README.md

---
title: Research Paper Metadata Database
emoji: 📚
colorFrom: blue
colorTo: indigo
sdk: static
pinned: false
license: apache-2.0
---

# 📚 Research Paper Metadata Database

A centralized metadata repository for scientific research papers, designed to enable AI-powered visualization and analysis of research structure with the goal of expanding research in interesting, useful, and practical ways.

## 📚 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 (not a file archive)
- **AI-Powered Preprocessing:** LLM-based entity extraction and annotation for research papers
- **Citation Network Analysis:** Cross-reference linking and relationship mapping between papers
- **Integration Framework:** Designed for integration with CopernicusAI Knowledge Engine components

### ⚙️ Technical Achievements
- **JSON-Based Storage:** Structured metadata format enabling programmatic access and analysis
- **Entity Extraction:** Automated extraction of genes, proteins, chemical compounds, equations, and key concepts
- **Quality Assessment:** Automated quality scoring and relevance metrics for research papers
- **API Architecture:** RESTful API design for external access and integration

### 🎯 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:** Structured metadata enables relationship mapping - **✅ Now Fully Operational** (December 2025) with 12,000+ mathematics papers indexed, interactive knowledge graph visualization, and relationship extraction (citations, semantic similarity, categories)
- **Research Tools Dashboard** (✅ Implemented December 2025) - Fully operational web interface providing unified access to research papers through knowledge graph visualization, vector search, RAG queries, and content browsing. Live at: https://copernicus-frontend-phzp4ie2sq-uc.a.run.app/knowledge-engine
- **Vector Search:** Semantic search using Vertex AI embeddings across papers, podcasts, and processes
- **RAG System:** Retrieval-augmented generation with citation support and multi-modal content integration
- **Integration with AI Podcast Generation:** Links research papers to generated podcast content
- **Support for GLMP:** Provides source paper references for biological process visualizations
- **Science Video Database Integration:** Potential linking between papers and related video content
- **Programming Framework Support:** Supplies structured data for process analysis applications

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. The Knowledge Engine now provides a fully operational system for exploring research papers through multiple interfaces.

## 🎯 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 Information:** Standard identifiers and publication details
- **Abstracts and Key Findings:** Extracted summaries and main contributions
- **Extracted Entities:** Genes, proteins, chemical compounds, equations, mathematical concepts
- **Citation Networks:** Cross-references and relationship mapping
- **Paradigm Shift Indicators:** Flags for revolutionary vs. incremental research
- **Interdisciplinary Connections:** Links between different research domains
- **Quality Scores:** Relevance metrics and validation scores

### AI-Powered Preprocessing
- LLM-based entity extraction and annotation
- Automatic categorization by discipline and subdomain
- Keyword extraction and semantic tagging
- Citation tracking and relationship mapping
- Quality assessment and validation

### Integration Features
- DOI/arXiv ID resolution and metadata enrichment
- Cross-reference linking between papers
- Podcast-to-paper relationship tracking
- Search and query capabilities
- API access for programmatic retrieval

## 🔗 Related Projects

- [CopernicusAI](https://huggingface.co/spaces/garywelz/copernicusai) - Main knowledge engine integrating metadata with AI podcasts
- [GLMP](https://huggingface.co/spaces/garywelz/glmp) - Genome Logic Modeling Project using metadata for source references
- [Programming Framework](https://huggingface.co/spaces/garywelz/programming_framework) - Universal process analysis tool that can utilize metadata
- [Science Video Database](https://huggingface.co/spaces/garywelz/sciencevideodb) - Video content management with potential metadata linking

## 💻 Technology Stack

- **Database:** Firestore NoSQL for flexible JSON storage
- **Processing:** Google Cloud Functions for automated metadata processing
- **AI/ML:** Vertex AI for entity extraction and analysis
- **API:** RESTful API for external access
- **Storage:** Google Cloud Storage for associated assets

## 🔗 Resources

- **GitHub Repository:** [garywelz/copernicusai-research-metadata](https://github.com/garywelz/copernicusai-research-metadata)
- **Hugging Face Space:** [garywelz/metadata_database](https://huggingface.co/spaces/garywelz/metadata_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.

---

**Part of the CopernicusAI Knowledge Engine**

© 2025 Gary Welz. All rights reserved.