# Data Similarity Map
## Find it in `Dataset` -> `Overview`
## Select Data
Use b-box or polygon tool to select data on the map.
## View Selected Data
Selected data can be displayed and save as a new dataset.
The data close to each other in the map shows that they have a high degree of similarity.
## Learn more tech details, see these two open source repos:
* [A PyTorch implementation of MobileNetV3](https://github.com/xiaolai-sqlai/mobilenetv3) is a convolutional neural network that is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm, and then subsequently improved through novel architecture advances.
* [openTSNE](https://github.com/pavlin-policar/openTSNE) is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE), a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings, massive speed improvements, enabling t-SNE to scale to millions of data points and various tricks to improve global alignment of the resulting visualizations.
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://docs.xtreme1.io/xtreme1-docs/product-guides/data-curation/data-similarity-map.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.