DataJoint Documentation¶
Documentation for DataJoint 2.0+
Python code examples in this documentation use DataJoint 2.0 syntax. If you're using DataJoint pre-2.0, see the Migration Guide or visit legacy docs.
About DataJoint¶
DataJoint is a framework for scientific data pipelines built on the Relational Workflow Model—a paradigm where your database schema is an executable specification of your workflow.
Unlike traditional databases that merely store data, DataJoint pipelines process data: tables represent workflow steps, foreign keys encode computational dependencies, and the schema itself defines what computations exist and how they relate. Combined with Object-Augmented Schemas for seamless large-data handling, DataJoint delivers reproducible, scalable scientific computing with full provenance tracking.
- Concepts
Understand the Relational Workflow Model and DataJoint's core principles
- Tutorials
Build your first pipeline with hands-on Jupyter notebooks
- How-To Guides
Practical guides for common tasks and patterns
- Reference
Specifications, API documentation, and technical details
- DataJoint Elements
Reusable pipeline modules for neurophysiology experiments
- DataJoint Platform
A cloud platform for automated analysis workflows. It relies on DataJoint Python and DataJoint Elements.
New to DataJoint? Start with the Quick Start tutorial.