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Alex Spinov
Alex Spinov

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5 Free Academic APIs You Should Know (250M+ Papers, No Scraping Needed)

#api #python #tutorial #discuss

Stop Scraping Google Scholar

If you're scraping Google Scholar, you're doing it wrong. There are 5 completely free APIs that give you structured access to 250M+ academic papers — with proper data fields, citation graphs, and even AI summaries.

I've been building research tools with these APIs for months. Here's what each one does best.

1. OpenAlex — The Discovery Engine (250M+ papers, no key)

Best for: Finding papers, tracking research trends, author analytics

import requests

# Search papers — sorted by most cited
resp = requests.get("https://api.openalex.org/works", params={
    "search": "CRISPR gene editing",
    "sort": "cited_by_count:desc",
    "per_page": 5
})

for work in resp.json()["results"]:
    print(f"[{work['publication_year']}] {work['title']}")
    print(f"  {work['cited_by_count']} citations")
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Why it's special: Broadest coverage (250M+ works), includes h-index for authors, tracks institutional affiliations. No API key needed.

Full tutorial →

2. Semantic Scholar — The AI-Powered One (200M+ papers, free key)

Best for: AI summaries of papers, recommendation engine, citation intent

# Get AI-generated TLDR for any paper
resp = requests.get(
    "https://api.semanticscholar.org/graph/v1/paper/search",
    params={"query": "attention mechanism", "limit": 3,
            "fields": "title,tldr,citationCount"}
)

for paper in resp.json()["data"]:
    print(f"{paper['title']}")
    if paper.get("tldr"):
        print(f"  AI Summary: {paper['tldr']['text']}")
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Why it's special: Only API with AI-generated paper summaries and "papers like this" recommendations. Built by Allen AI.

Full tutorial →

3. Crossref — The Metadata Authority (140M+ papers, no key)

Best for: DOI lookups, journal metadata, publisher info

# Look up any paper by DOI
doi = "10.1038/nature12373"
resp = requests.get(f"https://api.crossref.org/works/{doi}")
work = resp.json()["message"]

print(f"Title: {work['title'][0]}")
print(f"Journal: {work['container-title'][0]}")
print(f"Citations: {work['is-referenced-by-count']}")
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Why it's special: Canonical source for DOI metadata. Used by every academic publisher.

Full tutorial →

4. arXiv API — The Preprint Source (2M+ papers, no key)

Best for: CS, physics, math preprints before peer review

import requests

resp = requests.get("http://export.arxiv.org/api/query", params={
    "search_query": "all:transformer+AND+cat:cs.CL",
    "sortBy": "submittedDate",
    "sortOrder": "descending",
    "max_results": 5
})

# Returns Atom XML — parse with feedparser or xml.etree
import xml.etree.ElementTree as ET
root = ET.fromstring(resp.text)
ns = {"atom": "http://www.w3.org/2005/Atom"}

for entry in root.findall("atom:entry", ns):
    title = entry.find("atom:title", ns).text.strip()
    published = entry.find("atom:published", ns).text[:10]
    print(f"[{published}] {title}")
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Why it's special: Fastest access to new research. Papers appear here weeks before journal publication.

5. OpenCitations — The Citation Graph (1.5B+ citations, no key)

Best for: Citation network analysis, finding influential papers

# How many times has this paper been cited?
doi = "10.1038/nature12373"
resp = requests.get(f"https://opencitations.net/index/api/v2/citation-count/{doi}")
print(f"Citations: {resp.json()[0]['count']}")

# Who cited this paper?
resp = requests.get(f"https://opencitations.net/index/api/v2/citations/{doi}")
for cite in resp.json()[:5]:
    print(f"  Cited by: {cite['citing']}")
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Why it's special: 1.5 billion citation links, completely open. No other free API has this scale of citation data.

Which API Should You Use?

Need Use This
Find papers on a topic OpenAlex
Get AI summary of a paper Semantic Scholar
Look up DOI metadata Crossref
Latest CS/physics preprints arXiv
Citation network analysis OpenCitations
All of the above Combine them!

The Complete Collection

I maintain a curated list of all free academic APIs (not just these 5):

awesome-free-academic-apis on GitHub — patents, datasets, funders, institutions, and more.

Plus ready-to-run Python scripts:

python-data-scripts on GitHub — copy-paste scripts for all these APIs.

Which academic API do you use most? Any hidden gems I should add to the collection?

I write practical API tutorials every week. Follow for more.

More from me: 10 Dev Tools I Use Daily | 77 Scrapers on a Schedule | 150+ Free APIs

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