/openalex-database

name: "openalex-database"
description: "Query OpenAlex REST API for 250M+ scholarly works, authors, institutions, journals, concepts. Search by keyword, author, DOI, ORCID, or ID; filter by year, OA, citations, field; retrieve citations, references, author disambiguation. Free, no auth. For PubMed use pubmed-database; preprints use biorxiv-database."
license: "CC0-1.0"

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 use `biorxiv-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.com` query param to join polite pool (higher priority, same limit)

pip install requests pandas

Quick 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'