DataJoint 2.0: A Computational Substrate for Agentic Scientific Workflows

• URL: http://arxiv.org/abs/2602.16585v1
• Date: Wed, 18 Feb 2026 16:35:47 GMT
• Title: DataJoint 2.0: A Computational Substrate for Agentic Scientific Workflows
• Authors: Dimitri Yatsenko, Thinh T. Nguyen,
• Abstract summary: DataJoint creates a substrate for SciOps where agents can participate in scientific transformations without risking data corruption.<n>Tables represent workflow steps, rows represent artifacts, foreign keys prescribe execution order.<n>Single formal system where data structure, computational dependencies, and integrity constraints are all queryable, enforceable, and machine-readable.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Operational rigor determines whether human-agent collaboration succeeds or fails. Scientific data pipelines need the equivalent of DevOps -- SciOps -- yet common approaches fragment provenance across disconnected systems without transactional guarantees. DataJoint 2.0 addresses this gap through the relational workflow model: tables represent workflow steps, rows represent artifacts, foreign keys prescribe execution order. The schema specifies not only what data exists but how it is derived -- a single formal system where data structure, computational dependencies, and integrity constraints are all queryable, enforceable, and machine-readable. Four technical innovations extend this foundation: object-augmented schemas integrating relational metadata with scalable object storage, semantic matching using attribute lineage to prevent erroneous joins, an extensible type system for domain-specific formats, and distributed job coordination designed for composability with external orchestration. By unifying data structure, data, and computational transformations, DataJoint creates a substrate for SciOps where agents can participate in scientific workflows without risking data corruption.

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