Federated research discovery with S-Index metrics across a network of digital twins.
io.github.martinfrasch/researchtwin MCP Server
This MCP server, io.github.martinfrasch/researchtwin, supports federated research discovery and uses S-Index metrics across a network of digital twins. It is associated with an open-source federated platform that transforms a researcher’s publications, datasets, and code repositories.
🛠️ Key Features
Federated research discovery
S-Index metrics
Digital-twin network context
MCP integration
🚀 Use Cases
Discovering research across a federated digital-twin network
Connecting publications, datasets, and code repositories for research workflows
⚡ Developer Benefits
Standardized Model Context Protocol (MCP) server availability
Topics align with tools and workflows: semantic-scholar, RAG, schema-org, and inter-agentic
⚠️ Limitations
Provided source text includes only a truncated description; tool capabilities and exact MCP tools are not specified.
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.
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
Architecture Overview
BGNO (Bimodal Glial-Neural Optimization)
code
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
