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      Trackdem: Automated particle tracking to obtain population counts and size distributions from videos in R

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      Creators
      Bruijning, M.
      Visser, M.D.
      Hallmann, C.A.
      Jongejans, E.
      Date of Archiving
      2018
      Archive
      DANS EASY
      DOI
      https://doi.org/10.17026/dans-zte-ywex
      Related publications
      Trackdem: Automated particle tracking to obtain population counts and size distributions from videos in R  
      Publication type
      Dataset
      Spatial coverage
      ~~~~~~
      Please use this identifier to cite or link to this item: https://hdl.handle.net/2066/183995   https://hdl.handle.net/2066/183995
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      Organization
      Experimental Plant Ecology
      Animal Ecology & Physiology
      Audience(s)
      Biology
      Languages used
      English
      Key words
      automated population counts; image analysis; individual trajectories; movement behaviour; neural net; noise filtering; particle identification; size distribution
      Abstract
      This dataset includes two example videos on Daphnia magna populations with each containing 12 individuals and the R-package trackdem. This package automatically counts and tracks moving individuals, and version 0.3.1 of the package is included. See https://CRAN.R-project.org/package=trackdem and https://github.com/marjoleinbruijning/trackdem for more information and the most recent version of the package. The Daphnia were recorded in the lab at KU Leuven (Belgium) in September 2017. The aim of trackdem is to obtain unbiased, automated estimates of population densities and body size distributions, using video material or image sequences as input. It is meant to assist in evolutionary and ecological studies, which often rely on accurate estimates of population size, population structure and/or individual behaviour. The package trackdem includes a set of functions to convert a short video into an image sequence, background detection, particle identification and linking, and the training of an artificial neural network for noise flltering. A tutorial is included, providing a step-by-step introduction on the usage of all functions to analyse image sequences of moving organisms.
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      • Faculty of Science [31818]
       
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