Our Data & FAIR Infrastructure Workflow
A practical framework for designing interoperable, machine-readable, and reusable scientific data systems
Assess Data Landscape & FAIR Gaps
Review existing repositories, spreadsheets, metadata practices, and governance constraints to identify where findability, accessibility, interoperability, and reusability need to be strengthened.
Design the FAIR Data Architecture
Define metadata schemas, ontologies, identifiers, access rules, and repository structure so datasets remain machine-readable, traceable, and ready for long-term reuse.
Harmonise & Curate Scientific Data
Transform fragmented experimental and computational data into structured, validated assets with consistent terminology, provenance tracking, and cross-domain compatibility.
Build Interoperable Platforms & Repositories
Deploy scientific databases, cloud services, and data interfaces that support controlled access, repository integration, and scalable collaboration across teams and projects.
Enable AI-Ready Reuse
Prepare datasets and metadata for modelling, analytics, regulatory workflows, and machine learning pipelines so scientific data can be reused beyond a single project.
Core Infrastructure Capabilities
Service areas that turn fragmented research data into governed scientific infrastructure
FAIR Data Architecture
Design metadata models, persistent identifiers, access policies, and linked structures that make datasets findable, governed, and interoperable from the start.
- Metadata standards & schemas
- Persistent identifiers
- Ontology and vocabulary mapping
- Machine-readable data models
Scientific Repositories & Platforms
Build domain-specific repositories and cloud-native data platforms that connect experimental, computational, and curated knowledge assets across projects.
- Scientific databases
- Cloud-native repository design
- API and workflow integration
- Cross-domain data ecosystems
AI-Ready Dataset Engineering
Prepare curated datasets for modelling, analytics, and decision support by improving structure, provenance, and consistency across heterogeneous research sources.
- Dataset harmonisation
- Provenance tracking
- ML-ready data preparation
- Reproducible analytics workflows
See It in Action
Three real-world examples showing how NovaMechanics turns FAIR principles into usable scientific infrastructure, modelling-ready repositories, and governed AI-ready data systems.
FAIR-by-Design for Ethical AI Governance
NovaMechanics mapped FAIR, FAIR for computational workflows, and FAIR4RS principles against major AI ethics frameworks, showing how metadata, provenance, identifiers, and governance mechanisms can operationalise transparency, traceability, accountability, and reproducibility in AI systems.
Read the paperNanoPharos: FAIR Infrastructure for Modelling-Ready NanoEHS Data
NovaMechanics developed NanoPharos as a FAIR-native registry for nanomaterials environmental health and safety data, combining structured metadata, persistent identifiers, programmatic access, and direct reuse in modelling workflows across interoperable scientific platforms.
Explore the paperFrom Fragmented Data to FAIR-Ready Scientific Repositories
NovaMechanics expanded nanoPharos into a scalable FAIR-compliant repository for modelling-ready nanomaterials datasets, integrating rich metadata, project-specific instances, machine-actionable records, and infrastructure for long-term reuse across collaborative research projects.
View the case study paper