Crossref Event Data API
Overview
Crossref Event Data tracks where scholarly publications are discussed, shared, and referenced across the open web — Wikipedia citations, Twitter/X mentions, Reddit posts, blog references, policy document citations, and more. Unlike traditional citation counts, Event Data captures real-time online attention to research. Free, no authentication required.
API Endpoints
Base URL
https://api.eventdata.crossref.org/v1
Query Events
# Get events for a specific DOI
curl "https://api.eventdata.crossref.org/v1/events?obj-id=10.1038/nature14539&rows=20"
# Filter by source
curl "https://api.eventdata.crossref.org/v1/events?\
obj-id=10.1038/nature14539&source=wikipedia"
# Filter by date range
curl "https://api.eventdata.crossref.org/v1/events?\
from-occurred-date=2024-01-01&until-occurred-date=2024-12-31&source=twitter&rows=100"
# Get events about a DOI prefix (publisher level)
curl "https://api.eventdata.crossref.org/v1/events?obj-id.prefix=10.1371&rows=50"
# Events from a specific source
curl "https://api.eventdata.crossref.org/v1/events?source=reddit&rows=50"
Event Sources
| Source |
Description |
What it tracks |
wikipedia |
Wikipedia article references |
DOIs cited in Wikipedia |
twitter |
Twitter/X posts |
Tweets linking to DOIs |
reddit |
Reddit posts/comments |
Reddit links to papers |
hypothesis |
Hypothesis annotations |
Web annotations on papers |
newsfeed |
News articles |
Media coverage of research |
stackexchange |
Stack Exchange Q&A |
Technical discussions |
web |
General web pages |
Blog posts, reports |
wordpressdotcom |
WordPress blogs |
Blog references |
datacite |
DataCite DOIs |
Dataset-paper linkages |
crossref |
Crossref metadata |
Reference list updates |
Query Parameters
| Parameter |
Description |
Example |
obj-id |
DOI of the paper |
obj-id=10.1038/nature14539 |
obj-id.prefix |
DOI prefix (publisher) |
obj-id.prefix=10.1371 |
source |
Event source |
source=wikipedia |
from-occurred-date |
Events from date |
2024-01-01 |
until-occurred-date |
Events until date |
2024-12-31 |
rows |
Results per page (max 10000) |
rows=100 |
cursor |
Pagination cursor |
Returned in response |
Response Structure
{
"status": "ok",
"message-type": "event-list",
"message": {
"total-results": 245,
"events": [
{
"obj_id": "https://doi.org/10.1038/nature14539",
"source_id": "wikipedia",
"subj_id": "https://en.wikipedia.org/wiki/Deep_learning",
"relation_type_id": "references",
"occurred_at": "2024-03-15T10:30:00Z",
"subj": {
"title": "Deep learning - Wikipedia",
"url": "https://en.wikipedia.org/wiki/Deep_learning"
}
}
],
"next-cursor": "abc123..."
}
}
Python Usage
import requests
from collections import Counter
BASE_URL = "https://api.eventdata.crossref.org/v1"
def get_events(doi: str, source: str = None,
rows: int = 100) -> list:
"""Get Event Data events for a DOI."""
params = {"obj-id": doi, "rows": rows}
if source:
params["source"] = source
resp = requests.get(f"{BASE_URL}/events", params=params)
resp.raise_for_status()
data = resp.json()
events = []
for ev in data.get("message", {}).get("events", []):
events.append({
"source": ev.get("source_id"),
"subject_url": ev.get("subj_id"),
"subject_title": ev.get("subj", {}).get("title", ""),
"relation": ev.get("relation_type_id"),
"date": ev.get("occurred_at", "")[:10],
})
return events
def get_attention_summary(doi: str) -> dict:
"""Summarize online attention for a paper."""
events = get_events(doi, rows=10000)
source_counts = Counter(e["source"] for e in events)
return {
"total_events": len(events),
"by_source": dict(source_counts),
"first_event": min((e["date"] for e in events), default=None),
"latest_event": max((e["date"] for e in events), default=None),
}
def find_wikipedia_citations(doi: str) -> list:
"""Find Wikipedia articles that cite a paper."""
events = get_events(doi, source="wikipedia")
return [
{"wikipedia_page": e["subject_title"],
"url": e["subject_url"],
"date": e["date"]}
for e in events
if e["relation"] == "references"
]
# Example: analyze online attention for a paper
doi = "10.1038/nature14539"
summary = get_attention_summary(doi)
print(f"Total events: {summary['total_events']}")
for source, count in sorted(summary["by_source"].items(),
key=lambda x: -x[1]):
print(f" {source}: {count}")
# Example: find Wikipedia coverage
wiki_refs = find_wikipedia_citations(doi)
for ref in wiki_refs:
print(f"Cited in: {ref['wikipedia_page']} ({ref['date']})")
Use Cases
- Altmetrics research: Measure non-traditional scholarly impact
- Public engagement: Track how research reaches public audiences
- Policy monitoring: Discover when research informs policy documents
- Social media analytics: Track paper sharing on Twitter, Reddit
- Wikipedia coverage: Find which papers are cited in encyclopedias
References