# Crossref Works Scraper (`neuton/crossref-works-scraper`) Actor

Search Crossref scholarly works by query or DOI. Extract titles, authors, publishers, journals, publication dates, DOI URLs, references, licenses, and citation counts.

- **URL**: https://apify.com/neuton/crossref-works-scraper.md
- **Developed by:** [Ashwin Prasad](https://apify.com/neuton) (community)
- **Categories:** Education
- **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

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

## 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 Works Scraper

Extract DOI and publication metadata from Crossref for research analytics, citation workflows, library enrichment, and scholarly market maps.

Use this actor when you need clean DOI-backed publication rows without maintaining your own Crossref integration. It is useful for publishers, universities, academic databases, AI/RAG teams, citation tools, research analysts, and market-intelligence workflows that need publication metadata at predictable cost.

### What it returns

- DOI, title, abstract, publisher, journal/container title
- Authors, publication dates, type, subject
- Reference count, indexed date, license URLs
- Canonical DOI and Crossref source URLs

### Common use cases

- Enrich DOI lists with titles, authors, journal names, publishers, and dates
- Build scholarly datasets for RAG, literature reviews, or semantic search
- Monitor publisher, journal, author, or keyword activity over time
- Power citation workflows, library metadata cleanup, and research market maps
- Feed publication metadata into AI agents for summarization and deduplication

### Input

Provide search queries, DOIs, or both. Crossref works best with publication titles, author names, journal names, or exact DOI values.

```json
{
  "queries": ["retrieval augmented generation"],
  "dois": ["10.1038/s41586-020-2649-2"],
  "maxResultsPerQuery": 25
}
```

### Output

Every result is saved to the default Apify dataset. Rows typically include `doi`, `title`, `abstract`, `publisher`, `container_title`, `authors`, `published_date`, `type`, `subjects`, `reference_count`, `is_referenced_by_count`, `license_urls`, `doi_url`, and Crossref source links.

### SEO keywords

Crossref scraper, DOI metadata scraper, Crossref works API, scholarly metadata export, academic publication scraper, citation data scraper, DOI to CSV, research paper metadata API.

### Pricing recommendation

Launch around $2 per 1,000 work rows. The actor is API-first and low-compute, so this leaves margin while staying affordable for research enrichment and bulk DOI cleanup.

### Responsible use

This actor extracts public metadata from Crossref. Respect Crossref API etiquette, publisher rights, and license terms for downstream use. Metadata can be incomplete or updated; verify critical bibliographic records before using them in formal publication, compliance, or procurement workflows.

### Automation ideas

Schedule runs for journal watchlists, DOI batches, competitor/publisher topics, or research categories. AI agents can deduplicate works, summarize abstracts, cluster publication themes, fill missing DOI fields, and route new papers into literature-review pipelines.

# Actor input Schema

## `queries` (type: `array`):

Search queries

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

DOIs

## `maxResults` (type: `integer`):

Max results per query

## Actor input object example

```json
{
  "maxResults": 50
}
```

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("neuton/crossref-works-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 = {}

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

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

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

```

## CLI example

```bash
echo '{}' |
apify call neuton/crossref-works-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=neuton/crossref-works-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/lJimRbdoZxXymZNsR/builds/hRI5plbSU8oyc6TKj/openapi.json