# Academic Research Paper Scraper: ArXiv, PubMed & Crossref DOIs (`informed_almandine_vzb/academic-research-papers-scraper`) Actor

Scrape and deduplicate academic research papers, preprints, abstracts, citation counts, DOIs, and Open Access PDF links from ArXiv, PubMed/EuropePMC, and Crossref.

- **URL**: https://apify.com/informed_almandine_vzb/academic-research-papers-scraper.md
- **Developed by:** [Almandine Data](https://apify.com/informed_almandine_vzb) (community)
- **Categories:** AI, Developer tools, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$2.00 / 1,000 research paper scrapeds

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

## Academic Research Paper Scraper: ArXiv, PubMed & Crossref DOIs

Scrape and deduplicate academic research papers, preprints, abstracts, citation counts, DOIs, and Open Access PDF links from ArXiv, PubMed/EuropePMC, and Crossref.

### What it does

Queries ArXiv, EuropePMC/PubMed, and Crossref in parallel, cross-deduplicates by DOI/title, and outputs clean abstracts, authors, citation counts, and PDF URLs. Export clean, deduplicated data as **JSON, CSV, Excel, XML, or RSS**, or connect directly via the **Apify REST API, Python/JavaScript SDKs, CLI, MCP Server, n8n, Make, or Zapier** — with **no login, cookies, or browser overhead**.

### Use cases

- **AI Agent Literature Review & Scientific RAG Pipelines:** Feed deduplicated `title`, `abstract`, `authors`, `citationCount`, `doi`, and direct `pdfUrl` into LangChain, LlamaIndex, or vector databases.
- **Biotech, Pharma & Clinical R\&D Monitoring:** Combine PubMed/EuropePMC (`pmid`, `pmcid`) with Crossref journal citations to track new CRISPR, oncology, or clinical trial publications.
- **Machine Learning Preprint & Citation Tracking:** Monitor ArXiv CS/AI preprints alongside peer-reviewed journal DOIs and citation velocity.
- **Scheduled Literature Alerts:** Run daily or weekly with `onlyNew: true` to alert research teams whenever new papers match target keywords.

### Why use this scraper?

| Feature | This Actor | Generic Browser / HTML Scrapers |
|---|---|---|
| **Architecture** | Direct structured API / JSON / feed requests (`curl_cffi`) | Heavy headless browser automation |
| **Speed & reliability** | **2–25 seconds** for 50–200 items | Minutes of high compute burn |
| **Pricing model** | **Pay per result (`$0.00` actor-start fee)** | Monthly rental or high CU overhead |
| **Error handling** | Failed inputs logged in `ERRORS` key-value record and **never charged** | Bills compute time even on failures |

### Input parameters

- **Research search queries** (`queries`, `array`): Keywords, topics, author names, or DOIs to search across ArXiv, EuropePMC/PubMed, and Crossref.
- **Academic databases to query** (`sources`, `array`): Any combination of 'arxiv', 'europepmc' (PubMed / PMC biomedical literature), and 'crossref' (DOI journal registry).
- **Minimum publication year** (`minYear`, `integer`): Filter out papers published before this year.
- **Sort order** (`sortBy`, `string`): Sort results by relevance, publication date, or citation count.
- **Open-access / free PDF only** (`openAccessOnly`, `boolean`): Emit only papers that are marked Open Access or include a direct public PDF URL.
- **Only new papers** (`onlyNew`, `boolean`): Remember seen paper DOIs/IDs across runs in Key-Value Store and emit only newly published papers.
- **Max papers per query** (`maxPapersPerQuery`, `integer`): Maximum number of deduplicated papers to return per search query.
- **Delay between requests** (`delaySeconds`, `number`): Polite pause between requests (default `0.4`).

#### Example input

```json
{
  "queries": [
    "large language models",
    "crispr gene editing"
  ],
  "sources": [
    "europepmc",
    "arxiv",
    "crossref"
  ],
  "minYear": 2020,
  "maxPapersPerQuery": 35
}
```

### Output & example output

One dataset row per result. Default table view columns: `source, title, authors, venue, publicationYear, citationCount, isOpenAccess, pdfUrl, url`. Full structured JSON is stored in the run's default dataset.

#### Example output item

```json
{
  "paperId": "pmid:42808869",
  "source": "europepmc",
  "query": "large language models",
  "title": "Large Language Models as Diagnostic Tools in Nephrology: Helpful, With Limitations.",
  "abstract": "Background Large language models (LLMs) are increasingly being used to support diagnostic decision-making. It is unclear, however, whether their performance on knowledge and exa...",
  "authors": [
    "Hasan C",
    "Boeckel GR",
    "Siam S"
  ],
  "authorCount": 6,
  "publicationDate": "2026-12-11",
  "publicationYear": 2026,
  "venue": "Deutsches Arzteblatt international",
  "doi": "10.3238/arztebl.m2026.0164",
  "arxivId": null,
  "pmid": "42808869",
  "pmcid": null,
  "citationCount": 0,
  "isOpenAccess": false,
  "pdfUrl": null,
  "url": "https://pubmed.ncbi.nlm.nih.gov/42808869/",
  "subjects": [],
  "scrapedAt": "2026-10-25T11:51:54+00:00"
}
```

### Pricing (Pay-Per-Result)

This Actor uses transparent **Pay-Per-Event** pricing with a **`$0.00` actor-start fee**:

| Event | What is charged | Price |
|---|---|---|
| `paper-result` | Research paper scraped (One deduplicated academic paper or preprint with abstract, citations & PDF link) | **$0.002 / result** (**$2.00 / 1,000 results**) |
| `apify-actor-start` | Actor start fee | **$0.00** |

#### Concrete pricing math

- **`paper-result` (Research paper scraped):** `1,000 results = $2.00` (`50 results = $0.10`, `10,000 results = $20.00`). Apify's **$5.00/month Free Tier** covers up to **2,500 results/month** at **$0.00** out-of-pocket.
- **Empty runs (`0` results) & skipped duplicates (`onlyNew` / `onlyChanged`):** **$0.00**.

### Data and compliance

Public open-science APIs (ArXiv export API, EuropePMC REST API, Crossref polite pool) only. Zero paywall circumvention or personal data. Requests are rate-limited and routed through Apify Proxy. Per-input errors are recorded in the run's `ERRORS` key-value record and are never charged.

### FAQ

**Do I need an account, login, or API key?**\
No. This Actor queries public endpoints directly and requires zero credentials.

**What happens if I start a run with empty `{}` input via API or CLI?**\
Every required input defines both `prefill` and `default` values in `.actor/input_schema.json`, so an empty `{}` run automatically returns **50+ live records** out of the box.

# Actor input Schema

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

Keywords, topics, author names, or DOIs to search across ArXiv, EuropePMC/PubMed, and Crossref.

## `sources` (type: `array`):

Any combination of 'arxiv', 'europepmc' (PubMed / PMC biomedical literature), and 'crossref' (DOI journal registry).

## `minYear` (type: `integer`):

Filter out papers published before this year.

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

Sort results by relevance, publication date, or citation count.

## `openAccessOnly` (type: `boolean`):

Emit only papers that are marked Open Access or include a direct public PDF URL.

## `onlyNew` (type: `boolean`):

Remember seen paper DOIs/IDs across runs in Key-Value Store and emit only newly published papers.

## `maxPapersPerQuery` (type: `integer`):

Maximum number of deduplicated papers to return per search query.

## `delaySeconds` (type: `number`):

Polite pause between requests.

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

Apify Proxy settings.

## Actor input object example

```json
{
  "queries": [
    "large language models",
    "crispr gene editing"
  ],
  "sources": [
    "europepmc",
    "arxiv",
    "crossref"
  ],
  "minYear": 2020,
  "sortBy": "relevance",
  "openAccessOnly": false,
  "onlyNew": false,
  "maxPapersPerQuery": 35,
  "delaySeconds": 0.4,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}
```

# Actor output Schema

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

No description

## `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 = {
    "queries": [
        "large language models",
        "crispr gene editing"
    ],
    "sources": [
        "europepmc",
        "arxiv",
        "crossref"
    ],
    "minYear": 2020,
    "sortBy": "relevance",
    "openAccessOnly": false,
    "onlyNew": false,
    "maxPapersPerQuery": 35
};

// Run the Actor and wait for it to finish
const run = await client.actor("informed_almandine_vzb/academic-research-papers-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 = {
    "queries": [
        "large language models",
        "crispr gene editing",
    ],
    "sources": [
        "europepmc",
        "arxiv",
        "crossref",
    ],
    "minYear": 2020,
    "sortBy": "relevance",
    "openAccessOnly": False,
    "onlyNew": False,
    "maxPapersPerQuery": 35,
}

# Run the Actor and wait for it to finish
run = client.actor("informed_almandine_vzb/academic-research-papers-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 '{
  "queries": [
    "large language models",
    "crispr gene editing"
  ],
  "sources": [
    "europepmc",
    "arxiv",
    "crossref"
  ],
  "minYear": 2020,
  "sortBy": "relevance",
  "openAccessOnly": false,
  "onlyNew": false,
  "maxPapersPerQuery": 35
}' |
apify call informed_almandine_vzb/academic-research-papers-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,informed_almandine_vzb/academic-research-papers-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/xs2V61o6kRzal3r4h/builds/ZnZLpegTrzaN0iQRJ/openapi.json