# Crossref Scraper | Scholarly DOIs, Citations & Metadata (`muzafferkadir/crossref-scraper`) Actor

Scrape scholarly metadata from Crossref by keyword: title, authors, DOI, journal, citations, subjects, dates and abstract across all publishers. Official API, no API key.

- **URL**: https://apify.com/muzafferkadir/crossref-scraper.md
- **Developed by:** [Muzaffer Kadir YILMAZ](https://apify.com/muzafferkadir) (community)
- **Categories:** Education, AI, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.50 / 1,000 works

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Crossref Scraper

Scrape **scholarly metadata across all publishers** from **Crossref** by keyword — **title, authors, DOI, journal, citations, subjects, publication dates, and abstracts**. Built on the **official Crossref REST API**: no API key or setup needed.

Search a topic like `graph neural network`, get a clean, dated list of matching works with DOIs — ready to export to CSV, Excel, or JSON, or to power a citation tracker or literature review.

### Features

- **Official, reliable source** — reads the public Crossref REST API.
- **Every publisher** — Crossref indexes 150M+ works across journals, conferences, books, preprints and datasets, all linked by DOI.
- **Rich metadata** — authors, journal, citation counts, subjects, reference counts and plain-text abstracts.
- **Deep pagination** — cursor-based paging fetches thousands of works per query.

### Use cases

- **Literature reviews** — build a dated, deduplicated corpus of works on a topic.
- **Citation & impact tracking** — sort by citation count to find the most influential papers.
- **DOI enrichment** — resolve keywords to DOIs and clean metadata for downstream tools.
- **Research monitoring** — schedule daily on your keywords and catch new publications.

### Input

| Field | Type | Description |
| --- | --- | --- |
| `searchQueries` | string\[] | Search queries (required). |
| `fromDate` | string | `YYYY-MM-DD`; only works published on/after this date. |
| `type` | enum | Work type filter (journal article, proceedings, book chapter, preprint, dataset). |
| `sortBy` | enum | `relevance`, `published` (newest), or `citations` (most cited). |
| `maxItems` | integer | Max works per query. Default 100. |

#### Example

```json
{
  "searchQueries": ["graph neural network"],
  "type": "journal-article",
  "sortBy": "citations",
  "fromDate": "2023-01-01",
  "maxItems": 100
}
```

### Output

```json
{
  "doi": "10.1109/tnnls.2020.2978386",
  "title": "A Comprehensive Survey on Graph Neural Networks",
  "authors": ["Zonghan Wu", "Shirui Pan", "Fengwen Chen"],
  "publisher": "Institute of Electrical and Electronics Engineers (IEEE)",
  "journal": "IEEE Transactions on Neural Networks and Learning Systems",
  "type": "journal-article",
  "publishedDate": "2021-01",
  "citedByCount": 4821,
  "referenceCount": 187,
  "subjects": ["Artificial Intelligence", "Computer Networks and Communications"],
  "abstract": "Deep learning has revolutionized many machine learning tasks...",
  "url": "https://doi.org/10.1109/tnnls.2020.2978386",
  "query": "graph neural network",
  "scrapedAt": "2026-08-11T19:00:00.000Z"
}
```

Download the dataset as **JSON, CSV, Excel, or XML**, or pull it via the Apify API.

### Pricing

Pay-per-event: **$0.01 per run start** (per GB of run memory) plus **$0.50 per 1,000 works**. You only pay for works actually returned. See the **Pricing** tab for current rates.

### Notes

- Data comes from the public [Crossref REST API](https://api.crossref.org).
- Abstracts are only available for a subset of works (publishers that deposit them) and are cleaned from JATS XML to plain text.

### Disclaimer

This is an **unofficial** Actor. It is not affiliated with or endorsed by Crossref. It returns publicly available scholarly metadata. You are responsible for how you use the data.

# Actor input Schema

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

Search queries (required). Each term is searched across titles, authors and metadata, e.g. "graph neural network".

## `fromDate` (type: `string`):

YYYY-MM-DD; only works published on/after this date (from-pub-date filter). Leave empty for no lower bound.

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

Work type filter. Leave as Any to include all types.

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

How to sort results within each query.

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

Max works per query. Stop after this many works for each query.

## `proxyConfiguration` (type: `object`):

Optional. Leave empty for most runs.

## Actor input object example

```json
{
  "searchQueries": [
    "graph neural network"
  ],
  "type": "",
  "sortBy": "relevance",
  "maxItems": 100
}
```

# Actor output Schema

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

No description

# 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": [
        "graph neural network"
    ],
    "maxItems": 100
};

// Run the Actor and wait for it to finish
const run = await client.actor("muzafferkadir/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": ["graph neural network"],
    "maxItems": 100,
}

# Run the Actor and wait for it to finish
run = client.actor("muzafferkadir/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": [
    "graph neural network"
  ],
  "maxItems": 100
}' |
apify call muzafferkadir/crossref-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,muzafferkadir/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/drVt6f7XoGamZcefB/builds/ObaCwe2GhCTtHuwpv/openapi.json