Understanding the Data Model & Provenance Ontology
Learn how TARE digitally models physical reality through Execution, Telemetry, Evidence, and Graph data structures.
TARE was engineered from the ground up to provide high-assurance, forensic-grade chain of custody for modern laboratories. To achieve this, the platform utilizes a unified Provenance Ontology to track the continuous lifecycle of data.
Unlike traditional LIMS systems that rely on flat, disconnected tables, TARE organizes reality into four interconnected pillars. This structure ensures that every action, reading, and resulting artifact can be cryptographically traced back to its origin.
1. Execution
Represents the intentional actions taken by human operators or automated agents. This includes top-level Runs (often tied to Experiments) and granular Process Steps.
2. Telemetry
Captures the passive or automated signals generated during operations. Events track state changes, while Observations log subjective or objective streams from sensors.
3. Evidence
The tangible outputs of the system. Artifacts represent physical or digital objects produced, while Derived Results are analytical conclusions drawn from the data.
4. Graph
The connective tissue of the platform. Using Provenance Edges, TARE binds all elements together into a cryptographically secured Merkle-DAG graph.
Projects, Experiments, and Cases
On top of this low-level ontology, TARE surfaces human-readable organizational layers:
Projects
The primary container for work over time (e.g., a grant or client program).
Experiments
An R&D study tracked through a status lifecycle (planned → in progress → completed).
Cases
An investigation or work order requiring strict evidence tracking.