datajoint 2.3.0

DataJoint for Python is a framework for scientific workflow management based on relational principles. DataJoint is built on the foundation of the relational data model and prescribes a consistent method for organizing, populating, computing, and querying data.

DataJoint for Python

DataJoint is a framework for scientific data pipelines based on the Relational Workflow Model — a paradigm where your database schema is an executable specification of your workflow.

• Tables represent workflow steps — Each table is a step in your pipeline
• Foreign keys encode dependencies — Parent tables must be populated before child tables
• Computations are declarative — Define what to compute; DataJoint handles when
• Results are immutable — Full provenance and reproducibility

Documentation: https://docs.datajoint.com

📘 Upgrading from legacy DataJoint (pre-2.0)? See the Migration Guide for a step-by-step upgrade path.

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Installation

pip install datajoint

or with Conda:

conda install -c conda-forge datajoint

Example Pipeline

Cite DataJoint: Yatsenko et al., 2026 — RRID: SCR_014543

Resources

• Documentation — Complete guides and reference
  • Tutorials — Learn by example
  • How-To Guides — Task-oriented guides
  • API Reference — Complete API documentation
  • Migration Guide — Upgrade from legacy versions
• DataJoint Elements — Example pipelines for neuroscience
• GitHub Discussions — Community support

Contributing

See CONTRIBUTING.md for development setup and guidelines.