Academic Paper Search (Semantic Scholar)

Find and Extract 200M+ Research Papers in Seconds

Stop spending hours manually searching through academic databases. Academic Paper Search lets you programmatically search, filter, and extract structured data from 200 million peer-reviewed papers, complete with citations, abstracts, DOIs, and open-access links, in a single API call.

Perfect for researchers, competitive intelligence teams, patent analysts, and institutions that need systematic access to academic literature at scale.

What This Actor Does

This actor connects to Semantic Scholar's vast database and returns structured research paper data for any search query. Whether you're tracking citations across a field, conducting systematic literature reviews, monitoring competitor research, or feeding papers into machine learning pipelines, this actor handles it automatically.

What you get: Paper titles and abstracts, author names and affiliations, citation counts and impact metrics, publication year and venue information, DOIs, and open-access PDF links when available.

Use Cases

Literature Review Automation

Find "all papers on transformer models published since 2021" and return 5,000 results with citation counts, filtered by year, ranked by relevance. Perfect for grad students, postdocs, and faculty building comprehensive literature maps.

Competitive Intelligence in Biotech and Pharma

Search "CRISPR gene therapy 2023-2024" or "GLP-1 receptor agonists clinical trials" to track what competitors and leading researchers are publishing. Identify emerging trends weeks before commercial applications.

Patent Prior Art Research

Query "blockchain supply chain verification" to build comprehensive prior art dossiers. Cross-reference patent applications against published research.

AI/ML Paper Tracking

Automate searches like "large language model fine-tuning techniques" to build proprietary datasets of recent papers for your knowledge base or citation graphs.

University Department Benchmarking

Extract faculty publication data to analyze publication volume, citation impact, and collaboration networks across departments.

Input Parameters

{
  "searchQuery": "transformer neural networks",
  "maxResults": 100,
  "yearFrom": 2020,
  "yearTo": 2024
}

Parameter	Type	Required	Default	Notes
searchQuery	string	Yes	-	The research topic or keyword phrase
maxResults	number	No	100	How many papers to return (1-10,000)
yearFrom	number	No	-	Minimum publication year filter
yearTo	number	No	-	Maximum publication year filter

Sample Output

{
  "title": "Attention Is All You Need",
  "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks...",
  "authors": [
    {"name": "Ashish Vaswani", "affiliation": "Google Brain"},
    {"name": "Noam Shazeer", "affiliation": "Google Brain"}
  ],
  "publicationYear": 2017,
  "citationCount": 78342,
  "venue": "NeurIPS",
  "doi": "10.5555/3122622.3125895",
  "openAccessUrl": "https://arxiv.org/pdf/1706.03762.pdf"
}

Pricing: $5 per 1,000 Results

Option	Cost	Cost Per Paper
This Actor	$5 per 1K	$0.005
Google Scholar (manual)	~$15/hour	$0.15
Web of Science (annual)	$3,000+	$0.06+
Scopus (annual)	$4,000+	$0.067+

For an academic team running 10 literature reviews per year (500 papers each), this actor costs $25 total vs. $3,000+ annual subscriptions.

FAQ

How often is the data updated? Semantic Scholar updates its database continuously. New papers appear within days of publication. The actor queries live data, so you always get current results.

Can I use this for commercial research? Yes. Semantic Scholar's content is freely available for research and business purposes.

Do I get the actual PDF files? The actor returns direct links to open-access PDFs when available. For papers behind paywalls, you get the DOI and citation.

What's the maximum number of results? Up to 10,000 results per search. For broader searches, filter by year or venue for more targeted results.

💻 Code Example — Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("nexgendata/google-scholar-scraper").call(run_input={
    # Fill in the input shape from the actor's input_schema
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

🌐 Code Example — cURL

curl -X POST "https://api.apify.com/v2/acts/nexgendata~google-scholar-scraper/run-sync-get-dataset-items?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{ /* input schema */ }'

❓ FAQ

Q: How do I get started? Sign up at apify.com, grab your API token from Settings → Integrations, and run the actor via the Apify console, API, Python SDK, or any integration (Zapier, Make.com, n8n).

Q: What's the typical cost per run? See the pricing section below. Most runs finish under $0.10 for typical batches.

Q: Is this actor maintained? Yes. NexGenData maintains 165+ Apify actors and ships updates regularly. Bug reports via the Apify console issues tab get responses within 24 hours.

Q: Can I use the output commercially? Yes — you own the output data. Check the target site's Terms of Service for any usage restrictions on the scraped content itself.

Q: How do I handle rate limits? Apify manages concurrency and retries automatically. For very large batches (10K+ items), run multiple smaller jobs in parallel instead of one mega-job for better reliability.

💰 Pricing

Pay-per-event pricing — you only pay for what you actually extract.

• Actor Start: $0.0001
• result: $0.0050

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🚀 Apify Affiliate Program

New to Apify? Sign up with our referral link — you get free platform credits on signup, and you help fund the maintenance of this actor fleet.

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