# CORE Open Access Scraper - Papers, Abstracts, Authors & PDFs (`ninhothedev/core-ac-scraper`) Actor

$0.5/1K 🔥 CORE scraper! 300M+ open-access papers with abstracts, authors & PDF links. No key. JSON, CSV, Excel or API in seconds. Build RAG corpora & literature reviews ⚡

- **URL**: https://apify.com/ninhothedev/core-ac-scraper.md
- **Developed by:** [ninhothedev](https://apify.com/ninhothedev) (community)
- **Categories:** AI, Developer tools
- **Stats:** 2 total users, 1 monthly users, 50.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.50 / 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/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.
Actors are written with capital "A".

## 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.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## CORE Open Access Papers Scraper

Scrape **300M+ open-access research papers** from [CORE](https://core.ac.uk), the world's largest aggregator of open-access research. Search any topic and get clean, structured JSON with titles, abstracts, authors, DOIs, publication years, publishers, fields of study, citation counts and **direct PDF download links** — ready for literature reviews, RAG pipelines and bibliometric analysis.

**No API key. No login. No proxy required.**

***

### Why this actor

CORE indexes papers from more than 10,000 repositories and journals worldwide — university archives, arXiv mirrors, institutional repositories and open-access publishers. This actor turns that index into a spreadsheet or a JSON feed in seconds.

- **Zero configuration** — type a search query, press Start.
- **PDF links included** — most records carry a direct `download_url` to the open-access full text.
- **Datacenter-clean** — the public CORE search endpoint answers without proxies, so runs stay cheap.
- **Permissive output** — every field is nullable, so one odd repository record never fails your run.

***

### Pricing

Roughly **$0.50 per 1,000 papers** on the Apify platform (compute units only — no proxy costs, no API subscription). A 100-paper test run costs a few cents.

***

### Use cases

- **Literature review** — pull every paper matching a topic, then sort by year or citation count in Excel.
- **Open access research** — build a list of freely downloadable PDFs on a subject, with no paywall friction.
- **RAG corpora** — harvest titles + abstracts + full-text links to feed a retrieval-augmented generation pipeline or a custom research assistant.
- **Bibliometrics** — analyse publication trends, prolific authors, repository coverage and citation distributions across a field.

***

### Input

| Field | Type | Default | Description |
| --- | --- | --- | --- |
| `mode` | select | `search` | Scraping mode. `search` runs your queries against the CORE index. |
| `queries` | array | `["machine learning", "climate change"]` | One or more free-text queries. CORE query syntax is supported, e.g. `title:(deep learning) AND yearPublished>2020`. |
| `maxItems` | integer | `100` | Maximum total papers across all queries (max `1000`). |

#### Example input

```json
{
    "mode": "search",
    "queries": ["quantum computing", "CRISPR gene editing"],
    "maxItems": 500
}
```

***

### Output

One dataset item per paper.

```json
{
    "core_id": "171513974",
    "title": "Learning curves for decision making in supervised machine learning: a survey",
    "abstract": "Learning curves are a concept from social sciences that has been adopted in the context of machine learning...",
    "authors": ["van Rijn J.N.", "Mohr F."],
    "year": 2024,
    "published_date": "2024-01-01T00:00:00+00:00",
    "doi": "10.1007/s10994-024-06619-7",
    "publisher": null,
    "document_type": null,
    "language": "English",
    "fields_of_study": ["Computer Science"],
    "citation_count": 0,
    "download_url": "https://core.ac.uk/download/641684400.pdf",
    "full_text_url": null,
    "data_providers": ["Intellectum (Universidad de La Sabana)"],
    "url": "https://core.ac.uk/works/171513974",
    "source": "core",
    "scraped_at": "2026-07-28T12:00:00+00:00"
}
```

#### Field reference

| Field | Description |
| --- | --- |
| `core_id` | CORE work identifier |
| `title` | Paper title |
| `abstract` | Abstract, capped at 5,000 characters |
| `authors` | List of author names |
| `year` | Publication year |
| `published_date` | Publication date, ISO-8601 |
| `doi` | Digital Object Identifier |
| `publisher` | Publisher name, when reported |
| `document_type` | Document type, when reported |
| `language` | Language name |
| `fields_of_study` | Subject / discipline labels |
| `citation_count` | Citations known to CORE |
| `download_url` | Direct open-access PDF link |
| `full_text_url` | Alternative full-text location |
| `data_providers` | Repositories that supplied the record |
| `url` | Canonical CORE page |
| `source` | Always `core` |
| `scraped_at` | UTC scrape timestamp |

Every field is nullable — coverage varies by source repository.

***

### Notes and limits

- Results are paginated 50 per request; `maxItems` caps the total.
- CORE's public endpoint occasionally rate-limits; the actor retries with backoff.
- Full text bodies are not exposed to public API users — use `download_url` for the PDF.
- Data is aggregated open-access metadata, published by CORE for reuse.

***

### Related actors

- [OpenAlex Scraper](https://apify.com/ninhothedev/openalex-scraper) — scholarly works, authors and institutions graph
- [Crossref Scraper](https://apify.com/ninhothedev/crossref-scraper) — DOI metadata for 150M+ registered records
- [Unpaywall Scraper](https://apify.com/ninhothedev/unpaywall-scraper) — find legal free PDFs for any DOI
- [Figshare Scraper](https://apify.com/ninhothedev/figshare-scraper) — datasets, figures and preprints

***

### Support

Issues and feature requests: open an issue on the actor page.

# Actor input Schema

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

Scraping mode. 'search' runs each of your queries against the public CORE search index and returns the matching open-access papers.

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

One or more free-text search queries (keywords, phrases, author names, or CORE query syntax such as 'title:(deep learning) AND yearPublished>2020'). Each query is paginated separately until the total item limit is reached.

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

Maximum total number of papers to push to the dataset across all queries. Keeps runs cheap and predictable. Minimum 1, maximum 1000.

## Actor input object example

```json
{
  "mode": "search",
  "queries": [
    "machine learning",
    "climate change"
  ],
  "maxItems": 100
}
```

# 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": [
        "machine learning",
        "climate change"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("ninhothedev/core-ac-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 = { "queries": [
        "machine learning",
        "climate change",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("ninhothedev/core-ac-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 '{
  "queries": [
    "machine learning",
    "climate change"
  ]
}' |
apify call ninhothedev/core-ac-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,ninhothedev/core-ac-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/mm88e6cVzEaLiYV7O/builds/lihJ2OX16TCK2dCKc/openapi.json