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

# Dataset overview

> Learn about datasets provided by Siverse, their mount paths, and their relationship to caching, and use them in development environments or training jobs.

Datasets provide input data for development environments and training jobs. You can select the data you need from the datasets provided by Siverse and mount it for reading when you create a workload.

## Basic concepts

| Concept | Description |
| - | - |
| Data source | The location where files are stored, such as a Volume directory or an object storage path. |
| Dataset | Data that a workload can select, mount, and read. |
| Container mount path | The path that code uses to read data inside the container after a dataset is mounted to a development environment or training job, for example, `/mnt/datasets/corpus`. |
| Dataset cache | An optional capability that accelerates data reads and improves access efficiency when the same dataset is read repeatedly. A cache is not a data backup or a prerequisite for using a dataset. |

## Select a dataset

| Dataset | Suitable scenarios | How to use |
| - | - | - |
| Preset dataset | Use a public dataset maintained by Siverse to quickly verify a development environment, Notebook, or training job workflow. | Select and mount the dataset when creating a development environment or training job. |

Before selecting a dataset, confirm that it is available to the workload in the current region and cluster.

## Usage flow

1. Confirm that the target dataset is available to the workload in the current region and cluster.
2. When creating a development environment or training job, select a dataset, specify a subdirectory as needed, and configure the container mount path.
3. Read data in your code through the mount path. Write training outputs, checkpoints, Notebooks, or temporary files to a writable Volume.
4. If the same dataset will be read repeatedly and the project already uses dataset caching, you can configure a cache for it.

## Read and write boundaries

Datasets are typically used as input data. Mounting a dataset does not automatically modify scripts or convert data formats. The data path in your code must match the container mount path.

If your program needs to write output files, cache files, training artifacts, or model files, use a writable Volume. Do not write outputs to the dataset mount path or mount a dataset over a critical directory provided by the image.

## Related documentation

* [Use preset datasets](/siverse/datasets/use-preset-datasets.md)
* [Mount datasets in development environments and training jobs](/siverse/datasets/mount-datasets-in-workloads.md)
* [Configure and manage dataset cache](/siverse/datasets/manage-dataset-cache.md)


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