Crossref Scholarly Works Scraper

Get Crossref Scholarly Works results as clean JSON, CSV or Excel, from the Apify API or on a schedule. No login, about $2.00 for 1,000 results.

What does Crossref Scholarly Works Scraper do?

Crossref Scholarly Works Scraper extracts structured data from api.crossref.org. Collect scholarly works from Crossref's public REST API, the registry behind DOIs: articles, preprints, books and proceedings matching a search, DOI prefix (publisher), journal ISSN, type and date range, as clean records (DOI, title, authors, journal, publisher, dates, citation count, licence, subjects, corrections and retractions), watched for new works, citation jumps and corrections. It works as an API for api.crossref.org data: run it from Apify Console, on a schedule, or from your own code, and get clean, typed JSON with numbers as numbers and dates in ISO 8601.

What you get

Data	17 fields per item: doi, title, authors, journal, issns, publisher, ...
Formats	JSON, CSV, Excel, HTML, or the Apify API
Price	$2.00 per 1,000 results, pay per result
Access	Public api.crossref.org data only: no login, no cookies, robots.txt respected
Licence	Crossref metadata: 'almost none of the metadata is subject to copyright, and you may use it for any purpose' (Crossref REST API documentation); abstracts are not included

Why use Crossref Scholarly Works Scraper?

• Analysts and researchers: export the data to a spreadsheet or database.
• Developers: call it from your own code through the Apify API.
• Monitoring: run it on a schedule and compare results over time.
• AI agents and RAG: give an LLM live structured data.

Main features:

• Follows pagination up to maxPages pages per start URL and stops at maxItems results.
• Filters: query (search), doiPrefixes (doi prefixes), issns (journal issns), types (work types), registeredAfter (registered after), publishedAfter (published after), publishedBefore (published before), onlyChanges (only changes since the last run), so you only get (and pay for) the results you need.
• Polite by default: respects robots.txt, at most maxConcurrency parallel requests and a delay between requests.
• Checks every result against field validators, so layout changes show up as clear data-quality warnings.
• Runs on the Apify platform: scheduling, API access, integrations, monitoring and datasets you can export.

What data can Crossref Scholarly Works Scraper extract?

Field	Type	Description
doi	string	DOI
title	string	Title
authors	array	Author names, in order
journal	string	Journal, book or proceedings title
issns	array	ISSNs
publisher	string	Publisher
type	string	Work type, e.g. journal-article, posted-content
publishedDate	string	Publication date (YYYY-MM-DD, or year/month when the day isn't given)
registeredAt	ISO 8601 date	First registered at Crossref (ISO 8601)
citations	integer	Times cited (Crossref's is-referenced-by-count)
licenseUrl	string	Licence URL, when deposited
subjects	array	Subjects, when deposited
updates	array	Corrections, retractions or other updates to this work (type: DOI)
url	string	DOI link
source	string	Always 'Crossref'
changeType	string	new, changed, unchanged since the last run with the same input
changes	object	What changed since the last run: {field: {from, to}}

How to scrape api.crossref.org

1. Open Crossref Scholarly Works Scraper in Apify Console and go to the Input tab.
2. Enter what to scrape (see the Input section below), for example the start URLs.
3. Set Max items to the number of results you need.
4. Click Start and wait for the run to finish.
5. Download the results from the Output tab, or fetch them with the API.

How much will it cost to scrape api.crossref.org?

This Actor is priced per result: $2.00 per 1,000 results, with no extra charge for platform usage. That is about $2.00 for 1,000 results: 100 results cost $0.20 and 10,000 results cost $20.00. Set a maximum cost per run and the Actor stops when it is reached.

Input

See the Input tab for full configuration options.

Field	Type	Required	Description
query	string	no	Words to search titles, authors and journals for, e.g. 'large language models'.
doiPrefixes	array	no	Only works from these DOI prefixes (publishers), e.g. 10.1038 (Nature).
issns	array	no	Only works in these journals, e.g. 1476-4687.
types	array	no	e.g. journal-article, posted-content (preprints), book-chapter, proceedings-article.
registeredAfter	string	no	YYYY-MM-DD: works first registered at Crossref on or after this date. Default: the last 7 days.
publishedAfter	string	no	YYYY-MM-DD (publication date).
publishedBefore	string	no	YYYY-MM-DD (publication date).
onlyChanges	boolean	no	For scheduled runs: return (and pay for) only new and changed records, compared with the last run with the same input. The snapshot is kept in a named key-value store; delete it to start over.
maxItems	integer	no	Maximum number of items to return (0 = unlimited).
maxPages	integer	no	Maximum listing pages to follow per start URL (pagination).
maxConcurrency	integer	no	Maximum parallel requests (politeness; 1-10).
requestDelayMs	integer	no	Minimum delay between requests, in milliseconds (at least 250).
proxyType	string	no	none (direct connection), datacenter (Apify Proxy, cheapest) or residential (opt-in, billed per GB, fewer blocks). The actor never switches by itself.
proxyCountry	string	no	Two-letter country code for the proxy IP (optional).

Example input:

{
  "query": "large language models",
  "maxItems": 300,
  "maxPages": 2,
  "maxConcurrency": 1,
  "requestDelayMs": 500,
  "proxyType": "none"
}

Output

You can download the dataset in various formats such as JSON, HTML, CSV, or Excel. Example results from a real run:

[
  {
    "doi": "10.56855/j-llans.v5i3.2471",
    "title": "Nilai-Nilai Moral dalam Peribahasa Nias Masyarakat Hiliserangkai: Kajian Etnolinguistik",
    "authors": [
      "Weywin Sri Nenci Laoli",
      "Noveri Amal Jaya Harefa",
      "Iman Sudi Zega",
      "Noibe Halawa"
    ],
    "journal": "Journal of Literature Language and Academic Studies",
    "issns": [
      "2964-2973",
      "2985-6000"
    ],
    "publisher": "Edupedia Publisher",
    "type": "journal-article",
    "publishedDate": "2026-08-18",
    "registeredAt": "2026-09-27T00:06:24+00:00",
    "citations": 0,
    "licenseUrl": null,
    "subjects": [],
    "updates": [],
    "url": "https://doi.org/10.56855/j-llans.v5i3.2471",
    "source": "Crossref",
    "changeType": "new",
    "changes": {}
  },
  {
    "doi": "10.32603/2071-2340-2025-3-65-80",
    "title": "Перспективы использования языка описания онтологий ONTOL V2 в автоматизации процедуры проверки знаний Prospects for using the ontology description language ONTOL V2 in automating the knowledge verification procedure",
    "authors": [
      "Данил Денисович Пестряков"
    ],
    "journal": "Computer Tools in Education",
    "issns": [
      "2071-2359",
      "2071-2340"
    ],
    "publisher": "St. Petersburg Electrotechnical University LETI",
    "type": "journal-article",
    "publishedDate": "2025-10-01",
    "registeredAt": "2026-09-27T01:14:24+00:00",
    "citations": 0,
    "licenseUrl": "https://creativecommons.org/licenses/by/4.0",
    "subjects": [],
    "updates": [],
    "url": "https://doi.org/10.32603/2071-2340-2025-3-65-80",
    "source": "Crossref",
    "changeType": "new",
    "changes": {}
  },
  {
    "doi": "10.32603/2071-2340-2026-1-74-90",
    "title": "Модели автоматической генерации учебных заданий: сравнительный обзор Models for automatic generation of educational tasks: a comparative analysis",
    "authors": [
      "Дмитрий Сергеевич Бутенко"
    ],
    "journal": "Computer Tools in Education",
    "issns": [
      "2071-2359",
      "2071-2340"
    ],
    "publisher": "St. Petersburg Electrotechnical University LETI",
    "type": "journal-article",
    "publishedDate": "2026-03-31",
    "registeredAt": "2026-09-27T01:15:55+00:00",
    "citations": 0,
    "licenseUrl": "https://creativecommons.org/licenses/by/4.0",
    "subjects": [],
    "updates": [],
    "url": "https://doi.org/10.32603/2071-2340-2026-1-74-90",
    "source": "Crossref",
    "changeType": "new",
    "changes": {}
  }
]

Integrations and API

• Apify API: start a run and get the results in one HTTP request:

curl -X POST "https://api.apify.com/v2/acts/compass_lab~crossref-works-scraper/run-sync-get-dataset-items?token=<YOUR_APIFY_TOKEN>" \
  -H "Content-Type: application/json" -d '{"query": "large language models", "maxItems": 300}'

• Python (pip install apify-client):

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("compass_lab/crossref-works-scraper").call(run_input={"query": "large language models", "maxItems": 300})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

• JavaScript (npm install apify-client):

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' });
const run = await client.actor('compass_lab/crossref-works-scraper').call({"query": "large language models", "maxItems": 300});
const { items } = await client.dataset(run.defaultDatasetId).listItems();

• Make, Zapier, n8n, Google Sheets, webhooks: use the Apify integrations (Integrations tab) to send each run's results where you need them, or to start a run from your workflow.
• Schedules: run it hourly, daily or weekly from Apify Console (Schedules) and always have fresh results.

Tips and advanced options

• Keep Max items and Max pages as low as you need: fewer pages means a faster, cheaper run.
• Raise Delay between requests if the site responds slowly; keep Max concurrency low to stay polite.
• Missing values are null. Fields that often come back empty are listed in the run log as data-quality warnings.
• Watch for changes: every record has changeType (new, changed or unchanged compared with the last run with the same input) and changes ({"field": {"from": ..., "to": ...}}). Schedule the Actor with Only changes since the last run on to get, and pay for, only what moved. new means new to this input: not returned by an earlier run with the same filters. The last snapshot is kept in a named key-value store called <actor-name>-watch-<id> (the run log prints its name); delete it, or change the input, to start over.

FAQ, disclaimers and support

Is it legal to scrape api.crossref.org?

D91 (Mouad, 2026-10-04) confirmed the scholarly line, covering the missing robots.txt on api.crossref.org. Crossref: 'almost none of the metadata is subject to copyright, and you may use it for any purpose'; abstracts are left out (they may be copyrighted). Polite pool: mailto and User-Agent with contact email, one request at a time (live limit 3/s).

The data is published under the Crossref metadata: 'almost none of the metadata is subject to copyright, and you may use it for any purpose' (Crossref REST API documentation); abstracts are not included: you may reuse it, including commercially, provided you credit the source as the licence requires.

Our Actors are ethical and do not extract any private user data, such as email addresses, gender, or location. They only extract what the user has chosen to share publicly. We therefore believe that our Actors, when used for ethical purposes by Apify users, are safe. However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers.

How many results can I get?

Up to maxItems per run (0 means no limit), as many as the source lists. Each result is one dataset item, and you are only charged for items that are saved.

Can I run it on a schedule or from my own code?

Yes. Schedule it in Apify Console (Schedules), or call it with the run-sync-get-dataset-items endpoint or the Python/JavaScript clients shown in Integrations and API above.

What are the limitations?

• None identified so far.

Where can I get help?

Report problems or ideas on the Issues tab. To call this Actor from your own code, see the API tab.

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