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1,896 results for “call”
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>
Example of simulated bat-call recording from known trajectory
<p>The archive "ExampleOfSimulatedRecordingFromKnownTrajectory.ZIP" contains an example of virtual bat Myotis myotis, flying along some random trajectory and emitting an echolocation sequence, recorded by 4 microphones, placed near this trajectory. This example is to compare our method of reconstruction of trajectory with a baseline. The trajectory is chosen to contain 1 point where echo is stronger than a direct signal (point 3), 1 point baseline fails to reconstruct (point 5) and other points where calculation of TDOAs by baseline is good (close to real TDOA).</p> <p><br> Subdirectory "whole_simulated_signal" contains signal as it is emitted by bat and as it is received by all 4 microphones, recording both the direct signal and the echo.</p> <p>Subdirectory "parts_of_signal,recorded_during_fly_near_each_point" contains division of echolocation sequence into 5 parts, emitted by bat while flying near each of 5 trajectory points. Direct signal and Echo are plotted in black and red correspondingly. At the end of signal (point 5) there is an additional noise, to make reconstruction of this point difficult.</p> <p>Supplementary subdirectory "separately_echo_and_direct_signal" contains zoom on smaller parts of a signal (time-segments, used for calculation of TDOAs and FDOAs). Echoes and Direct signal are plotted in 2 different colors.</p> <p>Subdirectories "pointX_time-segments_and_resulting_TFDOAs" reflect the process of calculation of TDOAs and FDOAs as correlations of signals of different microphones on the same time-segment. Some of images correspond to images in the paper "Joint time delay and Doppler passive acoustic 3D tracking, applied to studies of bats in natural habitats." by Ales Mishchenko and Herve Glotin</p> <p>Subdirectory "trajectory" contains resulting TDOAs in the form of distance difference, as well as the resulting trajectory</p> <p><br> The archive "ExampleOfSimulatedRecordingFromKnownTrajectory.ZIP" duplicates information in images(.PNG) and figures in Matlab format (.FIG), as well as in tables(.CSV) for small data or in Matlab data files(.MAT) for big data<br> -------------<br> For additional information contact Ales Mishchenko (Ales.Mishchenko@inria.fr)</p>
FIGURE 5. Calling song spectrograms. A in New Neotropical species of Hygronemobius Hebard, 1913 (Orthoptera: Grylloidea: Nemobiinae), including a brief discussion of male genitalia morphology and preliminary biogeographic considerations of the genus
FIGURE 5. Calling song spectrograms. A—Hygronemobius indaia sp. n., B—Hygronemobius iperoigae sp. n.
FIGURES 15, 16 in Review of potter wasps with a petiolate metasoma excluding so-called " Zethinae " (Hymenoptera: Vespidae: Eumeninae) in the Lesser Sunda Islands of the Indonesian Archipelago
FIGURES 15, 16. Eumenes pius, Ƥ, body, lateral view. 15. E. p. pius. 16. E. p. nigrorufus. Scale 1 mm.
FIGURE 7. Habitat photos. A. P in New species of Pyrgulopsis Call & Pilsbry, 1886 (Mollusca: Caenogastropoda Hydrobiidae) from two Chihuahuan Desert springs
FIGURE 7. Habitat photos. A. P. madridensis sp. nov., Type locality, Madrid Falls, Presidio Co., TX. B. Crawford-Smith Canyon, Presidio Co., TX. Pool at bottom of image about 1 m across and 0.25 m deep. Photos by B. Schwartz.
FIGURE 6 in New species of Pyrgulopsis Call & Pilsbry, 1886 (Mollusca: Caenogastropoda Hydrobiidae) from two Chihuahuan Desert springs
FIGURE 6. Scanning electron micrographs of radula of Pyrgulopsis madridensis sp. nov. Individuals in A, C were from Madrid Falls and B, D from Crawford-Smith Canyon. A = portion of radular ribbon; B = portion of radular ribbon; C = portion of radular ribbon; D = lateral teeth, inner and outer marginal teeth. Scale bars = 10 µm.
FIGURE 4 in New species of Pyrgulopsis Call & Pilsbry, 1886 (Mollusca: Caenogastropoda Hydrobiidae) from two Chihuahuan Desert springs
FIGURE 4. Shells and anatomical features of Pyrgulopsis madridensis sp. nov. A–C. holotype, Madrid Falls Spring, ANSP A492826. D. Animals with shell removed to show variation in pigmentation. E–F. Penial anatomy illustrating glandular features. Pf=Penial filament, Tg=terminal gland, Tl=terminal lobe, Vg=ventral gland, Dp=penial gland, Dg1=dorsal gland 1, Dg2=dorsal gland 2, Dg3=dorsal gland 3. E. Penis, dorsal surface (yellow color due to Bouin's solution). F. Penis, ventral surface. G. Operculum, outer surface. H. Operculum, inner surface. Scale bars A–F = 100 µm; G–H = 200 µm.
FIGURE 2 in New species of Pyrgulopsis Call & Pilsbry, 1886 (Mollusca: Caenogastropoda Hydrobiidae) from two Chihuahuan Desert springs
FIGURE 2. Morphometric analyses of shell shape of P. metcalfi, P. harrymilleri, P. rubra, and P. madridensis sp. nov. Oversize black markers indicate centroids for each species. Colored ellipses reflect 95% concentrations. Principal component analysis illustrating PC1 compared to PC2. Discriminant function analysis (LDA), illustrating DF1 compared to DF2. In both comparisons, the populations of Pyrgulopsis are distinguishable in shape from each other as well as other Pyrgulopsis species. MF = Madrid Falls, CSC = Crawford-Smith Canyon.
Data accompanying "Calibrated microphone array recordings reveal that a gleaning bat emits low-intensity echolocation calls even in open-space habitat"
<p>Introduction to the dataset: </p> <p>In this manuscript, we analyse recordings of brown long-eared bats in the wild collected with a 4-microphone array, which we use to measure the distance between the bat and the microphone.<br> The distance is then used to correct the recordings for distance-dependent frequency and amplitude attenuation.<br> To ensure that our measurements and corrections are correct, we fully calibrated the microphone array and analysis set-up. <br> The calibration includes a comparison between real distances and calculated distances between a loudspeaker and the microphone array as well as comparisons between the real amplitude and the calculated amplitude of the loudspeaker.<br> To obtain the original amplitude of the calibration recording, we calibrated the loudspeaker as well</p> <p>In this folder, we provide the original bat recordings as well as the datasheet with all analysed call measurements, and the original data from the microphone calibration.</p> <p>DATA & FILE OVERVIEW</p> <p>Folder list: <br> - Bat data: folder containing the original Plecotus recordings and raw call parameters<br> - Calibration data : folder containing the array calibration raw data, as well as codes and recordings necessary to calibrate the loudspeaker. <br> </p>
Emergency-line calls as an indicator to assess human-wildlife interaction in urban areas
<p><span>Human-wildlife interactions (HWI) are increasingly common as human disturbance and development continue to remove wildlife habitats. Documenting HWIs is critical for management agencies to develop strategies and management decisions that meet the needs of both people and wildlife. However, evaluating the frequency and types of HWI at broad spatial scales (e.g., national or regional level) can be costly and difficult to implement by managers. In this study, we apply a novel method for evaluating the patterns of HWI in urban areas using publicly available data from emergency calls placed by inhabitants of Romania's >300 urban areas. We used information from 4,601 emergency calls (Romanian National Emergency Call System 112), consisting of (1) wildlife species, (2) spatial location, (3) date and time, and (4) a short description of the emergency. Out of the 318 analyzed cities, 300 cities documented emergency calls on HWI between 2015–20120, with roe deer and brown bear as the most frequent species. There was an increasing trend in HWI-related emergency calls in 73% of the urban areas. We mapped the large-scale distribution of HWI by species and type of interactions, capturing variations at the national level, and further, we analyze social and biophysical factors influencing the occurrence and frequency of HWI. The results showed that social factors have the same positive or negative effect on all species, while the effect of the biophysical factors varied between species. Particularly, the presence of large natural habitats, represented by forests, influenced the number of calls only for brown bears. Seminatural landscapes with agricultural land have a different influence in terms of effect and significance for the considered species. Our results suggest that publicly available data from emergency calls can be used for the rapid assessment of HWI and for evaluating trends and predictors of HWI at broad spatial scales.</span></p>
Variant Calling Files supporting leptospira analysis
<p>This data set contains the results of a variant calling analysis performed on 20 samples named X_SX for sample 1 to 16 and named B3288_2 B3288_8 B3288_10 and B3288_16 for samples named 17 to 20.</p> <p> </p> <p>All variant calling analysis were performed with Sequana variant calling pipeline (https://github.com/sequana/variant_calling) but only the final VCF files are provided in this dataset. One VCF file per sample and per reference genome used. There were 273 core genomes used; Therefore we have here 273 time 20 VCF files. In the summary directory one can also find the CSV files for SNPs and INDELs that were extracted from the VCF files using several filtering (depth of 10, strand balance >0.2, min frequency >=0.5).</p> <p>More information and notebooks generating and using those files can be found here: https://github.com/biomics-pasteur-fr/manuscript_capture_leptospira/ A tagged version of this github repository is provided as version 1 : manuscript_capture_leptospira-1.tar.gz</p>
FIGURES 15–26. Handianus spp., calling signal oscillograms. 15–17, 21–23―H. eurotiae, 18–20, 24–26―H in An enigma of Handianus Ribaut, 1942 (Hemiptera: Cicadellidae: Deltocephalinae Athysanini): identical host preferences and male calling signals in two morphologically distinctive sympatric species
FIGURES 15–26. Handianus spp., calling signal oscillograms. 15–17, 21–23―H. eurotiae, 18–20, 24–26―H. fartilis. Faster oscillograms of the parts of signals indicated as "21–26" are given under the same numbers.
FIGURES 27–29. 27 in An enigma of Handianus Ribaut, 1942 (Hemiptera: Cicadellidae: Deltocephalinae Athysanini): identical host preferences and male calling signals in two morphologically distinctive sympatric species
FIGURES 27–29. 27―map of July air temperature in Kazakhstan and adjacent countries of Central Asia, 28―map of annual precipitation in Kazakhstan and adjacent countries of Central Asia, 29―distribution map of Krascheninnikovia ceratoides in Russia and adjacent countries. On each map, collection sites of Handianus eurotiae and H. fartilis are shown by squares and circles, respectively.
Detecting microservice anti-patterns using interactive service call graphs: Effort Assessment
<p>Supplemental Figures for <em>Detecting microservice anti-patterns using interactive service call graphs: Effort Assessment</em></p>
Detecting microservice anti-patterns using interactive service call graphs: Effort Assessment
<p>Case Study Results for <em>Detecting microservice anti-patterns using interactive service call graphs: Effort Assessment</em></p>
FIG. 3 in Intraspecific Call Variation in the Mimic Poison Frog Ranitomeya imitator
FIG. 3.—Regressions between (a) elevation and average male mass, (b) average male mass and note length residuals, (c) average male mass and pulse rate residuals, and (d) average male mass and dominant frequency residuals of Ranitomeŋa imitator. Individual dots represent population averages.
FIG. 2 in Intraspecific Call Variation in the Mimic Poison Frog Ranitomeya imitator
FIG. 2.—Clines in advertisement call parameters across three mimetic transition zones of Ranitomeŋa imitator. (a) Banded-striped transition zone, (b) spotted-striped transition zone, (c) striped-varadero transition zone. In all panels, call parameter values for individual R. imitator (represented by dots) are plotted along the sampling transect (x-axis). For all panels, the fit line represents the best-supported model according to the AICc (see Table 2).
FIG. 1 in Intraspecific Call Variation in the Mimic Poison Frog Ranitomeya imitator
FIG. 1.—Advertisement calls of Ranitomeŋa imitator, showing waveforms (above) and spectrograms (below). (a) varadero morph, recording locality Varadero Forest 1, recorded at 27°C (note length = 0.983 s, pulse rate = 37.6 pulses/s, dominant frequency = 5059 Hz); (b) striped morph, recording locality Varadero South Bank, recorded at 26°C (note length = 0.692 s, pulse rate = 34.7 pulses/s, dominant frequency = 5668 Hz); (c) banded morph, recording locality Sauce, recorded at 26°C (note length = 1.044 s, pulse rate = 28.7 pulses/s, dominant frequency = 5003 Hz); (d) spotted morph, recording locality San Jose, recorded at 22°C (note length = 0.777 s, pulse rate = 33.5 pulses/s, dominant frequency = 5194 Hz). The nearly ubiquitous noise occurring at 7 kHz in all recordings is background noise caused by calling insects.
FIG. 6 in Differences in Advertisement Calls and Vocal Behavior in HIJpsiboas atlanticus (Anura: Hylidae) among Microhabitats
FIG. 6.—Mean values (±1 SD) for abundance of calling Hŋpsiboas atlanticus (A) and temperature in each microhabitat (B) as a function of the hourly intervals during the sampled nights for July 2010–August 2011.
FIG. 4 in Differences in Advertisement Calls and Vocal Behavior in HIJpsiboas atlanticus (Anura: Hylidae) among Microhabitats
FIG. 4.—Response of male Hŋpsiboas atlanticus in each of two microhabitats (perch or water) to playback stimuli having different acoustic parameters. Dots represent means with their respective standard deviations. Absent, parameter before males be stimulated by playback; Present, parameter during playback stimulus.
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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.