# Academic Paper Search: Crossref, arXiv & PubMed Scraper (`pankajbhatia/academic-paper-search`) Actor

Search research papers across Crossref, arXiv and PubMed (plus OpenAlex) in one run. Titles, authors, abstracts, DOIs, citation counts, journals and PDF links, merged and de-duplicated. For literature reviews, RAG and research.

- **URL**: https://apify.com/pankajbhatia/academic-paper-search.md
- **Developed by:** [Pankaj Bhatia](https://apify.com/pankajbhatia) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 1,000 papers

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 Paper Search: Crossref, arXiv & PubMed Scraper

**Search research papers across the biggest open scholarly databases in one run**, and get one clean, merged table:

- **Crossref**: over 150 million journal articles, books and conference papers with DOIs and **citation counts**.
- **arXiv**: preprints in AI, computer science, physics, math, biology and economics, with **free PDFs**.
- **PubMed**: biomedical and life-science literature with abstracts and MeSH topics.
- **OpenAlex** (optional): 250+ million works. Bring your own free API key.

For every paper you get **title, authors, publication date and year, journal or venue, publisher, DOI link, abstract, citation count, open-access status, PDF or full-text link, topics** and source IDs (PMID, PMCID, arXiv ID). The same paper found in several databases is **merged into one row**.

### Who is it for?

- **Researchers and students**: run literature reviews and systematic searches in minutes and export to Excel or Zotero-friendly CSV.
- **AI and RAG builders**: collect abstracts and open PDFs on any topic for embeddings, chatbots and summarizers.
- **Pharma, biotech and healthcare**: monitor new PubMed papers on drugs, diseases and targets with a scheduled run.
- **R\&D, analysts and consultants**: map who publishes what, in which journals, and how influential it is.
- **Science writers**: find the most cited and newest papers on a subject.

### How to use it

1. Enter a **search query**, e.g. `large language models` or `crispr gene therapy`. Every word must match, so results stay on topic.
2. Pick **sources** (Crossref, arXiv and PubMed are on by default).
3. Optionally set years, **open access only**, and sort by relevance, date or citations.
4. Click **Start** and export to Excel, CSV or JSON.

**Tip:** schedule a weekly run with **Newest first** to get alerts for new papers on your topic.

### Input example

```json
{
  "query": "crispr gene therapy",
  "sources": ["crossref", "arxiv", "pubmed"],
  "maxResultsPerSource": 50,
  "fromYear": 2022,
  "sortBy": "citations",
  "openAccessOnly": false
}
```

### Output example

```json
{
  "source": "pubmed",
  "title": "Large Language Models in Medicine: Applications, Challenges, and Future Directions",
  "authors": ["Erlan Yu", "Xuehong Chu", "Wanwan Zhang", "Xiangbin Meng", "..."],
  "publishedDate": "2025-01-01",
  "year": 2025,
  "venue": "International journal of medical sciences",
  "doi": "10.7150/ijms.111780",
  "doiUrl": "https://doi.org/10.7150/ijms.111780",
  "url": "https://pubmed.ncbi.nlm.nih.gov/40520893/",
  "pdfUrl": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12163604/",
  "isOpenAccess": true,
  "citationCount": null,
  "abstract": "In recent years, large language models (LLMs) represented by GPT-4 have...",
  "type": "journal article",
  "topics": ["Humans", "Natural Language Processing", "Large Language Models"],
  "externalIds": { "pmid": "40520893", "pmcid": "PMC12163604" }
}
```

### How much does it cost?

Pay-per-event: a small fee **per paper returned** plus a tiny per-run start fee, with no subscription and no separate platform usage costs. For example, 1,000 papers cost about $2. Duplicates merged across sources count once. Set **Maximum cost per run** to cap your spending.

### Good to know

- Citation counts come from Crossref and OpenAlex. arXiv and PubMed don't provide them.
- Some publishers don't share abstracts with Crossref. Turn on **Only papers with an abstract** if you need them.
- arXiv and PubMed limit request speed, so very large searches take a little longer.

### Use it through the API, integrations or AI agents

Run it from the Apify API, on a schedule, or connect it to Make, Zapier, n8n or Google Sheets. AI agents can call it through the Apify MCP server to find and cite real papers.

### FAQ

**Is it legal?** It uses the official, free public APIs of Crossref, arXiv, PubMed (NCBI) and OpenAlex, which are made for exactly this kind of use.

**Can I download the full PDFs?** The `pdfUrl` field links to free full text when it's available (all arXiv papers, PubMed Central and open-access journals).

**Found a bug or want another source?** Open an issue. We usually reply within 24 hours.

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# Actor input Schema

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

Topic, keywords or a paper title, e.g. 'large language models' or 'crispr gene therapy'. For arXiv you can also use fields like ti:, au:, cat:.

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

Which scholarly databases to search. Crossref covers most journals and DOIs, arXiv covers preprints (CS, physics, math...), PubMed covers biomedicine. OpenAlex needs your free API key.

## `maxResultsPerSource` (type: `integer`):

How many papers to take from each source.

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

Most relevant, newest, or most cited first (citation counts come from Crossref and OpenAlex).

## `fromYear` (type: `integer`):

Only papers published in or after this year.

## `toYear` (type: `integer`):

Only papers published in or before this year.

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

Only free-to-read papers (where the source reports it).

## `onlyWithAbstract` (type: `boolean`):

Skip papers whose abstract isn't available.

## `removeDuplicates` (type: `boolean`):

The same paper found in several sources (same DOI or title) is returned once, with the best fields merged.

## `openAlexApiKey` (type: `string`):

Free key from openalex.org. Only needed if you select OpenAlex as a source.

## `contactEmail` (type: `string`):

Crossref gives faster, more reliable service to requests that include a contact email (their 'polite pool'). Not stored or used for anything else.

## Actor input object example

```json
{
  "query": "large language models",
  "sources": [
    "crossref",
    "arxiv",
    "pubmed"
  ],
  "maxResultsPerSource": 50,
  "sortBy": "relevance",
  "openAccessOnly": false,
  "onlyWithAbstract": false,
  "removeDuplicates": true
}
```

# Actor output Schema

## `results` (type: `string`):

One item per paper with title, authors, date, venue, DOI, abstract, citations and links.

## `summary` (type: `string`):

Results per source.

# 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": "large language models"
};

// Run the Actor and wait for it to finish
const run = await client.actor("pankajbhatia/academic-paper-search").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": "large language models" }

# Run the Actor and wait for it to finish
run = client.actor("pankajbhatia/academic-paper-search").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 '{
  "query": "large language models"
}' |
apify call pankajbhatia/academic-paper-search --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,pankajbhatia/academic-paper-search"
        }
    }
}
```

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/95CcYV7Eb4XGkGzfm/builds/KdAUn3g09YYSY8oSe/openapi.json