# CrossRef Scraper — Scholarly Works & Citations (`ponderable_hydrometer/crossref-scraper`) Actor

Search 150M+ scholarly works from CrossRef — DOI, title, authors, journal, publisher, citations & dates. Filter by date & type. Free keyless API.

- **URL**: https://apify.com/ponderable\_hydrometer/crossref-scraper.md
- **Developed by:** [Ponderable Hydrometer](https://apify.com/ponderable_hydrometer) (community)
- **Categories:** Developer tools, Automation, SEO tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.50 / 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/platform/actors/running/actors-in-store#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 & Citations

**Search CrossRef's catalogue of 150M+ scholarly works — journal articles, books, conference papers, datasets — and get rich, structured metadata.** DOI, title, authors with ORCID and affiliation, journal, publisher, publication date, citation count and subjects. Full CrossRef filter grammar, deep cursor paging, look up specific DOIs. Free keyless API.

For research tools, bibliometrics, publishing workflows and literature reviews.

### What you get

Per work:

- **Identity** — `doi`, `title`, `type`, `url`
- **Authors** — array of `{ given, family, name, orcid, affiliation[] }`
- **Publication** — `containerTitle` (journal/book), `publisher`, `published` (YYYY-MM-DD), `year`, `volume`, `issue`, `page`, `issn[]`, `isbn[]`
- **Impact** — `citedByCount`, `referencesCount`
- **Content** — `subjects[]`, `abstract` (when available), `language`

### Output sample

```json
{
  "doi": "10.1038/nature14539",
  "title": "Deep learning",
  "type": "journal-article",
  "authors": [
    {
      "given": "Yann",
      "family": "LeCun",
      "name": "Yann LeCun",
      "orcid": "http://orcid.org/0000-0002-...",
      "affiliation": ["Facebook AI Research"]
    }
  ],
  "containerTitle": "Nature",
  "publisher": "Springer Science and Business Media LLC",
  "published": "2015-05-28",
  "year": 2015,
  "volume": "521",
  "issue": "7553",
  "page": "436-444",
  "issn": ["0028-0836", "1476-4687"],
  "isbn": [],
  "citedByCount": 68210,
  "referencesCount": 0,
  "subjects": ["Multidisciplinary"],
  "abstract": null,
  "language": "en",
  "url": "https://doi.org/10.1038/nature14539"
}
```

### Input

| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `query` | string | — | Free-text search across all fields |
| `queryBibliographic` | string | — | Combined title/author/year (good for citation matching) |
| `queryAuthor` | string | — | Search by author name |
| `queryTitle` | string | — | Search within work titles |
| `filter` | string | — | Raw CrossRef filter, e.g. `from-pub-date:2024-01-01,type:journal-article,has-abstract:true` |
| `sort` | string | — | e.g. `relevance`, `published`, `is-referenced-by-count`, `score`, `updated` |
| `order` | string | `desc` | `desc` or `asc` |
| `dois` | array | — | Look up specific DOIs |
| `maxResults` | integer | `100` | Cap on works returned |

### Example input

```json
{
  "query": "large language models",
  "filter": "from-pub-date:2024-01-01,type:journal-article",
  "sort": "is-referenced-by-count",
  "maxResults": 200
}
```

Look up specific DOIs with `{"dois":["10.1038/nature14539","10.1126/science.1231143"]}`.

### Why this actor

- **Depth few match** — full author detail (ORCID + affiliation), citation counts, subjects and CrossRef's complete filter grammar.
- **Whole result sets** — deep cursor paging, not just the first 1,000.
- **Fast & polite** — a `mailto` User-Agent puts requests in CrossRef's faster "polite pool" with backoff on errors.

### Pricing

Pay per result — **$1.50 per 1,000 results** (one result = one scholarly work). No subscription or platform fees; you pay only for the results you get.

### Notes & limits

- Source is CrossRef's official free REST API — no key, no anti-bot. Abstracts are only present where the publisher deposited them.
- Public metadata; you are responsible for compliant use. Not affiliated with CrossRef.

### Related actors

- **arXiv Scraper** — preprints with abstracts and PDF links (pairs with CrossRef to cover preprints + published).
- **Semantic Scholar Scraper** — papers, citations and influence metrics.
- **Open Library Scraper** — books and ISBN metadata.

# Actor input Schema

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

Free-text search across all fields, e.g. "machine learning healthcare".

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

Search title, author, year etc. together (good for citation matching).

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

Search by author name, e.g. "Hinton".

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

Search within work titles.

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

Raw CrossRef filter, e.g. "from-pub-date:2024-01-01,type:journal-article,has-abstract:true".

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

e.g. relevance, published, is-referenced-by-count, score, updated.

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

"desc" or "asc".

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

Look up specific DOIs, e.g. \["10.1038/nature14539"].

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

Cap on number of works returned.

## Actor input object example

```json
{
  "query": "machine learning",
  "order": "desc",
  "maxResults": 100
}
```

# 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 = {
    "query": "machine learning"
};

// Run the Actor and wait for it to finish
const run = await client.actor("ponderable_hydrometer/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 = { "query": "machine learning" }

# Run the Actor and wait for it to finish
run = client.actor("ponderable_hydrometer/crossref-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 '{
  "query": "machine learning"
}' |
apify call ponderable_hydrometer/crossref-scraper --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/SAtshtX3g2Lm1IrTc/builds/C5URqMLfjJhT5QX6N/openapi.json