Research Paper Metadata Database

📚 Prior Work & Research Contributions

Overview

The Research Paper Metadata Database represents prior work that demonstrates the creation of a structured metadata repository for scientific research papers. This project establishes a foundation for using AI tools to visualize and analyze the structure of scientific research, enabling systematic exploration of research patterns, citation networks, and interdisciplinary connections.

🔬 Research Contributions

• Structured Metadata Repository: Centralized database of research paper metadata
• AI-Powered Preprocessing: LLM-based entity extraction and annotation
• Citation Network Analysis: Cross-reference linking and relationship mapping
• Integration Framework: Designed for CopernicusAI Knowledge Engine integration

⚙️ Technical Achievements

• JSON-Based Storage: Structured metadata format for programmatic access
• Entity Extraction: Automated extraction of genes, proteins, compounds, equations
• Quality Assessment: Automated quality scoring and relevance metrics
• API Architecture: RESTful API design for external access

🎯 Position Within CopernicusAI Knowledge Engine

The Research Paper Metadata Database serves as a core data infrastructure component of the CopernicusAI Knowledge Engine, providing:

• Foundation for knowledge graph construction
• Integration with AI podcast generation
• Support for GLMP source references

• Science Video Database integration
• Programming Framework support

This work establishes a proof-of-concept for AI-assisted research metadata management, demonstrating how structured data can enable systematic analysis and visualization of scientific research patterns.

🎯 Project Goals

This project creates a database of scientific research paper metadata for the purpose of:

• Using AI tools to visualize and analyze the structure of scientific research
• Expanding research in interesting, useful, and practical ways
• Enabling systematic exploration of research patterns and connections
• Supporting knowledge graph construction and semantic search

🔧 Technical Architecture

Metadata Structure

• DOI, arXiv ID, publication info
• Abstracts & key findings
• Extracted entities
• Citation networks
• Paradigm shift indicators
• Quality scores

AI-Powered Preprocessing

• LLM-based entity extraction
• Automatic categorization
• Keyword extraction
• Citation tracking
• Quality assessment

Integration Features

• DOI/arXiv ID resolution
• Cross-reference linking
• Podcast-to-paper tracking
• Search & query capabilities
• API access

🔗 Related Projects

🔬 Copernicus AI

Main knowledge engine integrating metadata with AI podcasts and research synthesis.

Visit Copernicus AI →

🧬 GLMP

Genome Logic Modeling Project using metadata for source paper references.

Explore GLMP →

🛠️ Programming Framework

Universal process analysis tool that can utilize metadata for research analysis.

Explore Framework →

🎬 Science Video Database

Video content management with potential metadata linking.

Visit Video Database →

How to Cite This Work

Welz, G. (2024–2025). Research Paper Metadata Database.
Hugging Face Spaces. https://huggingface.co/spaces/garywelz/metadata_database

This project serves as infrastructure for AI-assisted research analysis, enabling systematic visualization and exploration of scientific research patterns through structured metadata management.

The Research Paper Metadata Database is designed as infrastructure for AI-assisted science, providing the foundational data layer for knowledge graph construction and semantic search capabilities within the CopernicusAI Knowledge Engine.