# Academic Papers Search - Crossref, arXiv & PubMed (`pushbuild/academic-papers`) Actor

Search scholarly papers across Crossref (150M+ works), arXiv and PubMed in one run. Get title, abstract, authors, affiliations, DOI, journal, date, citation count, subjects, MeSH terms and PDF links. Filter by date, sort by newest or most cited, or look up DOIs.

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

## Pricing

$0.80 / 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 Papers Search - Crossref, arXiv & PubMed

Search the scholarly literature from one actor. Query Crossref (150M+ journal articles, books and proceedings), arXiv (preprints) and PubMed (biomedicine) together, and get clean, de-duplicated paper records ready for a spreadsheet, a database or an AI pipeline.

### What you get per paper

Title, abstract, authors, affiliations, DOI, publication date and year, journal or venue, publisher, type, citation count (Crossref), reference count, subjects or arXiv categories, MeSH terms and keywords (PubMed), licence, volume, issue and pages, and links to the landing page and PDF (arXiv, PubMed Central, open-access publishers).

### Features

- Several sources in one run, de-duplicated by DOI
- Sort by relevance, newest, or most cited
- Publication date filters
- DOI lookup
- Native query syntax: arXiv `ti:`, `au:`, `cat:cs.CL`; PubMed `[tiab]`, `[au]`, MeSH terms

### Input example

```json
{ "searchQueries": ["large language models clinical"], "sources": ["crossref", "pubmed", "arxiv"], "sort": "newest", "fromDate": "2025-01-01", "maxResultsPerQuery": 200 }
```

### Use cases

Literature reviews, research monitoring on a schedule, building RAG and training corpora from abstracts, competitive R\&D intelligence, and finding experts by topic.

### Pricing

Pay per paper returned.

Data comes from the official public APIs of Crossref, arXiv and NCBI PubMed. Thank you to arXiv for use of its open access interoperability.

# Actor input Schema

## `searchQueries` (type: `array`):

Topics or keywords. arXiv field syntax (ti:, au:, cat:) and PubMed syntax (\[tiab], \[au], MeSH) work for those sources.

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

Which databases to search. Results from several sources are de-duplicated by DOI.

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

Look up specific papers by DOI or doi.org link.

## `maxResultsPerQuery` (type: `integer`):

Cap for each query in each source.

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

Result order.

## `fromDate` (type: `string`):

Earliest publication date (YYYY-MM-DD).

## `untilDate` (type: `string`):

Latest publication date (YYYY-MM-DD).

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

Crossref only: keep works that carry a licence (mostly open access).

## `crossrefType` (type: `string`):

Crossref only: restrict to one type of work.

## Actor input object example

```json
{
  "searchQueries": [
    "large language models clinical"
  ],
  "sources": [
    "crossref",
    "arxiv",
    "pubmed"
  ],
  "maxResultsPerQuery": 100,
  "sort": "relevance",
  "openAccessOnly": false
}
```

# 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 = {
    "searchQueries": [
        "large language models clinical"
    ],
    "sources": [
        "crossref",
        "arxiv",
        "pubmed"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("pushbuild/academic-papers").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 = {
    "searchQueries": ["large language models clinical"],
    "sources": [
        "crossref",
        "arxiv",
        "pubmed",
    ],
}

# Run the Actor and wait for it to finish
run = client.actor("pushbuild/academic-papers").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 '{
  "searchQueries": [
    "large language models clinical"
  ],
  "sources": [
    "crossref",
    "arxiv",
    "pubmed"
  ]
}' |
apify call pushbuild/academic-papers --silent --output-dataset

```

## MCP server setup

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

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/mvdj32hlgeMbLQDKN/builds/mzzRERThlaVFycy7b/openapi.json