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Development of Federated Data Science Collaboration Platform with Cloud Interoperability

softwaremind.com

Education

Information technology

Research & Development

Challenges in Federated Scientific Collaboration

Scientific institutions face significant barriers in sharing large datasets, collaborating on data analysis, and maintaining consistent workflows across disparate cloud environments. Existing tools lack seamless integration with federated cloud infrastructures, hindering efficient research collaboration and resource sharing.

About the Client

A leading research organization focused on particle physics, requiring advanced cloud interoperability solutions for global scientific collaboration.

Key Project Goals

Develop a JupyterLab extension for full integration with ScienceMesh federated cloud infrastructure
Enable real-time collaborative data science workflows across distributed institutions
Implement secure file sharing and concurrent editing capabilities within Data Science environments
Ensure compatibility with existing research collaboration tools and data services

Core System Capabilities

CS3APIs client integration for complete cloud federation access
Federated file browsing and management within JupyterLab
Real-time collaborative editing of notebooks across institutions
Secure sharing of data science resources and workflows
Integration with ScienceMesh data services for archival, publishing, and transfer

Technology Stack Requirements

Golang

Python

Kubernetes

JupyterLab

gRPC

Node.js

REVA platform

System Integration Needs

ScienceMesh infrastructure
CERN SWAN service
Existing collaboration tools (document editing, data archiving)
CS3APIs ecosystem

Operational Requirements

High scalability for large dataset transfers
Enterprise-grade security for cross-institution data sharing
Low-latency collaboration features
High availability for global research access
Compliance with scientific data governance standards

Expected Business Impact

Enables seamless cross-institutional scientific collaboration through federated data access, reduces data transfer bottlenecks by 70%, and accelerates research workflows through integrated tooling. The solution will become the standard for EU-funded scientific projects requiring distributed data science capabilities.