Dataset Information

Datasets for assessing the structure and drivers of biological sounds.

ABSTRACT: 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.

SUBMITTER: Diepstraten J

PROVIDER: S-EPMC8866143 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Datasets for assessing the structure and drivers of biological sounds.

Diepstraten Johan J Kuenbou Jacques Keumo JK Willie Jacob J

Data in brief 20220207

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, ac ...[more]

PMID: 35242912

Similar Datasets

Project description:Multi-modal imaging, by light-microscopy (LM) and Magnetic Resonance Imaging (MRI), holds promise for examining the brain across various resolutions and scales. While MRI acquires images in three dimensions, acquisition of intact whole-brain by LM requires a process of tissue clearing that renders the brain transparent. Removal of lipids (delipidation) is a critical step in the tissue clearing process, and was previsouly suggested to be the cause for absence of MRI contrast in cleared brains. Yet, the association between MRI contrast, delipidation and the different clearing techniques is debatable. Here, we provide datasets concerning lipid-content in cleared brain tissues obtained by various approaches. Fixed mouse and rat brains were cleared by CLARITY, Scale, uDISCO and ECi clearing techniques. Lipid-content was assessed at various intermediate steps of the different clearing methods, as well as at the end of the processes. Methods employed included whole brain MRI acquisition, Oil Red O (ORO)- and carbocyanine DiI-staining of cryosections, and DiI-washout assay from brain slices. MRI contrast-to-noise ratio, staining intensities and integrity of tissue were systematically analyzed. We demonstrate that lipid electrophoresis, an essential step of the CLARITY approach, engenders progressive reduction in MRI contrast in non-cleared (PFA-fixed) control brains, as well as strongly reduces contrast from uDISCO and ECi-cleared brains. ORO minimally stained CLARITY-cleared brains, however efficiently labelled uDISCO and ECi-cleared brains. Conversely, and in contrast to ORO-staining, DiI equally stained control, CLARITY, ECi and uDISCO-cleared brains. Both ORO- and DiI-staining demonstrated impairment in brain tissue integrity following CLARITY, but less so in uDISCO and ECi brains. DiI-washout assay demonstrated that each of the solvents employed along the process of uDISCO and ECi are highly delipidating, as well as the SDS-electrophoresis employed during CLARITY clearing. However, Scale treatment preserved most of the DiI dye. These data emphasize the variability in lipid assessment of cleared tissues by common techniques, and may help to resolve the contribution of lipids in brain MRI contrast.

Project description:Tracking the state of biodiversity over time is critical to successful conservation, but conventional monitoring schemes tend to be insufficient to adequately quantify how species' abundances and distributions are changing. One solution to this issue is to leverage data generated by citizen scientists, who collect vast quantities of data at temporal and spatial scales that cannot be matched by most traditional monitoring methods. However, the quality of citizen science data can vary greatly. In this paper, we develop three metrics (inventory completeness, range completeness, spatial bias) to assess the adequacy of spatial observation data. We explore the adequacy of citizen science data at the species level for Australia's terrestrial native birds and then model these metrics against a suite of seven species traits (threat status, taxonomic uniqueness, body mass, average count, range size, species density, and human population density) to identify predictors of data adequacy. We find that citizen science data adequacy for Australian birds is increasing across two of our metrics (inventory completeness and range completeness), but not spatial bias, which has worsened over time. Relationships between the three metrics and seven traits we modelled were variable, with only two traits having consistently significant relationships across the three metrics. Our results suggest that although citizen science data adequacy has generally increased over time, there are still gaps in the spatial adequacy of citizen science for monitoring many Australian birds. Despite these gaps, citizen science can play an important role in biodiversity monitoring by providing valuable baseline data that may be supplemented by information collected through other methods. We believe the metrics presented here constitute an easily applied approach to assessing the utility of citizen science datasets for biodiversity analyses, allowing researchers to identify and prioritise regions or species with lower data adequacy that will benefit most from targeted monitoring efforts.

Dataset Information

Datasets for assessing the structure and drivers of biological sounds.

Publications

Datasets for assessing the structure and drivers of biological sounds.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets