Apify Actor · EDUCATION, AI

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

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 …

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,…

Run it on the Apify Store $2.00 per 1,000 results, billed by Apify; Apify's free plan includes monthly credit.

Inputs

Field	Type	What it does	Example
`query`	string	Search terms: Keywords to search for in titles, abstracts and full text.	`"graph neural networks"`
`year_from`	integer	From year: First publication year to include.	`2020`
`year_to`	integer	To year: Last publication year to include.	`2026`
`oa_status`	string	Open-access status: Keep only works with this open-access status (empty: all works).	`"green"`
`maxItems`	integer	Max results: Stop after this many results (keeps a run's cost predictable).	`500`

What each result holds

Field	Type
`openalex_id` (always set)	url
`title` (always set)	text
`doi`	text
`year`	integer
`publication_date`	date
`venue`	text
`type`	text
`cited_by_count`	integer
`is_open_access`	bool
`oa_status`	text
`open_access_url`	url
`language`	text

Sample output

The first 3 results of its latest test run on the platform (long values shortened).

[
  {
    "cited_by_count": 573,
    "doi": "10.48550/arxiv.2003.00982",
    "is_open_access": true,
    "language": "en",
    "oa_status": "green",
    "open_access_url": "https://arxiv.org/pdf/2003.00982",
    "openalex_id": "https://openalex.org/W3007332492",
    "publication_date": "2020-03-02",
    "title": "Benchmarking Graph Neural Networks",
    "type": "preprint",
    "venue": "arXiv (Cornell University)",
    "year": 2020
  },
  {
    "cited_by_count": 11009,
    "doi": "10.1109/tnnls.2020.2978386",
    "is_open_access": true,
    "language": "en",
    "oa_status": "green",
    "open_access_url": "https://arxiv.org/pdf/1901.00596",
    "openalex_id": "https://openalex.org/W2907492528",
    "publication_date": "2020-03-24",
    "title": "A Comprehensive Survey on Graph Neural Networks",
    "type": "review",
    "venue": "IEEE Transactions on Neural Networks and Learning Systems",
    "year": 2020
  },
  {
    "cited_by_count": 549,
    "doi": "10.1145/3394486.3403076",
    "is_open_access": true,
    "language": "en",
    "oa_status": "green",
    "open_access_url": "https://arxiv.org/pdf/2007.09296",
    "openalex_id": "https://openalex.org/W3042770487",
    "publication_date": "2020-08-20",
    "title": "Towards Deeper Graph Neural Networks",
    "type": "conference-paper",
    "venue": "ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)",
    "year": 2020
  }
]

First call in 60 seconds

Get an Apify token (the Store page; Apify's free plan includes monthly credit)

Run one call with the example input (the Apify API):

curl -X POST "https://api.apify.com/v2/acts/nonstop_poacher~store-scholarly-search/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"maxItems": 500, "oa_status": "green", "query": "graph neural networks", "year_from": 2020, "year_to": 2026}'

Read the answer. The first result of the record's own sample run, fields openalex_id, title, doi, year, publication_date, venue:

{
  "cited_by_count": 573,
  "doi": "10.48550/arxiv.2003.00982",
  "is_open_access": true,
  "language": "en",
  "oa_status": "green",
  "open_access_url": "https://arxiv.org/pdf/2003.00982",
  "openalex_id": "https://openalex.org/W3007332492",
  "publication_date": "2020-03-02"
}

Call it from code

With the Apify API (one request; the response is the results):

curl -X POST "https://api.apify.com/v2/acts/nonstop_poacher~store-scholarly-search/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"maxItems": 500, "oa_status": "green", "query": "graph neural networks", "year_from": 2020, "year_to": 2026}'

With the Python client (pip install apify-client):

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("nonstop_poacher/store-scholarly-search").call(run_input={'maxItems': 500,
 'oa_status': 'green',
 'query': 'graph neural networks',
 'year_from': 2020,
 'year_to': 2026})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

From an AI agent: connect Apify's MCP server and add the Actor nonstop_poacher/store-scholarly-search.

Guides

Finding open-access graph neural network papers — with Scholarly Paper Search (OpenAlex): DOI, year, venue, citations, open-access link

Questions

What does it cost? $2.00 per 1,000 results, charged per result through your Apify account. You pay only for results it returns.

Something wrong? Open an issue on the Issues tab of its Store page.