Dual‐fluorescence imaging and automated trophallaxis detection for studying multi‐nutrient regulation in superorganisms

In the related manuscript, we present a dual-fluorescence imaging setup designed to track two food sources, each labeled with a different fluorophore, as they are disseminated throughout a freely behaving colony of individually tagged ants. Additionally, our image-based deep learning algorithm for automatic detection of ant trophallaxis events efficiently yields a detailed record of all food-transfer interactions.

Using a series of calibration experiments, we demonstrate the reliability of our measurements. We then exemplify the capabilities of our new method by tracking food dissemination in a colony of Camponotus sanctus ants supplied with two nutritionally-distinct food sources.

This dataset contains data and Matlab code related to:

1. Calibration and validation of the dual-fluorescence imaging technique

2. Training and employing a deep neural network for detecting trophallaxis

3. Sample data from a multinutrient feeding experiment

Methods
This data was collected using the experimental setup described in the manuscript.

For further details, see README files within and related manuscript.