Research data foundations
Organize experimental data, results, and metadata into documented, validated models so R&D teams can find, reproduce, and reuse their work.
Research teams generate data from instruments, experiments, simulations, and analyses. Without consistent structure and metadata, data ends up in personal folders, results cannot be reproduced, and past work is repeated because nobody can find it.
We design data models and pipelines that capture data with its metadata, validate it on the way in, and document every transformation. Researchers can then find past results, reproduce analyses, and build on each other's work.
Things to keep in mind
- Involve researchers in defining metadata so it gets used.
- Partner and proprietary data need access controls.
- Keep raw data immutable and transformations documented.
How it works
Map the data
Identify instruments, experiments, and analyses that produce data.
Define metadata
Agree what must be recorded with every dataset.
Build pipelines
Ingest, validate, and store data with versioning.
Make it findable
Index datasets so researchers can search and reuse them.
Frequently asked questions
Can you work with instrument outputs?
Usually. We review the formats your instruments produce.
How do you handle sensitive research data?
Access is role-based and limited to the people and projects that need it.
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