Data sections

Both Agrolab Excel files are translated in a single notebook cell. Like the sample translate step, this cell calls a process_file directly — the translate process file contains two entries, one per table.

Prerequisites

Load sample data must be complete. The observation log links to a provision record (ai4sh-agrolab) that must exist from Load utility data.

Notebook cell

In load_ai4sh_wetlab_data.ipynb, the Translate Agrolab (wetlab) Observation log cell runs:

process_file = 'import_data/wetlab/agrolab/process/translate_agrolab_observation.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)

Note: the cell uses process_file, not job_file.

Translate process file

Path: ./ai4sh/import_data/wetlab/agrolab/process/translate_agrolab_observation.json

{
  "process": [
    {
      "process": "translate_tabular_data",
      "overwrite": true,
      "parameters": {
        "process": "manage_observation_log",
        "tabular_data_path": "../../../wetlab/agrolab/excel/AI4SH_observation_log_agrolab.xlsx",
        "dst_path": "../../../wetlab/agrolab/manage_process"
      }
    },
    {
      "process": "translate_tabular_data",
      "overwrite": true,
      "parameters": {
        "process": "manage_observation",
        "tabular_data_path": "../../../wetlab/agrolab/excel/AI4SH_observation_agrolab.xlsx",
        "dst_path": "../../../wetlab/agrolab/manage_process"
      }
    }
  ]
}

The two entries are executed in sequence. Each reads an Excel source file and writes one manage process file to ./ai4sh/import_data/wetlab/agrolab/manage_process/.

Source files

AI4SH_observation_log_agrolab.xlsx — one row per sampling log / provision combination. Required columns:

Column Description
sampling_log_id__sampling_log_name FK lookup: parent sampling log
provision_id__provision_name FK lookup: the Agrolab provision record (e.g. ai4sh-agrolab)
contact_name Name of the responsible person
contact_email Email of the responsible person
name Observation log name — auto generates <sampling_log>@<provision>
preparation_id__preparation_name FK lookup: sample preparation method
preservation_id__preservation_name FK lookup: sample preservation method
storage_id__storage_name FK lookup: sample storage condition
transportation_id__transportation_name FK lookup: transportation method
laboratory Boolean — true for laboratory-based observations

AI4SH_observation_agrolab.xlsx — one row per sample. Columns beyond the identifier fields carry the measured values and use the @ prefix convention described below.

Required identifier columns:

Column Description
observation_log_id__observation_log_name FK lookup: parent observation log
sample_id__sample_name FK lookup: sample record
observation_date Date of analysis as integer (YYYYMMDD)

The @ prefix for observation values

Columns whose names begin with @ in the Excel file are observation value columns. The @ is a signal to the framework that the column maps to a provision_indicator in the database — not a regular field or FK.

For example, @ph-h2o in the spreadsheet maps to the ph-h2o provision indicator defined in the ai4sh-agrolab provision. The value is stored as an observation record for that indicator.

Values can be:

  • Numeric: stored directly (e.g. 5.9, 32, 2.18)
  • String with qualifier: below-detection values such as "<0.10" are stored as strings

Output

Two manage process files are written:

File Source
manage_observation_log.json AI4SH_observation_log_agrolab.xlsx
manage_observation.json AI4SH_observation_agrolab.xlsx

Next step

Proceed to Manage Agrolab observation log.

Updated: