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1,896 results for “call”
Calling structural variants with confidence from short-read data in wild bird populations
<p>Comprehensive characterisation of structural variation in natural populations has only become feasible in the last decade. To investigate the population genomic nature of structural variation (SV), reproducible and high-confidence SV callsets are first required. We created a population-scale reference of the genome-wide landscape of structural variation across 33 Nordic house sparrows (<em>Passer domesticus</em>) individuals. To produce a consensus callset across all samples using short-read data, we compare heuristic-based quality filtering and visual curation (Samplot/PlotCritic and Samplot-ML) approaches. We demonstrate that curation of SVs is important for reducing putative false positives and that the time invested in this step outweighs the potential costs of analysing short-read discovered SV datasets that include many potential false positives. We find that even a lenient manual curation strategy (e.g. applied by a single curator) can reduce the proportion of putative false positives by up to 80%, thus enriching the proportion of high-confidence variants. Crucially, in applying a lenient manual curation strategy with a single curator, nearly all (>99%) variants rejected as putative false positives were also classified as such by a more stringent curation strategy using three additional curators. Furthermore, variants rejected by manual curation failed to reflect the expected population structure from SNPs, whereas variants passing curation did. Combining heuristic-based quality-filtering with rapid manual curation of structural variants in short-read data can therefore become a time- and cost-effective first step for functional and population genomic studies requiring high-confidence SV callsets.</p>
A labelled dataset of the loud calls of four vertebrates collected using passive acoustic monitoring in Malaysian Borneo
<p><em>Passive acoustic monitoring data collection</em></p> <p>We collected data using first generation Swift autonomous recording units (ARUs) (Koch et al. 2016) with a microphone sensitivity of −44 (+/−3) dB re 1 V/Pa. The microphone frequency response was not measured but is assumed to be flat (+/− 2 dB) in the frequency range 100 Hz to 7.5 kHz. The analog signal was amplified by 40 dB and digitized (16-bit resolution) using an analog-to-digital converter (ADC) with a clipping level of −/+ 0.9 V. We collected acoustic data from one primary conservation area in Sabah, Malaysia: Danum Valley Conservation Area (with 11 recording units from March to July 2018). Danum Valley covers an area of roughly 440 km², and is characterized by lowland dipterocarp forest. Unlike many tropical forest regions, this area is considered 'aseasonal' due to its lack of clearly differentiated wet and dry seasons (Walsh and Newbery 1999). In Danum Valley, the ARUs recorded at a sampling rate of 16 kHz. All recordings were saved in waveform audio (.wav) format, with files of 2-hr duration. We affixed each recording unit to trees approximately 2-m above the ground and recorded continuously over 24 hours. We set the units on a 750 m grid structure, and preliminary field tests indicate that with these recording settings the detection range of gibbon vocalizations is ~ 400 m.</p> <p> </p> <p><em>Acoustic data processing </em></p> <p>We randomly chose approximately 500 h of recordings from Danum Valley Conservation Area to use to create a training dataset. We used a band-limited energy detector (BLED) to identify potential sounds of interest in the gibbon frequency range. For the BLED detector, we convert the 2-hr recordings into a spectrogram using a 1,600-point (100 ms) Hamming window (3 dB bandwidth = 13 Hz) with 0% overlap and a 2,048-point DFT, with the "seewave" package (Sueur et al. 2008). We then filtered the spectrogram to focus on the desired frequency range, specifically 0.5–1.6 kHz for Northern grey gibbons. For each unique time window in the recording, we determined the total energy across frequency bins which gave a single value for every 100 ms interval. Utilizing the "quantile" function in base R, we established the threshold to delineate signal from noise. Preliminary tests with varied quantile values revealed that the 15th quantile led to optimized recall for our target signal. This approach resulted in 1,439 unique sound events. The sound events were then annotated by a single observer (DJC) using a custom-written function in R to visualize the spectrograms into the following categories: great argus pheasant (<em>Argusianus argus</em>) long and short calls (Clink et al. 2021), helmeted hornbills (<em>Rhinoplax vigil</em>), rhinoceros hornbills (<em>Buceros rhinoceros</em>), female gibbons (<em>Hylobates funereus</em>) and a catch-all “noise” category. </p> <p><em>Update Version 5 and later</em></p> <p>Includes labeled test clips from a second conservation area, Maliau Basin Conservation Area, Sabah, Malaysia recorded during August 2019. The ARUs recorded at a sampling rate of 16 kHz. All recordings were saved in waveform audio (.wav) format, with files of 2-hr duration. We affixed each recording unit to trees approximately 2-m above the ground and recorded continuously over 24 hours.</p> <p> </p> <p><strong>References</strong></p> <p>Clink, D. J., Groves, T., Ahmad, A. H., & Klinck, H. (2021). Not by the light of the moon: Investigating circadian rhythms and environmental predictors of calling in Bornean great argus. <em>PloS one</em>, <em>16</em>(2), e0246564.</p> <p>Koch, R., Raymond, M., Wrege, P., & Klinck, H. (2016). SWIFT: A small, low-cost acoustic recorder for terrestrial wildlife monitoring applications. In <em>North American Ornithological Conference</em> (p. 619). Washington, D.C.</p> <p>Sueur, J., Aubin, T., & Simonis, C. (2008). Seewave: a free modular tool for sound analysis and synthesis. <em>Bioacoustics</em>, <em>18</em>, 213–226.</p> <p>Walsh, R. P., & Newbery, D. M. (1999). The ecoclimatology of Danum, Sabah, in the context of the world’s rainforest regions, with particular reference to dry periods and their impact. <em>Philosophical transactions of the Royal Society of London. Series B, Biological sciences</em>, <em>354</em>(1391), 1869–83. https://doi.org/10.1098/rstb.1999.0528</p> <p>Webb, C. O., & Ali, S. (2002). Plants and vegetation of the Maliau Basin Conservation Area, Sabah, East Malaysia. <em>Final Report to Maliau Basin Management Committee</em>.</p> <p> </p>
LYCEUM: Learning to call copy number variants on low coverage ancient genomes
<p>Copy number variants (CNVs) are pivotal in driving phenotypic variation that facilitates species adaptation. They are significant contributors to various disorders, making ancient genomes crucial for uncovering the genetic origins of disease susceptibility across populations. However, detecting CNVs in ancient DNA (aDNA) samples poses substantial challenges due to several factors: (i) aDNA is often highly degraded; (ii) contamination from microbial DNA and DNA from closely related species introduce additional noise into sequencing data; and finally, (iii) the typically low coverage of aDNA renders accurate CNV detection particularly difficult. Conventional CNV calling algorithms, which are optimized for high coverage read-depth signals, underperform under such conditions. To address these limitations, we introduce LYCEUM, the first machine learning-based CNV caller for aDNA. To overcome challenges related to data quality and scarcity, we employ a two-step training strategy. First, the model is pre-trained on whole genome sequencing data from the 1000 Genomes Project, teaching it CNV-calling capabilities similar to conventional methods. Next, the model is fine-tuned using high- confidence CNV calls derived from only a few existing high-coverage aDNA samples. During this stage, the model adapts to making CNV calls based on the downsampled read depth signals of the same aDNA samples. LYCEUM achieves accurate detection of CNVs even in typically low-coverage ancient genomes. We also observe that the segmental deletion calls made by LYCEUM show correlation with the demographic history of the samples and exhibit patterns of negative selection inline with natural selection.</p>
Thyolo alethe (Chamaetylas choloensis) calls for passive acoustic monitoring
<p>Data accompanying the paper: "Passive Acoustic Monitoring and Transfer Learning"</p> <p><strong>Please cite this dataset as:</strong></p> <blockquote> <p>Dufourq, Emmanuel and Batist, Carly and Foquet, Ruben and Durbach, Ian. (2022). Passive Acoustic Monitoring and Transfer Learning. BioRxiv doi: </p> </blockquote> <p>This dataset contains approximately 10 hours of audio that contained calls of the vulnerable Thyolo Alethe (Chamaetylas choloensis). The audio data was collected in the Mount Mulanje Biosphere Reserve, Malawi using 10 Audiomoths. The sampling rate was set to 32,000Hz and the recordings were obtained over five days in November 2020. A larger dataset exists.</p> <p>The annotations files are in (.svl) format which is compatible with SonicVisualiser (https://www.sonicvisualiser.org/). Each audio file has a corresponding .svl file. Each .svl has segments of audio that were manually annotated as either ''thyolo-alethe" (presence class) or "noise" (absence class) -- this dataset can be used to train a binary classification model.</p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip".</p>
Pin-tailed whydah (Vidua macroura) calls for passive acoustic monitoring
<p>Data accompanying the paper: "Passive Acoustic Monitoring and Transfer Learning"</p> <p><strong>Please cite this dataset as:</strong></p> <blockquote> <p>Dufourq, Emmanuel and Batist, Carly and Foquet, Ruben and Durbach, Ian. (2022). Passive Acoustic Monitoring and Transfer Learning. BioRxiv doi: </p> </blockquote> <p>This dataset contains approximately 6 hours of audio that contained calls of the pin-tailed whydah (Vidua macroura). The audio data was collected in the Intaka Island Nature Reserve in Cape Town, South Africa using 1 Audiomoth. The sampling rate was set to 48,000Hz and the recordings were obtained over four days in January 2021. A larger dataset exists.</p> <p>The annotations files are in (.svl) format which is compatible with SonicVisualiser (https://www.sonicvisualiser.org/). Each audio file has a corresponding .svl file. Each .svl has segments of audio that were manually annotated as either ''thyolo-alethe" (presence class) or "noise" (absence class) -- this dataset can be used to train a binary classification model.</p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip".</p>
Black-and-white ruffed lemur (Varecia variegata) calls for passive acoustic monitoring
<p>Data accompanying the paper: "Passive Acoustic Monitoring and Transfer Learning"</p> <p><strong>Please cite this dataset as:</strong></p> <blockquote> <p>Dufourq, Emmanuel and Batist, Carly and Foquet, Ruben and Durbach, Ian. (2022). Passive Acoustic Monitoring and Transfer Learning. BioRxiv doi: </p> </blockquote> <p>This dataset contains approximately 60 hours of audio that contained calls of the critically endangered Black-and-white ruffed lemur (Varecia variegata). The audio data was collected in a sub-humid rainforest site (Mangevo) in the southeast of Ranomafana National Park in Madagascar using 2 Swift recorders (Cornell Center for Conservation Bioacoustics). The sampling rate was set to 48,000Hz and the recordings were collected intermittently between May 2019 and November 2020. A larger dataset exists and further recordings will be released.</p> <p>The annotations files are in (.svl) format which is compatible with SonicVisualiser (https://www.sonicvisualiser.org/). Each audio file has a corresponding .svl file. Each .svl has segments of audio that were manually annotated as either ''thyolo-alethe" (presence class) or "noise" (absence class) -- this dataset can be used to train a binary classification model.</p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip".</p>
Hainan gibbons (Nomascus hainanus) calls for passive acoustic monitoring
<p><em>This dataset extends an existing one (10.5281/zenodo.3991714).</em></p> <p>Data accompanying the paper: "Passive Acoustic Monitoring and Transfer Learning"</p> <p><strong>Please cite this dataset as:</strong></p> <blockquote> <p>Dufourq, Emmanuel and Batist, Carly and Foquet, Ruben and Durbach, Ian. (2022). Passive Acoustic Monitoring and Transfer Learning. BioRxiv doi: </p> </blockquote> <p>This dataset contains approximately 10 hours of audio that contained calls of the critically endangered Hainan gibbons (Nomascus hainanus). The audio data was collected in the Bawangling National Nature Reserve, Malawi using 8 Song Meter SM3 recorders. The sampling rate was set to 9,600Hz and the recordings were collected between March to August 2016.</p> <p>The annotations files are in (.svl) format which is compatible with SonicVisualiser (https://www.sonicvisualiser.org/). Each audio file has a corresponding .svl file. Each .svl has segments of audio that were manually annotated as either ''gibbon" (presence class) or "no-gibbon" (absence class) -- this dataset can be used to train a binary classification model.</p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip".</p>
Twindroid - System calls traces - Android apps
<p>TwinDroid - a dataset of over 10 000 system calls traces, from both benign and infected Android apps. A large part of the dataset is composed of traces from pairs of benign and infected apps.</p> <p>For more traces you can visit https://zenodo.org/record/6465271#.Yls3F1yZNH4</p> <p>To trace Android application, you can use our script in https://github.com/RaphaelKhoury/automated-apk-tracing</p>
Call combinations in chimpanzees: a social tool?
<p><span>A growing body of evidence suggests the capacity for animals to combine calls into larger communicative structures is more common than previously assumed. Despite its cross-taxa prevalence, little is known regarding the evolutionary pressures driving such combinatorial abilities. One dominant hypothesis posits that social complexity and vocal complexity are linked, with changes in social structuring (e.g. group size) driving the emergence of ever-more complex vocal abilities, such as call sequencing. In this paper, we tested this hypothesis through investigating combinatoriality in the vocal system of the highly social chimpanzee. Specifically, we predicted combinatoriality to be more common in socially-driven contexts and in females and lower-ranked males (socially challenging contexts and socially challenged individuals respectively). Firstly, through applying methods from computational linguistics (i.e. collocation analyses), we built an objective repertoire of combinatorial structures in this species. Second, we investigated what potential factors influenced call combination production. We show that combinatoriality is predominant in i) social contexts vs. non-social contexts, ii) females vs. males and iii) negatively correlates with male rank. Together, these results suggest one function of combinatoriality in chimpanzees may be to help individuals navigate their dynamic social world. More generally, we argue these findings provide support for the hypothesised link between social and vocal complexity and can provide insight into the evolution of our own highly combinatorial communication system, language.</span></p>
Application demonstrating results of application of show view updates developer option on MainActivity with onBind Recyclerview call
<p>Results demonstrating results of application of show view updates developer option on MainActivity with onBind Recyclerview call</p>
GHGA_sarek3_variant_calling_Agilent200M_WES
<p>Using the sarek pipeline default values. Aligned to HG38 using bwa, and dragmap. Variants are called using either haplotypecaller, strelka, deepvariant, or freebayes. Data input was Agilent 200M WES reads. Sample A006850052 (GIAB: HG001)</p>
Nitzschia sp. Nitz4 variant calling
<p>BAM file of read alignments used to call variants in the Nitzschia sp. Nitz4 genome. The BAM file was processed to mark duplicates and add read group names.</p>
CWL run of Somatic Variant Calling Workflow (CWLProv 0.5.0 Research Object)
<p>The somatic variant calling workflow included in this case study is designed by <a href="http://bcb.io/">Blue Collar Bioinformatics (bcbio)</a>, a community-driven initiative to develop best-practice pipelines for variant calling, RNA-seq and small RNA analysis workflows. According to the documentation, the goal of this project is to facilitate the automated analysis of high throughput data by making the resources quantifiable, analyzable, scalable, accessible and reproducible.</p> <p>All the underlying tools are containerized, facilitating software use in the workflow. The somatic variant calling workflow defined in CWL is available on GitHub and equipped with a well defined test dataset.</p> <p>This dataset folder is a CWLProv Research Object that captures the Common Workflow Language execution provenance, see <a href="https://w3id.org/cwl/prov/0.5.0">https://w3id.org/cwl/prov/0.5.0</a> or use <a href="https://pypi.org/project/cwlprov/">https://pypi.org/project/cwlprov/</a> to explore</p> <p><strong>Steps to reproduce</strong></p> <p>To build the research object again, use Python 3 on macOS. Built on:</p> <ul> <li>Processor 2.8GHz Intel Core i7</li> <li>Memory: 16GB</li> <li>OS: macOS High Sierra, Version 10.13.3</li> <li>Storage: 250GB</li> </ul> <p>To run the workflow:<br> </p> <pre><code class="language-bash">pip3 install cwltool==1.0.20180912090223 git clone https://github.com/FarahZKhan/bcbio_test_cwlprov cd bcbio_test_cwlprov/somatic/somatic-workflow/ cwltool --provenance somaticwf_0.5.0_mac main-somatic.cwl main-somatic-samples.json</code></pre> <p>To package the research object:<br> </p> <pre><code class="language-bash">zip -r somaticwf_0.5.0_mac.zip somaticwf_0.5.0_mac/ sha256sum somaticwf_0.5.0_mac.zip > somaticwf_0.5.0_mac.zip.sha256</code></pre> <p>The <a href="https://github.com/FarahZKhan/bcbio_test_cwlprov">cloned git repository</a> is a fork of <a href="https://github.com/bcbio/test_bcbio_cwl">https://github.com/bcbio/test_bcbio_cwl</a>. It was obtained using:</p> <pre><code class="language-bash">wget -O test_bcbio_cwl.tar.gz https://github.com/bcbio/test_bcbio_cwl/archive/master.tar.gz</code></pre> <p>The content is from an archived version from the documentation here: <a href="https://bcbio-nextgen.readthedocs.io/en/latest/contents/cwl.html#install-bcbio-vm-with-containers">https://bcbio-nextgen.readthedocs.io/en/latest/contents/cwl.html#install-bcbio-vm-with-containers</a></p>
Dataset of "Tracking Urban Human Activity from Mobile Phone Calling Patterns" PLOS Computational Biology paper
<p>This are the dataset file for "Tracking Urban Human Activity from Mobile Phone Calling Patterns", to be published in PLOS Computational Biology.</p> <p>The files contain probability distributions of finding a first, last, or any call as a function of time, derived from anonymized call detail records for a 12 months period in the year 2007 from a mobile phone service provider in a European country. The first data file contains the data obtained fom 30 different cities. the second for the six most populated cities, splitting the data into different age and gender groups.</p> <p>Details in README files.</p>
Túngara frog call-timing decisions arise as internal rhythms interact with fluctuating chorus noise
<p>For chorusing males, optimally timing their calls relative to nearby rivals' calls and fluctuations in background chorus noise is crucial for reproductive success. A caller's acoustic environment will vary by chorus density and the properties of his chorus-mates' calls and will fluctuate unpredictably due to chorusing dynamics emerging among his chorus-mates. Thus, callers must continuously monitor moment-to-moment fluctuations in the acoustic scene they perceive at the chorus for advantageous times to call. In live experimental choruses, we investigated the factors influencing túngara frog call-timing responses to chorus-mates' calls on an interaction-by-interaction basis, revealing that intrinsic and extrinsic factors influenced call-timing decisions. Callers were more likely to overlap calls from smaller chorus-mates and chorus-mates at intermediate distances, as well as calls containing lower frequencies and exhibiting lower final amplitude minima. Consequently, variation among males in call properties led to variation in levels of call-interference received when calling in the same social environment. Additionally, callers were more likely to overlap chorus-mates' calls after experiencing extended periods of inhibition and were less likely to overlap synchronized chorus-mates' calls relative to single calls. In chorusing species, female choice is influenced by inter-caller dynamics, selecting for male call-timing strategies which, in turn, constitute the selective environment further refining these same strategies. Thus, understanding the specific factors driving call-timing decisions is essential for understanding how sexual selection operates in chorusing taxa.</p>
Data and Code: Detecting Blue Whale Calls in the Northeast Pacific Using Seismic Systems (Undergraduate Thesis)
<p><strong>SeismoData.ipynb</strong>: This Jupyter notebook is adapted from seismosocialdistancing.ipynb created by Thomas Lecocq, Fred Massin and Claudio Satriano. SeismoData.ipynb was used to retrieve seismic waveform data from the Incorporated Research Institutions for Seismology (IRIS) Data Management Center (DMC) (https://ds.iris.edu/ds/ nodes/dmc/) and convert files from miniSEED to SAC format. It was also used to preview waveform and spectrogram plots.</p> <p><strong>BlueWhaleDetectionResults_J53A_Dec132011.mat</strong>: This .mat file summarizes whale detection results from OBS J53A on December 13th 2011. The objective was to calibrate a detection algoritm created for Northwest Atlantic blue whale A calls by Plourde and Nedimovic (2022), so that it can target Northeast Pacific blue whale B calls using seismometers off Washington and California. Three tests were performed to find optimal parameters. <em>BlueWhaleDetections_J53A_Test1</em><strong> </strong>are the detection results of a control that uses Northwest Atlantic blue whale parameters (16.25-18Hz frequency and 68-78s period ranges). <em>BlueWhaleDetections_J53A_Test2</em> <strong> </strong>are the detection results using Northeast Pacific blue whale B call parameters (14-17Hz frequency and 45-55s period ranges). <em>BlueWhaleDetections_J53A_Test2 </em>are the detection results using Northeast Pacific blue whale B call and C call parameters (10.5-12Hz and 14-17Hz frequency and 45-55s period ranges). <em>BlueWhaleDetections_J53A_SCC</em> are the 95% probability detection results from the Wilcock and Hilmo (2021) blue whale catalogue created using spectrogram cross-correlation. </p> <p><strong>NEPBlueWhaleMATLABcodes.zip</strong>: Contains scripts to run the recurrence interval power ratio method created by Plourde and Nedimovic (2022), adjusted to detect Northeast Pacific blue whale B calls and plot waveforms/spectrograms. First run <em>DetectBlueWhales.m </em>to calculate the recurrence power ratio every 12 minutes, then run C<em>reateBlueWhaleDetectionList.m </em>to classify detections with high power ratios and likely blue whale call detections.</p> <p><strong>BlueWhaleDetectionResults_CapeMendocino_Dec15to292014.mat</strong>: This .mat file summarizes the blue whale detection results from 2 OBS (FS02D and FS07D) and 1 land seismometer (CM09A) in close proximity, using Test 2 parameters. Note if there are less than 3 BWD in a given day, these are likely false detections.</p>
Behavioural changes in aposematic Heliconius melpomene butterflies in response to their predatory bird calls
<p>Prey-predator interactions have resulted in the evolution of many anti-predatory traits. One of them is the ability of prey to listen to predators and avoid them. Although prey anti-predatory behavioural responses to predator auditory cues are well described in a wide range of taxa, studies on whether butterflies change their behaviours in response to their predatory calls are lacking. <em>Heliconius </em>butterflies are unpalatable and form Müllerian mimicry rings as morphological defence strategies against their avian predators. Like many other butterflies in the <em>Nymphalidae </em>family, some <em>Heliconius </em>butterflies possess auditory organs, which are hypothesized to assist with predator detection. Here we test whether <em>Heliconius melpomene </em>changes their behaviour in response to their predatory bird calls by observing the behaviour of male and female <em>H. m. plessini </em>exposed to calls of <em>Heliconius</em> avian predators: rufous-tailed jacamar, migratory Eastern kingbird, and resident tropical kingbird. We also exposed them to the calls of the toco toucan, a frugivorous bird as a control bird call, and an amplified greenhouse background noise as a noise control. We found that individuals<em> </em>changed their behaviour in response to Jacamar calls only. Males increased their walking and fluttering behaviour, while females did not change their behaviour during the playback of the jacamar call. Intersexual behaviours like courtship, copulation, and abdomen lifting did not change in response to bird calls. Our findings suggest that despite having primary predatory defences like toxicity and being in a mimicry ring, <em>H. m. plessini </em>butterflies changed their behaviour in response to predator calls. Furthermore, this response was predator-specific, as <em>H. m. plesseni</em> did not respond to either the Eastern kingbird or the tropic kingbird calls. This suggests that <em>Heliconius</em> butterflies may be able to differentiate predatory calls, and potentially the birds associated with those calls.</p>
Helperfiles for using hmmix for calling archaic introgression into present day humans (both hg19 and hg38)
<p>These files are:</p> <p>1) Strict callability masks from 1000 genomes project in hg19 and hg38 coordinates</p> <p>2) Outgroup files:</p> <p>hg38_Outgroup_1000g_HGDP.txt: Frequencies of derived alleles in 490 present individuals with Sub-Saharan related ancestry: 426 from 1000genomes project and 64 from HGDP (total=490) Only first two columns are used by hmmix. The remaining columns what the reference base (hg38 refgenome), ancestral base, derived bases and the frequency of the derived bases in HGDP and 1000genomes</p> <p>hg19_Outgroup_1000g.txt: Frequencies of derived alleles in 292 present individuals with Sub-Saharan related ancestry from 1000genomes</p> <p>3) The mutation rate files are based on the outgroup files. They report the mutation rate in 1 Mb window scaled by the genomewide mutation rate </p> <p>4) The reference genome for hg19 and hg38</p> <p>5) The ancestral allele calls for hg19 and hg38</p>
Figure 3 in The fast-calling short-tailed cricket Anurogryllus celerinictus Walker, 1973 (Orthoptera: Gryllidae) occurs in Cuba, Greater Antilles
Figure 3. Male genitalia of Anurogryllus celerinictus, views: a) dorsal, b) lateral, c) ventral.
Fig. 4 in New records, range extension and call description for the stream-breeding frog Hyloscirtus lascinius (Rivero, 1970) in Venezuela
Fig. 4. Oscillogram (A) and spectrogram (B) of the advertisement call of Hyloscirtus lascinius.
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.