Manage Agrolab Observation Log
An observation log record bridges a sampling log (a field collection event) and a provision (a laboratory protocol). For the Agrolab wetlab data, each entry records which sampling log’s samples were analysed, under which protocol, and how they were handled in the lab. Individual observations are later linked to these log records.
Prerequisites
- Translate Agrolab wetlab data must be complete.
- The Agrolab provision (
ai4sh-agrolab) and its indicators must exist from Load utility data. - Sampling logs must exist from Load dataset metadata.
Notebook cell
In load_ai4sh_wetlab_data.ipynb, the Manage Agrolab observation log cell runs:
process_file = 'import_data/wetlab/agrolab/manage_process/manage_observation_log.json'
structured_process_D, scheme_params_D = Initiate_process(notebook_path, scheme_file, process_file)
if structured_process_D is not None:
Run_process(structured_process_D, scheme_params_D)
Manage process file
Path: ./ai4sh/import_data/wetlab/agrolab/manage_process/manage_observation_log.json
{
"process": [
{
"root_process_id": "import_tabular_data",
"process": "manage_observation_log",
"delete": false,
"overwrite": false,
"parameters": {
"sampling_log_id__sampling_log_name": "ai4sh_dk_foulum_2024",
"provision_id__provision_name": "ai4sh-agrolab",
"contact_name": "thomas gumbricht",
"contact_email": "thomas.gumbricht@natgeo.su.se",
"name": "auto",
"preparation_id__preparation_name": "ds2",
"preservation_id__preservation_name": "ds2",
"storage_id__storage_name": "amb",
"transportation_id__transportation_name": "none",
"laboratory": true
}
}
]
}
The example file contains 12 entries — one per field site sampling log.
Parameters
| Parameter | Description |
|---|---|
sampling_log_id__sampling_log_name |
FK lookup: parent sampling log by name |
provision_id__provision_name |
FK lookup: Agrolab provision record (defines the indicators measured) |
contact_name |
Name of the responsible person |
contact_email |
Email of the responsible person |
name |
Log name — "auto" generates <sampling_log_name>@<provision_name> |
preparation_id__preparation_name |
FK lookup: sample preparation method used (e.g. ds2 = dried and sieved to 2 mm) |
preservation_id__preservation_name |
FK lookup: preservation method |
storage_id__storage_name |
FK lookup: storage condition (e.g. amb = ambient) |
transportation_id__transportation_name |
FK lookup: transportation method |
laboratory |
Boolean — true for laboratory-based analyses |
Auto-naming
When name is set to "auto", the framework generates the observation log name as <sampling_log_name>@<provision_name>. For example, the entry above produces the name ai4sh_dk_foulum_2024@ai4sh-agrolab. This name is then used as the FK reference in the observation manage file via observation_log_id__observation_log_name.
Next step
Proceed to Manage Agrolab observation.