Colony 11 - January 11 Public

Supplementary videos of ants foraging on trails

README

TITLE: Wandering with a purpose: Variability in individual ant trajectories that may improve foraging flexibility uncovered through automated ant tracking

CREATORS:
Natalie Imirzian, nsi2@psu.edu, Graduate Student, Pennsylvania State University

CONTRIBUTORS:
Yizhe Zhang, Yizhe.Zhang.190@nd.edu, Graduate Student, University of Notre Dame
Christoph Kurze, cuk210@psu.edu, Postdoctoral Associate, Pennsylvania State University
Danny Z. Chen, dchen@nd.edu, PI, University of Notre Dame
David P. Hughes, dph14@psu.edu, PI, Pennsylvania State University

PROJECT DATES: 11 January 2017 to 1 August 2018

PROJECT DESCRIPTION:
This project filmed ants in the rainforest on trails to investigate how ants behave in an area of high disease pressure. The videos were processed using machine learning algorithms to automate ant tracking.

DATA DESCRIPTION:
The raw videos of ants walking on trails. We filmed 3-5 nights at five different colonies, and each night of video has 6-10 videos.

USAGE:
The video files can be opened with any standard media player (Quicktime, Windows Media Player, etc.).

FILE MANIFEST or DATA STRUCTURE

Collection -> Colony/Date -> Videos

Each group of videos is organized into a separate group based on its colony and date. Each Colony/Date combination has 6-10 videos and are numbered in the order that they were recorded.

Video filename: The name of each video file indicates the colony and date it was recorded. The number after 'MP' indicates the colony number, and the number after 'jan' indicates the date in January. The number after 'vid' indicates the order that the videos were recorded for that night of filming.

CITATION:
Imirzian, N., Zhang, Y., Kurze, C., Chen, D.Z., and Hughes, D.P. (submitted). Wandering with a purpose: Variability in individual ant trajectories that may improve foraging flexibility uncovered through automated ant tracking.

Imirzian, N., Zhang, Y., Kurze, C., Loreto, R.G., Chen, D.Z. and Hughes, D.P., 2018. Computer vision and deep learning automates nocturnal rainforest ant tracking to provide insight into behavior and disease risk. BioRxiv, p.454207.

LICENSING: Creative Commons Attribution-ShareAlike 4.0 International

Items in this Work

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User Cynthia Vitale has updated Colony 11 - January 11 about 1 month ago
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