NASA Space Biology Knowledge Engine

A futuristic dashboard for exploring space biology research with AI-powered insights, knowledge graphs, and interactive visualizations.

🚀 Features

• Interactive Knowledge Graph: Visualize connections between organisms, experiments, missions, and results
• Timeline Visualization: Explore experiments across years and missions with D3.js charts
• AI-Powered Summaries: Generate plain-language and technical summaries of research papers
• Advanced Search: Filter by organism, experiment type, mission, and year
• NASA-Style UI: Professional, clean design inspired by NASA's official website
• Real-time Updates: Live data synchronization and smooth animations

🛠 Technology Stack

Frontend

• React 18 with modern hooks and functional components
• Tailwind CSS with custom NASA-themed configuration
• Framer Motion for smooth animations and transitions
• Cytoscape.js for interactive graph visualization
• D3.js for timeline charts and data visualization
• Vite for fast development and building

Backend

• FastAPI for high-performance API endpoints
• PostgreSQL for metadata and structured data
• Neo4j for knowledge graph storage
• FAISS for vector similarity search
• HuggingFace Transformers with BioBERT/SciBERT for AI summarization

📦 Installation

Prerequisites

• Node.js 18+
• Python 3.9+
• PostgreSQL 13+
• Neo4j 5.0+

Frontend Setup

# Install dependencies
npm install

# Start development server
npm run dev

Backend Setup

# Navigate to backend directory
cd backend

# Create virtual environment
python -m venv venv
venv\\Scripts\\activate  # Windows
# source venv/bin/activate  # Linux/Mac

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
copy .env.example .env
# Edit .env with your configuration

# Start the API server
python main.py

🚦 Quick Start

1. Start the backend server:

   cd backend
   python main.py

2. Start the frontend development server:

   npm run dev

3. Open your browser to http://localhost:3000

🎨 Design System

The application uses a custom design system based on NASA's visual identity:

• Colors: NASA blue (#0B3D91), NASA red (#FC3D21), space gold (#FFD700)
• Typography: Orbitron for headings, Roboto for body text
• Components: Rounded cards, glass effects, smooth animations
• Layout: Grid-based, mobile-responsive design

📊 Features Overview

Knowledge Graph

• Interactive node-link visualization
• Filter by node types (organisms, experiments, missions, results)
• Click nodes to view detailed information
• Dynamic layout with force-directed positioning

Timeline View

• Stacked bar charts showing experiments by year
• Color-coded by experiment type
• Interactive selection and filtering
• Smooth transitions and hover effects

AI Summaries

• Plain Language: Easy-to-understand summaries for general audiences
• Technical: Preserves scientific terminology and key findings
• Space Relevance: Highlights connections to human space exploration

Search & Filtering

• Full-text search across titles, abstracts, and keywords
• Multi-dimensional filtering by organism, experiment type, mission, year
• Real-time results with smooth animations
• Export functionality for selected results

🔧 Configuration

Tailwind Configuration

The design system is configured in tailwind.config.js using the theme from style.json:

// Custom colors, fonts, spacing, and animations
// Based on NASA brand guidelines

API Configuration

Backend endpoints in backend/main.py:

• GET /api/search - Search publications with filters
• GET /api/graph - Get knowledge graph data
• GET /api/summary/{id} - Get AI-generated summaries
• GET /api/timeline - Get timeline data

📱 Responsive Design

The dashboard is fully responsive with:

• Mobile-first approach
• Adaptive layouts for tablets and desktops
• Touch-friendly interactions
• Optimized performance on all devices

🤖 AI Features

Summarization Models

• BioBERT: For biomedical text understanding
• SciBERT: For scientific literature processing
• BART: For general language summarization

Vector Search

• FAISS: Fast similarity search
• Sentence embeddings: Semantic search capabilities
• Real-time indexing: Auto-update search index

🚀 Deployment

Production Build

# Frontend
npm run build

# Backend
pip install -r requirements.txt
uvicorn main:app --host 0.0.0.0 --port 8000

Docker Deployment

# Build and run containers
docker-compose up -d

📄 License

MIT License - Built for educational and research purposes.

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

🎯 Future Enhancements

• Real NASA data integration
• Advanced ML models for research recommendations
• Collaborative features for researchers
• 3D visualization modes
• Voice-controlled navigation
• Integration with NASA APIs

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Built with ❤️ for space biology research and exploration