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Datasets for assessing the structure and drivers of biological sounds

Obtaining and analysing sound data can be a tedious and lengthy process. We present sound data consisting of 20,485 1 min sound recordings obtained in three sites within a rainforest landscape in southeast Cameroon. The sites differ in anthropogenic disturbance. We also present meta data correspondi...

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Detalles Bibliográficos
Autores principales: Diepstraten, Johan, Kuenbou, Jacques Keumo, Willie, Jacob
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8866143/
https://www.ncbi.nlm.nih.gov/pubmed/35242912
http://dx.doi.org/10.1016/j.dib.2022.107930
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author Diepstraten, Johan
Kuenbou, Jacques Keumo
Willie, Jacob
author_facet Diepstraten, Johan
Kuenbou, Jacques Keumo
Willie, Jacob
author_sort Diepstraten, Johan
collection PubMed
description Obtaining and analysing sound data can be a tedious and lengthy process. We present sound data consisting of 20,485 1 min sound recordings obtained in three sites within a rainforest landscape in southeast Cameroon. The sites differ in anthropogenic disturbance. We also present meta data corresponding to these recordings with the identification of all animal vocalisations in each 1 min sound recording. Additionally, we provide a raw database with data on habitat, human activities, remoteness, accessibility, temperature, humidity, rainfall, moon phase, and mammal and bird observations in the area during the recording period. The data were used by Diepstraten & Willie (2021) to investigate the structure and drivers of biological sounds along a disturbance gradient. The data contribute to call libraries of tropical species and can also be used to build classifiers for automatic detection and classification of animal vocalisations.
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spelling pubmed-88661432022-03-02 Datasets for assessing the structure and drivers of biological sounds Diepstraten, Johan Kuenbou, Jacques Keumo Willie, Jacob Data Brief Data Article Obtaining and analysing sound data can be a tedious and lengthy process. We present sound data consisting of 20,485 1 min sound recordings obtained in three sites within a rainforest landscape in southeast Cameroon. The sites differ in anthropogenic disturbance. We also present meta data corresponding to these recordings with the identification of all animal vocalisations in each 1 min sound recording. Additionally, we provide a raw database with data on habitat, human activities, remoteness, accessibility, temperature, humidity, rainfall, moon phase, and mammal and bird observations in the area during the recording period. The data were used by Diepstraten & Willie (2021) to investigate the structure and drivers of biological sounds along a disturbance gradient. The data contribute to call libraries of tropical species and can also be used to build classifiers for automatic detection and classification of animal vocalisations. Elsevier 2022-02-07 /pmc/articles/PMC8866143/ /pubmed/35242912 http://dx.doi.org/10.1016/j.dib.2022.107930 Text en © 2022 The Author(s). Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Diepstraten, Johan
Kuenbou, Jacques Keumo
Willie, Jacob
Datasets for assessing the structure and drivers of biological sounds
title Datasets for assessing the structure and drivers of biological sounds
title_full Datasets for assessing the structure and drivers of biological sounds
title_fullStr Datasets for assessing the structure and drivers of biological sounds
title_full_unstemmed Datasets for assessing the structure and drivers of biological sounds
title_short Datasets for assessing the structure and drivers of biological sounds
title_sort datasets for assessing the structure and drivers of biological sounds
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8866143/
https://www.ncbi.nlm.nih.gov/pubmed/35242912
http://dx.doi.org/10.1016/j.dib.2022.107930
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