Research platforms & infrastructure architecture

Practical reference designs and integration patterns to build reproducible, auditable, and scalable research platforms—covering compute, storage, LIMS/ELN, provenance capture, and analytics.

Why this resource matters

Technical choices about compute, storage, laboratory systems, and data flows shape whether your experiments are reproducible, auditable, and able to scale. Fragmented tools, undocumented integrations, and ad-hoc migrations create hidden failure modes that waste time, increase risk, and block discovery. This resource helps teams make deliberate architecture decisions so research work stays usable, verifiable, and portable as projects grow.

What you'll understand and be able to do

Using these reference designs and patterns you will be able to:

• Map roles for compute, object/block storage, databases, and cold archives in different research contexts.
• Choose where a LIMS vs. an ELN adds value, and what metadata and interfaces each must expose for reproducibility.
• Design provenance capture and immutable audit trails that follow samples, data, and analysis pipelines.
• Plan integration patterns (APIs, event streams, message buses, ETL) that reduce brittle point-to-point wiring and simplify migration.
• Prepare migration and versioning strategies so systems can evolve without losing history or reproducibility.

Practical examples — from small labs to enterprise R&D

Examples show how reference choices differ by scale and outcome:

• Academic research group: lightweight ELN plus cloud object storage and containerized compute for reproducible scripts and published provenance.
• Biotech startup: integrated LIMS for sample tracking, secure storage for sequencing data, and reproducible pipelines with provenance stored alongside results.
• Clinical or regulated lab: hardened audit trails, role-based access controls, validated integrations to laboratory instruments, and retention policies to meet compliance needs.
• Manufacturing R&D: hybrid on-premise compute for sensitive data, synchronized metadata catalogs, and a unified lineage model that ties experiments to product decisions.

How to use this resource in your organization

Start by reviewing the reference architecture diagrams and the modeling & simulation playbook included in this resource. Use the patterns as a starting point—adapt them to your data volumes, compliance needs, and team skills. Capture your decisions (service choices, schema, retention rules, integration methods) so the architecture becomes an auditable artifact that can be reviewed, copied, and evolved.

Platform affordances that make adoption easier:

• Adaptive Ownable Domains — acquire or copy the reference architecture into your workspace and tailor it to site-specific needs instead of starting from scratch.
• Interactive form and JSON storage — record architecture decisions, integration checklists, and migration plans as structured data to support later audits or dashboards.