# Academic Paper Search – Crossref, arXiv & PubMed (Europe PMC) (`pinkish_armada/academic-paper-search`) Actor

Search 150M+ scholarly papers across Crossref, arXiv and Europe PMC/PubMed in one run: titles, authors, abstracts, DOIs, citation counts, journals, open-access PDF links. Year filters, most-cited sort, DOI lookup, dedup.

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

## Pricing

from $1.00 / 1,000 returned 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 (Europe PMC)

**Search more than 150 million scholarly papers across Crossref, arXiv and Europe PMC (PubMed + PubMed Central) in one run — and get clean, deduplicated JSON with titles, authors, abstracts, DOIs, citation counts, journals and open-access PDF links.** A fast, reliable **Google Scholar alternative** built only on official open scholarly APIs: no CAPTCHAs, no blocked runs, no grey-zone scraping.

- 📚 **Three sources, one schema:** Crossref (all disciplines, 150M+ DOIs, citation counts), arXiv (CS, AI, physics, maths, stats preprints with PDFs), Europe PMC (life sciences & medicine: PubMed, PMC, bioRxiv/medRxiv preprints)
- 🔎 **Filters & sorting:** year range, open access only, sort by relevance, newest or **most cited**
- 🧬 **Rich metadata:** authors with ORCID and affiliation, abstract, venue, publisher, volume/issue/pages, subjects/MeSH keywords, license, PMID/PMCID/arXiv ID
- 📄 **Open-access PDF links** where available (always for arXiv)
- 🔁 **Deduplication** across sources by DOI, arXiv ID or title + year
- 🔗 **DOI lookup:** paste DOIs to fetch full metadata for a reference list
- 💸 **$1 per 1,000 papers** — no subscription, no per-query fees

### Who is it for?

- **Researchers & students:** literature reviews, systematic review screening exports, reading lists
- **AI / RAG builders:** feed abstracts and PDFs into LLM pipelines, build domain corpora, ground agents in real citations
- **R\&D, pharma & biotech:** monitor new publications on a target, compound or method (schedule it weekly with `sort: newest`)
- **Analysts & funders:** track research trends, top-cited work, venues and institutions in a field
- **Librarians & publishers:** enrich reference lists by DOI

### Pricing

| Event | Price |
|---|---|
| Paper returned | **$0.001** ($1 per 1,000 papers) |

Example: 3 queries × 3 sources × 50 papers = up to 450 papers ≈ **$0.45** (less after deduplication). Duplicates removed by dedup are not charged; failed DOI lookups are free.

For comparison, Google-Scholar-based Actors in the Apify Store charge roughly $0.0015–$0.008 per paper or $0.015 per query (October 2026) and depend on scraping a site that blocks bots. Set **Maximum cost per run** to cap spending.

### Input example

```json
{
  "queries": ["retrieval augmented generation evaluation", "\"large language models\" hallucination"],
  "sources": ["crossref", "arxiv", "europepmc"],
  "maxResultsPerSource": 50,
  "sort": "citations",
  "yearFrom": 2023,
  "openAccessOnly": false
}
```

DOI lookup:

```json
{ "dois": ["10.1038/nature14539", "https://doi.org/10.1145/3641289"] }
```

### Output example

```json
{
  "query": "retrieval augmented generation evaluation",
  "status": "ok",
  "source": "arxiv",
  "sourceId": "2411.18583v1",
  "title": "Automated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation",
  "authors": [{ "name": "Nurshat Fateh Ali", "orcid": null, "affiliation": null }],
  "authorNames": "Nurshat Fateh Ali, Md. Mahdi Mohtasim, Shakil Mosharrof, T. Gopi Krishna",
  "authorCount": 4,
  "year": 2024,
  "publishedDate": "2024-11-27",
  "venue": "arXiv",
  "type": "preprint",
  "doi": null,
  "url": "https://arxiv.org/abs/2411.18583",
  "pdfUrl": "https://arxiv.org/pdf/2411.18583v1",
  "abstract": "This research presents and compares multiple approaches to automate the generation of literature reviews…",
  "citationCount": null,
  "isOpenAccess": true,
  "subjects": ["cs.CL", "cs.AI", "cs.IR", "cs.LG"],
  "arxivId": "2411.18583"
}
```

Crossref and Europe PMC rows add `citationCount`, `venue` (journal), `publisher`, `volume`/`issue`/`pages`, `license`, and `pmid`/`pmcid` where available.

Views: **Papers** (title, authors, year, venue, citations, links) and **Abstracts**. Export to CSV/Excel for Zotero, Mendeley, EndNote or spreadsheets, or use the API/JSON.

### Tips

- **Exact phrases:** wrap in quotes — `"graph neural networks"`.
- **arXiv power syntax:** `ti:transformer AND cat:cs.CL`, `au:hinton`.
- **Europe PMC syntax** works for the Europe PMC source, e.g. `TITLE:"long covid" AND METHODS:cohort`.
- **Most cited** uses each source's citation counts (Crossref "is-referenced-by", Europe PMC "cited by"); arXiv has no citation counts and is returned by relevance.
- **Monitoring:** schedule weekly with `sort: "newest"` and `yearFrom` = this year, and send results to email/Slack/Sheets via integrations.

### Data sources and terms

All data comes from official, free scholarly metadata APIs used within their published rate limits and terms:

- **Crossref REST API** — bibliographic metadata deposited by publishers (facts and metadata; abstracts where publishers provide them)
- **arXiv API** — "Thank you to arXiv for use of its open access interoperability." Requests are throttled to arXiv's limit of one call every 3 seconds.
- **Europe PMC RESTful API** — PubMed/MEDLINE, PMC and preprint records

This Actor returns metadata and links; it does not download or redistribute full-text articles. Respect each paper's license when using full texts.

### FAQ

**Is this Google Scholar?** No — Google Scholar has no API and forbids automated access. This Actor uses open scholarly databases that cover the vast majority of peer-reviewed literature and preprints, with stable, structured results.

**Why do some papers lack abstracts?** Not all publishers deposit abstracts in Crossref. Europe PMC and arXiv records almost always include them.

**Why are citation counts different from Google Scholar?** Each database counts citations from its own index; Crossref and Europe PMC counts are usually lower than Google Scholar's but consistent and reproducible.

**How many results can I get?** Up to 5,000 per source per query; Crossref and Europe PMC use cursor paging, so large exports are reliable.

### Related Actors

- **PDF to Markdown** — convert open-access PDFs into clean Markdown for LLMs
- **AI Web Scraper** — extract structured fields (methods, datasets, results) from paper landing pages
- **RSS Feed Reader & Monitor** — follow journal RSS feeds for new issues

# Actor input Schema

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

Keywords or phrases, e.g. "retrieval augmented generation", "CRISPR off-target". Use quotes for exact phrases. arXiv field syntax (ti:, au:, cat:) is supported for the arXiv source.

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

Crossref (150M+ DOIs, all disciplines, citation counts), arXiv (physics, CS, maths, stats preprints with PDFs) and Europe PMC (PubMed + PMC life-science literature and preprints).

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

Papers to fetch from each source for each query.

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

Relevance, newest first, or most cited first (citations available for Crossref and Europe PMC; arXiv falls back to relevance).

## `yearFrom` (type: `integer`):

Only papers published in or after this year.

## `yearTo` (type: `integer`):

Only papers published in or before this year.

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

Only open-access papers (arXiv is always open access).

## `includeAbstract` (type: `boolean`):

Include the abstract when the source provides it.

## `dedupe` (type: `boolean`):

Return each paper once (matched by DOI, arXiv ID or title + year), even if several sources have it.

## `strictMatching` (type: `boolean`):

When sorting Crossref by newest or most cited, keep only papers whose title/abstract/subjects contain all query terms (Crossref otherwise matches any term).

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

Fetch full metadata for specific DOIs (e.g. 10.1038/nature14539 or https://doi.org/...). Returned regardless of filters.

## `includeAllCrossrefTypes` (type: `boolean`):

Also include book chapters, datasets, components etc. Default: journal articles, conference papers and preprints only.

## Actor input object example

```json
{
  "queries": [
    "retrieval augmented generation evaluation"
  ],
  "sources": [
    "crossref",
    "arxiv",
    "europepmc"
  ],
  "maxResultsPerSource": 50,
  "sort": "relevance",
  "openAccessOnly": false,
  "includeAbstract": true,
  "dedupe": true,
  "strictMatching": true,
  "includeAllCrossrefTypes": false
}
```

# Actor output Schema

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

Title, authors, year, venue, citations, DOI, links.

## `abstracts` (type: `string`):

Abstracts and subjects.

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

Papers per source and any source errors.

# 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": [
        "retrieval augmented generation evaluation"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("pinkish_armada/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 = { "queries": ["retrieval augmented generation evaluation"] }

# Run the Actor and wait for it to finish
run = client.actor("pinkish_armada/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 '{
  "queries": [
    "retrieval augmented generation evaluation"
  ]
}' |
apify call pinkish_armada/academic-paper-search --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,pinkish_armada/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/bMFHsd5vv5zDMqvlc/builds/tdCDwbYAeN9lHat4i/openapi.json