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Solutions

Built for the groups that share research data.

A single lab, a multi-site consortium and an institute with many programs all need the same guarantee: every result traces back to the data behind it. What differs is who has to agree on what.

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Research labs

Keep your own data reproducible.

For a principal investigator and a small team who want their analyses to survive the next grad student.

One workspaceInstrument data, imaging and genomic files, notebooks and findings in a single collection.
Pinned cohortsDefine a cohort once and re-run it exactly, even after new participants are added.
Notebooks with nothing to installAnalyze in the browser and save figures straight into a finding.
Compute when you need itOptional pipelines with an estimate before launch and spend limits per person.
Release on your termsKeep work in progress private, then publish a versioned release to collaborators.
Consortia and programs

Harmonize data across sites.

For multi-site studies that must measure the same things the same way and release on a schedule.

Shared instrumentsStandardize on common data elements such as PHQ-9 and GAD-7, with about 22,000 NLM CDEs available.
Site-level controlEach site works in its own collection. Permission groups combine them for cross-site analysis.
Curator reviewData structures and collections are approved before anything is released.
Access requestsOutside researchers ask for access through a queue, and every decision is recorded.
Bring existing dataImport collections from NDA with their structures.
Institutes

Run many programs on one governed platform.

For an institute that has to keep programs separate, keep data in its own account, and answer a security review.

Tenant isolationA separate bucket and catalog for each institution, with administrators scoped to their own tenant.
Your cloud accountData stays in your AWS account, in open Iceberg tables you can read without us.
Budgets that match grantsEach collection is tied to a billing account, and a lab with two grants can have two budgets.
Storage limitsQuotas per institution and per member keep a shared pool fair.
A security review partnerWe complete your questionnaire and walk your team through the architecture.
Example scenarios

What this looks like in practice.

Illustrative scenarios, not customer stories.

Consortium

Depression screening across three sites

Each site uploads PHQ-9 data to its own collection. A curator approves the shared structure, a permission group unlocks cross-site queries, and release-3 is published with its snapshot recorded.

Lab

Re-running a published analysis

Two years after publication, a reviewer opens the finding, sees the release and snapshot it used, and re-runs the notebook. The result matches, although the live table has since grown.

Institute

Variant calling on a curated pipeline

An owner launches nf-core sarek from a collection, sees the maximum cost before launch, and gets the outputs back as files in the same collection, with VCFs opening in the genome viewer.

Tell us who needs to agree.

We will map your groups, data and review process onto the platform.

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