# Scholarly Papers by DOI or Search (Crossref) (`scraperworks/crossref-papers`) Actor

Look up research papers by DOI, or search 160 million+ scholarly works by topic, year and type. Get title, authors, journal, year, citation count, abstract and a ready APA citation, from Crossref's open metadata. Clean JSON for researchers and AI agents.

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

## Pricing

from $2.00 / 1,000 results

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

## Scholarly Papers by DOI or Search (Crossref)

Look up research papers by **DOI**, or search more than 160 million scholarly works (journal articles, books, conference papers, preprints, datasets) by topic, year and type. Each paper comes back with its title, authors, journal, year, citation count, abstract and a **ready-made APA citation**.

The data is Crossref's open metadata, read through Crossref's official REST API. Crossref is the registry most publishers use for their DOIs.

### Who it's for

- **AI agents and research assistants** that need "the full citation for this DOI" or "the most-cited papers on X" in the middle of a task. Inputs are one line and outputs are small.
- **Researchers and students** building reading lists and reference lists.
- **Librarians, analysts and publishers** checking metadata and citation counts in bulk.

### Input

| Field | What it does |
|---|---|
| **DOIs** | Papers to look up: `10.1038/nature12373`, `doi:10.1038/...` or a `https://doi.org/...` link. Up to 500 |
| **Search for** | Words to search titles, authors, journals and abstracts for |
| **Type** | Search only: journal articles, book chapters, books, conference papers, preprints, datasets, dissertations or reports |
| **From year / To year** | Search only: the publication year range |
| **Order** | Search only: *Best match*, *Newest first* or *Most cited first* |
| **Max search results** | Up to 1,000 (default 20). DOIs you list are always looked up |
| **Include abstracts** | On by default |

Examples:

```json
{ "dois": ["10.1038/nature12373", "https://doi.org/10.1371/journal.pone.0115253"] }
```

```json
{ "query": "crispr gene editing", "type": "journal-article", "fromYear": 2020, "sort": "most-cited", "maxItems": 50 }
```

### Output

One row per paper. A real example:

```json
{
  "doi": "10.1038/nature12373",
  "title": "Nanometre-scale thermometry in a living cell",
  "authors": ["G. Kucsko", "P. C. Maurer", "N. Y. Yao", "M. Kubo", "H. J. Noh", "P. K. Lo", "H. Park", "M. D. Lukin"],
  "year": 2013,
  "published": "2013-07-31",
  "journal": "Nature",
  "publisher": "Springer Science and Business Media LLC",
  "type": "journal-article",
  "volume": "500",
  "issue": "7460",
  "pages": "54-58",
  "citedBy": 1851,
  "references": 30,
  "abstract": null,
  "subjects": [],
  "issn": ["0028-0836", "1476-4687"],
  "url": "https://doi.org/10.1038/nature12373",
  "citation": "Kucsko, G., Maurer, P. C., Yao, N. Y., Kubo, M., Noh, H. J., Lo, P. K., Park, H., & Lukin, M. D. (2013). Nanometre-scale thermometry in a living cell. Nature, 500(7460), 54-58. https://doi.org/10.1038/nature12373",
  "scrapedAt": "2026-10-19T12:00:00Z"
}
```

A DOI Crossref doesn't know gets one free row with an `error` that says so.

### Pricing

Pay per result: **$2.00 per 1,000 papers**. Error rows are free. You can set a spending limit on any run, and the run stops cleanly when it's reached.

### Good to know

- **Abstracts come from the publisher.** Many publishers deposit them with Crossref and some don't. Where there's none, `abstract` is `null`.
- **`citedBy` counts citations within Crossref's data.** It's usually lower than Google Scholar's figure, which also counts sources outside Crossref.
- **Some DOIs aren't Crossref's.** Most datasets, for example, are registered with DataCite. Those DOIs get an error row.
- **Authors are listed by name only, as printed on the paper.** We don't return their affiliations, ORCID iDs or contact details.

# Actor input Schema

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

Papers to look up, one per line: 10.1038/nature12373, doi:10.1038/..., or a doi.org link. Up to 500.

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

Words to search titles, authors, journals and abstracts for, e.g. "crispr gene editing". Best matches come first, unless you pick another order.

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

Only this kind of work.

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

Only works published in or after this year.

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

Only works published in or before this year.

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

Best match, newest first, or most cited first.

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

How many search results to return, up to 1,000. DOIs you list are always looked up.

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

Add the abstract as plain text, when the publisher deposited one with Crossref.

## Actor input object example

```json
{
  "dois": [
    "10.1038/nature12373"
  ],
  "type": "any",
  "sort": "relevance",
  "maxItems": 20,
  "includeAbstract": true
}
```

# Actor output Schema

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

One row per paper, plus an error row for each DOI Crossref doesn't know.

# 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 = {
    "dois": [
        "10.1038/nature12373"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("scraperworks/crossref-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 = { "dois": ["10.1038/nature12373"] }

# Run the Actor and wait for it to finish
run = client.actor("scraperworks/crossref-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 '{
  "dois": [
    "10.1038/nature12373"
  ]
}' |
apify call scraperworks/crossref-papers --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,scraperworks/crossref-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/1u7je8MUzEgnkwjt3/builds/dTzQjFXmRAJf0YbWs/openapi.json