Building knowledge repositories for High-Performance Computing (HPC) and academic research goes beyond mere storage; it involves creating a searchable, structured, and permanent intellectual ecosystem. Modern repositories must handle massive datasets while ensuring that findings are "machine-actionable" for future AI-driven discovery.

Here is the blueprint for architecting a comprehensive research repository.

1. The Core Architecture: The "Data Lakehouse" Model

Modern academic repositories are moving away from simple file servers toward a Data Lakehouse architecture. This combines the flexibility of a Data Lake (storing raw, unstructured data) with the management and performance of a Data Warehouse.

Ingestion Layer: Connects directly to HPC scratch space or lab instruments via Globus or S3 connectors.
Storage Layer: Uses a "Shared Nothing" architecture to scale horizontally, ensuring that as your petabytes grow, your access speed doesn't drop.
Processing Layer: Automated pipelines (using Nextflow or Apache Beam) that extract metadata, generate thumbnails, or run checksums as data is uploaded.
Semantic Layer: A Knowledge Graph (RDF/OWL) that links papers to the specific versions of datasets and software used to produce them.

2. Implementation: Selecting the Right Platform

Rather than building from scratch, most institutions implement a "Base Platform" and customize it.

Platform	Best For	Key Advantage
InvenioRDM	Large-scale Research Data	Built by CERN; handles massive datasets and complex metadata natively.
DSpace 8	Institutional Publications	The industry standard for "Grey Literature," theses, and open-access papers.
Dataverse	Social Science & Humanities	Excellent versioning and "Data Citation" tools; allows users to "guest-edit" datasets.
CKAN	Public & Open Data	Ideal for government-linked research or data that needs a public-facing API.

3. The FAIR Data Mandate

A repository is only successful if it follows the FAIR Principles. In 2026, most funding agencies (NIH, NSF, Horizon Europe) will not approve grants unless findings are stored in a FAIR-compliant repository.

Findable: Every dataset gets a Digital Object Identifier (DOI).
Accessible: Metadata is always open, even if the data itself is restricted (e.g., HIPAA genomic data).
Interoperable: Use standardized schemas like Dublin Core or Schema.org so search engines like Google Scholar can index your repository.
Reusable: Every entry must include a clear license (e.g., Creative Commons CC-BY) and provenance (who created it and how).

4. Advanced Search: AI & Semantic Discovery

Keyword search is no longer sufficient for millions of research entries.

Vector Search (Semantic Scholar Style): Implement a vector database (like Milvus or Pinecone) that allows researchers to search by concept rather than just keyword.

Example: Searching for "extreme weather events" should return papers on "hurricanes," "cyclones," and "monsoons" even if the specific words "extreme weather" aren't in the title.

Knowledge Graphs: Link entities within the repository.

Linkage: Researcher A used Software B (v1.2) to process Dataset C resulting in Publication D.

AI Chat with Data: Use "Retrieval-Augmented Generation" (RAG) to allow researchers to "chat" with the repository. A user can ask, "What were the average temperature findings in all Arctic studies from 2022?" and the repository synthesizes an answer with citations.

5. Governance & Curation Workflow

A repository can quickly become a "Data Swamp" without a clear curation policy.

The "Deposit Agreement": A digital contract where researchers attest they have the right to share the data and that it contains no PII (Personally Identifiable Information).
Metadata Templates: Provide "ReadME" templates to researchers during upload. If the metadata is incomplete (e.g., missing units of measurement), the system should flag it for review.
Review Workflows: Implement a "Librarian Review" stage where experts check the data's integrity before it is assigned a permanent DOI.

6. Repository Success Checklist

[ ] Persistent IDs: Does every entry have a DOI or Handle?
[ ] Redundancy: Is data replicated across at least two geographic locations (3-2-1 backup rule)?
[ ] Analytics: Can you track "Impact Metrics" (downloads, views, and citations) to report back to funders?
[ ] Scalability: Can the system handle a 10x increase in data volume over the next 5 years?