OpenAlex Research Works Scraper

Search over 250 million research works on OpenAlex. Get title, authors with institutions, abstract, citations, open access links, topics, keywords and journal for every match.

What you get

• Keyword search across titles and abstracts
• Filter by year range, type, open access and minimum citations
• Authors with institutions and countries
• Abstract rebuilt as plain text, topics and keywords
• Best open access PDF link when one exists
• Deep pagination with no page limit

How to use

1. Open the Input tab. The form is already filled with a working example.
2. Change the fields to your own search, or keep the example to see the output format.
3. Click Start and download the results as JSON, CSV, Excel or HTML from the Output tab, or read them through the Apify API.

Input

Field	What it does
Search query	Words to search for.
Published from	DD-MM-YYYY (optional).
Published to	DD-MM-YYYY (optional).
Type	Kind of work.
Open access only	Only works with a free full text.
Minimum citations	Skip works cited fewer times.
Sort by	Order of results.
Maximum results	Stop after this many works.

Example input:

{
  "query": "retrieval augmented generation",
  "sort": "relevance_score",
  "maxItems": 100
}

Output

Each result is one row. Main fields:

Field	Description
openAlexId	OpenAlex ID
url	OpenAlex page
doi	DOI
title	Title
type	Work type
publicationDate	Publication date
year	Publication year
citations	Times cited
authors	Authors with institutions
journal	Journal or source
publisher	Publisher
isOpenAccess	Open access
openAccessUrl	Best free full text link
abstract	Abstract (plain text)
topics	Topics
keywords	Keywords
referencesCount	Number of references
language	Language
isRetracted	Retracted
countries	Author countries

Example output (shortened):

{
  "openAlexId": "https://openalex.org/W4389519118",
  "url": "https://openalex.org/W4389519118",
  "doi": "https://doi.org/10.18653/v1/2023.emnlp-main.495",
  "title": "Active Retrieval Augmented Generation",
  "type": "conference-paper",
  "publicationDate": "2023-01-01",
  "year": 2023,
  "citations": 462,
  "authors": [
    {
      "name": "Zhengbao Jiang",
      "orcid": "https://orcid.org/0000-0002-0315-6727",
      "institutions": [
        "Carnegie Mellon University"
      ],
      "position": "first"
    },
    {
      "name": "Frank F. Xu",
      "orcid": "https://orcid.org/0000-0002-9662-7582",
      "institutions": [
        "Carnegie Mellon University"
      ],
      "position": "middle"
    },
    {
      "name": "Luyu Gao",
      "orcid": "https://orcid.org/0009-0006-5806-3022",
      "institutions": [
        "Carnegie Mellon University"
      ],
      "position": "middle"
    }
  ],
  "journal": "Conference on Empirical Methods in Natural Language Processing (EMNLP)",
  "isOpenAccess": true,
  "openAccessUrl": "https://aclanthology.org/2023.emnlp-main.495.pdf",
  "abstract": "Zhengbao Jiang, Frank Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig. Pr...",
  "topics": [
    "Topic Modeling",
    "Information Retrieval and Search Behavior",
    "Natural Language Processing Techniques"
  ],
  "keywords": [
    "retrieval-augmented generation",
    "active learning (machine learning)",
    "document retrieval"
  ],
  "referencesCount": 62,
  "language": "en",
  "isRetracted": false,
  "countries": [
    "US"
  ]
}

Good to know

• The Actor never fails on a bad or empty input. It finishes with a clear message in the run status explaining what to change.
• Results are saved while the run is going, so you can open the dataset before it finishes.

Support

Found a field that is missing or a result that looks wrong? Open an issue from the Actor page and include the run link.