smell-datasets
Overview
Unless otherwise noted, this work is derived from the Institute for Biomedical Informatics Innovation Core at the University of Kentucky.
Electronic nose (e-nose) technology is a type of sensory system that mimics the olfactory system of mammals to detect, identify, and quantify odors or volatile organic compounds (VOCs) in the air.
E-noses consist of a combination of chemical sensors, pattern recognition algorithms, and data analysis software that work together to identify and classify different smells. The chemical sensors used in e-noses can detect a wide range of VOCs, including gases such as carbon dioxide, nitrogen oxides, and methane, as well as volatile organic compounds like benzene, formaldehyde, and ethanol.
This repository provides measurement data, data parsers, data visualization, and pretrained models for e-nose applications.
From the project’s README on GitHub.
At a glance
| Repository | Kentucky-Open-Science/smell-datasets |
|---|---|
| Research area | Robotics, drones & sensing |
| Primary language | Python |
| License | Apache-2.0 |
| Stars / forks | 9 / 0 |
| Open issues and pull requests | 1 |
| Created | 2023-04-04 |
| Last push | 2024-01-06 |
| Default branch | main |
| Topics | dataset, sensors |
What the README covers
Top contributors
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Get the code
git clone https://github.com/Kentucky-Open-Science/smell-datasets.git