--- 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.