Hi everyone,
I wanted to share a workflow I’ve been building in n8n to automate academic trend tracking and literature reviews for our team.
Initially, I tried building this using a standard HTTP Request node combined with a custom Python script to scrape Google Scholar and arXiv for new papers matching specific keywords. However, as many of you have probably experienced, Google Scholar’s anti-bot system triggers CAPTCHAs and blocks the execution IP almost immediately, causing the n8n workflow to fail constantly.
To build a production-grade, reliable automation, I swapped out the fragile scraping nodes and connected n8n to ScholarAPI (scholarapi.net/case_study/monitor). It provides a clean, stable endpoint specifically meant for citation and publication monitoring, so the workflow never breaks due to IP bans.
Here is the logic and structure of the workflow that sends a daily digest of new papers to Slack/Email:
Workflow Logic:
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Schedule Trigger: Runs every morning at 9:00 AM.
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HTTP Request Node (ScholarAPI): Fetches the latest published papers based on specific research keywords.
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Code Node (Data Transformation): Normalizes the JSON output to extract titles, authors, and PDF download links.
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IF Node: Checks if any new papers were published in the last 24 hours.
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Slack / Email Node: Sends a formatted digest directly to the research channel.
The n8n JSON Structure (Paste this into your canvas):
JSON
Why this setup is efficient for n8n:
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Error-Free Execution: No more handling
503 Service Unavailableerrors or proxy rotation overhead inside n8n loops. -
Pre-structured Data: The incoming payload is already clean JSON, meaning you don’t need heavy HTML parsing nodes (like HTML Extract) which consume excessive server memory.
Is anyone else running academic workflows or automated literature reviews inside n8n? How are you handling deduplication of citations across multiple sources? Let me know if you want the full sub-workflow json!
