Project
National Data Platform Search
Federated scientific-data discovery with metadata, ontology-driven search, and national-scale APIs.
Overview
This project emphasizes discovery across a national-scale scientific platform. The public-facing problem is search; the deeper system problem is preserving meaning, provenance, and access constraints while still making data easy to find.
Problem
Scientific datasets may be distributed across institutions, engines, and curation levels. Search has to work across those boundaries while respecting domain semantics.
Approach
This project aligns with the National Data Platform’s federated and extensible data ecosystem, AI-ready datasets, integrated HPC/cloud resources, and collaborative workspaces for integrated workflow development.
Contributions
- Metadata-aware discovery design
- Ontology-driven indexing and retrieval
- Search architecture for heterogeneous scientific users
- API-oriented access to platform resources
Placeholder notes
The public NDP documentation emphasizes data registration, discovery, distributed endpoints, AI-integrated workflows, and catalog services, which map directly to the search and discovery role represented here.
Project details
Problem: How can a national data platform make distributed scientific datasets discoverable without flattening their semantics or provenance?
Approach: Built federated-search services around metadata normalization, text and semantic retrieval, APIs, and policy-aware access.
Architecture: Federated search architecture with metadata, ontology components, and indexed discovery services.
Contributions
- Built or shaped search-facing data models for scientific discovery.
- Worked on metadata and semantic access patterns.
- Helped design APIs for community use.
Outcomes
- Connected distributed scientific catalogs to a reusable discovery layer.
- Aligned with the National Data Platform's AI-ready data and workspace model.