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Mastering the Complexity of Brain Research
Neuroscience takes on the most complex of all biological systems. DataJoint was built by scientists to handle the complexity of reverse engineering the brain.
Wrangling Multi-Modal Recordings
Experimental neuroscience records physiology and behavior to capture the brain in action. That means massive sets of multi-modality data – often 10 or more distinct types of data, each with its own distinct complexity (e.g., the 1,280 recording sites of a single Neuropixels 2.0 probe).
DataJoint tames that complexity. It tracks your subjects, sessions, and equipment. It takes custody of your recordings, organizing and managing them across your storage infrastructure. No more difficulties finding or identifying data – it’s always easy to find, identify, and access. Most important, every recording automatically stays linked to its upstream metadata and to every downstream computation, visualization, or conclusion that depends on it.
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Signal Extraction and Synchronization
DataJoint automatically kicks off processing when new recordings are ready. That can vary from fully automated processes like spike sorting to interactive processes like pose estimation that involve curation and model training.
DataJoint supports algorithms of any complexity to synchronize your data streams in time and space. That can mean aligning multiple events and streams with a common timeline, or 3D volume registration that correlates in vivo functional recordings with ex vivo structural images.
You can even use DataJoint to develop and disseminate a new research methodology.
The Cadwell Lab at UCSF
The Cadwell Lab at UCSF is developing DataJoint pipelines for Patch Seq, a new methodology that combines patch-clamp, morphology, and single-cell sequencing.
Project Aeon at University College London
Project Aeon from the Sainsbury Wellcome Centre at UCL has released a DataJoint-powered system using dozens of cameras and sensors for weeks to months to understand the neural basis of natural mouse behaviors.
The Shcheglovitov Lab at the University of Utah
The Shcheglovitov Lab at the University of Utah is developing DataJoint pipelines as a standard resource for the study of human brain organoids, solving a major challenge in this nascent field.
Consistent Quality Control
Bad data can cost months – or sink a project entirely. Of course, some level of error is inevitable in experimental science. So we have algorithms to identify common issues and statistical anomalies.
The problem?
Researchers are human. Even when they know the appropriate tests, they rarely apply them consistently in a manual process. Too often, mistakes that could have been flagged immediately surface months later, if ever.
DataJoint solves that.
A well-built DataJoint pipeline doesn’t just support quality control – it enforces it. Validation and error correction algorithms run automatically as data flows in, reliably catching issues right away. Adding these steps once removes the burden from an entire team forever.
Good data enables great science. With DataJoint, you get both – and the peace of mind that comes from knowing your data is clean, complete, and trustworthy.
From Complex Signals to Clear Insights
DataJoint offers an integrated data workbench for science. Work in Jupyter notebooks, backed by a customizable hardware and software environment that matches the work you’re doing. Create dashboards and visualizations using the embedded Plotly Dash system. Integrate Weights and Biases or other tools for specialized ML modeling. Instantly filter an analysis by any criteria using the simple, powerful query operators built into DataJoint Python (no SQL required!).
Imagine isolating neural activity by specific behaviors, genetic markers, or reagent batches. This interactive approach to exploring the patterns in your data paves the way to profound insights.
Comprehensive Neuroscience Capabilities
DataJoint open-source scientific software is no longer a pain eliminates the pain of wrangling open-source neuroscience software—no more hunting for the right versions, configuring tools for your lab, or stitching them into a pipeline. DataJoint Elements are ready-to-use reference implementations for major neuroscience modalities, built from real lab practices and integrated with powerful analysis tools. They give you a validated, automated foundation you can use as-is or customize for your experiment.
Supported Instruments and Tools
Our team, our customers, and the broader DataJoint community have built pipelines around a wide array of instruments, software, and methodologies for neuroscience and behavioral analysis.
Infinite Flexibility to *Meet Research Needs*
DataJoint is ultimately a programmable framework for science. Its true power lies in its limitless adaptability. Seamlessly incorporate your existing algorithms and tools, and create new ones. As long as your code can run in a container, DataJoint can handle it. And DataJoint can handle, without hassle, code that depends on old versions of libraries or stringent CPU/GPU/memory configurations.
With DataJoint, you are in control. It’s anything but a black box. You can go as deep as you like, understand what’s happening beneath the surface, and adjust as you see fit.
There really are no limits, and we’re always excited to see what the community creates. If you have developed a new tool or capability, please get in touch with our team!
It's *Your* Pipeline
Reproducible
Everything is recorded, versioned, and linked – a digital replica of your study
Transparent
Your pipeline reveals every scientifically relevant aspect of your study
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Open
Access, inspect, and modify any component of your workflow
Secure
Data and code protected by strict user-based permissions system
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Shareable
Easy publishing and controlled sharing of pipelines and segments
Portable
Yes, you can take it with you
We're here for you
See how DataJoint's SciOps team is here to help, including neuroscientists and data scientists with skills that span neuroscience methodologies, pipeline design, systems integration, and operations .
Accelerate Your Research
Find out how DataJoint can help.
