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

# Raw data

The **Raw Data** block generates windows from data samples without any specific signal processing. It is great for signals that have already been pre-processed and if you just need to feed your data into the Neural Network block.

GitHub repository containing all DSP block code: [edgeimpulse/processing-blocks](https://github.com/edgeimpulse/processing-blocks).

## Raw data parameters

**Normalize features**

To enable data normalization, you can use the **Normalize features** option in the processing blocks **generate features** tab. This will learn the mean and standard deviation of each output column during the feature generation step, and apply a normalization step during training and inference.

<Frame caption="Normalize features">
  <img src="https://mintcdn.com/edgeimpulse/JEriF3YpqtMjSUxH/.assets/images/normalization/enable.png?fit=max&auto=format&n=JEriF3YpqtMjSUxH&q=85&s=0e4b4773a3ea8f53500ba59e7b84b902" width="1494" height="1000" data-path=".assets/images/normalization/enable.png" />
</Frame>

When enabled, the block learns the mean and standard deviation of every output column during **Generate features**, then applies a normalization step at training and inference. Recommended when raw numeric ranges differ by more than \~10×. see [Why normalize?](/studio/projects/processing-blocks#why-normalize) for more details.

**Scaling**

* Scale axes: Multiplies each axis by this number. This can be used to normalize your data between 0 and 1.

## How does the raw data block work?

The **Raw Data** block retrieves raw samples and applies the *Scaling* parameter.