OpenAlex Scholarly Database
Overview
OpenAlex is a free, open-access index of 250M+ scholarly works, 90M+ authors, 110,000+ journals, and 10,000+ institutions. It succeeds Microsoft Academic Graph and provides rich metadata: abstracts, open-access URLs, citation counts, referenced works, author disambiguated IDs (ORCID), and concept tags. The REST API requires no authentication for up to 100,000 requests/day; a polite pool (email parameter) gives priority processing.
When to Use
- Building systematic literature review corpora by searching across all academic disciplines (not just biomedical)
- Retrieving citation networks for bibliometric analysis, co-citation clustering, or reference graph traversal
- Disambiguating author identities across institutions using ORCID/OpenAlex author IDs
- Finding open-access full-text URLs for a set of DOIs to build downloadable paper corpora
- Analyzing publication trends by year, institution, country, or research concept
- Enriching a paper list with metadata (citation count, abstract, venue) from DOIs or titles
- For PubMed-indexed biomedical literature use
pubmed-database; for bioRxiv preprints usebiorxiv-database
Prerequisites
- Python packages:
requests,pandas - Data requirements: DOIs, OpenAlex Work IDs (W…), author names, ORCID IDs, or search terms
- Environment: internet connection; no API key required
- Rate limits: 10 req/s anonymous; add
mailto=your@email.comquery param to join polite pool (higher priority, same limit)
pip install requests pandasQuick Start
import requests
BASE = "https://api.openalex.org"
# Search for works on CRISPR
r = requests.get(f"{BASE}/works",
params={"search": "CRISPR gene editing",
"filter": "publication_year:2023",
"per_page": 5,
"mailto": "your@email.com"})
r.raise_for_status()
data = r.json()
print(f"Total results: {data['meta']['count']}")
for work in data["results"][:3]:
print(f" {work['title'][:80]} ({work['publication_year']}) cites={work['cited_by_count']}")Core API
Query 1: Works Search
Search works by title/abstract keywords with filters.
import requests, pandas as pd
BASE = "https://api.openalex.org"
def search_works(query, filters=None, per_page=25, mailto="your@email.com"):
params = {"search": query, "per_page": per_page, "mailto": mailto}
if filters:
params["filter"] = ",".join(f"{k}:{v}" for k, v in filters.items())
r = requests.get(f"{BASE}/works", params=params)
r.raise_for_status()
return r.json()
# Search with filters
data = search_works("single-cell RNA sequencing",
filters={"publication_year": "2020-2024",
"open_access.is_oa": "true"},
per_page=10)
print(f"Open-access scRNA-seq papers 2020-2024: {data['meta']['count']}")
rows = []
for w in data["results"]:
rows.append({
"title": w["title"],
"year": w["publication_year"],
"citations": w["cited_by_count"],
"doi": w.get("doi"),
"oa_url": w.get("open_access", {}).get("oa_url"),
})
df = pd.DataFrame(rows)
print(df[["title", "year", "citations"]].head())# Paginate through all results
def paginate_works(query, filters=None, max_results=200, mailto="your@email.com"):
"""Retrieve up to max_results works, paginating automatically."""
all_results = []
cursor = "*"
while len(all_results) < max_results:
params = {"search": query, "per_page": 200,
"cursor": cursor, "mailto": mailto}
if filters:
params["filter"] = ",".join(f"{k}:{v}" for k, v in filters.items())
r = requests.get(f"{BASE}/works", params=params)
data = r.json()
all_results.extend(data["results"])
cursor = data["meta"].get("next_cursor")
if not cursor:
break
return all_results[:max_results]
papers = paginate_works("transformer protein structure", max_results=100)
print(f"Retrieved {len(papers)} papers")Query 2: Lookup by DOI or OpenAlex ID
Retrieve a single work by DOI or OpenAlex ID.
import requests
BASE = "https://api.openalex.org"
# By DOI
doi = "10.1038/s41592-019-0458-z" # Scanpy paper
r = requests.get(f"{BASE}/works/https://doi.org/{doi}",
params={"mailto": "your@email.com"})
r.raise_for_status()
work = r.json()
print(f"Title : {work['title']}")
print(f"Year : {work['publication_year']}")
print(f"Citations: {work['cited_by_count']}")
print(f"Journal : {work.get('primary_location', {}).get('source', {}).get('display_name')}")
abstract = work.get("abstract_inverted_index")
if abstract:
# Reconstruct abstract from inverted index
words = {pos: word for word, positions in abstract.items() for pos in positions}
text = " ".join(words[i] for i in sorted(words))
print(f"Abstract (first 200): {text[:200]}")Query 3: Author Search and ORCID Lookup
Find author records, resolve ORCID identifiers, retrieve publication lists.
import requests, pandas as pd
BASE = "https://api.openalex.org"
# Search for an author
r = requests.get(f"{BASE}/authors",
params={"search": "Jennifer Doudna",
"per_page": 5,
"mailto": "your@email.com"})
authors = r.json()["results"]
for a in authors[:3]:
print(f"Author: {a['display_name']}")
print(f" OpenAlex ID : {a['id']}")
print(f" ORCID : {a.get('orcid', 'n/a')}")
# 2024+: singular `last_known_institution` was replaced by plural list `last_known_institutions[0]`
insts = a.get("last_known_institutions") or []
print(f" Institution : {insts[0]['display_name'] if insts else 'n/a'}")
print(f" Works count : {a['works_count']}")
print(f" h-index : {a['summary_stats'].get('h_index', 'n/a')}")
print()# Get all papers by an author (by ORCID)
orcid = "0000-0001-9161-999X" # Jennifer A. Doudna (correct ORCID; the 8742-3594 variant 404s)
r = requests.get(f"{BASE}/works",
params={"filter": f"author.orcid:{orcid}",
"sort": "cited_by_count:desc",
"per_page": 10,
"mailto": "your@email.com"})
papers = r.json()["results"]
for p in papers[:5]:
print(f" [{p['publication_year']}] {p['title'][:70]} (cites: {p['cited_by_count']})")Query 4: Citation Network Retrieval
Get referenced works and citing works for a paper.
import requests, pandas as pd
BASE = "https://api.openalex.org"
work_id = "W2018426904" # CRISPR paper
# Get what this paper references
r = requests.get(f"{BASE}/works/{work_id}",
params={"select": "referenced_works,cited_by_count,title",
"mailto": "your@email.com"})
work = r.json()
ref_ids = work.get("referenced_works", [])
print(f"'{work['title']}' cites {len(ref_ids)} papers")
print(f"Total citations: {work['cited_by_count']}")
# Fetch metadata for references (batch)
if ref_ids:
ids_str = "|".join(id.split("/")[-1] for id in ref_ids[:10])
r2 = requests.get(f"{BASE}/works",
params={"filter": f"openalex_id:{ids_str}",
"per_page": 10,
"mailto": "your@email.com"})
refs = r2.json()["results"]
for ref in refs[:5]:
print(f" [{ref['publication_year']}] {ref['title'][:70]}")Query 5: Concept/Topic Filtering and Trend Analysis
Filter by research concepts and analyze publication trends.
import requests, pandas as pd
BASE = "https://api.openalex.org"
# Get concept ID for "Machine Learning". OpenAlex concept search is brittle for
# multi-word phrases ("machine learning biology" returns 0); use the single core term.
r = requests.get(f"{BASE}/concepts",
params={"s