They deploy
Your data partner runs one script on their infrastructure.
A shared workspace on their hardware, under their control.
You never touch their systems. You never see individual records.
The data you need already exists. Getting to it is another story.
Every collaboration starts with a data access agreement. Every agreement triggers legal, ethics, IT security.
Months pass — or it never happens. tracebloc removes data transfer and direct access — the rest is routine.
How It Works
Your model travels to the data. Not the other way around.
You submit
Submit your model or pipeline to their workspace.
You see the dataset structure, never raw patient records.
Their data never leaves their site.
You get results
Every submission benchmarked identically on their data.
External validation evidence without a single data transfer.
Results in days, not months.
The problem
The data exists. The ecosystem refuses to expose it.
The cohort your reviewer wants exists somewhere, but you can't get to it in time. An MTA takes eight months, an Epic integration costs $25k before any line of code is written. The revision deadline is long gone before access can be set up.
For Multi-Omics data, your n is typically small, and so is everyone else's in your network. Multi-omics needs numbers no single site can produce, which is why bioinformaticians keep asking whether they can combine datasets from different studies. The answer: statistically yes, legally no.
tracebloc is what you give your data partner so they can say yes. They deploy a workspace on their infrastructure. You submit your model. No data leaves their site — so the approval that used to take months is now routine.
Who This Is For
For researchers who've found the right dataset and hit a governance wall.
PIs Facing Peer Review
The challenge
Your reviewer wants external validation on an independent cohort. The cohort exists at a partner institution. But access triggers an MTA, ethics resubmission, and IT security review. Months pass. Your revision deadline doesn’t wait.
How we help
Give your data partner tracebloc. They deploy a workspace on their infrastructure. You submit your model. Independent validation evidence in days, not months. No data transfer required.
Clinical Bioinformaticians
The challenge
"Can I combine scRNA-seq datasets from different studies?" Statistically, yes. Legally, no. Each dataset sits behind a separate governance wall. No transfer agreement, no pooling. The analysis you need is blocked, not slow.
How we help
Each dataset trains locally. You get combined model performance without pooling a single patient record. "Can I combine datasets from different studies?" Yes — without moving any of them.
Rare Disease Researchers
The challenge
Your n is too small. Every institution in your network has the same problem. For rare disease, 200 patients with confirmed diagnoses is often the ceiling.
How we help
Pool statistical power across rare disease registries without pooling data. Each site keeps full governance. You build the model that no single site could build alone.
The Platform
See the workspace in action.
Use cases
Five workflows. Real pharma problems.
Each scenario maps to how pharma actually acquires and validates clinical data — shaped by conversations with clinical genomics leads, bioinformatics heads, and commercial teams.
WHY Reviewers Trust us
Same seed. Same hardware. Same conditions. Every submission.
Identical Run Conditions
Every model runs in the same container, on the same hardware, with the same seed. No drift between submissions. No "it worked on my machine."
Cryptographic Audit Trail
Each run records a hash of the model, data version, and environment. Reviewers can verify the exact run behind every number in your paper.
Independent External Cohort
Validation runs on a partner institution's data — not a held-out slice of your own. The independent cohort your reviewer asks for.
No Data Transfer, No Retraction Risk
Only results leave the site. Nothing to share, nothing to govern, nothing to unwind if a sample is withdrawn.