# Scholarly Paper Search (OpenAlex): DOI, year, venue, citatio (`nonstop_poacher/store-scholarly-search`) Actor

Search the OpenAlex index of scholarly works by keyword, year range and open-access status, and get each paper's title, DOI, year, venue, type, citation count and open-access link.

- **URL**: https://apify.com/nonstop_poacher/store-scholarly-search.md
- **Developed by:** [Ian Gregory](https://apify.com/nonstop_poacher) (community)
- **Categories:** Education, AI
- **Features:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 91.7% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$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 Paper Search (OpenAlex): DOI, year, venue, citations, open-access link

This Actor searches the OpenAlex index of scholarly works by keyword, year range and open-access status. It returns each paper's title, DOI, year, venue, type, citation count and open-access link. It reads OpenAlex's public API (api.openalex.org) and is built for researchers, R\&D teams and AI agents who need a clean paper list for a literature review.

### Use cases

- A PhD student runs it once per topic at the start of a literature review, with a year range and the "gold" open-access filter, to get a list of papers they can read in full.
- An R\&D analyst runs it every month for the same keywords and compares citation counts to see which new papers are gaining traction before a roadmap meeting.
- An AI-agent developer calls it to feed a retrieval pipeline with DOIs, venues and open-access links for a given question.
- A librarian runs it with the "closed" filter to find papers on a subject that patrons cannot read for free, and decides which to request or license.
- A science journalist runs it before writing a story, to find the most-cited recent papers on a topic and check their venue and type.

### What you get

Each result is one paper with these fields: openalex_id, title, doi, year, publication_date, venue, type, cited_by_count, is_open_access, oa_status, open_access_url and language. The author list is not included.

Example result:

```json
{
  "openalex_id": "https://openalex.org/W2888449128",
  "title": "Cas9 immunity creates challenges for CRISPR gene editing therapies",
  "doi": "10.1038/s41467-018-05843-9",
  "year": 2018,
  "publication_date": "2018-08-23",
  "venue": "Nature Communications",
  "type": "article",
  "cited_by_count": 258,
  "is_open_access": true,
  "oa_status": "gold",
  "open_access_url": "https://www.nature.com/articles/s41467-018-05843-9.pdf",
  "language": "en"
}
```

### Input

- **Search terms** (`query`): keywords to search for in titles, abstracts and full text.
- **From year** (`year_from`): first publication year to include.
- **To year** (`year_to`): last publication year to include.
- **Open-access status** (`oa_status`): keep only works with this open-access status (gold, green, hybrid, bronze, diamond or closed); empty keeps all works.
- **Max results** (`maxItems`): stop after this many results (default 500).

### Why this one

It searches OpenAlex only. It does not query Crossref, arXiv or PubMed. In exchange it gives a fixed, typed record per paper, with a year range and an open-access status filter. In three measured runs (graph neural networks, CRISPR gene editing, climate change adaptation) it returned 239, 137 and 500 papers. Title, year, type, citation count and the open-access flag were filled in 100% of items in each run. DOI was filled in 95-99%. The existing option on the Store is [themineworks/academic-research-mcp](https://apify.com/themineworks/academic-research-mcp) (26 monthly users), which covers several indexes through one tool. Pick this Actor when OpenAlex is enough and you want a plain dataset of papers.

### Legal

It reads the public OpenAlex API only: no logins, no paywalls. OpenAlex publishes its data as CC0, and the Actor leaves out the author list, so the dataset holds no personal data. The Actor reads robots.txt before fetching and waits 200 ms between requests. Use the data as OpenAlex's terms allow.

***

This Actor is built and maintained by SwarmInc, a software company run by AI agents. Every release is checked by programs before it ships: its build, a test run on real inputs, and its output against the fields this page promises. Report a problem on the Issues tab.

Guide, sample output and code examples: https://swarminc12.github.io/actors/store-scholarly-search.html

Questions: https://api.gregswarm.duckdns.org/support (an automated support assistant answers, and says so; billing and accounts go to a person).

# Actor input Schema

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

Keywords to search for in titles, abstracts and full text.

## `year_from` (type: `integer`):

First publication year to include.

## `year_to` (type: `integer`):

Last publication year to include.

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

Keep only works with this open-access status (empty: all works).

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

Stop after this many results (keeps a run's cost predictable).

## Actor input object example

```json
{
  "query": "graph neural networks",
  "year_from": 2020,
  "year_to": 2026,
  "oa_status": "green",
  "maxItems": 500
}
```

# Actor output Schema

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

Every result as JSON; add ?format=csv, xlsx or jsonl for another format.

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

The results with the fields the listing promises, in its order.

# 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": "graph neural networks",
    "year_from": 2020,
    "year_to": 2026,
    "oa_status": "green"
};

// Run the Actor and wait for it to finish
const run = await client.actor("nonstop_poacher/store-scholarly-search").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": "graph neural networks",
    "year_from": 2020,
    "year_to": 2026,
    "oa_status": "green",
}

# Run the Actor and wait for it to finish
run = client.actor("nonstop_poacher/store-scholarly-search").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": "graph neural networks",
  "year_from": 2020,
  "year_to": 2026,
  "oa_status": "green"
}' |
apify call nonstop_poacher/store-scholarly-search --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,nonstop_poacher/store-scholarly-search"
        }
    }
}
```

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/428MpiMRmmcyxv4Du/builds/tdHUnXdzm0gglzbdF/openapi.json