Academic Research Search Across Five Databases with PDF Vector & Multiple Exports

Nodes

Unified Academic Search Across Major Research Databases

This powerful workflow enables researchers to search multiple academic databases simultaneously, automatically deduplicate results, and export formatted bibliographies. By leveraging PDF Vector's multi-database search capabilities, researchers can save hours of manual searching and ensure comprehensive literature coverage across PubMed, ArXiv, Google Scholar, Semantic Scholar, and ERIC databases.

Target Audience & Problem Solved

This template is designed for:

Graduate students conducting systematic literature reviews
Researchers ensuring comprehensive coverage of their field
Librarians helping patrons with complex searches
Academic teams building shared bibliographies

It solves the critical problem of fragmented academic search by providing a single interface to query all major databases, eliminating duplicate results, and standardizing output formats.

Prerequisites

n8n instance with PDF Vector node installed
PDF Vector API credentials with search permissions
Basic understanding of academic search syntax
Optional: PostgreSQL for search history logging
Minimum 50 API credits for comprehensive searches

Step-by-Step Setup Instructions

Configure PDF Vector Credentials
- Go to n8n Credentials section
- Create new PDF Vector credentials
- Enter your API key from pdfvector.io
- Test the connection to verify setup
Import the Workflow Template
- Copy the template JSON code
- In n8n, click "Import Workflow"
- Paste the JSON and save
- Review all nodes for any configuration needs
Customize Search Parameters
- Open the "Set Search Parameters" node
- Modify the default search query for your field
- Adjust the year range (default: 2020-present)
- Set results per source limit (default: 25)
Configure Export Options
- Choose your preferred export formats (BibTeX, CSV, JSON)
- Set the output directory for files
- Configure file naming conventions
- Enable/disable specific export types
Test Your Configuration
- Run the workflow with a sample query
- Check that all databases return results
- Verify deduplication is working correctly
- Confirm export files are created properly

Implementation Details

The workflow implements a sophisticated search pipeline:

Parallel Database Queries: Searches all configured databases simultaneously for efficiency
Smart Deduplication: Uses DOI matching and fuzzy title comparison to remove duplicates
Relevance Scoring: Combines citation count, title relevance, and recency for ranking
Format Generation: Creates properly formatted citations in multiple styles
Batch Processing: Handles large result sets without memory issues

Customization Guide

Adding Custom Databases:

// In the PDF Vector search node, add to providers array:
"providers": ["pubmed", "semantic_scholar", "arxiv", "google_scholar", "eric", "your_custom_db"]

Modifying Relevance Algorithm:
Edit the "Rank by Relevance" node to adjust scoring weights:

// Adjust these weights for your needs:
const titleWeight = 10;    // Title match importance
const citationWeight = 5;  // Citation count importance
const recencyWeight = 10;  // Recent publication bonus
const fulltextWeight = 15; // Full-text availability bonus

Custom Export Formats:
Add new format generators in the workflow:

// Example: Add APA format export
const apaFormat = papers.map(p =&gt; {
  const authors = p.authors.slice(0, 3).join(', ');
  return `${authors} (${p.year}). ${p.title}. ${p.journal || 'Preprint'}.`;
});

Advanced Filtering:
Implement additional filters: