Data from: The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data ...
The recent increase in data accuracy from high resolution accelerometers offers substantial potential for improved understanding and prediction of animal movements. However, current approaches used for analysing these multivariable datasets typically require existing knowledge of the behaviors of th...
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ftdatacite:10.5061/dryad.5rg72 2024-10-13T14:01:03+00:00 Data from: The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data ... Chimienti, Marianna Cornulier, Thomas Owen, Ellie Bolton, Mark Davies, Ian M. Travis, Justin M. J. Scott, Beth E. Travis, Justin M.J. 2016 https://dx.doi.org/10.5061/dryad.5rg72 https://datadryad.org/stash/dataset/doi:10.5061/dryad.5rg72 en eng Dryad https://dx.doi.org/10.1002/ece3.1914 Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 Dataset dataset 2016 ftdatacite https://doi.org/10.5061/dryad.5rg7210.1002/ece3.1914 2024-10-01T11:10:49Z The recent increase in data accuracy from high resolution accelerometers offers substantial potential for improved understanding and prediction of animal movements. However, current approaches used for analysing these multivariable datasets typically require existing knowledge of the behaviors of the animals to inform the behavioral classification process. These methods are thus not well-suited for the many cases where limited knowledge of the different behaviors performed exist. Here, we introduce the use of an unsupervised learning algorithm. To illustrate the method's capability we analyse data collected using a combination of GPS and Accelerometers on two seabird species: razorbills (Alca torda) and common guillemots (Uria aalge). We applied the unsupervised learning algorithm Expectation Maximization to characterize latent behavioral states both above and below water at both individual and group level. The application of this flexible approach yielded significant new insights into the foraging ... : COGUsubset of 1 Common guillemot behavioural data recorded with accelerometer tags.RAZOsubset of 1 Razorbill behavioural data recorded with accelerometer tags. ... Dataset Alca torda common guillemot Razorbill Uria aalge uria DataCite |
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English |
description |
The recent increase in data accuracy from high resolution accelerometers offers substantial potential for improved understanding and prediction of animal movements. However, current approaches used for analysing these multivariable datasets typically require existing knowledge of the behaviors of the animals to inform the behavioral classification process. These methods are thus not well-suited for the many cases where limited knowledge of the different behaviors performed exist. Here, we introduce the use of an unsupervised learning algorithm. To illustrate the method's capability we analyse data collected using a combination of GPS and Accelerometers on two seabird species: razorbills (Alca torda) and common guillemots (Uria aalge). We applied the unsupervised learning algorithm Expectation Maximization to characterize latent behavioral states both above and below water at both individual and group level. The application of this flexible approach yielded significant new insights into the foraging ... : COGUsubset of 1 Common guillemot behavioural data recorded with accelerometer tags.RAZOsubset of 1 Razorbill behavioural data recorded with accelerometer tags. ... |
format |
Dataset |
author |
Chimienti, Marianna Cornulier, Thomas Owen, Ellie Bolton, Mark Davies, Ian M. Travis, Justin M. J. Scott, Beth E. Travis, Justin M.J. |
spellingShingle |
Chimienti, Marianna Cornulier, Thomas Owen, Ellie Bolton, Mark Davies, Ian M. Travis, Justin M. J. Scott, Beth E. Travis, Justin M.J. Data from: The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data ... |
author_facet |
Chimienti, Marianna Cornulier, Thomas Owen, Ellie Bolton, Mark Davies, Ian M. Travis, Justin M. J. Scott, Beth E. Travis, Justin M.J. |
author_sort |
Chimienti, Marianna |
title |
Data from: The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data ... |
title_short |
Data from: The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data ... |
title_full |
Data from: The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data ... |
title_fullStr |
Data from: The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data ... |
title_full_unstemmed |
Data from: The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data ... |
title_sort |
data from: the use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data ... |
publisher |
Dryad |
publishDate |
2016 |
url |
https://dx.doi.org/10.5061/dryad.5rg72 https://datadryad.org/stash/dataset/doi:10.5061/dryad.5rg72 |
genre |
Alca torda common guillemot Razorbill Uria aalge uria |
genre_facet |
Alca torda common guillemot Razorbill Uria aalge uria |
op_relation |
https://dx.doi.org/10.1002/ece3.1914 |
op_rights |
Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 |
op_doi |
https://doi.org/10.5061/dryad.5rg7210.1002/ece3.1914 |
_version_ |
1812819479954980864 |