OpenAlex Scraper - Research Authors & Funders

What does OpenAlex Scraper do?

Academic publications as one row per author and affiliation — with ORCID, corresponding-author flag, institution, ROR and field-normalised citation impact.

No login. No API key. No proxies.

One row per researcher, not per paper

A paper with five authors across three institutions is five researchers and three labs, not one row. For anyone selling into research, recruiting, or mapping a field, the nested author array is the wrong shape.

An author affiliated with two institutions gets two rows — because that is two labs to approach, not one.

Three fields that do the work

isCorresponding — the corresponding author handles the paper's correspondence. It is the only author you know is reachable. It is also a sharply better list: measured on CRISPR papers, corresponding authors carry 96 % ORCID coverage against 69–80 % for all authors, at 3.5 rows per paper instead of 10.9.

orcid — the researcher's permanent identifier. It survives name changes and moves between institutions, which plain names do not.

fwci — field-weighted citation impact. 1.0 is the world average for that field and year; 16.0 is sixteen times it. A raw citation count cannot be compared between disciplines — a well-cited maths paper and a well-cited oncology paper differ by an order of magnitude. This one can.

Output

Field	Example
authorName, orcid	Martin Jínek, 0000-0002-…
authorPosition	first / middle / last
isCorresponding	true
institution, institutionRor	University of Zurich, 02crff812
institutionCountry, institutionType	CH, education
rawAffiliation	the department line as printed
title, doi, workUrl
journal, publisher, issn	Nature Reviews Molecular Cell Biology
publicationYear, publicationDate, type, language
citedByCount, fwci, referencedWorksCount	117, 16.05
isOpenAccess, oaStatus, oaUrl
isRetracted	flagged rather than hidden
topics, keywords, funders

Input

{
  "searchText": "machine learning",
  "fromYear": 2025,
  "correspondingOnly": false,
  "maxItems": 500
}

Field	Default	Notes
searchText	machine learning	Titles and abstracts
fromYear / toYear	2025	Publication year range
correspondingOnly	false	The contactable list
maxItems	500	Counts author rows, not papers
contactEmail	—	See below
institutionCountry	—	US, FR, DE, CN
institutionRor	—	One institution, e.g. 03vek6s52
workType	all	Articles, reviews, preprints, datasets
minCitations	—	Filters uncited work
openAccessOnly	false	Fills the PDF link

About contactEmail. OpenAlex reserves a much larger rate limit for callers who identify themselves. Without it, large runs hit throttling and slow down — the Actor backs off and continues, but it takes longer. The address goes only to OpenAlex, and the field is optional by design.

Limits, honestly

• An institution filter matches papers, not authors. Filtering by Harvard's ROR returns papers with at least one Harvard author — and every author on those papers is exported, including the other forty-nine. In testing, one 50-author physics collaboration filled an entire run. Filter rows on institutionRor after export when you want only that institution's people.
• fwci needs time to mean anything. A paper published last month has not been cited yet. Combining a recent year with minCitations will return almost nothing.
• Author rows multiply fast. Large collaborations carry hundreds of signatories; maxItems counts rows, so set it with that in mind.
• ORCID coverage varies by field and is best among corresponding authors.
• Some authors have no institution recorded; those rows keep the author and leave the institution empty rather than being dropped.
• Retracted papers are flagged, not removed. Check isRetracted.
• Author names, ORCIDs and affiliations are personal data published in the scholarly record. Using them for outreach in the EU or UK makes you the data controller.
• Not affiliated with OpenAlex or OurResearch.

How much does it cost?

You pay per author row returned: $0.003 each, that is $3.00 per 1,000. There is no start fee, and subscription plans pay less per author row.

The example input below asks for up to 500 author rows, so it costs $1.50 at most.

If a run reaches the spending limit you set, the output stops at that limit and never goes past it. You are never charged for rows that were not delivered.

Use OpenAlex Scraper as an API

Call it from your own code with the Apify client, here in Python:

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("DataIO/openalex-authors-affiliations").call(run_input={
    'searchText': 'machine learning',
    'fromYear': 2025,
    'maxItems': 500,
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

It also works from JavaScript, Make, Zapier, n8n, and from AI agents through the Apify MCP server.

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FAQ

Is it legal to use this data?

The actor reads public data from its official source, without logging in and without bypassing any access control. What you do with the data, for example contacting people listed in it, is your responsibility under the laws that apply to you, such as GDPR in Europe.

Can I run it on a schedule?

Yes. Create a schedule in Apify Console, daily or weekly for example, and each run delivers a fresh dataset, which you can send by email, webhook or integration.

Can AI agents use it?

Yes. It is available through the Apify MCP server, and every input field is described in its input schema, so an agent can call it directly.