# Crossref Scraper - Scholarly Works, DOIs & Funders (`scrapesage/crossref-scraper`) Actor

Search Crossref (160M+ scholarly works) or look up DOIs and get clean rows: title, authors, affiliation, publisher, journal, year, citation count, funders with award numbers, license, ISSN/ISBN and abstract. Search, filter, or paste DOIs. No API key, no browser.

- **URL**: https://apify.com/scrapesage/crossref-scraper.md
- **Developed by:** [Scrape Sage](https://apify.com/scrapesage) (community)
- **Categories:** Agents, Integrations, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.10 / 1,000 work scrapeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Crossref Scraper - Scholarly Works, DOIs & Funders

Search **Crossref** - the DOI registry of **160M+ scholarly works** - or look up DOIs directly, and get
clean rows for every work: **title**, **authors** and **affiliation**, **publisher**, **journal**,
**year**, **citation count**, **funders with award numbers**, **license**, **ISSN/ISBN** and the
**abstract**. Search, filter, or paste a list of DOIs. Built on the official API - no key, no browser.

### What you get per work

| Field | Meaning |
|---|---|
| `doi` / `doiUrl` / `title` | DOI, resolvable link, and title |
| `authors` / `authorCount` / `firstAuthorAffiliation` | Authors and the lead author's institution |
| `publisher` / `containerTitle` / `volume` / `issue` / `page` | Publisher and journal placement |
| `publishedYear` / `publishedDate` / `workType` | Dates and type (journal-article, preprint, book...) |
| `citedByCount` / `referenceCount` | Citations received and references made |
| `funders` / `funderNames` | Funding bodies with **award / grant numbers** |
| `licenseUrls` / `isOpenAccess` | License links and an open-access flag |
| `issn` / `isbn` / `subjects` / `language` / `abstract` | Identifiers, subjects and abstract |

### Input

```json
{ "searchQueries": ["machine learning"], "sortBy": "cited", "maxItemsPerQuery": 100 }
```

- **Search queries** - one per line (title, author, abstract, metadata). **DOIs** - exact lookups.
- **Filter** - a Crossref filter expression, e.g. `from-pub-date:2024-01-01,type:journal-article,has-funder:true`.
- **Import from a file** - paste a list, or link a public `.txt`/`.csv`, a Google Sheet/Drive link, or an
  Apify key-value-store record. Queries and DOIs are auto-detected.
- **Sort by** relevance, most recent, most cited, or recently indexed. **Output fields** trim the record.

Leave everything empty and the run returns a small free sample so you can see the shape first.

### Reliability

Reads the official [Crossref REST API](https://api.crossref.org/) on the polite pool (UA + mailto),
keyless, cursor-paginated for deep result sets. A run that returns nothing bills **$0**.

### Honest limits

- **Metadata, not full text.** Crossref indexes the scholarly record - you get the DOI, citation graph
  and funders; for the article body follow `url`/`doiUrl` to the publisher.
- **`abstract` is present only when the publisher deposited one** with Crossref (many do not) - an honest
  null, not a scraping gap.
- **`funders` / `licenseUrls` depend on publisher deposits** too - richly populated for recent works,
  sparser for older ones. Filter with `has-funder:true` / `has-license:true` when you need them.
- **`citedByCount` is Crossref's open citation count**, which can differ from Scopus/Web of Science.

### Pricing

**$0.002 per work** on the FREE tier (tiered pricing lowers it with volume). Only works actually saved
are billed; an empty run costs nothing.

### Output views

- **Works** - title, authors, journal, year, citations, funders and DOI.

### Use with AI assistants (MCP)

Available through the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp) - an agent
can pull a funder's grant output, build a citation-ranked reading list with DOIs, or assemble a RAG
index of a field's literature in one call. Pairs with our arXiv, PubMed and OpenAlex scrapers.

### Agent-ready: autonomous payments (x402 & Skyfire)

This actor is **agent-ready** - AI agents can discover it, run it, and **pay for it autonomously**, with no Apify account and no human in the loop. It uses [pay-per-event](https://docs.apify.com/platform/actors/publishing/monetize/pay-per-event) pricing and [limited permissions](https://docs.apify.com/platform/actors/development/permissions), so it qualifies for Apify's agentic-payment standards:

- **[x402](https://docs.apify.com/platform/integrations/x402)** - an open, HTTP-native payment protocol. Agents pay per run in USDC on the Base network directly through the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp) - no account, no API key.
- **[Skyfire](https://docs.apify.com/platform/integrations/skyfire)** - agent-to-service payments for fully autonomous AI-agent workflows.

Building an AI agent, MCP tool, or autonomous data pipeline? This scraper is ready to plug in and pay as it goes.

# Actor input Schema

## `searchQueries` (type: `array`):

What to search Crossref for, one per line - e.g. <code>machine learning</code>, <code>climate policy</code>. Searches titles, authors, abstracts and metadata. <b>Leave empty and the run returns a small free sample.</b>

## `dois` (type: `array`):

Specific DOIs to look up exactly, one per line - e.g. <code>10.1038/nature14539</code> or a full <code>https://doi.org/...</code> URL.

## `filter` (type: `string`):

A Crossref filter expression, combined with the search - e.g. <code>from-pub-date:2024-01-01,type:journal-article</code> or <code>has-funder:true,funder:10.13039/100000001</code>. See the Crossref REST API docs.

## `searchQueriesFromFile` (type: `string`):

Bulk-load search queries or DOIs (auto-detected). Either <b>paste the whole list</b> (one per line), or give <b>a single link</b> to a public <code>.txt</code>/<code>.csv</code>, a Google Sheet/Drive link, or an Apify key-value-store record. A file that cannot be read says so and charges nothing.

## `sortBy` (type: `string`):

How to order search results (DOI lookups are always exact).

## `maxItemsPerQuery` (type: `integer`):

How many works to collect for each search query.

## `maxItems` (type: `integer`):

Overall cap across all queries, DOIs and the filter. You are only charged for works actually saved.

## `outputFields` (type: `array`):

Pick the fields you want and every record is trimmed to exactly those - handy for lean CSV/Sheets exports.

## Actor input object example

```json
{
  "searchQueries": [
    "machine learning"
  ],
  "sortBy": "relevance",
  "maxItemsPerQuery": 50,
  "maxItems": 1000
}
```

# Actor output Schema

## `results` (type: `string`):

Each scraped record - a Crossref scholarly work with authors, funders and license - as a JSON item in the default dataset.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "searchQueries": [
        "machine learning"
    ],
    "searchQueriesFromFile": ""
};

// Run the Actor and wait for it to finish
const run = await client.actor("scrapesage/crossref-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "searchQueries": ["machine learning"],
    "searchQueriesFromFile": "",
}

# Run the Actor and wait for it to finish
run = client.actor("scrapesage/crossref-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "searchQueries": [
    "machine learning"
  ],
  "searchQueriesFromFile": ""
}' |
apify call scrapesage/crossref-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,scrapesage/crossref-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/aMhAb69a9su2D6ti9/builds/75TqkMt3ehVZbU3R3/openapi.json