Researchtwin

🚀 ResearchTwin: Federated Agentic Web of Research Knowledge

ResearchTwin is an open - source, federated platform that transforms a researcher's publications, datasets, and code repositories into a conversational Digital Twin. Built on a Bimodal Glial - Neural Optimization (BGNO) architecture, it enables dual - discovery where both humans and AI agents collaborate to accelerate scientific discovery.

Live at researchtwin.net | Join the Network

🚀 Quick Start

Hosted (Tier 3) — Zero Setup

1. Visit researchtwin.net/join.html
2. Register with your name, email, and research identifiers
3. Your Digital Twin is live immediately

Local Node (Tier 1) — Full Control

git clone https://github.com/martinfrasch/researchtwin.git
cd researchtwin
pip install -r backend/requirements.txt
cp node_config.json.example node_config.json
# Edit node_config.json with your details
python run_node.py --config node_config.json

Docker Deployment

cp .env.example .env  # Add your API keys
docker-compose up -d --build

Required API keys: ANTHROPIC_API_KEY (for Claude RAG) Optional: S2_API_KEY, GITHUB_TOKEN, DISCORD_BOT_TOKEN, SMTP credentials

✨ Features

The exponential growth of scientific outputs has created a "discovery bottleneck." Traditional static PDFs and siloed repositories limit knowledge synthesis and reuse. ResearchTwin addresses this by:

• Integrating multi - modal research artifacts from Semantic Scholar, Google Scholar, GitHub, and Figshare
• Computing a real - time S-Index metric (Quality × Impact × Collaboration) across all output types
• Providing a conversational chatbot interface for interactive research exploration
• Exposing an Inter - Agentic Discovery API with Schema.org types for machine - to - machine research discovery
• Enabling a federated, Discord - like architecture supporting local nodes, hubs, and hosted edges

🔧 Technical Details

BGNO (Bimodal Glial - Neural Optimization)

Data Sources          Glial Layer          Neural Layer         Interface
┌──────────────┐    ┌─────────────┐    ┌──────────────┐    ┌────────────┐
│Semantic Scholar│───▶│             │    │              │    │  Web Chat  │
│Google Scholar │───▶│  SQLite     │───▶│  RAG with    │───▶│  Discord   │
│GitHub API     │───▶│  Cache +    │    │  Claude API  │    │  Agent API │
│Figshare API   │───▶│  Rate Limit │    │              │    │  Embed     │
└──────────────┘    └─────────────┘    └──────────────┘    └────────────┘

• Connector Layer: Pulls papers (S2+GS with deduplication), repos (GitHub), datasets (Figshare), and ORCID metadata
• Glial Layer: SQLite caching with 24h TTL, rate limiting, S2+GS title - similarity merge (0.85 threshold)
• Neural Layer: RAG with Claude — context assembly, prompt engineering, conversational synthesis
• Interface Layer: D3.js knowledge graph, chat widget, Discord bot, REST API

Federated Network Tiers

Inter - Agentic Discovery API

Machine - readable endpoints with Schema.org @type annotations:

📦 Installation

Local Node (Tier 1)

git clone https://github.com/martinfrasch/researchtwin.git
cd researchtwin
pip install -r backend/requirements.txt
cp node_config.json.example node_config.json
# Edit node_config.json with your details
python run_node.py --config node_config.json

Docker Deployment

cp .env.example .env  # Add your API keys
docker-compose up -d --build

📚 Documentation

📄 License

MIT License. See LICENSE.

📦 Repository Structure

researchtwin/
├── backend/
│   ├── main.py              # FastAPI endpoints (REST + Discovery API)
│   ├── researchers.py        # SQLite researcher CRUD + token management
│   ├── database.py           # SQLite schema, WAL mode, migrations
│   ├── models.py             # Pydantic models for all endpoints
│   ├── rag.py                # RAG context assembly for Claude
│   ├── qic_index.py          # S-Index / QIC computation engine
│   ├── email_service.py      # SMTP service for profile update codes
│   ├── connectors/           # Data source connectors
│   │   ├── semantic_scholar.py
│   │   ├── scholarly_lib.py  # Google Scholar via scholarly
│   │   ├── github_connector.py
│   │   └── figshare.py
│   └── discord_bot/          # Discord bot with /research and /sindex
├── frontend/
│   ├── index.html            # Main dashboard with D3.js knowledge graph
│   ├── join.html             # Self - registration page
│   ├── update.html           # Email - verified profile updates
│   ├── privacy.html          # Privacy policy
│   └── widget - loader.js    # Embeddable chat widget
├── run_node.py               # Tier 1 local node launcher
├── node_config.json.example  # Local node configuration template
├── docker - compose.yml      # Docker orchestration
├── nginx/                    # Nginx reverse proxy + SSL
└── whitepaper.tex            # LaTeX manuscript

🌐 Ecosystem

This repository is part of the ResearchTwin Ecosystem project:

💻 Usage Examples

Embeddable S - Index Widget

Show your S - Index on your lab website, Google Sites page, or personal homepage:

<iframe
  src="https://researchtwin.net/embed.html?slug=YOUR - SLUG"
  width="440" height="180"
  style="border:none; border - radius:12px;"
  loading="lazy">
</iframe>

Replace YOUR - SLUG with your researcher slug (e.g. martin - frasch).

Google Sites: Edit page > Insert > Embed > "By URL" tab > paste https://researchtwin.net/embed.html?slug=YOUR - SLUG

WordPress: Add a Custom HTML block and paste the iframe code.

The widget displays the researcher's name, S - Index score, h - index, citation count, and paper count. Data updates automatically from live API sources.

[See it in action](https://researchtwin.net/embed.html?slug=martin - frasch) | Full embed instructions

🤝 Contributing

Contributions welcome! See the project board for tracked issues.

• New connectors (ORCID enrichment, PubMed, OpenAlex)
• Affiliation - based geographic mapping
• MCP server for inter - agentic discovery
• UI/UX improvements
• Bug fixes and optimizations

📞 Contact

• Platform: researchtwin.net
• Email: martin@researchtwin.net
• Issues: GitHub Issues

Empowering researchers and AI agents to discover, collaborate, and innovate together.