# CORE Open Access Research Scraper (`parseforge/core-ac-uk-scraper`) Actor

Scrapes open access research paper metadata from CORE by keyword or title query. Returns each paper as a flat row with title, authors, abstract, year, language, and links. Supports year and language filters, up to 1,000,000 papers per run.

- **URL**: https://apify.com/parseforge/core-ac-uk-scraper.md
- **Developed by:** [ParseForge](https://apify.com/parseforge) (community)
- **Categories:** Education, Automation, Integrations
- **Stats:** 2 total users, 1 monthly users, 89.7% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $7.50 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#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

[![ParseForge](https://raw.githubusercontent.com/ParseForge/apify-assets/main/banner.jpg)](https://apify.com/parseforge?fpr=vmoqkp)

### CORE Open Access Research Scraper

**Scrape open access research papers from CORE, the world's largest aggregator.** Get titles, authors, abstracts, publication years, and full-text links for any query, filtered by year and language. No API key required. Export to CSV, JSON, Excel, or XML.

CORE's official API requires an API key and has rate limits that slow down large-scale research. This Actor queries the public CORE v3 search endpoint directly, so you can collect metadata for thousands of open access papers without registration. Each result is returned as a flat row with consistent fields, ready for analysis or export.

| Who uses it | What they scrape CORE for |
|---|---|
| Academic researchers | Building a literature review dataset for a specific topic |
| Librarians | Monitoring new open access publications in a subject area |
| Data scientists | Gathering paper metadata for bibliometric analysis |
| PhD students | Finding recent papers in their field without manual searching |

### What it does

This Actor collects open access research paper metadata from CORE by keyword or title query, and returns each paper as a flat row.

- 🔍 **Keyword search:** Enter any title or keyword and get matching papers from CORE's index.
- 📅 **Year filters:** Set a minimum and maximum publication year to narrow results to a specific time range.
- 🌐 **Language filter:** Restrict results to papers written in a specific language using a two-letter ISO code.
- ⚙️ **Flexible limits:** Collect anywhere from 1 to 1,000,000 papers per run, depending on your needs.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

### What you can do with CORE data

**📚 Build a literature review dataset.**

A researcher enters a topic keyword and a year range, then exports the resulting paper metadata to a CSV for screening in a spreadsheet.

**📈 Track publication trends.**

A data scientist runs the Actor monthly with the same query and year filters to collect new papers, then analyzes counts by year and language.

**🌍 Monitor open access output by language.**

A librarian sets the language code to 'es' and a recent year range to find Spanish-language papers added to CORE in the last quarter.

**🔎 Discover papers for a systematic review.**

A PhD student searches for a specific title phrase and collects all matching records, then deduplicates by CORE ID before full-text screening.

### Why choose this scraper

|  | What you get |
|---|---|
| **No API key** | Uses the public CORE search endpoint, so you can start scraping immediately without registration. |
| **Large scale** | Collect up to a million paper records in a single run, limited only by CORE's public search. |
| **Consistent schema** | Every paper is returned with the same fields, making it easy to merge datasets across runs. |
| **Export ready** | Download results as CSV, JSON, Excel, or XML for use in any analysis tool. |

### How it compares

No other Store actor targets CORE the same way, so the honest comparison is with the alternatives teams actually weigh.

| | CORE Open Access Research Scraper | Build it in-house | By hand |
|---|---|---|---|
| Setup | Run it now, zero config | Days of engineering | None, but hours per pull |
| When CORE changes | Maintained for you | You fix it | You re-learn the page |
| Proxies, retries, anti-bot | Built in | Your problem | Browser only |
| Output | Fixed JSON schema, CSV/Excel export | Whatever you build | Copy-paste |
| Cost | Pay per result | Engineering time | Analyst hours |

### Configure the run

Drive the Actor with a search query, and optionally filter by publication year range and language. The maximum number of papers to collect is set per run. The Input tab lists every parameter.

A first run with the defaults:

```json
{
  "maxItems": 10
}
```

A larger pull:

```json
{
  "maxItems": 200
}
```

### Pricing

Pay-per-result: **$0.0085 per result** collected. You pay only for the results written to your dataset.

| Results collected | Approximate cost |
|---|---|
| 100 results | $0.85 |
| 1,000 results | $8.50 |
| 10,000 results | $85.00 |

New Apify accounts start with $5 in free credit.

### Free users

Free-plan runs return up to 10 results as a preview. [Upgrade your Apify plan](https://console.apify.com/sign-up?fpr=vmoqkp) to collect up to 1,000,000 results per run.

### Run it

1. [Create a free Apify account with $5 in credit](https://console.apify.com/sign-up?fpr=vmoqkp).
2. Open the [CORE Open Access Research Scraper](https://apify.com/parseforge/core-ac-uk-scraper?fpr=vmoqkp).
3. Set your inputs and any filters, then click **Start**.
4. Export the results as CSV, Excel, JSON, or XML from the **Dataset** tab.

Run it programmatically through the [Apify API](https://docs.apify.com/api/v2) (`run-sync-get-dataset-items`) or the [ApifyClient](https://docs.apify.com/api/client/js) for JavaScript and Python.

### Use with AI agents (MCP)

Give an AI agent live access to CORE through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

```bash
claude mcp add --transport http apify "https://mcp.apify.com/?tools=parseforge/core-ac-uk-scraper"
```

Then prompt it in plain language to run the scraper and read back the results.

### Troubleshooting

**Why am I getting no results?**

Check your query for typos or overly specific phrases. Try a broader keyword. Also verify that your year range and language code are not too restrictive. CORE's public search may not index very recent papers immediately.

**Why are some papers missing from the results?**

CORE's public search endpoint may not return all matching records, especially for very large result sets. Try narrowing your query or year range, or run the Actor multiple times with different filters.

**The Actor returns fewer papers than my 'Maximum papers' setting.**

This means CORE found fewer matching papers than your limit. The Actor returns all available matches. If you expected more, broaden your query or remove filters.

**I get an error about the language code.**

Make sure you are using a valid two-letter ISO 639-1 code, such as 'en', 'fr', or 'de'. If you are unsure, leave the field empty to include all languages.

**The run takes a long time.**

Large result sets take longer to collect. Reduce the 'Maximum papers' value or narrow your query to speed up the run.

### FAQ

| Question | Answer |
|---|---|
| Do I need a CORE API key to use this Actor? | No. This Actor uses the public CORE search endpoint that does not require an API key. You can run it immediately without any registration. |
| What data does each paper record include? | Each record includes the paper title, authors, abstract, publication year, language, and links to the full text and CORE page. The exact fields are shown in the sample output. |
| Can I filter by publication year? | Yes. Use the 'Year from' and 'Year to' inputs to set a minimum and maximum publication year. Both are optional, so you can set only one if needed. |
| How do I filter by language? | Enter a two-letter ISO 639-1 language code in the 'Language code' field, for example 'en' for English, 'fr' for French, or 'es' for Spanish. Leave it empty to include all languages. |
| What is the maximum number of papers I can collect? | You can set 'Maximum papers' up to 1,000,000 per run. The actual number returned depends on how many results match your query on CORE. |
| Can I search for an exact paper title? | Yes. Enter the full title or a distinctive phrase in the 'Paper title or keyword' field. The Actor sends it as a full-text search query to CORE. |
| What export formats are supported? | You can export the results as CSV, JSON, Excel, or XML directly from the Apify dataset. |
| Is this Actor suitable for large-scale bibliometric studies? | Yes. You can collect up to a million records per run, and the flat schema makes it easy to combine multiple runs into one dataset. |
| Does this Actor get the full text of papers? | No. It collects metadata and links to the full text where available, but it does not download the PDF or full text content. |
| Can I run this Actor on a schedule? | Yes. You can set up a scheduled run in Apify to collect new papers matching your query at regular intervals. |

### Related actors

Browse the full [ParseForge collection](https://apify.com/parseforge?fpr=vmoqkp) for more scrapers.

🆘 **Need help?** Email parseforge@protonmail.com with your run ID, your input, and what you expected.

⚠️ **Disclaimer.** This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by CORE, The Open University. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.

# Actor input Schema

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

Full-text search query forwarded to the CORE v3 API.

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

Minimum publication year.

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

Maximum publication year.

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

Two-letter ISO 639-1 language code, for example en, fr, es.

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

How many papers to collect per run.

## Actor input object example

```json
{
  "query": "machine learning",
  "maxItems": 10
}
```

# 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 = {
    "query": "machine learning",
    "maxItems": 10
};

// Run the Actor and wait for it to finish
const run = await client.actor("parseforge/core-ac-uk-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 = {
    "query": "machine learning",
    "maxItems": 10,
}

# Run the Actor and wait for it to finish
run = client.actor("parseforge/core-ac-uk-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 '{
  "query": "machine learning",
  "maxItems": 10
}' |
apify call parseforge/core-ac-uk-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,parseforge/core-ac-uk-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/UWa4TrxWi2wY8zRhd/builds/G0M7mNcBK5HM3CMgO/openapi.json