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693 results for “Vocalization”
Data from: Voice efficiency for different voice qualities combining experimentally derived sound signals and numerical modeling of the vocal tract
<p>This dataset contains Stereo-Lithographic (STL) surface models of a human vocal tract, derived Finite-Element-Models, numerical results, and scripts for analyzing these results and (re-)running the computation.</p> <p> </p> <p><strong>In the main folder, this dataset contains:</strong></p> <p>1) Python files (*fig*.py) for the creation of figures and tables (*tab*.py)</p> <p>2) Python files (*.py) for analyzing Finite-Element (FE) calculations (x_resonances.py, x_libs.py, x_fem2excel.py)</p> <p>3) Python-files (*.py) for analyzing stl-data (x_analyzeSTL.py)</p> <p>4) Python files (*.py) for deriving Infinite-Impulse-Response (IIR) filter and their impulse responses (x_IIR.py)</p> <p>5) Excel files (*.xlsx) containing Volume-velocity-transfer-functions (Vlg.xlsx), Pressure-transfer-functions at the lips (Hlg.xlsx), and the glottis (Hgg.xlsx) based on FE, the sound spectra of audio signals (sound_spectra.xlsx), the polynomials describing the IIR (IIR_polynomial.xlsx) and their impulse responses (IIR_impulse_responses.xlsx), and glottal waveforms (glottal_waveform.xlsx) and spectra (glottal_spectra.xlsx)</p> <p>6) Several figures (*.pdf)</p> <p> </p> <p><strong>In folder „x_fenics/x_Subject-1“ (and sub-folders), this data set contains:</strong></p> <p>1) Surface models of the human vocal tract for different voice qualities (glottis.stl, wall.stl, lips.stl)</p> <p>2) Sub-volumes of the vocal tract cavities (*ET.stl, *HPl.stl, *HPu.stl, *OPf.stl, *OPr.stl, *SP.stl, *.VV.stl)</p> <p>3) Derived gmsh volume meshes (*.msh) (www.gmsh.info)</p> <p>3) Derived volume models applicable to FE-Solvers (*.h5, *.xdmf)</p> <p>4) Results of the FE-calculation (*pvtf*.txt, *vvtf*.txt, *pglottis*.txt)</p> <p>5) Formant frequencies computed by inverse filtering (*.for)</p> <p> </p> <p><strong>In folder „x_fenics/x_misc“ the data set contains:</strong></p> <p>1) Python-files (*.py) for (re-)running the calculations using the FE-Method</p>
Distinctive, fine-scale distribution of Eastern Caribbean sperm whale vocal clans reflects island fidelity rather than environmental variables
<p>Environmental variables are often the primary drivers of species' distributions as they define their niche. However, individuals, or groups of individuals, may sometimes adopt a limited range within this larger suitable habitat as a result of social and cultural processes. This is the case for Eastern Caribbean sperm whales. While environmental variables are reasonably successful in describing the general distribution of sperm whales in the region, individuals from different cultural groups have distinct distributions around the Lesser Antilles islands. Using data collected over two years of dedicated surveys in the Eastern Caribbean, we conducted habitat modelling and habitat suitability analyses to investigate the mechanisms responsible for such fine-scale distribution patterns. Vocal clan-specific models were dramatically more successful at predicting distribution than general species models, showing how a failure to incorporate social factors can impede accurate predictions. Habitat variation between islands did not explain vocal clan distributions, suggesting that cultural group segregation in the Eastern Caribbean sperm whale is driven by traditions of site/island fidelity (most likely maintained through conformism and homophily) rather than habitat type specialization. Our results provide evidence for the key role of cultural knowledge in shaping habitat use of sperm whales within suitable environmental conditions and highlight the importance of cultural factors in shaping sperm whale ecology. We recommend that social and cultural information be incorporated into conservation and management as culture can segregate populations on fine spatial scales in the absence of environmental variability.</p>
Higher-order sequences of vocal mimicry performed by male Albert's lyrebirds are socially transmitted and enhance acoustic contrast
<p>Most studies of acoustic communication focus on short units of vocalisation such as songs, yet these units are often hierarchically organised into higher-order sequences, and outside human language, little is known about the drivers of sequence structure. Here we investigate the organisation, transmission, and function of vocal sequences sung by male Albert's lyrebirds (<i>Menura alberti</i>), a species renowned for vocal imitations of other species. We quantified the organisation of mimetic units into sequences and examined the extent to which these sequences are repeated within and between individuals and shared among populations. We found that individual males organised their mimetic units into stereotyped sequences. Sequence structures were shared within and to a lesser extent among populations, implying that sequences were socially transmitted. Across the entire species range, mimetic units were sung with immediate variety and a high acoustic contrast between consecutive units, suggesting that sequence structure is a means to enhance receiver perceptions of repertoire complexity. Our results provide evidence that higher-order sequences of vocalisations can be socially transmitted, and that the order of vocal units can be functionally significant. We conclude that, to fully understand vocal behaviours, we must study both the individual vocal units and their higher-order temporal organisation.</p>
Figure 4 in Vocal repertoire and group-specific signature in the Smooth-billed Ani, Crotophaga ani Linnaeus, 1758 (Cuculiformes, Aves)
Figure 4. Boxplots (median and quartiles) of acoustic parameters of the similar vocalizations of Charqueada and Guararema groups of Smooth-billed Ani. Vocalizations:"Ahnee","Whine", "Pre-flight", "Flight" and "Vigil". Acoustic parameters: DUR = duration; MPF = maximum peak frequency; MFF = maximum fundamental frequency; MIF = minimum frequency; MAF = maximum frequency.
Figure 3 in Vocal repertoire and group-specific signature in the Smooth-billed Ani, Crotophaga ani Linnaeus, 1758 (Cuculiformes, Aves)
Figure 3. Spectrograms of the ten types of vocalizations of the Smooth-billed Ani: "Ahnee" (A), "Whine" (B, C, D, E, F and G), "Pre-flight" (H), "Shout" (I), "Flight" (J and K), "Hoot" (L), "Grunt" (M), "Ee-oo-ee" (N), "Vigil" (O),"INR" (P and Q).
Figure 1 in Vocal repertoire and group-specific signature in the Smooth-billed Ani, Crotophaga ani Linnaeus, 1758 (Cuculiformes, Aves)
Figure 1. Location of the studied groups of Smooth-billed Ani in the municipality of Alegre, ES, Brazil.
Fig. 1 in Main Functions Of Loud Vocalization In Populations Of Edible Dormouse Glis Glis
Fig. 1. Mean number of "performances" (a) and loud calls (b) found in all studied edible
Tissue-engineered vocal fold replacement in swine: Methods for functional and structural analysis.
<p>S2 Data. 5534 pig squeal events investigated in this study, provided in a folder structure sorted by pigs, pre- / post-treatment and recording date as .wav files.</p>
Marmoset Functional Maps, "A Vocalization-Processing Network in Marmosets"
<p>Marmoset data and functional maps used in the article "A vocalization-processing network in marmosets". (Jafari et al., 2023)</p>
Data from: Nasty neighbours in the Neotropics: seasonal variation in physical and vocal aggressions in a montane forest songbird, the Grey-browed Brushfinch
<p>Many territorial animals exhibit differences in their responses against intruders based on the level of threat that they pose. The dear-enemy and the nasty-neighbour effects refer to situations in which territorial aggressions are stronger against stranger and neighbour individuals, respectively. Using playback experiments during pre-breeding and post-breeding seasons in a songbird from Neotropical montane forests (Grey-browed Brushfinch, <em>Arremon</em> <em>assimilis</em>), we found that males exhibit the nasty-neighbour effect because they responded more aggressively towards neighbours than to strangers. However, territorial behaviour varied seasonally: (1) aggressions to all intrusions by neighbours were equally strong regardless of the location from which they were perceived prior to reproduction and (2) individuals were more aggressive towards neighbour males when perceived at a different border of their territory to the one they share during the post-breeding season. We conclude that territorial males respond to neighbours by assessing their threat to paternity and territoriality and thus modulate their aggressive response based on the season. In contrast, limited responses to strangers suggest that these individuals do not represent a serious threat to males of <em>A. assimilis</em> during the seasons we studied them. Thus, territorial aggressions against neighbours appear to be a mate-guarding mechanism in this species. Our results differ from those found in temperate zones, where strangers often elicit responses indicating they may represent a stronger threat than neighbours. Additional studies on the behavioral ecology of tropical birds are required to understand the generality of nasty-neighbour effects and the drivers of territorial behaviors.</p>
Vocal performance increases rapidly during the dawn chorus in Adelaide's warbler
<p><span>Many songbirds sing intensely during the early morning, resulting in a phenomenon known as the dawn chorus. We tested the hypothesis that male Adelaide's warblers (<em>Setophaga</em> <em>adelaidae</em>) warm up their voices during the dawn chorus. If warming up the voice is one of the functions of the dawn chorus, we predicted that vocal performance would increase more rapidly during the dawn chorus compared to the rest of the morning and that high song rates during the dawn chorus period contribute to the increase in vocal performance. The performance metrics <em>recovery time, voiced frequency modulation</em>, and <em>unvoiced</em> <em>frequency</em> <em>modulation</em> were low when birds first began singing, increased rapidly during the dawn chorus, and then leveled off or gradually diminished after dawn. These changes are attributable to increasing performance within song types. Reduction in the duration of the silent gap between notes is the primary driver of improved performance during the dawn chorus. Simulations indicated that singing at a high rate during the dawn chorus period increases performance in two of the three performance measures (<em>recovery</em> <em>time</em> and <em>unvoiced</em> <em>frequency</em> <em>modulation</em>) relative to singing at a low rate during this period. These findings are consistent with the hypothesis that vocal warm-up is one benefit of participation in the dawn chorus. </span></p>
Unmasking hidden genetic, vocal, and size variation in the Masked Flowerpiercer along the Andes supports two species separated by Northern Peruvian Low
<p>Genetic divergence among isolated populations is not always reflected in phenotypic differentiation. We investigated the genetic and phenotypic differentiation in <em>Diglossa cyanea</em> (Thraupidae; Masked Flowerpiercer), a widely distributed species in the tropical Andes. We found strong evidence for two main lineages separated by the Marañón River valley in the Northern Peruvian Low (NPL). These two lineages show a deep sequence divergence in mitochondrial DNA (mtDNA; ~6.7% uncorrected <em>p</em>-distance, n = 122), spectral frequency and song structure (with exclusive final whistles in southern populations, n = 88), and wing length (the northern populations are smaller, n = 364). The two divergent <em>D. cyanea</em> mitochondrial lineages were not sister to each other, suggesting a possible paraphyly with respect to <em>D. caerulescens</em> (Bluish Flowerpiercer) that remains to be tested with nuclear genomic data. No genetic variation, size difference or song structure was observed within the extensive range of the southern group (from the NPL to central Bolivia) or within all sampled northern populations (from the NPL to Venezuela). These vocal differences appear to have consequences for song discrimination, and species recognition, according to a previously published playback experiment study. We propose that the southern taxon be elevated to species rank as <em>D. melanopis</em>, a monotypic species (with the proposed name Whistling Masked-Flowerpiercer). In turn, we provide a redefinition of <em>D. cyanea</em> (Warbling Masked-Flowerpiercer), which is now restricted to the northern half of the tropical Andes as a polytypic species with three subspecies (<em>tovarensis</em>, <em>obscura</em>, and <em>cyanea</em>). Based on our results, the subspecies <em>dispar </em>should be treated as a junior synonym of <em>cyanea</em>. Our study highlights the need to continue amassing complementary datasets from field observations, experiments, and collection-based assessments to better characterize the evolutionary history, biogeography, bioacoustics, and taxonomy of Neotropical montane birds.</p>
The EmoHI Test stimuli: Measuring vocal emotion recognition in hearing-impaired populations
<p>Before reading this file, make sure you have read the <strong>README.1.pdf</strong> file. That file also contains information about the <strong>license</strong> these materials are distributed under.</p> <p><em><strong>Versions</strong></em></p> <ul> <li><strong>Version 2</strong>: This is the current version. To cite this version specifically, use DOI 10.5281/zenodo.7997063. In this version we fixed some naming mistakes in the files (in 8 of the files the sentence was identified as <code>t2</code> instead of <code>s2</code>), and added two missing stimuli (<code>t5_neutral_t2_u04.wav</code> and <code>t5_sad_t1_u05.wav</code>).</li> <li><strong>Version 1</strong>: This was the initial version. To cite that version specifically, use DOI 10.5281/zenodo.3689710.</li> </ul> <p>The latest version of the EmoHI material can be downloaded from <a href="https://doi.org/10.5281/zenodo.3689709">https://doi.org/10.5281/zenodo.3689709</a>. Please always check that you have the latest version, and that you comply with the current license requirements.</p> <p><em><strong>The EmoHI Test</strong></em></p> <p>The EmoHI Test was developed to measure the accuracy at which participants can recognize vocal emotions based on pseudospeech sentences that were produced in a happy, angry sad, or neutral manner. The EmoHI Test recordings are particularly suitable for testing hearing-impaired populations due to their high sound quality. All recordings, including the ones that were used in Nagels <em>et al.</em> (2020, <em>PeerJ</em>, <a href="https://doi.org/10.7717/peerj.8773">doi: 10.7717/peerj.8773</a>), are made available here.</p> <p>The stimuli were recorded in an anechoic room at a sampling rate of 44.1 kHz. The microphone was placed at a distance of approximately 30 cm (12 in) from the speaker. The recordings were made by connecting a standing Røde NT1 microphone to a Presonus TubePre V2 preamplifier and a TASCAM DR-100 portable digital recorder. The gain of the recordings was adjusted for each emotion production using the preamplifier to record the stimuli at an intensity level that was approximately the same across emotions to reduce large intensity differences between the recordings of different emotions. The files are not RMS equalized.</p> <p><em><strong>Citation</strong></em></p> <p>When using this repository in your research, please cite the repository itself. For this version:</p> <blockquote> <p>Nagels L., Gaudrain E., Hendriks P., & Başkent D. (2023, June 2). The EmoHI Test stimuli: Measuring vocal emotion recognition in hearing-impaired populations. Version 2. <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.7997063">https://doi.org/10.5281/zenodo.7997063</a></p> </blockquote> <p>Also cite the PeerJ article that describes the material:</p> <blockquote> <p>Nagels L., Gaudrain E., Vickers D., Matos Lopes M., Hendriks P., Başkent D. (2020). Development of vocal emotion recognition in school-age children: The EmoHI test for hearing-impaired populations. <em>PeerJ</em> 8:e8773 <a href="https://doi.org/10.7717/peerj.8773">https://doi.org/10.7717/peerj.8773</a></p> </blockquote> <p><em><strong>Sound file name structure</strong></em></p> <p>The sound files are named using the following convention:</p> <p><code>t[1-6]_{emotion}_s{1,2}_u[01-18].wav</code></p> <ul> <li><code>t[1-6]</code> represents the <strong>talker</strong> who produced the stimulus: <code>t1</code>, <code>t2</code>, <code>t3</code>, <code>t4</code>, <code>t5</code>, or <code>t6</code></li> <li><code>{emotion}</code> is the label of the <strong>emotion</strong> that was produced: <code>neutral</code>, <code>happy</code>, <code>angry</code>, or <code>sad</code></li> <li><code>s{1,2}</code> is the <strong>pseudospeech sentence</strong> that was used: <code>s1</code> for "Koun se mina lod belam." <code>s2</code> for "Nekal ibam soud molen."</li> <li><code>u[01-18]</code> is the <strong>utterance</strong> number: Number ranging from <code>u01</code> to <code>u18</code></li> </ul> <p>For instance, <code>t1_happy_s2_u01.wav</code> is utterance 1 of talker <code>t1</code> producing emotion "happy" using sentence 2.</p> <p><em><strong>Talker demographic information</strong></em></p> <p>The table below gives an overview of the voice characteristics from the talkers who produced the EmoHI test stimuli.</p> <table> <thead> <tr> <th scope="col">Talker</th> <th scope="col">Age (years)</th> <th scope="col">Gender</th> <th scope="col">Height (m)</th> <th scope="col">Mean F0 (Hz)</th> <th scope="col">F0 range (Hz)</th> </tr> </thead> <tbody> <tr> <td>t1</td> <td>48</td> <td>f</td> <td>1.72</td> <td>253.14</td> <td>179.97 – 421.81</td> </tr> <tr> <td>t2</td> <td>36</td> <td>f</td> <td>1.68</td> <td>302.23</td> <td>200.71 – 437.38</td> </tr> <tr> <td>t3</td> <td>27</td> <td>m</td> <td>1.85</td> <td>166.92</td> <td>100.99 – 296.47</td> </tr> <tr> <td>t4</td> <td>45</td> <td>m</td> <td>1.90</td> <td>149.41</td> <td>96.97 – 274.72</td> </tr> <tr> <td>t5</td> <td>25</td> <td>f</td> <td>1.63</td> <td>282.89</td> <td>199.49 – 429.38</td> </tr> <tr> <td>t6</td> <td>24</td> <td>m</td> <td>1.75</td> <td>167.76</td> <td>87.46 – 285.79</td> </tr> </tbody> </table> <p><em><strong>Supporting data</strong></em></p> <p>The behavioural data from the PeerJ article is accessible at <a href="https://doi.org/10.34894/BDMX6D">https://doi.org/10.34894/BDMX6D</a>.</p>
Nestling begging calls resemble maternal vocal signatures when mothers call slowly to embryos
<p><span>Vocal production learning (the capacity to learn to produce vocalizations) is a multi-dimensional trait that involves different learning mechanisms during different temporal and socio-ecological contexts. A key outstanding question is whether vocal production learning begins during the embryonic stage and whether mothers play an active role in this through pupil-directed vocalization behaviors. We examined variation in vocal copy similarity (an indicator of learning) in eight species from the songbird family Maluridae, using comparative and experimental approaches. We found that: (1) incubating females from all species vocalized inside the nest and produced call types including a signature 'B element' that was structurally similar to their nestlings' begging call; (2) in a prenatal playback experiment using superb fairywrens (<em>Malurus</em> <em>cyaneus</em>), embryos showed a stronger heart rate response to playbacks of the B element than to another call element (A); and (3) </span><span>mothers that produced slower calls had offspring with greater similarity between their begging call and the mother's </span><span>B element vocalization</span><span>. We conclude that malurid mothers display behaviors concordant with pupil-directed vocalizations and may actively influence their offspring's early-life through sound learning shaped by maternal call tempo. </span></p>
Vocal data in the Dioula language related to the numbers 1, 2, 3, and 4
<p>The dataset we have compiled for our research on "Setting up a speech recognition model for under-resourced languages" consists of audio recordings of Dioula speakers pronouncing the numbers 1, 2, 3, and 4. These recordings were collected under various conditions, featuring variability in speakers, accents, and environmental contexts. The data has been categorized into four distinct classes, each corresponding to one of the numbers (1, 2, 3, or 4), enabling the training and evaluation of a machine learning-based speech recognition model.</p>
Frequency jumps and subharmonic components in calls of female Odorrana tormota differentially affect the vocal behaviors of male frogs
<p><span>Many studies have demonstrated that sounds containing nonlinear phenomena (NLP) can influence the behavior of receivers. However, the specific functions of different NLP components have received less attention. In most frog species, females produce few or no vocalizations; in contrast, female <em>O</em></span><span><em>dorrana</em> <em>tormota</em></span><span> exhibit a diverse range of calls that are rich in NLP components. Previous field playbacks have shown that female calls can elicit responses from male frogs. Therefore, we conducted a phonotaxis experiment to investigate the differential effects of different NLP calls by female <em>O. tormota</em> on the vocal behavior of male frogs. The results of our study revealed that calls with subharmonics elicited a greater number of short calls and answering calls from male frogs than calls with frequency jumps. Conversely, calls with frequency jumps triggered more staccato calls from males than calls with subharmonics. Additionally, during the phonotaxis experiments, we recorded the initial vocalizations of males in response to playbacks of female calls. The majority of males first produced short calls. Under calls with frequency jumps, most of the male frogs approaching within 10 cm of the loudspeaker produced staccato calls instead of "meow" calls or short calls. While under calls with subharmonics, most male frogs preferred to produce short calls. Our findings demonstrate that frequency jumps and subharmonic components in the calls of female <em>O. tormota</em> have different effects on male vocal behaviors.</span></p>
Linguistic law-like compression strategies emerge to maximize coding efficiency in marmoset vocal communication
<p>Human language follows statistical regularities or linguistic laws. For instance, Zipf's law of brevity states that the more frequently a word is used, the shorter it tends to be. All human languages adhere to this word structure. However, it is unclear whether Zipf's law emerged de novo in humans or whether it also exists in the non-linguistic vocal systems of our primate ancestors. Using a vocal conditioning paradigm, we examined the capacity of marmoset monkeys to efficiently encode vocalizations. We observed that marmosets adopted vocal compression strategies at three levels: (i) increasing call rate, (ii) decreasing call duration, and (iii) increasing the proportion of short calls. Our results demonstrate that marmosets, when able to freely choose what to vocalize, exhibit vocal statistical regularities consistent with Zipf's law of brevity that go beyond their context-specific natural vocal behavior. This suggests that linguistic laws emerged in non-linguistic vocal systems in the primate lineage.</p>
Testing Context-Aware Software Systems in the Automotive Domain: A Multi Vocal Literature Review Protocol and Dataset
<h2>Testing Context-Aware Software Systems in the Automotive Domain: A Multi Vocal Literature Review Protocol and Dataset</h2><p>A Multi-Vocal Literature Review (MVLR) is a form of a systematic review that includes grey literature in addition to peer-review literature (Garousi, Felderer, and Mäntylä 2019). The decision to justify an MVLR is drawn from the results of recent literature reviews, in particular the recent results from (Matalonga et al. 2022) where it is shown that there is little evidence in the white literature on the approaches to testing non-academic CASS Systems.</p><p>Previous academic works (including our Quasi-Systematic Literature Reviews and Rapid Reviews) operate under the following assumptions and observations:</p><ul><li>Assumption 1. CASS are widespread and being deployed for commercial or industrial use.</li><li>Observation 1. The software engineering and software testing communities have had time to adopt (or develop new) techniques to deal with the context and effects of testing software systems.</li><li>Observation 2. Academics have been able to work with software organizations to transfer or study the approaches used to test CASS, yet the published case studies we are aware of describe a partial picture of the overall adoption and approach of the problem.</li></ul><p>In spite of these assumptions and the availability of systematic literature review studies, there is little evidence of how software organizations are testing CASS.</p><h3>Research Goals</h3><h4><strong>Aim: </strong>To uncover evidence on how the automotive industry reports their working with the dynamic testing process regarding CASS.</h4><p>We use the term industry to broaden our scope to include stakeholders with an interest in the quality of CASS like NGOs, standard-setting organizations and regulation-setting organizations who can influence how software must be treated in different domains.</p><p>The following research questions convey the general interest of our enquiries. These are driven by our previous research and expectations on the sources.</p><ul><li><strong>RQ1</strong> Are there sources to support the understanding and indicate directions to deal with the problem of testing CASS?</li><li><strong>RQ2</strong> What are the challenges of using these dynamic testing process solutions?</li><li><strong>RQ3 </strong>How are the dynamic testing processes that deal with the context of CASS described in the sources?</li></ul><h3>Dataset </h3><p>This dataset contains the following artifacts:</p><ol><li><strong>Testing CASS MVLR Protocol.pdf</strong>: protocol containing the methodological details of performing the MVLR</li><li><strong>Sources Identification, Selection and Data Extraction.xlsx</strong>: spreadsheet used to record and control discovered and selected sources</li><li><strong>Extraction_Documents.zip</strong>: compressed file containing all data collection forms filled with data extracted from the</li><li><strong>MVLR_Analysis-Codebook.xlsx</strong>: listing of codes emerging from the collected data</li></ol><p> </p>
Imagery Interventions for Auditory Vocal Hallucinations
ClinicalTrials.gov study NCT05603260. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
Effect of Semi-occluded Vocal Tract Therapy on the Phonation of Children With Vocal Fold Nodules
ClinicalTrials.gov study NCT05878197. IPD Sharing: NO. Countries: 1. Publications: 4.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.