Adoption
From working infrastructure
to lasting adoption.
Workflows
Technology
Three dimensions
One successful adoption.
People & Community
Successful adoption requires more than deploying technology. It depends on infrastructure that works, workflows that fit real research practices, and people who can confidently use and sustain them.
Start where research happens.
Scale where collaboration grows.
Start local
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Create structured data and working workflows around concrete research needs.
Standardize & reuse
Research data infrastructure can begin with a concrete research need and evolve across teams, institutes and research communities — while preserving shared standards, workflows and interoperability.
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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.
Scaling Adoption
Adoption does not end at deployment.
Successful adoption means moving beyond deployment and integrating infrastructure into everyday research practice. Researchers are enabled to use it confidently, workflows are adapted as research needs evolve, and knowledge is built within the organization so the infrastructure can be maintained, improved and scaled over time.
We support organizations throughout this journey — from first implementation and everyday use to continuous improvement and scale — so research data infrastructure can become a lasting part of scientific practice.
Making Adoption Last
Adoption in Practice
Different starting points.
The same principles.
From vision to first success.
From platform selection to active consortium adoption.
Scaling successful workflows across a growing research community.
Adoption looks different across research environments, but the same principles apply: start with real research needs, build around real workflows, reuse what works and create the conditions for adoption to grow and last.
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
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.
