# Crossref Scraper - Papers, Authors & Citations (`antishock/crossref-academic-publications-scraper`) Actor

Extract academic publication metadata from Crossref: DOI, title, authors, ORCIDs, affiliations, journal, publisher, citation count, subjects and funders. Search by keyword, author, journal, publisher, work type and date range. 160M+ records, no API key required.

- **URL**: https://apify.com/antishock/crossref-academic-publications-scraper.md
- **Developed by:** [Ryan Zinburg](https://apify.com/antishock) (community)
- **Categories:** Other, Business
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 result exporteds

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

## Crossref Scraper - Academic Papers, Authors, Citations & Journals

Extract scholarly publication metadata from **Crossref**, the DOI registration agency behind most academic publishing. More than 160 million records covering journal articles, book chapters, preprints, conference papers and datasets.

Public API, no key required, no proxy needed.

### What you get per publication

| Field | Example |
|---|---|
| `doi` | 10.1016/j.mlwa.2026.101002 |
| `title` | Machine learning model for cardiac sarcomere twitch contraction dynamics |
| `type` | journal-article, book-chapter, posted-content, proceedings-article |
| `journal`, `journalShort` | Machine Learning with Applications |
| `publisher` | Elsevier BV |
| `publishedDate`, `publishedYear` | 2026-09-01 |
| `citationCount` | how often the work has been cited |
| `referencesCount` | size of its own bibliography |
| `authors`, `firstAuthor`, `authorCount` | full author list |
| `orcids` | ORCID identifiers of the authors |
| `affiliations` | institutions, where deposited |
| `issn`, `volume`, `issue`, `page` | bibliographic details |
| `subjects` | subject categories |
| `funders` | funding organisations |
| `licenseUrl` | licence of the work |
| `url` | resolvable DOI link |

### Search filters

- **searchTerm** - bibliographic search across title, journal and abstract, e.g. `machine learning`
- **author** - author name, e.g. `Hinton`
- **journal** - journal or container title, e.g. `Nature Communications`
- **publisher** - e.g. `Elsevier`, `Springer`
- **workType** - journal article, book chapter, preprint, conference paper, dataset and more
- **publishedAfter** / **publishedBefore** - publication date range
- **openAccessOnly** - only works with a deposited licence
- **hasOrcid** - only works with at least one ORCID-identified author
- **sortBy** - newest first, most cited first or most relevant
- **maxResults** - up to 10 000 works per run

### Example input

```json
{
  "searchTerm": "large language models",
  "publishedAfter": "2026-01-01",
  "sortBy": "citations",
  "maxResults": 500
}
```

### Use cases

- **Competitive research intelligence** - see who publishes in your field, where and how often
- **Academic recruiting** - find prolific authors by topic, institution and ORCID
- **Publisher and journal analytics** - measure output and citation impact per journal or publisher
- **Literature reviews** - export a structured corpus instead of copying references by hand
- **R\&D scouting** - track which funders and institutions back a research area
- **Bibliometrics and reporting** - build citation and output dashboards

### Why Crossref

Crossref is the infrastructure publishers use to mint DOIs, so its metadata is the source that indexing services build on. It is open, complete for participating publishers and free to query at scale, with deep pagination through cursors rather than a hard result cap.

### Notes

- Affiliations and ORCIDs are only present when the publisher deposited them; coverage is good for recent works and thinner before roughly 2015.
- `citationCount` counts citations recorded within Crossref, which is usually lower than Google Scholar.
- Deep result sets are paged with Crossref cursors, so large exports stay reliable.
- "Newest first" orders by the date the record entered Crossref. Sorting by the printed publication date is not possible together with deep paging, and that field contains future-dated issues.

# Actor input Schema

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

Bibliographic search across title, journal and abstract.

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

Author name, e.g. Hinton.

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

Journal or container title, e.g. Nature Communications.

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

Publisher name, e.g. Elsevier, Springer.

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

Restrict to one kind of scholarly output.

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

Earliest publication date, YYYY-MM-DD or YYYY.

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

Latest publication date, YYYY-MM-DD or YYYY.

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

Restrict to works that have a deposited licence, a good proxy for open access.

## `hasOrcid` (type: `boolean`):

Restrict to works where at least one author has an ORCID.

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

Order of the results.

## `maxResults` (type: `integer`):

How many publications to save.

## Actor input object example

```json
{
  "searchTerm": "machine learning",
  "workType": "journal-article",
  "publishedAfter": "2026-01-01",
  "openAccessOnly": false,
  "hasOrcid": false,
  "sortBy": "newest",
  "maxResults": 100
}
```

# Actor output Schema

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

Scraped records in the default dataset.

# 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 = {
    "searchTerm": "machine learning",
    "workType": "journal-article",
    "publishedAfter": "2026-01-01",
    "maxResults": 100
};

// Run the Actor and wait for it to finish
const run = await client.actor("antishock/crossref-academic-publications-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 = {
    "searchTerm": "machine learning",
    "workType": "journal-article",
    "publishedAfter": "2026-01-01",
    "maxResults": 100,
}

# Run the Actor and wait for it to finish
run = client.actor("antishock/crossref-academic-publications-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 '{
  "searchTerm": "machine learning",
  "workType": "journal-article",
  "publishedAfter": "2026-01-01",
  "maxResults": 100
}' |
apify call antishock/crossref-academic-publications-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,antishock/crossref-academic-publications-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/HILNqjwaimsywnU3Y/builds/kT5BcfyDJx11s49yt/openapi.json