# Crossref Scientific Papers & DOI Citations Scraper (`litezero/crossref-research-citations-scraper`) Actor

Extract scholarly research articles, DOI citations, author affiliations, publishers, publication dates, and journal metadata from Crossref.

- **URL**: https://apify.com/litezero/crossref-research-citations-scraper.md
- **Developed by:** [LiteZero](https://apify.com/litezero) (community)
- **Categories:** Developer tools, AI, Open source
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.84 / 1,000 item 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?

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 Scientific Papers & DOI Citations Scraper

Extract peer-reviewed scholarly literature, DOI citations, academic authors, journal titles, and publisher metadata across **150M+ registered publications** from the official **Crossref** database.

***

### 🥊 Why This Actor Beats the Competition

| Feature | 🎓 This Crossref Scraper | 🐢 Google Scholar Web Scrapers | 🐌 Publisher Paywalls |
|---|---|---|---|
| **Speed** | **250+ papers / min** (Direct REST) | 10–15 papers / min (Captchas) | Slow manual navigation |
| **Pricing** | **$1.20 / 1,000 papers** (PPR) | High proxy & compute costs | Expensive subscriptions |
| **Data Cleanliness** | Official DOI, ISSN, Citations | Unstructured HTML parsing | Inconsistent format |
| **Platform Usage**| **FREE** (Zero compute charges) | Heavy CPU / RAM bills | N/A |
| **Reliability** | **100%** (Official Crossref API) | Frequent Cloudflare / 403 blocks| Manual research friction |

***

### 🛠️ Step-by-Step: How to Use

#### Method 1: Apify Console (No-Code UI)

1. Open the actor in Apify Console.
2. Enter your **Search Query** (e.g. `quantum computing`, `crispr therapeutics`, `transformer models`).
3. Set **Max Papers to Extract** (e.g. 50).
4. Click **Start** to run and download results in JSON or CSV.

#### Method 2: Python SDK

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run_input = {
    "query": "artificial intelligence large language models",
    "maxItems": 25
}
run = client.actor("litezero/crossref-research-citations-scraper").call(run_input=run_input)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["title"], item["doi"], "Citations:", item["citationCount"])
```

#### Method 3: Node.js SDK

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

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('litezero/crossref-research-citations-scraper').call({
    query: 'graph neural networks',
    maxItems: 25,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);
```

***

### 📥 Input Parameters

| Parameter | Type | Default | Description |
|---|---|---|---|
| `query` | String | `"artificial intelligence neural networks"` | Academic search query or keyword. |
| `maxItems` | Integer | `20` | Max publications to extract. |
| `filterYear` | String | `""` | Optional publication year filter (e.g. `2024`). |

***

### 📤 Output Data Dictionary & Sample

```json
{
  "doi": "10.1016/j.neunet.2023.11.021",
  "title": "Deep learning architectures for multimodal sensor fusion",
  "authors": ["Alexander Vance", "Elena Rostova", "Marcus Chen"],
  "publisher": "Elsevier BV",
  "journal": "Neural Networks",
  "type": "journal-article",
  "publishedDate": "2024-02-15",
  "citationCount": 42,
  "doiUrl": "https://doi.org/10.1016/j.neunet.2023.11.021",
  "issn": ["0893-6080"],
  "scrapedAt": "2026-10-04T05:40:00.000Z"
}
```

***

### 💡 Top Use Cases

1. **AI RAG & Vector Database Ingestion:** Ingest thousands of authoritative peer-reviewed papers into Pinecone, Qdrant, or Weaviate for specialized AI assistants.
2. **Academic & Research Citation Velocity:** Monitor emerging research trends, citation spikes, and influential authors in cutting-edge scientific fields.
3. **Patent Prior-Art Discovery:** Cross-reference patent claims against academic literature to substantiate IP novelty.

# Actor input Schema

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

Scientific topic, research title, or author to search across 150M+ scholarly works.

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

Maximum number of research publications to return.

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

Optional year to filter by (e.g. 2024, 2025, 2026). Leave empty for all years.

## Actor input object example

```json
{
  "query": "artificial intelligence neural networks",
  "maxItems": 20
}
```

# 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("litezero/crossref-research-citations-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("litezero/crossref-research-citations-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 '{}' |
apify call litezero/crossref-research-citations-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,litezero/crossref-research-citations-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/PAcW6Y0yj7ABs94bp/builds/EgzHaf7dv1PowqnV7/openapi.json