BUILT FOR THE MODERN SCIENTIFIC STACK
The Scientific Data Foundation for Accelerated Life Sciences R&D.
By codifying experiments, pipelines, and results as first-class scientific data, DataJoint becomes the foundation R&D leadership can defend: faster pipelines, defensible decisions, and AI investments that compound.
PLATFORMS
OUTCOMES
Where acceleration actually begins
Acceleration starts long before the lakehouse, the warehouse, or the model.
It starts where the experiment is designed, codified, and made reproducible;
and that's where DataJoint begins.
Why this matters now
Most platforms start at the instrument.
DataJoint starts at the experiment.
Lab platforms, data clouds, and AI tools assume the science is already clean. It isn’t. The provenance, the parameters, the pipeline logic; everything that makes a result reproducible; gets lost between the experiment and the system that’s supposed to receive it.
01
Reproducibility breaks.
You can’t rerun the analysis from six months ago. Scripts drift, environments change, people leave. The science isn’t defensible because it isn’t reconstructible.
02
AI investments stall.
Models trained on inconsistent, decontextualized experimental data don’t generalize. AI ROI hasn’t landed because the science underneath wasn’t structured in the first place.
03
Provenance breaks on audit.
When regulators, IP counsel, or QA ask ‘how did you get this result?’, the answer lives in a Slack thread. That works until it doesn’t.
The DataJoint Difference
A computational database that codifies experiments, pipelines, and results as first-class scientific assets: governed, reproducible, and upstream of every platform your team already runs.
Experiments are modeled, not stored.
Rerun an analysis from six months ago. Same result. Every time.
Reproducibility is structural, not optional.
Every output traces back to its exact inputs, code, and environment. Built in, not bolted on.
The work compounds, instead of disappearing.
Every experiment becomes a reusable asset. Every program builds on the last.
The precondition for trustworthy AI.
Audit-ready by default. Defensible in regulatory review.
The DataJoint Advantage
Four pillars. One foundation that compounds.
DataJoint is the experiment-first foundation R&D leadership can defend. Four pillars hold it up. Each one is a dimension of acceleration.
01
Data in Context
Scientific context preserved, not lost.
Every result carries the full record of how it was made. Experiments, pipelines, and results connected as one system. Data that explains itself to people, code, and AI.
02
Deterministic Workflows
Same inputs and code, same result every time.
Science expressed in code. Workflows codified, repeatable, and versioned. The exact code, parameters, and inputs preserved for rerun.
03
Reusable, AI-Ready Assets
Work that compounds across programs and sites.
Workflows and results that hold up beyond the moment. One-off analyses become assets others can extend. Every experiment adds to the foundation, never replaces it.
04
Defensible, Trusted Science
Faster decisions on a defensible foundation.
Stands up to internal review, regulatory scrutiny, and partner questions. Who did what, when, on which data, visible end to end. Suitable for higher-stakes decisions and AI training.
Trusted by Premier Research Institutions
Leading labs choose DataJoint to manage their most complex and valuable data.
Where we fit
We make the platforms you already run more valuable.
Every platform in your R&D stack has a job. Lab systems capture what’s done at the bench. Data platforms store and compute. AI tools build models. DataJoint sits upstream of all of them. We don’t replace your stack. We make it more valuable, so the science holds up.
SOURCE
SYSTEMS
Raw experimental output
DATAJOINT // THE SCIENTIFIC DATA FOUNDATION
Capture
Raw experimental output from labs, instruments, and sources lands in storage.
SCIENTIFIC CONTEXT PRESERVED, NOT LOST
Codify
DataJoint models experiments, pipelines, and results as first-class scientific data.
SAME INPUTS AND CODE, SAME RESULT EVERY TIME
Execute
Deterministic workflows run with full code, data, and compute context preserved.
WORK COMPOUNDS ACROSS PROGRAMS AND SITES
Activate
Trusted scientific assets publish into the platforms running R&D for AI, analytics, and governance.
FASTER DECISIONS ON A DEFENSIBLE FOUNDATION
Scientist-in-the-Loop
tune parameters, refine paths, or fork a workflow without losing traceability.
DOWNSTREAM PLATFORMS
Platforms become more reliable for science
RESEARCH
OUTCOMES
Where trusted science compounds into business value.
We exist so that scientific work compounds, instead of disappearing; across every platform that runs R&D.
What compounds
Six outcomes that change R&D economics.
When the science underneath holds up, the budget defends itself.
Pipeline throughput, compressed.
NME quality and quantity. Discovery cycle compression. Time to IND, measured in weeks instead of quarters.
AI investments that compound.
Trustworthy AI by construction. Models that survive audit. A defensible AI investment thesis at the board level.
Submissions, audit-ready by construction.
Defensible clinical evidence. Phase II and III integrity. Regulatory defensibility built in, not bolted on.
Program economics, protected upstream.
Earlier IP signal. Continuous FTO surveillance. Kill-issues caught before they kill the program.
Scientists, freed for harder work.
Your best people stay focused on designing the next experiment, not maintaining the last pipeline.
Science that reruns.
Reusable evidence across programs. Every new program inherits the foundation of the last, instead of starting from zero.
Built where the budget is on the line.
Proven at scale
Built where the experiment begins.
Proven where the science is hardest.
The institutions running the world’s most complex multimodal research run on DataJoint. The same upstream problem pharma R&D is now trying to solve at higher stakes.
Case Study · Johns Hopkins
Scaling Alzheimer's research with DataJoint.
With DataJoint, we save months of compute time. Without DataJoint, some of our experiments are not even doable.
Marshall Hussain Shuler
Associate Professor · Johns Hopkins School of Medicine
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DAY 0
Hypothesis
Prof. H. Shuler approaches DataJoint with a vision to boost productivity and reliably integrate AI into research.
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60 DAYS
Foundation Design
The team applies DataJoint principles to unify fragmented experimental workflows into a single, governed pipeline.
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6 MONTHS
Production
The automated pipeline is operational, processing 15h of recordings daily and generating 1 TB of data.
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8 MONTHS
Impact
DataJoint enables the lab to scale up research and unlock breakthroughs that would have taken years.
Case Study · Shriners Children's
Scaling pediatric motion analysis across Shriners Children's.
An advanced scientific data pipeline that turns synchronized multi-camera video into clinical-grade biomechanics, with no markers and no manual tracking.
Shriners Children's
Markerless Motion Capture Initiative
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DAY 0
Partnership
DataJoint partners with Dr. Seth Donahue to design a markerless motion capture pipeline for pediatric patients.
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3 MONTHS
Built & Operational
Pipeline goes live, from synchronized multi-camera capture through biomechanical modeling, with no markers and no manual tracking.
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6 MONTHS
Clinical Scale
Proven on 200+ pediatric patients, producing clinical-grade biomechanics from standard video.
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2026 & BEYOND
Network Rollout
Expanding across the Shriners network as a hospital-wide capability; making multi-site studies feasible, reproducible, and grant-ready.
Case Study · MICrONS
Proven on the $100M Apollo Project for the Brain.
We now have the technology to look into the detailed organization of the brain.
Andreas Tolias, PhD
Principal Investigator · MICrONS Program
-
2016
Collaboration
IARPA forms a consortium with 100+ scientists in 3 teams led by Baylor, Allen Institute, and Princeton.
-
2018
Data Collection
Baylor records neuronal activity, and the Allen Institute images the cellular structure.
-
2022
Analysis
Princeton constructs a 3D anatomical connectome, linking it to the functional data from across the consortium.
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2025
Publication
Nature publishes a special issue on MICrONS with 16 peer-reviewed papers from the consortium.
Experiment-first. Codified upstream. Proven at scale.
More than software
Behind every deployment is the SciOps team.
Scientists and engineers who design, build, and launch the foundation alongside your researchers, not in parallel to them.
See how DataJoint engagesApps
Composable by Design
Browse a library of reusable scientific pipeline components and supported integrations. Every app is built to drop into your DataJoint foundation without rebuilding from scratch.
Element Array Electrophysiology
A data pipeline for Neuropixels probes. End-to-end from acquisition to spike sorting.
DataJoint ELEMENTElement Calcium Imaging
A data pipeline for calcium imaging microscopy. Validated for multi-photon and miniscope setups.
DataJoint TOOLDeepLabCut
Markerless pose estimation toolbox using deep learning to track user-defined body parts.
Mackenzie Mathis, Harvard/EPFL TOOLKilosort
Spike sorting with accuracy and speed. The community-standard tool for Neuropixels analysis.
Marius Pachitariu · Janelia/UCL/HHMIWho DataJoint is built for
One foundation. Five seats at the table.
R&D & Translational Leaders
Move programs through key decision gates faster, with evidence that holds up to scrutiny.
See the questions R&D leaders askCode-Forward Scientists
The computational backbone that codifies experiments and pipelines once, then reuses them everywhere.
See the questions scientists askData & Platform Owners
Governed scientific data products that make the platforms you already run measurably more valuable.
See the questions platform owners askSecurity & Compliance Gatekeepers
A governed foundation carrying full provenance and access control. Not another shadow IT system.
See the questions compliance asksInstitutional Sponsors
The scientific foundation beneath every R&D and AI Initiative; and the precondition every downstream investment depends on.
See the questions sponsors askGet started
Build on a foundation that holds up.
Bring us your hardest scientific data problem. We will show you how DataJoint codifies it, connects it, and turns it into a foundation your R&D leadership can defend.
Book a Discovery