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15 results for “passive recording”
Data from: Performance of unmarked abundance models with data from machine-learning classification of passive acoustic recordings
<p>The ability to conduct cost-effective wildlife monitoring at scale is rapidly increasing due to availability of inexpensive autonomous recording units (ARUs) and automated species recognition, presenting a variety of advantages over human-based surveys. However, estimating abundance with such data collection techniques remains challenging because most abundance models require data that are difficult for low-cost monoaural ARUs to gather (e.g., counts of individuals, distance to individuals), especially when using the output of automated species recognition. Statistical models that do not require counting or measuring distances to target individuals in combination with low-cost ARUs provide a promising way of obtaining abundance estimates for large-scale wildlife monitoring projects but remain untested. We present a case study using avian field data collected in forests of Pennsylvania during the Spring of 2020 and 2021 using both traditional point counts and passive acoustic monitoring at the same locations. We tested the ability of the Royle-Nichols and time-to-detection models to estimate abundance of two species from detection histories generated by applying a machine-learning classifier to ARU-gathered data. We compared abundance estimates from these models to estimates from the same models fit using point-count data and to two additional models appropriate for point counts, the N-mixture model and distance models. We found that the Royle-Nichols and time-to-detection models can be used with ARU data to produce abundance estimates similar to those generated by a point-count based study but with greater precision. ARU-based models produced confidence or credible intervals that were on average 31.9% ( 11.9 SE) smaller than their point-count counterpart. Our findings were consistent across two species with differing relative abundance and habitat use patterns. The higher precision of models fit using ARU data is likely due to higher cumulative detection probability, which itself may be the result of greater survey effort using ARUs and machine-learning classifiers to sample significantly more time for focal species at any given point. Our results provide preliminary support the use of ARUs in abundance-based study applications, and thus may afford researchers a better understanding of habitat quality and population trends, while allowing them to make more informed conservation actions and recommendations.</p>
A novel method for estimating avian roost sizes using passive acoustic recordings
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Data from: Performance of unmarked abundance models with data from machine-learning classification of passive acoustic recordings
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Blainville’s beaked whale (Mesoplodon densirostris) echolocation clicks from autonomous passive acoustic recordings
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Active and passive surface-wave recordings along high-speed lines
<p>Seismic uniform linear arrays (ULAs) with a length of 23.75 m and composed by 96 geophones spaced at intervals of 25 cm were installed in 3 different sites (sites 1, 2 and 3). The ULAs were positioned on the cess (i.e. the track side) along the railway, at approximately 2 m from the rails, on track 1 for site 1 and track 2 for sites 2 and 3. Active seismic data was acquired by vertically striking a metal plate placed in-line, 12.5 cm away from the first (direct shot) and last (inverse shot) geophones.The impact was achieved with a hammer (1.5 kg) and waves propagation along the ULA was recorded for 2 s with a sampling interval of 0.5 ms (i.e. sampling rate of 2 kHz) and a pre-triggering delay of -0.02 s. Six direct and inverse shots were stacked in the time domain to enhance signal-to-noise ratio.HST passages on tracks 1 and 2 were also recorded with the exact same ULAs for durations of 120 s with a sampling interval of 2 ms (i.e. sampling rate of 500 Hz). The recordings were initiated manually upon visual confirmation of approaching trains at the site.</p>
Passive breathing of earth-air: 'fingerprint' evidence from moisture records
<p><em>Data in Brief.</em></p>
In vivo intracellular recording in the mouse V1 during passive visual stimulations and audiovisual discrimination task
<p>Whole cell recordings (Vm channel) performed in the layer 2/3 primary visual cortex (V1) of awake mice. Recordings are performed in current clamp (Im channel). A local ECoG was placed on the dura in the vincinity of the recorded neuron. Vistim channels indicate the orientation of the drifting grating (Vistim 1 frequency), contrast (Vistim 1 amplitude), temporal frequency (Vistim 2 frequency), and spatial frquency (Vistim 2 amplitude). The diode signal indicates the exact timing of the stimulus presentation. The two mouse channels (Mouse1 adn Mouse2) indicate the movement of the spherical treadmill on which the animal is standing. In the behavior database, the mice are performing a behavioral task (additional information can be found in those files: licking channel, auditory channel (one of two tones) reward (laser channel)). The script and the data are using an Igor Pro format. For converting the two mouse signals into locomotion, please refer to the script AnalysisMouseWaveProc.ipf or the functions imbeded in LoadExperiments 2013.ipf . Those data have been used for the article Einstein et al., 2017 (https://doi.org/10.1523/JNEUROSCI.3868-16.2017). The excel sheet provides additional information about the recording. If you want to open the ibw files directly in Matlab, use IBWread, m file</p> <p> </p>
Digital holographic microscope recordings of passive motion of small particles
<p>Digital holographic microscopy provides the ability to observe throughout a volume that is large compared to its resolution without the need to actively refocus to capture the entire volume. This enables simultaneous observations of large numbers of small objects within such a volume. We have constructed a microscope that can observe a volume of 0.4 x 0.4 x 1.0 µm with submicrometer resolution (in xy) and 2 µm resolution (in z) for observation of microorganisms and minerals in liquid environments on Earth and on potential planetary missions. Because environmental samples are likely to contain mixtures of inorganics and microorganisms of comparable sizes near the resolution limit of the instrument, discrimination between living and non-living objects may be difficult. The active motion of motile organisms can be used to readily distinguish them from non-motile objects (live or inorganic), but additional methods are required to distinguish non-motile organisms and inorganic objects that are of comparable size but different composition and structure. We demonstrate the use of passive motion to make this discrimination by evaluating diffusion and buoyancy characteristics of cells, styrene beads, alumina particles, and gas-filled vesicles of micron scale in the field of view.</p>
Passive acoustic records of Lake Sturgeon calling activity in Detroit River
<p>Lake sturgeon (<i>Acipenser fulvescens</i>) are endangered in the Laurentian Great Lakes with increasing binational efforts to establish spawning grounds to aid restoration. While SCUBA surveys can document spawning activity, these are labour-intensive and may disrupt spawning. We used passive acoustic monitoring to quantify spawning sounds of lake sturgeon as a first step to developing remote sensing of sturgeon spawning grounds. <i>Acipenser</i> sp. are known to make a variety of sounds including, "thunders" (aka drums), which have been documented in <i>A. fulvescens</i> during spawning. We quantified drums from a known spawning bed. We recorded 5 different potential sturgeon sounds but only quantified drums as a marker for spawning activity. Drums were low frequency with average frequency peaks at 40 and 92 Hz and a rapid drop-off thereafter. There was no relationship between calling activity and water temperature but calling activity increased as the summer progressed. Call production was most active from 0600-1500h with little calling activity during nighttime recordings. The presence of low frequency boat sounds did correlate with a reduction in maximum calling rate so it is possible that commercial shipping may disrupt sturgeon communication, but more research is necessary to separate correlational from causative effects. These recordings represent a promising approach to map sturgeon spawning activity and show the potential effect of human activity on communication in this threatened species.</p>
Passive acoustic records of Lake Sturgeon calling activity in Detroit River
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Digital holographic microscope recordings of passive motion of small particles
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NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 5
This data set provides a Climate Data Record (CDR) of sea ice concentration from passive microwave data. The CDR algorithm output is a rule-based combination of ice concentration estimates from two well-established algorithms: the NASA Team (NT) algorithm (Cavalieri et al. 1984) and NASA Bootstrap (BT) algorithm (Comiso 1986). The CDR is a consistent, daily and monthly time series of sea ice concentrations from 25 October 1978 through the most recent processing for both the north and south polar regions. All data are on a 25 km x 25 km grid.Note: A near-real-time version of this data set also exists to fill the gap between the time that this data set is updated through to the present. The data set is called the Near-Real-Time NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration (https://nsidc.org/data/g10016).
Near-Real-Time NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 3
This data set provides a near-real-time Climate Data Record (CDR) of sea ice concentration from passive microwave data. The Near-real-time NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration (NRT CDR) data set is the near-real-time version of the final NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration (G02202). The NRT CDR is designed to fill the temporal gap between updates of the final CDR, occurring every three to six months, and to provide the most recent data.
Continuous passive seismic data recorded at the Larderello-Travale geothermal field (June 2017-January 2018)
<p>The compressed files contain the continuous data (in mseed format) recorded from 23 June 2017 to 21 January 2018 by 8 temporary stations installed in the Larderello-Travale geothermal field (Lago Boracifero area). VN.dseed is a dataless SEED containing the metadata about the instruments.</p>
pogona vitticeps Neuropixels UHD passive probe recordings
<p>dataset of electrophysiological recordings in bearded dragons using passive UHD neuropixels probes</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.