Open Scholarly Evidence is a paid API for AI agents from foundry-open-scholarly-evidence-mainnet.inference-chip-index.workers.dev, paid per call via x402, $0.012/call, status unknown (last checked 2026-09-14).
Compares a single DOI across Crossref and DataCite registries and returns attributed, open-metadata agreement evidence for that scholarly work.
Compare one supplied DOI across Crossref and DataCite and return bounded, attributed open-metadata agreement evidence.
A structured comparison of the metadata for the supplied DOI as recorded in both Crossref and DataCite, including areas of agreement and discrepancy, attribution details, and bounded open-metadata evidence sourced from both registries.
POSThttps://foundry-open-scholarly-evidence-mainnet.inference-chip-index.workers.dev/v1/scholarly/evidenceChoose this endpoint when you need open, attributed cross-registry metadata evidence for a specific scholarly DOI and want to see how Crossref and DataCite records compare in a single call. It is ideal for metadata auditing, citation verification, and open-access compliance checks where dual-registry agreement matters. Prefer alternatives if you only need a simple DOI resolution or full-text retrieval rather than cross-registry metadata comparison.
| Field | Type | Description |
|---|---|---|
| doi | string |
{
"type": "json",
"example": {
"caveats": [
"Open metadata evidence only; not peer-review, identity, authorship, originality or legal verification.",
"Source absence or disagreement must not be interpreted as fraud or invalidity."
],
"matches": {
"doi": true,
"year": true,
"title": true
},
"product": "open-scholarly-evidence",
"sources": [
{
"doi": "10.5555/example",
"type": "journal-article",
"year": 2026,
"title": "Evidence: A Study",
"source": "crossref",
"status": "FOUND"
},
{
"doi": "10.5555/example",
"type": "JournalArticle",
"year": 2026,
"title": "Evidence - A Study",
"source": "datacite",
"status": "FOUND"
}
],
"version": "1.0.0",
"queriedDoi": "10.5555/example",
"disposition": "CONFIRMED",
"evidenceFingerprint": "0000000000000000000000000000000000000000000000000000000000000000"
}
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