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201 results for “singing”
Choral Singing Dataset
<p><strong>Choral Singing Dataset</strong></p> <p>This dataset was presented at the 15th ICMPC/10th ESCOM conference together with the paper: </p> <p>Cuesta, H., Gómez, E., Martorell, A., Loáiciga, F. (2018). <em>Analysis of Intonation in Unison Choir Singing</em>. In <em>Proceedings of the 15th International Conference on Music Perception and Cognition.</em></p> <p>It contains the individual audio recordings of 16 singers of the Anton Bruckner Choir from Barcelona (Spain) performing 3 different pieces <em>a cappella, </em>together with their associated MIDI files.</p> <p>Singers were recorded in groups of four (4 singers per choir section), with individual close microphones with cardioid polar pattern for directivity purposes. The performance was virtually conducted by the conductor of the choir through a video, which was displayed in all the recording sessions for synchronization purposes. Singers also had the possibility to hear a piano reference (through headphones) for tuning purposes.</p> <p>Three pieces were selected based on the choir repertoire and on the specific needs of the study, which were basically related to the language of the lyrics. These are the pieces we chose:</p> <ul> <li><em>Locus Iste</em>, written by Anton Bruckner (Latin).</li> <li><em>Niño Dios</em>, written by Francisco Guerrero (Spanish).</li> <li><em>El Rossinyol</em>, popular Catalan song.</li> </ul> <p>Overall, this dataset contains, for each of these pieces, the tracks of each individual singer (16 singers), together with frame-wise f0 annotations, and note annotations and semi-synchronized MIDI files for each choir section.</p> <p>Having the individual tracks also allows researchers to create the unison mix for each section, as well as the whole choir performance.</p> <p>The dataset covers the frequency range between 87 Hz and 783 Hz and is especially dense between 150 and 450 Hz. The notes have durations that range between 0.15 and 6.21 seconds, with an average of 0.84 seconds. </p> <p>If using this data in your research, please cite the aforementioned paper, which is also available for download. For any question or comment, please contact the first author.</p> <p>The dataset is compressed into a zip file. In the decompressed folder there is a README file with all the information regarding the annotation files and the filenames.</p>
Bioacoustic monitoring reveals shifts in breeding songbird populations and singing behaviour with selective logging in tropical forests
<b>Description: </b><p>Counts of individual male songbirds, males and females, songs and duets and original WAV audio recordings used to generate them</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/131"><b>Population and behavioral responses of songbirds to logging and rain forest fragmentation</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3366104">here</a></p><p><b>Files: </b>This dataset consists of 13 files: Pillay_et_al_Songbirds_Acoustic_Counts_Vegetation_Cover.xlsx, 2013_B.zip, 2013_D.zip, 2013_E.zip, 2013_F.zip, 2013_OG1.zip, 2013_OG2.zip, 2014_B.zip, 2014_D.zip, 2014_E.zip, 2014_F.zip, 2014_OG1.zip, 2014_OG2.zip</p><p><b>Pillay_et_al_Songbirds_Acoustic_Counts_Vegetation_Cover.xlsx</b></p><p>This file contains dataset metadata and 5 data tables:</p><ol><li><p><b>CountsMale</b> (described in worksheet CountsMale)</p><p>Description: Counts of male individuals of songbird species</p><p>Number of fields: 12</p><p>Number of data rows: 5700</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day</b>: Days 1 to 2 of sampling in each plot (Field type: numeric)</li><li><b>date</b>: Date of Sampling (Field type: date)</li><li><b>jul.date</b>: Julian Date of Sampling (Field type: numeric)</li><li><b>time1-6AM</b>: Counts of male individuals for 6:00-6:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time2-7AM</b>: Counts of male individuals for 7:00-7:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time3-8AM</b>: Counts of male individuals for 8:00-8:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li></ul></li><li><p><b>CountsMaleFemale</b> (described in worksheet CountsMaleFemale)</p><p>Description: Counts of male plus female individuals of songbird species</p><p>Number of fields: 12</p><p>Number of data rows: 1000</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day</b>: Days 1 to 2 of sampling in each plot (Field type: numeric)</li><li><b>date</b>: Date of Sampling (Field type: date)</li><li><b>jul.date</b>: Julian Date of Sampling (Field type: numeric)</li><li><b>time1-6AM</b>: Counts of male and female individuals for 6:00-6:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time2-7AM</b>: Counts of male and female individuals for 7:00-7:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time3-8AM</b>: Counts of male and female individuals for 8:00-8:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li></ul></li><li><p><b>CountsSong</b> (described in worksheet CountsSong)</p><p>Description: Counts of songs</p><p>Number of fields: 9</p><p>Number of data rows: 2850</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day3-6AM</b>: Counts of songs for 6:00-6:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-7AM</b>: Counts of songs for 7:00-7:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-8AM</b>: Counts of songs for 8:00-8:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li></ul></li><li><p><b>CountsDuet</b> (described in worksheet CountsDuet)</p><p>Description: Counts of duets</p><p>Number of fields: 9</p><p>Number of data rows: 500</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day3-6AM</b>: Counts of duets for 6:00-6:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-7AM</b>: Counts of duets for 7:00-7:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-8AM</b>: Counts of duets for 8:00-8:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li></ul></li><li><p><b>VegetationCover</b> (described in worksheet VegetationCover)</p><p>Description: Vegetation cover data</p><p>Number of fields: 6</p><p>Number of data rows: 50</p><p>Fields: </p><ul><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>forest.type</b>: Forest Type (Field type: categorical)</li><li><b>udens</b>: Proportion understory cover (Field type: numeric)</li><li><b>cc</b>: Proportion canopy cover (Field type: numeric)</li><li><b>can.ht</b>: Average canopy height (Field type: numeric)</li><li><b>max.canopy</b>: Maximum height of standing vegetation (Field type: numeric)</li></ul></li></ol><p><b>2013_B.zip</b></p><p>Description: WAV files from 2013 for site B</p><p><b>2013_D.zip</b></p><p>Description: WAV files from 2013 for site D</p><p><b>2013_E.zip</b></p><p>Description: WAV files from 2013 for site E</p><p><b>2013_F.zip</b></p><p>Description: WAV files from 2013 for site F</p><p><b>2013_OG1.zip</b></p><p>Description: WAV files from 2013 for site OG1</p><p><b>2013_OG2.zip</b></p><p>Description: WAV files from 2013 for site OG2</p><p><b>2014_B.zip</b></p><p>Description: WAV files from 2014 for site B</p><p><b>2014_D.zip</b></p><p>Description: WAV files from 2014 for site D</p><p><b>2014_E.zip</b></p><p>Description: WAV files from 2014 for site E</p><p><b>2014_F.zip</b></p><p>Description: WAV files from 2014 for site F</p><p><b>2014_OG1.zip</b></p><p>Description: WAV files from 2014 for site OG1</p><p><b>2014_OG2.zip</b></p><p>Description: WAV files from 2014 for site OG2</p><p><b>Date range: </b>2013-04-09 to 2014-07-26</p><p><b>Latitudinal extent: </b>4.6881 to 4.7530</p><p><b>Longitudinal extent: </b>116.9477 to 117.6249</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Aves <br> -  -  -  -  Passeriformes <br> -  -  -  -  -  Timaliidae <br> -  -  -  -  -  -  <i>Stachyris</i> <br> -  -  -  -  -  -  -  <i>Stachyris maculata</i> <br> -  -  -  -  -  -  -  <i>Stachyris erythroptera</i> <br> -  -  -  -  -  -  -  <i>Stachyris poliocephala</i> <br> -  -  -  -  -  -  <i>Macronus</i> <br> -  -  -  -  -  -  -  <i>Macronus bornensis</i> <br> -  -  -  -  -  -  -  <i>Macronus ptilosus</i> (as synonym: <i>Macronous ptilosus</i>)<br> -  -  -  -  -  -  <i>Stachyridopsis</i> <br> -  -  -  -  -  -  -  <i>Stachyridopsis rufifrons</i> (as synonym: <i>Stachyris rufifrons</i>)<br> -  -  -  -  -  -  <i>Pomatorhinus</i> <br> -  -  -  -  -  -  -  <i>Pomatorhinus montanus</i> <br> -  -  -  -  -  Pellorneidae <br> -  -  -  -  -  -  <i>Trichastoma</i> <br> -  -  -  -  -  -  -  <i>Trichastoma bicolor</i> <br> -  -  -  -  -  -  <i>Alcippe</i> <br> -  -  -  -  -  -  -  <i>Alcippe brunneicauda</i> <br> -  -  -  -  -  -  <i>Pellorneum</i> <br> -  -  -  -  -  -  -  <i>Pellorneum capistratum</i> <br> -  -  -  -  -  -  <i>Malacocincla</i> <br> -  -  -  -  -  -  -  <i>Malacocincla malaccensis</i> <br> -  -  -  -  -  -  <i>Malacopteron</i> <br> -  -  -  -  -  -  -  <i>Malacopteron magnirostre</i> <br> -  -  -  -  -  -  -  <i>Malacopteron magnum</i> <br> -  -  -  -  -  -  -  <i>Malacopteron cinereum</i> <br> -  -  -  -  -  -  -  <i>Malacopteron affine</i> <br> -  -  -  -  -  Pycnonotidae <br> -  -  -  -  -  -  <i>Alophoixus</i> <br> -  -  -  -  -  -  -  <i>Alophoixus bres</i> <br> -  -  -  -  -  -  -  <i>Alophoixus phaeocephalus</i> <br> -  -  -  -  -  -  <i>Tricholestes</i> <br> -  -  -  -  -  -  -  <i>Tricholestes criniger</i> <br> -  -  -  -  -  -  <i>Iole</i> <br> -  -  -  -  -  -  -  <i>Iole olivacea</i> <br> -  -  -  -  -  -  <i>Pycnonotus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus atriceps</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus simplex</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus eutilotus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus brunneus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus erythropthalmos</i> <br> -  -  -  -  -  Stenostiridae <br> -  -  -  -  -  -  <i>Culicicapa</i> <br> -  -  -  -  -  -  -  <i>Culicicapa ceylonensis</i> <br> -  -  -  -  -  Muscicapidae <br> -  -  -  -  -  -  <i>Cyornis</i> <br> -  -  -  -  -  -  -  <i>Cyornis superbus</i> <br> -  -  -  -  -  -  -  <i>Cyornis unicolor</i> <br> -  -  -  -  -  -  <i>Rhinomyias</i> <br> -  -  -  -  -  -  -  <i>Rhinomyias umbratilis</i> <br> -  -  -  -  -  -  <i>Trichixos</i> <br> -  -  -  -  -  -  -  <i>Trichixos pyrropygus</i> <br> -  -  -  -  -  -  <i>Copsychus</i> <br> -  -  -  -  -  -  -  <i>Copsychus stricklandii</i> <br> -  -  -  -  -  Monarchidae <br> -  -  -  -  -  -  <i>Terpsiphone</i> <br> -  -  -  -  -  -  -  <i>Terpsiphone paradisi</i> <br> -  -  -  -  -  -  <i>Hypothymis</i> <br> -  -  -  -  -  -  -  <i>Hypothymis azurea</i> <br></div><p></p>
Singing Insects of North America: SINA keys
Singing Insects of North America (SINA) is a unique resource providing extensive content covering the crickets and katydids (and, to a much lesser extent, cicadas) of North America north of Mexico. This content includes audio recordings, images, text, and identification keys. SINA was created and is still maintained by Dr. Thomas J. Walker of the University of Florida, a pioneer and leader in the study of North American crickets and katydids beginning in the late 1950s and continuing into the 21st century. <p></p>http://entnemdept.ifas.ufl.edu/walker/Buzz/
Singing Insects of North America: SINA images
Singing Insects of North America (SINA) is a unique resource providing extensive content covering the crickets and katydids (and, to a much lesser extent, cicadas) of North America north of Mexico. This content includes audio recordings, images, text, and identification keys. SINA was created and is still maintained by Dr. Thomas J. Walker of the University of Florida, a pioneer and leader in the study of North American crickets and katydids beginning in the late 1950s and continuing into the 21st century. <p></p>http://entnemdept.ifas.ufl.edu/walker/Buzz/
Singing Insects of North America: SINA audio
Singing Insects of North America (SINA) is a unique resource providing extensive content covering the crickets and katydids (and, to a much lesser extent, cicadas) of North America north of Mexico. This content includes audio recordings, images, text, and identification keys. SINA was created and is still maintained by Dr. Thomas J. Walker of the University of Florida, a pioneer and leader in the study of North American crickets and katydids beginning in the late 1950s and continuing into the 21st century. <p></p>http://entnemdept.ifas.ufl.edu/walker/Buzz/
Singing Insects of North America: SINA text
Singing Insects of North America (SINA) is a unique resource providing extensive content covering the crickets and katydids (and, to a much lesser extent, cicadas) of North America north of Mexico. This content includes audio recordings, images, text, and identification keys. SINA was created and is still maintained by Dr. Thomas J. Walker of the University of Florida, a pioneer and leader in the study of North American crickets and katydids beginning in the late 1950s and continuing into the 21st century. <p></p>http://entnemdept.ifas.ufl.edu/walker/Buzz/
Singing Insects of North America: SINA maps
Singing Insects of North America (SINA) is a unique resource providing extensive content covering the crickets and katydids (and, to a much lesser extent, cicadas) of North America north of Mexico. This content includes audio recordings, images, text, and identification keys. SINA was created and is still maintained by Dr. Thomas J. Walker of the University of Florida, a pioneer and leader in the study of North American crickets and katydids beginning in the late 1950s and continuing into the 21st century. <p></p>http://entnemdept.ifas.ufl.edu/walker/Buzz/
Female Passerine Singing Behavior from Odom et al. 2013: Odom et al, 2014
<p>This data set is derived from: Odom, K. J., Hall, M. L., Riebel, K., Omland, K. E. and Langmore, N. E. 2014. Female song is widespread and ancestral in songbirds. Nature Communications Article# 4379 doi:10.1038/ncomms 4501 Odom et al. scored passerine species with respect to whether (a) females sing, (b) females do not sing, or (c) neither males nor females have a true song. Using ancestral trait reconstruction, the authors concluded that having both males and females sing is likely the ancestral condition. The data can be downloaded from the Dryad Digital Repository at http://datadryad.org/resource/doi:10.5061/dryad.0sd41 </p> <p>Odom KJ, Hall ML, Riebel K, Omland KE, Langmore NE (2014) Data from: Female song is widespread and ancestral in songbirds. Dryad Digital Repository. </p>
Data from: Sexual signal loss in field crickets maintained despite strong sexual selection favoring singing males
Evolutionary biologists commonly seek explanations for how selection drives the emergence of novel traits. While trait loss is also predicted to occur frequently, few contemporary examples exist. In Hawaii, the Pacific field cricket (Teleogryllus oceanicus) is undergoing adaptive sexual signal loss due to natural selection imposed by eavesdropping parasitoids. Mutant male crickets ("flatwings") cannot sing. We measured the intensity of sexual selection on wing phenotype in a wild population. First, we surveyed the relative abundance of flatwings and "normal-wings" (non-mutants) on Oahu. Then, we bred wild-mated females' offspring to determine both female genotype with respect to the flatwing mutation and the proportion of flatwing males that sired their offspring. We found evidence of strong sexual selection favoring the production of song: females were predominantly homozygous normal-wing; their offspring were sired disproportionately by singing males; and at the population level, flatwing males became less common following a single sexual selection event. We report a selection coefficient describing the total (pre- and postcopulatory) sexual selection favoring normal-wing males in nature. Given the maintenance of the flatwing phenotype in Hawaii in recent years, this substantial sexual selection additionally suggests an approximate strength of opposing natural selection that favors silent males.
Dataset for: Vermilion flycatchers avoid singing during sudden peaks of anthropogenic noise
<p>Dataset from a playback experiment with free-living vermilion flycatchers where we show that they stop singing when traffic noise levels suddently increase (as would occur when a car passes by). This behavior may be a strategy to increase the probability of detection by singing in periods when noise levels are lower. Males' responses toward the noise playbacks were also related with the level of artificial light at night (ALAN) in males' territories, where males living in brighter territories shortened their song bout length to a lesser extent during noise exposure than those males living in territories with lower levels of ALAN, suggesting ALAN may decrease the extent at which traffic noise affects male singing behavior.</p>
Singing on the nest is a widespread behavior in incubating Northern Mockingbirds and increases probability of nest predation
<p>In this study, we documented for the first time singing on the nest (SOTN) in 74% of 65 Northern Mockingbird (<em>Mimus</em> <em>polyglottos</em>) nests that were monitored with continuous-running video cameras (8,353.9 hrs sampled). As predicted, higher rates of SOTN significantly decreased daily survival rates of nests. SOTN occurred almost exclusively by females during the egg stage and in 86% (48/56) of nests for which we had sampling from the egg stage. While extensive at the population level, the average rate of SOTN per individual was very low (5.24 ± 1.24 s SOTN per hr video sampled). We found mixed support for the hypothesis that SOTN functions in territory maintenance. We found no support for the hypotheses that SOTN functions to coordinate parental care, defend nests, or aid in vocal learning. Given the limited attention SOTN has received and the mostly anecdotal accounts of it, our understanding of its costs and benefits is lacking. We conclude that while individual rates of SOTN are quite low, SOTN may be more widespread in populations than previously thought and that studies specifically designed to test hypotheses regarding potential functions are critically needed. </p>
Data from: Natural singing interactions in Parus major
<p><span>Eavesdropping on interactions between conspecific animals provides a low-cost method for assessing other individuals. Asymmetries in territorial counter-singing interactions in songbirds provide a rich source of information for eavesdroppers about differences between the singers. Yet, little is known about the relationship between interactive singing in a natural, low-arousal context among territorial neighbours and individual traits of males. We used a microphone array to monitor natural counter-singing interactions in great tits (<em>Parus major</em>) during nest building, at the onset of the breeding season. We quantified song overlapping and song matching for 30 pairs (dyads) of interacting males, singing at their nest, respectively. We then compared these behaviours to five traits for 28 males: body condition, plumage ornamentation, offspring provisioning behaviour, offspring weight, and breeding site quality. We found no relationship between a male song overlapping or matching behaviour and any of the measured traits. Therefore, our results do not support the idea that short-term asymmetries in low-arousal long-range singing interactions among neighbours reflect differences in these fitness-related traits. Instead, our findings suggest that such singing asymmetries have less signal value in the absence of an immediate conflict but instead reflect short-term motivational differences, as shown in previous investigations.</span></p>
Data for: The relationships of breeding stage to daytime singing behaviour and song perch height in Bermuda White-eyed Vireos (Vireo griseus bermudianus)
<p><span>Bird song is crucial for attracting mates and defending territories, but different types of song or different singing behaviours may be involved in acquiring or maintaining each resource. Furthermore, male songbirds may adjust when and where they sing throughout the breeding season, depending on their breeding stage. However, such relationships remain untested in several avian taxa. Here, we studied male Bermuda White-eyed Vireos (<em>Vireo griseus bermudianus</em>), a passerine with two distinct song types (discrete and rambling), to test the mate attraction and territory defence hypotheses. We compare song production and song perch height among different stages of the breeding season and during the non-breeding season. We show that male vireos produce both song types during the breeding and non-breeding seasons, suggesting dual roles in mate choice and territorial defence. Song production did not differ significantly between the breeding and non-breeding seasons, but, within the breeding season, males without nesting duties sang significantly more songs than males with nesting duties. Song perch height was higher during the breeding season versus non-breeding season, among males without nesting duties compared to males with nesting duties, and when males produced discrete versus rambling songs. Our findings suggest that male vireos increase their conspicuousness to prospecting females by increasing song production and song perch height, and that they sing during the breeding and non-breeding seasons to defend year-round territories. Collectively, our study supports the mate attraction and territory defence hypotheses of bird song.</span></p>
Singing and Cardiovascular Health in Older Adults
ClinicalTrials.gov study NCT04121741. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Singing Your Negative Body-Related Thoughts
ClinicalTrials.gov study NCT03646305. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Online Singing Interventions for Postnatal Depression in Times of Social Isolation: a Single Arm Study
ClinicalTrials.gov study NCT04857593. IPD Sharing: YES. Countries: 1. Publications: 5.
Data for: The relationships of breeding stage to daytime singing behaviour and song perch height in Bermuda White-eyed Vireos (Vireo griseus bermudianus)
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Data for: Individual differences in song plasticity in response to social stimuli and singing position
Open the record for dataset details and reuse information.
Overtone focusing in biphonic Tuvan throat singing
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First singing data of Graptopsaltria nigrofuscata and weather data
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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.