# OpenAlex Scraper — Papers, Authors & Institutions (`haketa/openalex-scraper`) Actor

Scrape OpenAlex scholarly data: research papers (title, authors, DOI, citations, venue, abstract, open access), authors (ORCID, affiliation, h-index, topics) and institutions. Search any entity with filters. For research, bibliometrics and researcher lead gen. Not affiliated with OpenAlex.

- **URL**: https://apify.com/haketa/openalex-scraper.md
- **Developed by:** [Haketa](https://apify.com/haketa) (community)
- **Categories:** Lead generation
- **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.
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?

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

## OpenAlex Scraper — Papers, Authors & Institutions

> **Search and scrape the open index of science: research papers (title, authors, DOI, citations, venue, open access, reconstructed abstract), authors (ORCID, affiliation, h-index, topics) and institutions — all from OpenAlex.** Search any entity with powerful filters. Clean JSON/CSV/Excel in seconds. Built for research, bibliometrics, competitive science intelligence and researcher lead generation.

[![OpenAlex](https://img.shields.io/badge/OpenAlex-Open%20Science-0b5394)]()
[![Papers + Authors](https://img.shields.io/badge/Papers%20%2B%20Authors%20%2B%20Institutions-2da44e)]()
[![Research & Leads](https://img.shields.io/badge/Research%20%2F%20Researcher%20Leads-8250df)]()
[![Export](https://img.shields.io/badge/Export-JSON%20%2F%20CSV%20%2F%20Excel-fb8500)]()

***

### What This Actor Does

Pick an entity type and search the OpenAlex scholarly graph. You get one clean record per result:

#### Works (research papers)

Title, DOI, publication year/date, type, **citation count**, authors and their institutions, venue/journal, open-access status + URL, topics, language, and a **reconstructed plain-text abstract**.

#### Authors (researchers)

Name, **ORCID**, works count, citations, **h-index** and i10-index, last-known institution (+ country), affiliations and research topics — a ready researcher profile.

#### Institutions

Name, country, type, works count, citations, homepage, ROR ID, city/region and top topics.

Use **search terms** and/or OpenAlex **filters** (year, open access, country, and more).

***

### Why Use This

- **The whole graph of science, free.** 250M+ works, 90M+ authors and 100K+ institutions — no key, no login, no anti-bot.
- **Researcher lead generation.** Author records come with ORCID, affiliation and h-index — ideal for outreach, recruiting and expert discovery.
- **Bibliometrics & intelligence.** Citation counts, venues, topics and open-access status for any field or institution.
- **Clean abstracts.** Abstracts are reconstructed into plain text (OpenAlex stores them inverted).

***

### Quick Start

#### Run it in the console (no code)

1. Choose an **entity type**: Works, Authors or Institutions.
2. Add **search terms** and/or **filters**.
3. Set **Max results**, click **Start**, export as **JSON, CSV, Excel or HTML**.

#### Pull papers in a field (Python)

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {"entityType": "works", "searchTerms": ["machine learning"],
             "filters": ["publication_year:2023", "is_oa:true"], "maxItems": 1000}

run = client.actor("YOUR_USERNAME/openalex-scraper").call(run_input=run_input)

for w in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(w["publicationYear"], w["citedByCount"], "·", w["title"], "·", w["doi"])
```

#### Build a researcher list (Python)

```python
run = client.actor("YOUR_USERNAME/openalex-scraper").call(run_input={
    "entityType": "authors", "searchTerms": ["Yann LeCun", "Yoshua Bengio"], "maxItems": 200,
})
for a in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(a["name"], "·", a["orcid"], "·", f"h={a['hIndex']}", "·", a["lastKnownInstitution"])
```

***

### Input Parameters

| Field | Type | Description |
|---|---|---|
| `entityType` | string | `works`, `authors` or `institutions`. |
| `searchTerms` | array | Keywords to search. See the note below on how search works per entity. |
| `filters` | array | Raw OpenAlex filters, e.g. `publication_year:2023`, `is_oa:true`, `authorships.institutions.country_code:us`. |
| `email` | string | Optional email for OpenAlex's faster "polite pool" (recommended for large runs). |
| `reconstructAbstract` | boolean | Rebuild plain-text abstracts for works (default on). |
| `maxItems` | integer | Max results across all searches. `0` = no limit. |
| `maxPagesPerSearch` | integer | Pagination cap per search (200 per page). |
| `proxyConfiguration` | object | Apify Proxy. Datacenter is enough (public API). |

**How search works per entity:** for **works** and **institutions**, `searchTerms` matches titles/names and content (great for topic/keyword discovery). For **authors**, `searchTerms` matches the **author name** — to find researchers by topic instead, scrape `works` and read their authors, or use `filters`.

***

### Output (works example)

```json
{
  "id": "W2100837269",
  "title": "Scikit-learn: Machine Learning in Python",
  "doi": "https://doi.org/10.48550/arxiv.1201.0490",
  "publicationYear": 2012, "type": "article", "citedByCount": 63567,
  "authorNames": ["Fabián Pedregosa", "Gaël Varoquaux", "…"],
  "authorInstitutions": ["CEA"],
  "venue": "Journal of Machine Learning Research",
  "isOpenAccess": true, "oaUrl": "https://…",
  "topics": ["Machine Learning", "Python Applications"],
  "abstract": "Scikit-learn is a Python module integrating a wide range of …",
  "openAlexUrl": "https://openalex.org/W2100837269"
}
```

**About coverage:** identity fields (title/name, id, citation and works counts) are present for essentially every record. DOI, abstract, venue and ORCID are present where OpenAlex has them — not every work has a DOI or abstract, and not every author has a registered ORCID. This reflects the source data, not a gap in scraping.

***

### Use Cases

#### 1. Research & literature review

Pull all papers on a topic with citations, venues, open-access links and abstracts.

#### 2. Researcher discovery & lead generation

Build researcher lists with ORCID, affiliation and h-index for recruiting, outreach and expert networks.

#### 3. Bibliometrics & competitive science intelligence

Analyse citation trends, institutions, topics and open-access rates across a field.

#### 4. Institution benchmarking

Compare universities and labs by output, citations and research topics.

***

### Tips

- **`filters`** are powerful — combine with search, e.g. `publication_year:2020-2024`, `is_oa:true`, `cited_by_count:>100`.
- For **topic-based researcher discovery**, scrape `works` and collect their authors (author search is name-based).
- Add your **`email`** for OpenAlex's polite pool — faster and more consistent on large runs.
- **`citedByCount`** and **`hIndex`** make ranking and filtering easy.
- **Schedule it** with Apify Schedules to track new papers in your field.

***

### Frequently Asked Questions

**Do I need an account or key?**
No. OpenAlex is fully open — no login, key or anti-bot.

**How do I find researchers by topic, not name?**
Author search matches names. To find researchers in a topic, scrape `works` for that topic and read their authors, or use `filters`.

**Are abstracts included?**
Yes — for works, abstracts are reconstructed into plain text (OpenAlex stores them as an inverted index). Not every work has one.

**What filters can I use?**
Any OpenAlex filter, e.g. `publication_year`, `is_oa`, `type`, `authorships.institutions.country_code`, `cited_by_count`.

**What export formats are supported?**
JSON, CSV, Excel, HTML, or via API — plus Google Sheets, webhooks, Make and Zapier.

***

### Legal & Responsible Use

This Actor is an independent tool and is **not affiliated with, endorsed by, or sponsored by OpenAlex or OurResearch.** It reads only public, open scholarly data. Use the data responsibly and in line with applicable terms and laws.

# Actor input Schema

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

What to scrape: research papers, authors (researchers) or institutions.

## `searchTerms` (type: `array`):

Keywords to search (e.g. "machine learning", a researcher name, a university). Each runs as a separate search.

## `filters` (type: `array`):

Raw OpenAlex filter expressions, e.g. publication\_year:2023, is\_oa:true, authorships.institutions.country\_code:us. Combined with AND.

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

Optional email to join OpenAlex's faster 'polite pool'. Recommended for large runs.

## `reconstructAbstract` (type: `boolean`):

Rebuild the plain-text abstract for works from OpenAlex's inverted index.

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

Maximum results across all searches. 0 = no limit.

## `maxPagesPerSearch` (type: `integer`):

Pagination cap per search term (200 per page).

## `proxyConfiguration` (type: `object`):

Apify Proxy. The API is public — datacenter is enough and enabled by default.

## Actor input object example

```json
{
  "entityType": "works",
  "searchTerms": [
    "machine learning"
  ],
  "reconstructAbstract": true,
  "maxItems": 200,
  "maxPagesPerSearch": 20,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}
```

# Actor output Schema

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

OpenAlex ID

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

Work title

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

Author/institution

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

DOI (works)

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

Publication year

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

Publication date

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

Work/institution type

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

Citation count

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

Author names (works)

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

First author

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

Author institutions

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

Journal/source

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

Open access

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

Open-access URL

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

Language

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

Topics

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

Abstract (works)

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

ORCID (authors)

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

Works count

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

h-index (authors)

## `i10Index` (type: `string`):

i10-index

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

Last known institution

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

Institution country

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

Affiliations

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

Country (institutions)

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

Homepage URL

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

ROR ID

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

City

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

OpenAlex URL

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

ISO timestamp

# 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 = {
    "searchTerms": [
        "machine learning"
    ],
    "maxItems": 200,
    "proxyConfiguration": {
        "useApifyProxy": true
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("haketa/openalex-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 = {
    "searchTerms": ["machine learning"],
    "maxItems": 200,
    "proxyConfiguration": { "useApifyProxy": True },
}

# Run the Actor and wait for it to finish
run = client.actor("haketa/openalex-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 '{
  "searchTerms": [
    "machine learning"
  ],
  "maxItems": 200,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}' |
apify call haketa/openalex-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,haketa/openalex-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/3zswbG9Jmyalbm3hI/builds/xQiSGcy2vp3bjXhUu/openapi.json