# Crossref Scholar Scraper — Papers, Citations & DOIs (`eins332570/crossref-scholar-scraper`) Actor

Scrape academic works from Crossref — title, authors, journal, publisher, year, citation counts, DOI, ISSN & subjects — via the public Crossref API. Search by keyword or fetch by DOI. Great for literature reviews, research analytics & citation tracking. No login, no key, no proxy.

- **URL**: https://apify.com/eins332570/crossref-scholar-scraper.md
- **Developed by:** [thanachit singruang](https://apify.com/eins332570) (community)
- **Categories:** Other, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $5.00 / 1,000 results

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?

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 Scholar Scraper — Papers, Citations & DOIs

Scrape **academic works** — journal articles, books, and conference papers — from **Crossref**: title, authors, journal, publisher, year, **citation counts**, DOI, ISSN, and subjects, into **one unified dataset**. No login, no proxies, no browser, no API key.

Search the world's scholarly record by keyword, or fetch specific works by DOI.

### What you get

Every work is one row (`recordType: "work"`):

| Field | Description |
|---|---|
| `doi` | Digital Object Identifier |
| `title` | work title |
| `type` | journal-article / book-chapter / proceedings, etc. |
| `publisher` / `journal` | publisher and container title |
| `authors` / `authorCount` | author names and count |
| `year` / `publishedAt` | publication year and ISO date |
| `citations` | times referenced by other works |
| `referencesCount` | number of references it cites |
| `issn` / `subjects` | ISSNs and subject areas |
| `volume` / `issue` / `page` | bibliographic locators |
| `url` | doi.org link |

### Sample output

```json
{
  "recordType": "work",
  "doi": "10.1038/nature14539",
  "title": "Deep learning",
  "type": "journal-article",
  "publisher": "Springer Science and Business Media LLC",
  "journal": "Nature",
  "authors": ["Yann LeCun", "Yoshua Bengio", "Geoffrey Hinton"],
  "authorCount": 3,
  "year": 2015,
  "publishedAt": "2015-05-27T00:00:00.000Z",
  "citations": 55000,
  "referencesCount": 0,
  "issn": ["0028-0836", "1476-4687"],
  "volume": "521",
  "issue": "7553",
  "page": "436-444",
  "url": "https://doi.org/10.1038/nature14539"
}
```

### Input

```json
{
  "search": ["deep learning"],
  "dois": ["10.1038/nature14539"],
  "rows": 20
}
```

- **search** — keyword/title/author queries.
- **dois** — specific DOIs to fetch.
- **rows** — max works per search query (1–100).

### Use cases

- **Literature reviews** — pull a topic's works with citation counts in one dataset
- **Research analytics** — rank papers, authors, or journals by citations
- **Citation tracking** — monitor how a DOI or body of work is being cited
- **Reference enrichment** — resolve a DOI to full bibliographic metadata

### FAQ

**Do I need an API key or login?** No. Crossref's REST API is public and key-free.

**Is it free to try?** Yes — click **Try for free** and run the prefilled search (`deep learning`) in seconds.

**Can I look up an exact paper?** Yes — pass its DOI in `dois` (e.g. `10.1038/nature14539`).

**Is this allowed?** It uses Crossref's official public API (polite pool) — built for metadata reuse.

### Pricing

**Pay per result — $5 per 1,000 works.** Runs that return nothing cost nothing. No monthly fee, no proxy costs.

# Actor input Schema

## `search` (type: `array`):

Search scholarly works by title, author, or keyword. Each query returns up to `rows` works. Example: \["deep learning", "CRISPR gene editing"].

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

Fetch specific works by DOI, e.g. "10.1038/nature14539".

## `rows` (type: `integer`):

Cap works returned per search query (1–100).

## Actor input object example

```json
{
  "search": [
    "deep learning"
  ],
  "rows": 20
}
```

# Actor output Schema

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

All scraped work records as dataset items.

# 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 = {
    "search": [
        "deep learning"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("eins332570/crossref-scholar-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 = { "search": ["deep learning"] }

# Run the Actor and wait for it to finish
run = client.actor("eins332570/crossref-scholar-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 '{
  "search": [
    "deep learning"
  ]
}' |
apify call eins332570/crossref-scholar-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,eins332570/crossref-scholar-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/reIjVI95FXK4XEF8a/builds/4KOb6euo36Uvl2YSt/openapi.json