The DataJoint Library 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 lineage 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.

PyPI		Conda		Tests
License		Citation		Coverage

pip install datajoint

or with Conda:

conda install -c conda-forge datajoint

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

• 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

See CONTRIBUTING.md for development setup and guidelines.