The DataJoint Library is a Python framework for scientific data pipelines built on the Relational Workflow Model, a paradigm in which 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 are populated before their children.
• Computations are declarative: you define what to compute, and DataJoint handles when.
• Results are immutable: every result traces back to its declared inputs.

This organization hosts the DataJoint Library (datajoint-python), its documentation, codec plugins, and example pipelines.

Cite DataJoint: Yatsenko et al., 2026, DataJoint 2.0: A Computational Substrate for Agentic Scientific Workflows. RRID: SCR_014543

pip install datajoint

or with Conda:

conda install -c conda-forge datajoint

• Documentation: tutorials, how-to guides, explanations and API reference
  • Installation
  • Tutorials
  • Migration Guide: upgrading pipelines from DataJoint before 2.0
• DataJoint Elements: example pipelines for neuroscience experiments
• GitHub Discussions: questions and community support