Data from: Odor source distance is predictable from time-histories of odor statistics for large scale outdoor plumes
Creators: name: Nag, Arunava
name: van Breugel, Floris
Publisher: name: Zenodo
Year: 2024
Subjects: olfactory navigation
odor plume statistics
turbulent plume
Localization

description: Odor plumes in turbulent environments are intermittent and sparse. Lab-scaled experiments suggest that information about the source distance may be encoded in odor signal statistics, yet it is unclear whether useful and continuous distance estimates can be made under real-world flow conditions. Here we analyze odor signals from outdoor experiments with a sensor moving across large spatial scales in desert and forest environments to show that odor signal statistics can yield useful estimates of distance. We show that achieving accurate estimates of distance requires integrating statistics from 5-10 seconds, with a high temporal encoding of the olfactory signal of at least 20 Hz. By combining distance estimates from a linear model with wind-relative motion dynamics, we achieved source distance estimates in a 60x60 m2 search area with median errors of 3-8 meters, a distance at which point odor sources are often within visual range for animals such as mosquitoes.
description: These are data frames with .hdf and .h5 extension, processed with python pandas. They can be opened in using python pandas https://pandas.pydata.org/pandas-docs/version/1.5/getting_started/install.html

To open or produce the figures inskcape is needed.
Funding provided by: United States Air Force Research LaboratoryCrossref Funder Registry ID: https://ror.org/02e2egq70Award Number: FA8651-20-1-0002
Funding provided by: United States Air Force Office of Scientific ResearchCrossref Funder Registry ID: https://ror.org/011e9bt93Award Number: FA9550-21-0122
Funding provided by: Alfred P. Sloan FoundationCrossref Funder Registry ID: https://ror.org/052csg198Award Number: FG-2020-13422
Funding provided by: National Science FoundationCrossref Funder Registry ID: https://ror.org/021nxhr62Award Number: 2112085
description: The setup can be divided into two components, the first being the mobile sensor stack that was carried by a human for collecting odor signals, and the second component included the placement of 8 stationery wind sensors for ambient wind measurements around the odor source. The odor source was a propylene gas tank that was mounted with a stationary GPS antenna which sent Real Time Kinematics (RTK) correction data to the antenna mounted on the mobile sensor stack for a high-resolution position with an accuracy close to 1cm.The mobile sensor stack included an odor sensor, a GPS antenna to receive accurate location measurements, and an IMU that provided angular velocity measurements. The sensors stack was balanced on a gimbal for stability and ease of carrying. The odor sensor data was collected using a data acquisition (DAQ) unit, which was connected along with all the other sensors to a computer that was running ROS as middleware and recorded data in real-time. Due to the different sampling rates of sensors, the data was interpolated with respect to the rate of the odor sensor which sampled at 200Hz.Around the odor source, 7 ambient wind sensors were placed in a square fashion at approximately 30 meters away from the source, and one another wind sensor was placed 1 meter away from the odor source for accurate measurement of wind speed and direction near the source.Refer to the manuscript for more details.