> ## Documentation Index
> Fetch the complete documentation index at: https://docs.labelbox.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Manage datasets

> Instructions for managing datasets in the Labelbox UI.

This guide covers how to create, modify, and manage your datasets directly within the Labelbox user interface.

## Before you start

Before you can label data, you need to import it into Labelbox. Take a look at our guides for connecting your cloud data to Labelbox, then come back to this page.

<Card title="Connect to cloud storage" icon="sparkles" horizontal href="/docs/connect-to-cloud-storage" />

## Key definitions

The following are fundamental concepts for organizing your data in Labelbox. Understanding these terms is the first step to building powerful ML models.

All data in Labelbox is organized into **Datasets**, which are made up of individual **Data Rows**.

| Term       | Definition                                                                                                                                                                                                                 |
| :--------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Asset      | The file you want to label (e.g., an image, a video, a text file). This is your core, raw data.                                                                                                                            |
| Data row   | The container for a single asset and all its related information. This includes the asset itself, its metadata, attachments, and any annotations (labels) created for it.                                                  |
| Dataset    | A collection of data rows, typically from a single source or domain. For example, you might create one dataset for "Medical Images from Device X" and another for "Customer Support Transcripts."                          |
| Attachment | Supplementary information you can add to an asset to provide extra context for your labelers. For example, you could attach a PDF with instructions or a reference image.                                                  |
| Global key | An optional but highly recommended unique ID for each data row. Using global keys helps prevent duplicate data uploads and makes it easy to map data rows in Labelbox back to your own external databases or file systems. |

## Create a new dataset

1. Navigate to the **Catalog** tab.
2. Click the **+ New** button to open the "Create a new dataset" dialog.
3. Give your dataset a clear, descriptive name (e.g., `q1-2026-night-driving-images`).
4. Optionally, add a description for more context.
5. Choose an import method:
   * [Connect to your cloud storage (RECOMMENDED)](/docs/connect-to-cloud-storage)
   * [Signed URLs](/docs/signed-urls)
   * [Direct upload](/docs/upload-local-files)
6. Click **Create dataset**.

<Note>
  Best practices

  * Naming: Dataset names can be up to 256 characters and include letters, numbers, spaces, and `_-.,()/`. Use names that clearly explain the data's source and purpose.
  * Organization: Group data from a single domain or source into its own dataset. This simplifies labeling workflow setup. You can use Metadata to further organize and filter data rows within a dataset.
</Note>

## Append data to an existing dataset

You can add more data to a dataset at any time.

1. Go to **Catalog** and select your dataset from the list on the left.
2. Click the **Append to dataset** button.
3. You will be prompted to choose an import method to add your new data rows.

## Delete a dataset

1. In **Catalog**, select the dataset you wish to delete.
2. Click the three-dot menu and select **Delete dataset**.
3. A confirmation dialog will appear. Type `delete` and click the **Delete dataset** button to confirm.

<Warning>
  Deleting a dataset is a permanent, irreversible action. All associated data rows, annotations, metadata, and classifications will be lost.
</Warning>