---
title: Data augmentation | Adaption
description: Add domain-specific and general-purpose rows before AutoScientist training begins.
---

[Augmentation](/api/python/resources/autoscientist/methods/create/index.md) is optional, but recommended when a dataset is small or lacks diversity. AutoScientist can add two independent types of rows before its training iterations begin:

- **Domain rows** add examples specific to the dataset’s domain. Existing rows count toward the recommended 20,000-domain-row target.
- **General rows** add diverse examples that help the model retain broader capabilities.

Augmentation here is part of the AutoScientist request. To improve and restructure the source dataset first, use [Adaptive Data](/adaptive-data/overview/index.md).

## Calculate the additions

The following request targets at least 20,000 domain rows and adds 8,000 general rows:

```
from adaption import Adaption


client = Adaption()


dataset_id = "dataset_abc123"
dataset = client.datasets.get(dataset_id=dataset_id)


domain_rows_to_add = max(0, 20_000 - (dataset.row_count or 0))


run = client.autoscientist.create(
    dataset_id=dataset_id,
    augmentation_domain_rows=domain_rows_to_add,
    augmentation_general_rows=8_000,
)
print(run.id, run.status)
```

This produces up to 28,000 training rows: the existing dataset plus enough domain rows to reach 20,000, followed by 8,000 general rows. If the dataset already contains at least 20,000 rows, `domain_rows_to_add` is zero.

Omit either augmentation parameter to skip that type of addition. AutoScientist prices and sizes each requested expansion before training. See the [`create` API reference](/api/python/resources/autoscientist/methods/create/index.md) for current limits and request fields.