Development of a Federated Data Science Environment with Cloud Interoperability and Collaborative IDE Extensions

The client faces challenges in facilitating federated data sharing across multiple cloud storage services and research environments. Existing systems hinder efficient collaboration, limit interoperability between data repositories and analysis tools, and lack unified interfaces for distributed data science workflows, resulting in reduced productivity and slowed research progress.

A leading research university with multiple campuses seeking to enable seamless data sharing and collaborative data science workflows across its distributed infrastructure.

• Implement a federated cloud interoperability platform to enable seamless sharing of data and applications across diverse storage services.
• Develop and integrate extensions for popular data science IDEs to support federated file browsing, resource sharing, and collaborative editing within a unified environment.
• Integrate comprehensive data services to support complete research workflows, including data publishing, large dataset transfer, and remote analysis.
• Improve collaboration efficiency, reduce data sharing latency, and facilitate concurrent editing and resource sharing within data science environments.

• Federated API layer supporting full data and application sharing across multiple sync-and-share services.
• Extensions for integrated IDEs enabling file browsing, sharing, and collaborative coding within a federated environment.
• Concurrency support for real-time editing of notebooks and shared resources.
• Integration with existing data services for data publishing, archiving, and large dataset management.
• Remote data analysis capabilities supporting interactive workflows.

• Cloud storage and sync-and-share platforms
• Data publishing and archiving repositories
• Large data transfer tools for high-volume datasets
• Existing collaborative platforms and analysis tools

• Scalability to support growing datasets and concurrent users
• High performance for large data transfers and real-time collaboration
• Robust security, access controls, and data privacy compliance
• Availability and fault tolerance for continuous data analysis workflows

The implementation is expected to substantially enhance research collaboration efficiency by enabling federated data sharing across multiple institutions, reduce data transfer and collaboration latency, and support comprehensive distributed data science workflows. This will accelerate the research output, facilitate large-scale data analysis, and foster innovative scientific discovery workflows.