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4d • General Discussion 💬
Professor Thinks I'm Magic - Academic Research Automation That Pulls From 6 Databases
2 AM email from Dr. Martinez, Stanford biochemistry: "Need 300 citations for NIH grant. Due Friday. Help?"
HER MANUAL NIGHTMARE
- Search PubMed, ArXiv, Semantic Scholar, Google Scholar, ERIC, DOAJ
- Remove duplicates manually
- Format in 3 citation styles
- Verify impact factors
- Cross-reference relevance
- Estimated time: 60 hours
MY n8n SOLUTION (Built in 4 hours)
12-node workflow that changed everything:
Nodes 1-6: Parallel database search
- PubMed API for medical research
- ArXiv for preprints
- Semantic Scholar for CS/interdisciplinary
- Google Scholar for comprehensive coverage
- CORE for open access papers
- DOAJ for peer-reviewed journals
Node 7: Deduplication by DOI and title similarity
Node 8: Impact factor lookup and relevance scoring
Node 9: Citation formatting (APA/MLA/Chicago)
Node 10: Export to BibTeX/EndNote/Zotero
Node 11: PDF fetching for available papers
Node 12: Weekly monitoring for new publications
THE MAGIC DEMO
Input: 12 research topics in Google Sheet
Output (8 minutes later):
- 1,247 relevant papers found
- 387 duplicates removed
- 860 unique citations
- All formatted in required styles
- Ranked by impact factor and relevance
Her response: "This is impossible. You built a research assistant."
SCALING THE IMPOSSIBILITY
Current client base:
- 15 professors across 8 universities
- 25,000+ papers processed monthly
- Average search time: 3.7 minutes
- Manual equivalent: 400+ hours saved monthly
Revenue: $7,200/month from "impossible" research automation
The workflow that made professors think I was magic is now available as n8n template. Same 12 nodes. Same impossibility.
What "impossible" integration could you build this weekend?
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Professor Thinks I'm Magic - Academic Research Automation That Pulls From 6 Databases
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