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# Inspect datasets

Inspect a dataset after its [onboarding job](/ui-guides/data/onboarding-jobs) completes. Check its status and resource references, then review the distributions and lineage before using it for training.

## Before you begin

Identify the completed onboarding job and output dataset. Check that the dataset is **AVAILABLE** before training. Histogram and lineage inspection help verify the prepared data; anomaly analysis also requires a running deployed model.

## Inspect the dataset

Open the dataset to review its metadata and processing results.

**Overview** shows the dataset name and ID, increment, source processing job, datasource, parent dataset, status and audit information. Confirm that the references match the intended input and job.

![Existing dataset overview with AVAILABLE status](/_fern-files/_img/2406984a39b09b329f87312c71940cc1d1515c226b75995f8c893ca9821b5af1.webp)

## Inspect distributions and anomaly controls

**Histogram** lets you select a **Column** and view **Count** or **Percentage**. Use **Refresh** to refresh the display. Inspect the distribution and the target summary before moving to model development.

![Histogram controls and distribution for card\_present\_flag](/_fern-files/_img/1fb6be28b763ecff688f273a998b08fa3e393f6d4ed34af750a4ec18676898c9.webp)

**Anomalies** provides controls for a deployed model, bin source, binning strategy, features and rebinning. Return to this view after [deploying a model](/ui-guides/deployment/deployed-models). Select the deployed model, review the binning and feature settings, then select **Submit**. Interpret the results in the context of the selected model and dataset; an unusual observation does not necessarily indicate an incorrect source value.

![Anomaly analysis controls before selecting a deployed model](/_fern-files/_img/3a734f9636cea320441318a2f2f55bd5a4a1299436ace288af8ede1ea7fa338e.webp)

## Trace dataset lineage

**Lineage** links raw inputs, the datasource and dataset through their processing steps. Select a node to inspect its metadata. Review the row counts, source information and relationships when checking where the dataset came from.

![Dataset lineage and selected node metadata](/_fern-files/_img/d266ec9672486213abbd1b81469b1e5f10699a828d54935cf97112289f25e764.webp)

## Inspect the dataset payload

**Details** displays the raw dataset payload, including identifiers, references, increment information, status and audit fields.

![Raw dataset payload in Details](/_fern-files/_img/f68e27444a674244d8980974004ef965a8adb08c1604a8c087fb0e83d92540e5.webp)

## Decide whether the dataset is ready for training

| Check                 | What to look for                                                                                |
| --------------------- | ----------------------------------------------------------------------------------------------- |
| Identity and lineage  | The expected datasource, processing job and increment.                                          |
| Target distribution   | Class counts or numeric range that match the intended prediction task.                          |
| Feature distributions | Unexpected missing values, rare categories, changed units or values outside the expected range. |
| Preprocessing effects | Special categories and value handling consistent with the onboarding configuration.             |

Compare count and percentage views when assessing a distribution. Percentages help compare shapes; counts reveal how much data supports them. If a distribution is unexpected, trace it to the source and preprocessing settings before interpreting it as a model effect.

## Next steps

Continue to [Workspaces](/ui-guides/models/workspaces).