The Commons, the Data Browser, and LabChat surface the same versioned dataset with the same permissions and provenance — so a finding looks the same whether it’s in a paper, your workspace, or an AI answer.
The data graph, the storage underneath it, and the architecture that serves it to the Commons, the Data Browser, and LabChat. Here’s how each looks.
Raw sessions, analyses, and published datasets all flow through a single identity, permission layer, and provenance chain. Same data model from rig to citation.
Most labs live on scattered external drives, shared folders, and personal laptops. NDI Cloud is a single store where every session is a permanent, citable, downloadable object — still resolvable next semester, next decade.
Files live on backup drives, lab servers, and personal laptops. Nobody’s sure which copy is canonical, and when a drive dies or a student leaves, the data can go with them.
Every session is an addressable object you can pull up by ID, cite in a paper, and share with collaborators.
NDI Cloud runs on the NDI data model — an open spec, not a closed product. Storage, the workspace apps, the Commons, and LabChat are all inspectable layers above it. The data model is the foundation; everything else is pluggable.
We’ll walk you through the architecture, answer questions on storage, security, or SDK fit, and — if it’s a match — help you get your first sessions uploaded.