Crossref Scholarly Metadata Scraper

Here is one real result, with every field the actor returns (this record has all three AI add-ons enabled; authorsDetailed is trimmed with a note, values real; aiSummary is null because this work has no abstract to summarize):

{
  "doi": "10.1007/978-981-16-1212-1_4",
  "title": "Graphene Field-Effect Transistor Biosensor",
  "type": "book-chapter",
  "publisher": "Springer Singapore",
  "containerTitle": "Graphene Field-Effect Transistor Biosensors",
  "publishedYear": 2021,
  "authors": ["Shiyu Wang", "Zakir Hossain", "Yan Zhao", "Tao Han"],
  "authorsDetailed": [
    { "name": "Shiyu Wang", "given": "Shiyu", "family": "Wang", "orcid": null, "affiliation": [], "sequence": "first" },
    { "name": "Zakir Hossain", "given": "Zakir", "family": "Hossain", "orcid": null, "affiliation": [], "sequence": "additional" }
  ],
  "abstract": null,
  "issn": [],
  "isbn": ["9789811612114", "9789811612121"],
  "volume": null,
  "issue": null,
  "page": "45-67",
  "subjects": [],
  "language": "en",
  "licenseUrl": "http://www.springer.com/tdm",
  "fullTextLinks": ["https://link.springer.com/content/pdf/10.1007/978-981-16-1212-1_4"],
  "funders": [],
  "citationCount": 1,
  "referencesCount": 62,
  "url": "https://doi.org/10.1007/978-981-16-1212-1_4",
  "source": "Crossref",
  "observedAt": "2026-08-14T05:55:46.126Z",
  "aiSummary": null,
  "aiKeywords": ["Graphene", "Field-Effect Transistor", "Biosensor", "Nanotechnology", "Electronics", "Sensing", "Materials Science", "Device Fabrication", "Biomedical Applications", "Transistor Technology"],
  "aiField": "Nanotechnology"
}

The most complete Crossref scholarly-metadata scraper available. It returns every field the Crossref works API exposes, including DOI, title, abstract, journal, ISSN/ISBN, authors with ORCID and affiliation, funders, license, full-text links and citation count, plus three optional AI add-ons, and searches the entire scholarly index by keyword.

📥 Input · 📤 Output · 💰 Pricing · ▶️ Examples

Table of contents

• What it does
• Quickstart
• Input reference
• Output reference
• Example output record
• Run via API and CLI
• Fetch results
• Billing and limits
• FAQ and troubleshooting

What it does

The actor searches the public Crossref works index for your keyword, paginates through the matches, and writes one normalized record per scholarly work to the run's dataset. Authors are returned both as a flat name list and as detailed objects with ORCID and affiliation, dates are reduced to a publication year, abstracts are HTML stripped, and identifiers, funders, license and full-text links are surfaced as structured fields. Missing source values are returned as null or empty arrays, never invented.

Data covers the entire Crossref scholarly metadata index (journal articles, book chapters, conference papers, datasets and more). Three optional AI add-ons (withAiSummary, withAiKeywords, withAiField) can enrich each record; they are opt-in, billed only when they produce output, and disabled on free Apify plans.

Quickstart

Open the actor, paste this into the input, and press Run. It returns 3 works matching "graphene transistor".

{
  "searchQuery": "graphene transistor",
  "maxPapers": 3
}

Leave the AI add-ons off (default) for the raw metadata fields. With an empty input the actor searches climate change and returns the top 10 works.

Input reference

Field	Type	Required	Default	Description
searchQuery	string	no	climate change	Keyword to search works by title, author and other metadata, for example graphene, public health, machine learning.
maxPapers	integer	no	10	Maximum works to collect. Free Apify plans are capped at 10 per run.
withAiSummary	boolean	no	false	Add a 1-2 sentence AI summary from each abstract (skipped when no abstract). Paid add-on, disabled on free plans.
withAiKeywords	boolean	no	false	Extract 5-10 topical keywords via AI. Paid add-on, disabled on free plans.
withAiField	boolean	no	false	Classify the academic field/discipline via AI. Paid add-on, disabled on free plans.

Output reference

One dataset item per work. Types: string, number, string[], object[], or null/[] when the source value is absent.

Field	Type	Description
doi	string	Digital Object Identifier.
title	string	Work title.
type	string	Work type, for example journal-article, book-chapter.
publisher	string	Publisher name.
containerTitle	string	Journal or book title.
publishedYear	number	Publication year, or null.
authors	string[]	Flat list of author names.
authorsDetailed	object[]	Authors with name, given, family, orcid, affiliation, sequence.
abstract	string	Abstract, HTML stripped, or null.
issn	string[]	ISSN identifiers.
isbn	string[]	ISBN identifiers.
volume	string	Volume, or null.
issue	string	Issue, or null.
page	string	Page range, or null.
subjects	string[]	Subject categories.
language	string	Language code, or null.
licenseUrl	string	License URL, or null.
fullTextLinks	string[]	Full-text/PDF links.
funders	object[]	Funders with name, doi, awards.
citationCount	number	Times the work is cited (is-referenced-by count).
referencesCount	number	Number of references the work cites.
url	string	Resolvable DOI URL.
source	string	Constant source tag. Always Crossref.
observedAt	string	ISO 8601 timestamp of when the record was collected.
aiSummary	string	AI abstract summary. Present only when withAiSummary is enabled and an abstract exists.
aiKeywords	string[]	AI topical keywords. Present only when withAiKeywords is enabled.
aiField	string	AI field/discipline classification. Present only when withAiField is enabled.

On a failed run, a single item with a populated error field is written instead.

Example output record

Real record from a live run (input {"searchQuery":"graphene transistor","maxPapers":3}):

{
  "doi": "10.1007/978-981-16-1212-1_4",
  "title": "Graphene Field-Effect Transistor Biosensor",
  "type": "book-chapter",
  "publisher": "Springer Singapore",
  "containerTitle": "Graphene Field-Effect Transistor Biosensors",
  "publishedYear": 2021,
  "authors": ["Shiyu Wang", "Zakir Hossain", "Yan Zhao", "Tao Han"],
  "isbn": ["9789811612114", "9789811612121"],
  "page": "45-67",
  "language": "en",
  "licenseUrl": "http://www.springer.com/tdm",
  "fullTextLinks": ["https://link.springer.com/content/pdf/10.1007/978-981-16-1212-1_4"],
  "citationCount": 1,
  "referencesCount": 62,
  "url": "https://doi.org/10.1007/978-981-16-1212-1_4",
  "source": "Crossref",
  "observedAt": "2026-08-14T05:55:46.126Z"
}

Run via API and CLI

Start a run and wait for it to finish, then read the dataset. Replace <TOKEN> with your Apify API token.

Run synchronously and get dataset items in one call:

curl -X POST "https://api.apify.com/v2/acts/scrapers_lat~crossref-scraper/run-sync-get-dataset-items?token=<TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"searchQuery":"graphene transistor","maxPapers":25}'

Start a run asynchronously:

curl -X POST "https://api.apify.com/v2/acts/scrapers_lat~crossref-scraper/runs?token=<TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"searchQuery":"public health","maxPapers":100}'

Apify CLI:

apify call scrapers_lat/crossref-scraper \
  --input '{"searchQuery":"machine learning","maxPapers":10}'

Fetch results

Every run writes to a dataset. Fetch items as JSON, CSV, or Excel by changing format:

# JSON
curl "https://api.apify.com/v2/datasets/<DATASET_ID>/items?token=<TOKEN>&clean=true&format=json"

# CSV
curl "https://api.apify.com/v2/datasets/<DATASET_ID>/items?token=<TOKEN>&clean=true&format=csv"

# Paginate large datasets
curl "https://api.apify.com/v2/datasets/<DATASET_ID>/items?token=<TOKEN>&offset=1000&limit=1000"

<DATASET_ID> is returned as defaultDatasetId in the run object. Use offset and limit to page through large result sets. clean=true drops empty and internal fields.

Billing and limits

• Pay per result. You are charged per work returned (result event). See the pricing tab for the current per-result price.
• AI add-ons billed separately. withAiSummary, withAiKeywords and withAiField each charge their own event only when they produce output, and are disabled on free plans.
• No charge on failure. If a run errors, the actor writes a single item with a populated error field and does not charge for it. Empty runs cost nothing.
• Spend cap respected. Set maxTotalChargeUsd on the run; once reached, the actor stops emitting and charging further billable results.
• Free Apify plans are capped at 10 records per run. Upgrade for higher maxPapers.

FAQ and troubleshooting

A run returned 0 records. Why? The keyword matched no works. Try a broader keyword. Zero-result runs are not charged.

Why is abstract null? Crossref does not carry an abstract for every work (many publishers do not deposit them). Missing source values are returned as null, never invented. aiSummary is skipped when there is no abstract.

Do I get author ORCID and affiliation? Yes, in authorsDetailed, when the publisher deposited them. Not every record includes ORCID or affiliation.

How fresh is the citation count? citationCount is the Crossref is-referenced-by count read live at run time (see observedAt).

Do the AI add-ons work on a free plan? No. The AI add-ons require a paid Apify plan and are disabled automatically for free users. The base metadata fields are always returned.

Is this an official Crossref tool? No. This actor is independent and has no affiliation with Crossref. It reads only data that is publicly available through the Crossref API. Use the results in accordance with the source's terms.

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Independent tool, not affiliated with Crossref. Accesses only publicly available scholarly metadata. Use in accordance with the source's terms.