Papers

Look up scholarly metadata, full text where licensing allows, and citation graph edges.

When to use

You have a DOI, title, or paper ID and want metadata
You need references or citing works for a known paper
You are enriching your own dataset

When not to use

You have a question rather than an identifier → searches
You need a formatted bibliography entry → citations

Endpoints

Method	Path	Scope	Description
GET	`/v1/papers/{id}`	`papers:read`	Retrieve by `pap_` ID or DOI
GET	`/v1/papers`	`papers:read`	Lookup by `doi`, `title`, or `query`
GET	`/v1/papers/{id}/full-text`	`papers:read`	Sectioned full text
GET	`/v1/papers/{id}/references`	`papers:read`	Outgoing references
GET	`/v1/papers/{id}/citations`	`papers:read`	Incoming citations

The paper object

json

{
  "object": "paper",
  "id": "pap_3kf9wq2m8x",
  "doi": "10.48550/arXiv.1706.03762",
  "title": "Attention Is All You Need",
  "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks…",
  "tldr": "Replaces recurrence and convolution with self-attention alone, training faster and scoring higher on translation.",
  "publication_date": "2017-06-12",
  "publication_type": "preprint",
  "language": "en",
  "open_access": {
    "is_open_access": true,
    "status": "green",
    "licence": "arXiv perpetual non-exclusive",
    "pdf_url": "https://arxiv.org/pdf/1706.03762"
  },
  "is_retracted": false,
  "citation_count": 142387,
  "url": "https://scispace.com/papers/pap_3kf9wq2m8x",
  "venue": {
    "name": "arXiv",
    "type": "preprint_server",
    "issn": null,
    "publisher": "Cornell University"
  },
  "volume": null,
  "issue": null,
  "pages": null,
  "authors": [
    {
      "name": "Ashish Vaswani",
      "orcid": null,
      "affiliation": { "name": "Google Brain", "country": "US", "ror": "https://ror.org/00njsd438" }
    },
    {
      "name": "Noam Shazeer",
      "orcid": null,
      "affiliation": { "name": "Google Brain", "country": "US", "ror": "https://ror.org/00njsd438" }
    }
    …
  ],
  "subjects": ["Computer Science", "Machine Learning", "Computation and Language"]
}

Fields

Field	Type	Description
`id`	string	`pap_`-prefixed identifier. Stable; use it rather than the DOI as your key
`doi`	string	Canonical DOI. `null` for works that have none
`title`	string	As published
`abstract`	string	Publisher abstract. `null` where licensing forbids redistribution
`tldr`	string	One-sentence machine summary. Not a substitute for the abstract
`publication_date`	string	`YYYY-MM-DD`; day precision may be approximate for older works
`publication_type`	enum	`journal_article`, `preprint`, `conference_paper`, `book_chapter`, `thesis`, `report`
`language`	string	BCP-47
`open_access.is_open_access`	boolean	Whether a legally readable full text exists
`open_access.status`	enum	`gold`, `green`, `hybrid`, `bronze`, `closed`
`open_access.licence`	string	e.g. `CC-BY-4.0`. Governs what you may store — see content-licensing
`open_access.pdf_url`	string	Publisher-hosted PDF. `null` when closed
`is_retracted`	boolean	Check this before citing. Retraction propagates within 7 days
`citation_count`	integer	Inbound citations. Refreshed weekly
`venue`	object	`name`, `type`, `issn`, `publisher`
`volume` / `issue` / `pages`	string	Bibliographic locators. `null` for preprints
`authors[]`	array	Ordered as published. `affiliation` may be `null`
`authors[].affiliation.ror`	string	ROR identifier, for disambiguating institutions
`subjects[]`	array	Field-of-study labels, broadest first

Retracted work stays in the corpus

It is returned with is_retracted: true rather than removed, so citations to it can still resolve. Filter it out of anything user-facing.

Redistribution limits apply

Full text and abstracts are licensed content. What you may store, cache, or display is governed by content-licensing.

Full text availability

Full text is returned only where we are licensed to serve it. Check open_access.is_open_access before requesting it.

Case	Response
Open access, PDF held	`200` with sections
Licensed via publisher agreement	`200` with sections; `licence` field states the restriction
Not licensed to you	`403 full_text_not_licensed`
No PDF anywhere	`404 full_text_unavailable`

Sections are returned as an ordered array of { heading, level, text }. Reference lists, figures, and tables are separate arrays so you do not have to parse them out of the body.

All paper reads are free (credit), so check availability before you build a fallback.

Errors

Status	`code`	When
404	`paper_not_found`	unknown ID or DOI
403	`full_text_not_licensed`	full text exists but is not available to you

paper · citations · content-licensing · migrate-from-openalex

Last updated 2026-08-10