DOI: 10.5061/dryad.gb5mkkwrv
title: Data from: Active anemosensing hypothesis: How flying insects could estimate ambient wind direction

description: Estimating the direction of ambient fluid flow is a crucial step during
 chemical plume tracking for flying and swimming animals. How animals
 accomplish this remains an open area of investigation. Recent calcium
 imaging with tethered flying Drosophila has shown that flies encode the
 angular direction of multiple sensory modalities in their central complex:
 orientation, apparent wind (or airspeed) direction, and direction of. Here
 we describe a general framework for how these three sensory modalities can
 be integrated over time to provide a continuous estimate of ambient wind
 direction. After validating our framework using a flying drone, we use
 simulations to show that ambient wind direction can be most accurately
 estimated with trajectories characterized by frequent, large magnitude
 turns. Furthermore, sensory measurements and estimates of their
 derivatives must be integrated over a period of time that incorporates at
 least one of these turns. Finally, we discuss approaches that insects
 might use to simplify the required computations and present a list of
 testable predictions. Together, our results suggest that ambient flow
 estimation may be an important driver underlying the zigzagging maneuvers
 characteristic of plume tracking animals' trajectories.
description: This archive contains four datasets described below. For details
 on data collection please refer to the associated preprint manuscript. For
 details on data format and processing, please refer to our open source
 code repository: https://github.com/florisvb/active_anemosensing [1]. <strong>Raw data of real-world wind measurements.</strong> Data includes wind direction and speed data using two 3-D ultrasonic anemometers (Trisonica mini, Anemoment, Longmont CO) operating at 10 Hz positioned orthogonal to one another to ensure accurate readings of both horizontal and vertical wind components. We collected data in an open desert environment for 120 minutes. Raw data is provided as a collection of binary files for each anemometer. To extract the vertical wind velocity and horizontal wind speed and direction, synchronize the data, and interpolate the two sensors to the same master time base, please refer to the Jupyter notebooks provided here: https://github.com/florisvb/active_anemosensing/tree/main/preprocess_raw_wind_data. Data was collected using custom data logging firmware running on a teensy microcontroller, for details please refer to the following repository: https://github.com/florisvb/gps_wind_station.    <strong>Raw multisensory data from a quadrotor drone flight. </strong>Data includes multiple sensor streams from a ~3 minute manually piloted drone flight. The raw data consists of .csv files containing GPS, IMU, 3D ultrasonic anemometer, and 2D optic flow data. To process the raw data, interpolate to a single master time base, and reorient the coordinate frames for consistency, please refer to the Jupyter notebooks provided here: https://github.com/florisvb/active_anemosensing/tree/main/preprocess_raw_botfly_data. For details on the drone hardware and data collection, please refer to our preprint.  <strong>Curated flight trajectory data of freely flying fruit flies. </strong>Data includes 700 3D flight trajectories of fruit flies navigating an odor plume in a wind tunnel in an hdf file format. The trajectories were curated from a previously published raw dataset (Pang et al., 2019) and reformated into an hdf format to only include those with durations greater than 1 second during which an odor (ethanol) was encountered at least once while moving (on average) upwind in 0.4 m/s wind. For details on the original data collection refer to van Breugel & Dickinson (2014), and for details on the raw dataset refer to Pang et al. (2019). <strong>Simulation results.</strong> Data includes simulation results for ambient wind estimation under four different scenarios described in the associated manuscripts. For each scenario, several different parameter sweeps were done, and an hdf file is provided for each case. See manuscript and associated software (van Breugel, 2022) for details. <strong>References. </strong> van Breugel, F. Active Anemosensing. (2022). Zenodo, https://doi.org/10.5281/zenodo.6402594. Pang, Rich et al. (2019). Data from: History dependence in insect flight decisions during odor tracking, Dryad, Dataset, https://doi.org/10.5061/dryad.n0b8m van Breugel, F. and Dickinson, M. H. (2014). Plume-Tracking behavior of flying <em>Drosophila</em> emerges from a set of distinct sensory-motor reflexes. Current Biology, https://doi.org/10.1016/j.cub.2013.12.023
description: This archive is intended to be used in conjunction with the open
 source code repository provided here: https://github.com/florisvb/active_anemosensing (persistent DOI: https://doi.org/10.5281/zenodo.6914384).