AI-Ready Research Data Infrastructure
We help research and R&D organizations build FAIR and AI-ready research data infrastructure.
Glide with us.
Fragmented research data is a hidden cost in R&D
Time wasted searching, reformatting and reproducing data.
Lost productivity
Repetitive tasks and disconnected tools slow research down.
Manual workflows
Context gets lost.
People move on.
Insights disappear.
Knowledge loss
Unstructured and inconsistent data cannot be reliably used for Al/ML.
Slow Al adoption
Building this internally is slow, expensive and difficult to scale
Rare experts
Long implementation cycles
High internal dependency
AI needs more than data
It needs structured, contextualized and reusable research data
GlaideData connects research data and Al into one integrated infrastructure.
Technology
alone
is not
enough
Successful research data infrastructure combines
Successful adoption
Many projects focus on software deployment.
Successful projects focus on adoption.
Three dimensions.
One successful adoption.
Built for complex research environments
From individual research groups to large collaborative initiatives, GlaideData helps organizations build research data infrastructure that fits their scientific workflows, scale and goals.
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Build structured, reproducible data workflows around everyday research:
Instruments & experiments
Local data management
Reproducible workflows
Analysis & insights
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Connect distributed partners, data and workflows through interoperable infrastructure:
Distributed data integration
Interoperability & standards
Secure data sharing
Global collaboration
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Create shared infrastructure across teams, instruments and research domains:
Centralized data infrastructure
Cross-team collaboration
Governance & compliance
Resource optimization
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Build reliable research data foundations for R&D, automation and AI/ML:
R&D data foundation
Automation & pipelines
Al/ML enablement
Scalable & secure
Adoption
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Research data infrastructure can begin within a single group and evolve across institutes, consortia and research networks while preserving shared standards, workflows and interoperability.
Start local
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Create structured data and working workflows around concrete research needs.
Standardize & reuse
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Reuse data models, tools and practices so successful approaches can spread beyond their original users.
Connect & scale
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Shared standards and interoperable infrastructure allow workflows, data and knowledge to connect across teams and organizations.
One infrastructure. Growing with your research.
Built in research
Proven in practice
Focused on adoption
Research Group
Leibniz University Hannover
From interest to productive usage in 1.5 months.
Consortium
iEntrance
From platform selection to active consortium adoption.
Research Network
SolarTAP
Scaling successful workflows across a growing research community.
Where scientific expertise meets data infrastructure.
We understand the science behind the data.
And the infrastructure required to make it work.
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Expertise in structuring, managing and connecting scientific data for sustainable, reusable and FAIR research workflows.
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From requirements and architecture to integration, implementation and long-term adoption.
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Born from research, with first-hand experience in scientific data environments.
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Deep expertise in the NOMAD ecosystem and its implementation in real research environments.
GlaideData combines scientific understanding, research data expertise and infrastructure implementation — bridging the gap between research requirements and working systems.
Trusted by research communities
How we help
From research requirements to working infrastructure.
Strategy
Defining RDM goals, priorities and a practical path to implementation.
We support the full path from strategy and infrastructure to integrated scientific workflows and long-term adoption.
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first-hand experience in scientific data environments
Start where you need us. Expand as your requirements grow.
Not just deployed. Designed to be adopted.
TECHNOLOGY & INFRASTRUCTURE
Deploying and operating reliable research data infrastructure for scientific environments.
TECHNOLOGY & INFRASTRUCTURE
Defining RDM goals, priorities and a practical path to implementation.
Build your first AI-ready data workflow
Start with the right infrastructure. Scale from there.
