National Data Platform's Education Hub
Pedro Ramonetti, Melissa Floca, Kate O'Laughlin, Amarnath Gupta, Manish Parashar, Ilkay Altintas
TL;DR
The paper presents the NDP Education Hub, a federated, education-focused layer of the National Data Platform designed to bridge research-scale data and compute with classroom and data-challenge workflows. It introduces two educational spaces—Classrooms and Data Challenges—and a workspace abstraction that bundles datasets, services, and models for deployment on HPC, cloud, or on‑prem resources via JupyterHub, plus a Collaboration Space with shared storage for group work. Early case studies demonstrate the platform's ability to support data-intensive projects, including a UCSD Data Science and Engineering course leveraging Neo4j on NDP and a TLS data challenge with deployable ML workflows on Kubernetes, workloads previously hard to realize on personal infrastructure. The results indicate the Education Hub lowers barriers to entry for large-scale data work and HPC-enabled education, enabling scalable, realistic, collaborative pedagogy. The paper envisions broad adoption by educators and learners and outlines future enhancements such as LMS integration, grading tools, expanded environments beyond JupyterHub, and enhanced onboarding materials.
Abstract
As demand for AI literacy and data science education grows, there is a critical need for infrastructure that bridges the gap between research data, computational resources, and educational experiences. To address this gap, we developed a first-of-its-kind Education Hub within the National Data Platform. This hub enables seamless connections between collaborative research workspaces, classroom environments, and data challenge settings. Early use cases demonstrate the effectiveness of the platform in supporting complex and resource-intensive educational activities. Ongoing efforts aim to enhance the user experience and expand adoption by educators and learners alike.