Fragmented research data is a hidden cost in R&D
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Context gets lost. People move on. Insights disappear.
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Unstructured and inconsistent data cannot be reliably used for Al/ML.
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Repetitive tasks and disconnected tools slow research down.
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Time wasted searching, reformatting and reproducing data.
Building internally comes with challenges
Rare experts
Long implementation cycles
High internal dependency
Successful adoption requires
Many projects focus on software deployment.
Successful projects focus on adoption.
Three dimensions.
One successful adoption.
Adoption
Start where research happens. Scale where collaboration grows.
Build around real research needs, then reuse successful workflows, standards and practices across teams and organizations.
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.
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.
GlaideData combines scientific understanding, research data expertise and infrastructure implementation — bridging the gap between research requirements and working systems.
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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.
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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.
We understand the science behind the data.
And the infrastructure required to make it work.
Our research network
How we help
From research requirements to working infrastructure.
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We support the full path from strategy and infrastructure to integrated scientific workflows and long-term adoption.
Strategy
Defining RDM goals, priorities and a practical path to implementation.
Technology & Infrastructure
Deploying and operating reliable research data infrastructure for scientific environments.
Workflows & Integration
Structuring scientific workflows and connecting laboratory data and systems.
People & Community
Onboarding and enabling users, Data Stewards and internal communities to adopt sustainable RDM practices.
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Goals, priorities and first use cases.
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Reliable research data infrastructure.
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Structured data and integrated systems.
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Confident users and active communities.
Start where you need us.
Expand as your requirements grow.
Not just deployed.
Designed to be adopted.
Build your first AI-ready data workflow
Start with the right infrastructure. Scale from there.
