w', newline='') as f:
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writer = csv.writer(f)
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writer.writerow(['Title', 'Year', 'Citations', 'DOI', 'OA Status'])
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for paper in all_papers:
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writer.writerow([
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paper['title'],
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paper['publication_year'],
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paper['cited_by_count'],
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paper.get('doi', 'N/A'),
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paper['open_access']['oa_status']
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])
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```
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## Complex Multi-Filter Query
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**User query**: "Find recent, highly-cited, open access papers on AI from top institutions"
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**Approach**: Combine multiple filters
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**Python example**:
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```python
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# Get IDs for top institutions
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top_institutions = ['MIT', 'Stanford', 'Oxford']
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inst_ids = []
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for inst_name in top_institutions:
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response = client._make_request(
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'/institutions',
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params={'search': inst_name, 'per-page': 1}
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)
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if response['results']:
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inst_id = response['results'][0]['id'].split('/')[-1]
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inst_ids.append(inst_id)
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# Combine with pipe for OR
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inst_filter = '|'.join(inst_ids)
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# Complex query
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works = client.search_works(
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search="artificial intelligence",
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filter_params={
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"publication_year": ">2022",
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"cited_by_count": ">50",
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"is_oa": "true",
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"authorships.institutions.id": inst_filter
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},
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sort="cited_by_count:desc",
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per_page=200
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)
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print(f"Found {works['meta']['count']} papers matching criteria")
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for work in works['results'][:10]:
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print(f"{work['title']}")
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print(f" Citations: {work['cited_by_count']}, Year: {work['publication_year']}")
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```
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