We propose a clean separation between:

• Bonsai operators for acquisition (provided by ucl-open/acquisition)
• Python schemas describing experiments (provided by open_ucl_rigs)

Together, they make experiments config-driven, fully logged, and schema-described, so data can always be read and interpreted correctly later.

NuGet package: UclOpen.Acquisition

Provides standardised Bonsai operators for:

• Hardware (UclOpen.Acquisition.Device)
• Logging (UclOpen.Acquisition.Logging)
• Shared utilities (UclOpen.Acquisition.Utilities)

These operators read a YAML config and configure devices + logging in a consistent way across rigs and labs.

Provides core Pydantic schemas that describe all parts of an experiment:

• Rig (rig.py)
• Task (task.py)
• Experiment (experiment.py)

Experimenters / designers extend these schemas for their own experiments on a new repo.

This can then be run to produce:

• experiment.json — a combined schema detailing all experiment parameters, including rig configuration and metadata.

Automatically generates corresponding C# classes and associated operators in Extensions from the .json schema. These are then available in the Bonsai editor Toolbox, and can be wired in directly with experimenter defined friendly names and structure.

1. Bonsai loads experiment.yml.
2. UclOpen.Acquisition operators configure hardware + logging using these parameters.
3. Data is recorded along with the configuration parameters + schema.

Python readers use the schema to interpret:

• which devices and data streams exist
• how they map to each rig
• which workflow + git commit produced the data (To allow complete recreation of experiment)