OpenAlex Scholarly Papers, Citations & Research Scraper 🎓

Extract scholarly research papers, scientific literature, citation metrics, DOI links, author affiliations, ORCIDs, reconstructed abstracts, and open-access PDFs from the 250M+ OpenAlex global catalog without API key limits or browser overhead.

⚡ Instant Scholarly Data Extraction Guarantee (No Setup Required):

• 🟢 Zero-Config: Direct access to OpenAlex's comprehensive scientific index — no registration or API keys needed.
• 🚀 1-Click Free Test: Just click the green "Save & Start" button with the pre-filled inputs to test live scholarly data extraction in < 2 seconds.
• 📜 Abstract Reconstruction: Automatically reconstructs human-readable abstracts from OpenAlex's complex inverted index format.
• 📊 Instant Export: Download verified publications, citations, and researcher metadata directly to Excel (.xlsx), CSV, or JSON.
• 🧠 AI & RAG Ready: Pre-computed OpenAI token counts and semantic vector chunks for instant LangChain, LlamaIndex, and Vector DB ingestion.

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⚡ 1-Click API Quick-Start (cURL / Python / Node.js)

Run programmatically via HTTP API without opening the browser console:

# 1-Click cURL Instant Run (Replace YOUR_APIFY_TOKEN)
curl -X POST "https://api.apify.com/v2/acts/trusted_tech_spot~openalex-scholarly-citations-academic-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "searchQuery": "large language models",
    "entityType": "works",
    "minYear": 2022,
    "maxResults": 10
  }'

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⚡ Ready-to-Run Input Preview (Zero Configuration Required)

Setting	What You Provide	Default Value (Ready to Run)	Quick Link
Search Query	Research topic, keywords, or author name	"artificial intelligence"	👉 Run Default Search
Entity Type	Scholarly publications (works) or researchers (authors)	"works"	👉 Select Entity
Minimum Publication Year	Year filter boundary for recent literature	2020	👉 Set Publication Year
Maximum Results	Number of scholarly items to harvest	20 (Sub-2s test)	👉 Adjust Volume

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🌟 Why Choose This Actor?

• 📜 Automatic Abstract Reconstruction: OpenAlex represents abstracts as word-coordinate inverted indices (abstract_inverted_index). This scraper automatically stitches tokens together into coherent, readable English text blocks ready for human review or LLM prompting.
• 🔗 Direct DOI Resolver & Open-Access PDF Links: Provides clean direct resolver URLs (https://doi.org/...) and automatically detects open-access full-text PDFs (arXiv, PubMed Central, institutional repositories).
• 🏷️ Citation Impact Classification: Computes derived citation impact tiers (Monumental Landmark >500, Highly Cited >100, Established Influence 20-99, Emerging Impact 5-19) for rapid bibliometric triage.
• 🏛️ Deep Author Affiliations & ORCIDs: Captures complete authorship rosters, verified ORCID identifiers, institutional affiliations (with ROR identifiers and ISO country codes), and corresponding author flags.
• 🌳 Multi-Tier Research Taxonomy: Extracts OpenAlex's 4-level academic hierarchy (domain, field, subfield, primary topic) for deep topic modeling and domain clustering.
• 💰 Predictable Pay-Per-Event Pricing: Pay only $0.002 per run with zero hidden compute charges, zero proxy fees, and zero idle container waste.
• 🛡️ Zero-Crash Resilient Architecture: Includes a verified offline academic fallback index so that your production pipelines, automated tests, and scheduled agents never fail even during upstream network hiccups.

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🎯 Common High-Impact Use Cases

• 🤖 AI Literature Review & RAG Pipelines: Feed verified academic literature and synthesized abstracts directly into vector databases (Pinecone, ChromaDB, Qdrant) for automated state-of-the-art research assistants.
• 📊 Bibliometric & Citation Analysis: Track citation velocity, identify breakthrough papers in emerging subfields, and map foundational papers across computer science, biomedicine, physics, and social sciences.
• 🧪 Biotech & Pharmaceutical Intelligence: Monitor clinical trial publications, CRISPR discoveries, drug target validations, and key opinion leader (KOL) publication records.
• 🎓 Academic Talent & Faculty Sourcing: Analyze researcher portfolios, H-index metrics, i10-indices, institutional affiliations, and historical citation milestones.
• 📈 Patent Prior-Art Discovery: Search scientific preprints and conference proceedings to uncover prior art before filing patent applications.

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✨ Enterprise v3.0 Data Enrichment (Included Free)

Every extracted scholarly record is automatically normalized and enriched with 20+ derived high-value fields at zero additional cost:

Enrichment Capability	What It Delivers	How It Helps Your Research
🛡️ Data Quality Score (0–100%)	_dataQualityScore: 98	Quantifies record completeness across DOI, abstract, citations, and authors.
🔑 Deduplication Fingerprint	_deduplicationFingerprint: "sha256:..."	Cryptographic SHA-256 hash for 100% idempotent database upserts.
🏆 Citation Impact Tier	citationImpactTier: "Monumental Landmark (>500 citations)"	Instant heuristic filter to separate landmark papers from recent preprints.
🔗 Canonical DOI Resolver	directDoiResolverUrl: "https://doi.org/10.1145/..."	Direct clickable permalink resolving through standard Crossref / DOI foundation.
📖 Reconstructed Abstract	abstract: "The dominant sequence transduction models..."	Converts cryptic inverted indices into clean, continuous reading text.
🌐 Author ORCID & Affiliation Graph	Complete author lists with institutional RORs	Maps scientific collaboration networks and researcher affiliations.
🏷️ Field & Subfield Taxonomy	academicFieldTaxonomy: { domain, field, subfield, primaryTopic }	Categorizes research across standard UNESCO / OECD scientific disciplines.
🧠 AI Vector & RAG Chunking	_rag: { estimatedTokens, chunksCount, chunks }	Pre-chunked into semantic chunks ready for LangChain, LlamaIndex & Vector DBs.
⚡ 3-Tier Configurable Depth	Enriched (Default), Standard, Basic	Choose full enterprise enrichment, clean standard fields, or ultra-fast raw extract.

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📥 Input Parameters

Parameter	Type	Required	Default / Prefill	Description
Academic Search Query (searchQuery)	string	No	"artificial intelligence"	Topic, keyword, title, or author name to search across the OpenAlex catalog
Target Entity Type (entityType)	string	No	"works"	Choose whether to extract scholarly publications (works) or researcher profiles (authors)
Minimum Publication Year (minYear)	integer	No	2020	Filter scholarly works published from this year onwards
Maximum Results Count (maxResults)	integer	No	20	Number of scholarly records to extract (1 to 100)
Include Keywords (Filter) (includeKeywords)	string	No	""	Filter results to only include items containing these comma-separated keywords
Exclude Keywords (Filter) (excludeKeywords)	string	No	""	Filter out results containing these comma-separated keywords
Enable AI / RAG Token Chunking (enableRagEnrichment)	boolean	No	true	Compute OpenAI token counts and generate semantic vector chunks for RAG pipelines
Data Enrichment Tier (enrichmentMode)	string	No	"Enriched"	Enrichment depth: Enriched (20+ fields), Standard (core clean), Basic (raw)

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📤 Sample Output Data

Scholarly Work (Paper Record)

{
  "query": "artificial intelligence",
  "entityType": "works",
  "totalResults": 20,
  "minYear": 2020,
  "works": [
    {
      "id": "https://openalex.org/W3028248512",
      "openAlexId": "W3028248512",
      "doi": "https://doi.org/10.48550/arxiv.2005.14165",
      "directDoiResolverUrl": "https://doi.org/10.48550/arxiv.2005.14165",
      "title": "Language Models are Few-Shot Learners",
      "publicationYear": 2020,
      "publicationDate": "2020-05-28",
      "citedByCount": 38200,
      "citationImpactTier": "Monumental Landmark (>500 citations)",
      "academicFieldTaxonomy": {
        "primaryTopic": "Large Language Models & Few-Shot Learning",
        "subfield": "Artificial Intelligence",
        "field": "Computer Science",
        "domain": "Physical Sciences",
        "topicsList": [
          "Generative Pre-trained Transformers",
          "Prompt Engineering",
          "In-Context Learning",
          "GPT-3"
        ]
      },
      "authors": [
        {
          "name": "Tom B. Brown",
          "orcid": null,
          "openAlexAuthorId": "https://openalex.org/A5043829101",
          "institutions": [
            {
              "name": "OpenAI",
              "countryCode": "US",
              "ror": "https://ror.org/033eqas34"
            }
          ],
          "isCorresponding": true
        }
      ],
      "venue": {
        "name": "Advances in Neural Information Processing Systems (NeurIPS 2020)",
        "type": "conference",
        "issn": "1049-5258",
        "hostOrganization": "Neural Information Processing Systems Foundation",
        "landingPageUrl": "https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html"
      },
      "openAccess": {
        "isOpenAccess": true,
        "status": "green",
        "pdfUrl": "https://arxiv.org/pdf/2005.14165.pdf",
        "license": "cc-by"
      },
      "abstract": "Recent work has demonstrated substantial gains on many NLP tasks by pre-training on a large corpus of text followed by fine-tuning on a specific task. We show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches.",
      "fwci": 62.4,
      "type": "article",
      "referencedWorksCount": 76,
      "language": "en",
      "_dataQualityScore": 100,
      "_deduplicationFingerprint": "sha256:7f9b8c2d...",
      "_extractedAt": "2026-10-30T10:20:00.000Z"
    }
  ],
  "_ragMetadata": {
    "estimatedTokens": 1420,
    "wordsCount": 985,
    "chunksCount": 5,
    "chunks": ["..."],
    "ingestionReady": true
  }
}

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💻 Developer API & AI Framework Integration

Node.js (apify-client)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({
    token: 'YOUR_APIFY_TOKEN',
});

// Run actor with targeted parameters
const run = await client.actor('trusted_tech_spot/openalex-scholarly-citations-academic-scraper').call({
    searchQuery: 'quantum error correction',
    entityType: 'works',
    minYear: 2022,
    maxResults: 15
});

// Fetch structured items from the dataset
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Extracted ${items[0]?.works?.length || 0} research publications:`, items);

Python (apify-client)

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

# Run actor
run = client.actor("trusted_tech_spot/openalex-scholarly-citations-academic-scraper").call(
    run_input={
        "searchQuery": "CRISPR gene editing",
        "entityType": "works",
        "minYear": 2021,
        "maxResults": 20
    }
)

# Fetch dataset items
items = client.dataset(run["defaultDatasetId"]).list_items().items
for work in items[0].get("works", []):
    print(f"[{work['publicationYear']}] {work['title']} ({work['citedByCount']} citations)")
    print(f"DOI: {work['directDoiResolverUrl']} | PDF: {work['openAccess']['pdfUrl']}")

LangChain Vector Ingestion (AI & RAG)

from apify_client import ApifyClient
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_core.documents import Document

# 1. Fetch pre-chunked academic literature from Apify
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("trusted_tech_spot/openalex-scholarly-citations-academic-scraper").call(
    run_input={"searchQuery": "transformer architectures", "maxResults": 25}
)
items = client.dataset(run["defaultDatasetId"]).list_items().items

# 2. Ingest pre-chunked literature chunks into LangChain
docs = []
for result in items:
    for chunk in result.get("_ragMetadata", {}).get("chunks", []):
        docs.append(Document(page_content=chunk, metadata={"source": "openalex-scholarly-scraper"}))

# 3. Build Vector Store
vector_store = Chroma.from_documents(docs, OpenAIEmbeddings())
print(f"Successfully indexed {len(docs)} academic literature chunks into ChromaDB!")

LlamaIndex Knowledge Ingestion

from apify_client import ApifyClient
from llama_index.core import VectorStoreIndex, Document

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("trusted_tech_spot/openalex-scholarly-citations-academic-scraper").call(
    run_input={"searchQuery": "multimodal vision models", "maxResults": 20}
)
items = client.dataset(run["defaultDatasetId"]).list_items().items

# Extract reconstructed abstracts as knowledge documents
documents = [
    Document(text=f"Title: {w['title']}\nAbstract: {w['abstract']}\nYear: {w['publicationYear']}", extra_info={"doi": w['doi']})
    for w in items[0].get("works", []) if w.get("abstract")
]
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What are the key architectural innovations in recent multimodal vision models?")
print(response)

n8n / Make.com Automated Workflow

[Schedule Trigger (Weekly)] 
       ⬇
[Apify Actor: OpenAlex Scraper] 
  Query: "autonomous AI agents" | minYear: 2025
       ⬇
[Filter: citedByCount >= 10 OR isOpenAccess == true]
       ⬇
[Send Email / Slack Digest / Push to Notion Research DB]

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💬 Technical Support & Custom Demands

Need custom academic taxonomy extraction, batch DOI resolution, high-volume dedicated scraping, or specialized institution mapping?

• 🐛 Open an Issue: Open a ticket directly on the Actor Issues Tab for 24-hour engineer response.
• ✉️ Direct Email Support: Reach out to contact@trustedtechspot.com for custom data pipelines, enterprise feeds, or custom SLA requirements.
• 💡 Feature Requests: Request new endpoints, journal ISSN filters, or citation graph mappings anytime.

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🔒 Privacy & Compliance

• Public Scientific Open Data: Strictly accesses publicly indexed academic metadata under CC0 and OpenAlex open licensing terms.
• No Credentials or Passwords Required: Executes safely without user logins, authentication cookies, or private credentials.
• Enterprise Grade Sanitization: Automatically scrubs any tracking parameters before exporting data.

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🏷️ Search & Discovery Keywords

openalex scraper • academic paper scraper • scholarly citations • scientific literature extractor • doi resolver • research paper api • arxiv pdf scraper • author orcid lookup • rag literature review • apify actor