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Figure 1 in Calling behavior in virgin females of Diatraea saccharalis (Fabricius, 1794) (Lepidoptera: Crambidae) in laboratory
Figure 1. Temporal pattern of calling behavior exhibited by Diatraea saccharalis females. / Patrón temporal de llamado de hembras de Diatraea saccharalis.
Figure 3 in Calling behavior in virgin females of Diatraea saccharalis (Fabricius, 1794) (Lepidoptera: Crambidae) in laboratory
Figure 3. Call duration of one, two and three days old Diatraea saccharalis females. / Duración de llamado de hembras de uno, dos y tres días de edad de Diatraea saccharalis en laboratorio. * Error bars indicate standard error.
Figure 2 in Calling behavior in virgin females of Diatraea saccharalis (Fabricius, 1794) (Lepidoptera: Crambidae) in laboratory
Figure 2. Diatraea saccharalis calling temporal pattern exhibited by one, two and three-days-old females. / Horario de llamado de hembras de Diatraea saccharalis de uno, dos y tres días de edad en laboratorio.
Glass Frog Calls
<p>This dataset contains 5296 audio files in .WAV format, corresponding to calls of two glass frog species: <em>Hyalinobatrachium fleischmanni</em> (Hf) and <em>Espadarana prosoblepon </em>(Ep), recorded under controlled laboratory conditions. We further increased the dataset size using noise injection-based data augmentation in one species (Ep), and artificially shifting call frequency in both species. The dataset contains four labelled classes: Hf (1250 calls), Hf-shifted (1250 calls), Ep (1398 calls) and Ep-shifted (1398 calls); each class is in a separate folder.</p> <p>Noise injected files, with white and pink noise, were generated using noise factors of 0.010 and 0.020, respectively, using the random and powerlaw_psd_gaussian functions from numpy (v1.26.4) and colorednoise (v2.2.0) Python libraries.</p> <p>Frequency shifting placed calls in upper or lower parts of spectrum, incorporating additional variability and was used to create two new classes. Hf call frequency was increased by a factor of +4 semitones (4/12), while Ep call frequency was decreased by the same factor (-4), using librosa (v0.10.2) in Python (effects.pitch_shift). Modified calls (‘Hf-shifted’ and ‘Ep-shifted’, respectively) more closely resemble the calls of the other species in terms of their frequency band.</p> <p>Derived audio files partially share file names: '1104-18211-ROI1_pN.WAV' is the 'pink noise' version of '1104-18211-ROI1.WAV', etc.</p> <p>The audio collection is accompanied by a data table (.csv) with no missing or null values, and consists of 4 columns: “File_name”; “Data_Augmentation”, whether the file had noise injection or not, and its type (white or pink); “Frequency_Shift”, whether the audio frequency was artificially shifted or not; and “Class”, which includes the respective labels. The dataset is suitable for machine learning tasks, audio signal processing and statistical analysis.</p>
Fig. 1 in Joaquim José da Silva (c. 1755-1810): his life, natural history collecting activities, and involvement in the so-called first scientific expedition in the interior of Angola
Fig. 1. – "Aspecto da embocadura do Rio Dande" [Aspect of the mouth of the River Dande] with Joaquim José da Silva (left) and José António (right). [a. "Forno da cal" [lime oven]; b. "Armazem de a-guardar" [storage]; c. "Sanzallas" [dwellings]; d. "Armazem da madeira" [timber storage]; e. "Igreja que foi dos Jesuitas" [church that was of the Jesuits]; f. "Ponta do Mussule(?)" [Mussule(?) Tip] [SILVA, J.J. (post. 1785: fig. 84); painting executed by José António] [© Arquivo Histórico dos Museus da Universidade de Lisboa]
Fig. 2 in Joaquim José da Silva (c. 1755-1810): his life, natural history collecting activities, and involvement in the so-called first scientific expedition in the interior of Angola
Fig. 2. – Itinerary of Joaquim José da Silva in Angola from 1783 to 1810 according to TEIXEIRA (1962) in pink, SIMON (1983) in blue, and this work in red.
Metagenomes: gene calls
<p>Gene calls for all contigs in 1,782 metagenomes.</p> <p>Gene calls were made by Prodigal.</p>
TCGA ABSOLUTE auto-calling results with DoAbsolute
<p>The results are calculated with DoAbsolute (https://github.com/ShixiangWang/DoAbsolute) with cancer type specific and pan-cancer mode based on TCGA copy number segments data and mutation data. Take your own risk as the data are auto-processed and no manual check.</p> <p> </p> <p>array sample call status purity ploidy Genome doublings delta Coverage for 80% power Cancer DNA fraction Subclonal genome fraction tau E_CR<br> TCGA-OR-A5JO-01 TCGA-OR-A5JO-01 called 1 5 0 0.2 21 1 0.01 5.00281122783359 0<br> TCGA-OR-A5KQ-10 TCGA-OR-A5KQ-10 called 0.69 5 0 0.17 29 0.85 0 4.99022535717521 0<br> TCGA-OR-A5L8-10 TCGA-OR-A5L8-10 called 0.56 3.99 0 0.18 27 0.72 0 4.02086049427876 0<br> TCGA-OU-A5PI-10 TCGA-OU-A5PI-10 called 0.75 5 0 0.18 28 0.88 0.01 5.01366221195135 0</p> <p> </p> <p>Please cite the following paper if you use it in academic research:</p> <ul> <li>Wang, Shixiang, et al. "The predictive power of tumor mutational burden in lung cancer immunotherapy response is influenced by patients' sex." International journal of cancer (2019).</li> <li>Carter, Scott L., et al. “Absolute quantification of somatic DNA alterations in human cancer.” Nature biotechnology 30.5 (2012): 413.</li> </ul> <p> </p>
Semi-simulated dataset of indel calling tool evaluation
<p>The semi-simulated datasets used in Ning Wang, et al. </p> <p>100bp_5X_500_Venter_read1.fq.gz & 100bp_5X_500_Venter_read2.fq.gz: 5X coverage, 100bp read length semi-simulated paired-end sequencing FASTQ files.</p> <p>100bp_30X_500_Venter_read1.fq.gz & 100bp_30X_500_Venter_read2.fq.gz: 30X coverage, 100bp read length semi-simulated paired-end sequencing FASTQ files.</p> <p>250bp_30X_500_Venter_read1.fq.gz & 250bp_30X_500_Venter_read2.fq.gz: 30X coverage, 250bp read length semi-simulated paired-end sequencing FASTQ files.</p> <p>100bp_60X_500_Venter_read1.fq.gz & 100bp_60X_500_Venter_read2.fq.gz: 60X coverage, 100bp read length semi-simulated paired-end sequencing FASTQ files.</p> <p>Haplotype_1_no_gap.fa & Haplotype_2_no_gap.fa: the semi-simulated dipoid human genome hg19 chromosome 1 and chromosome 2, including HuRef indels used in Ning Wang, et al. In order to use ART (fastq simulator) to generate simulated fastq fiels, the gaps (Ns) of genome were removed.</p> <p>chr1_chr2_variants_truthset.txt: types, position, size and genotype of HuRef indels used in Ning Wang, et al. </p>
Echolocation call parameters of Daubenton's bats during exposure to masking noise
<p>Echolocating bats hunt prey on the wing under conditions of poor lighting by emission of loud calls and subsequent auditory processing of weak returning echoes. To do so, they need adequate echo-to-noise ratios (ENRs) to detect and distinguish target echoes from masking noise. Early obstacle avoidance experiments report high resilience to masking in free-flying bats, but whether this is due to spectral or spatiotemporal release from masking, advanced auditory signal detection or an increase in call amplitude (Lombard effect) remains unresolved. We hypothesized that bats with no spectral, spatial or temporal release from masking noise, defend a certain ENR via a Lombard effect. We trained four bats (<em>Myotis daubentonii</em>) to approach and land on a target that broadcasted broadband noise at four different levels. An array of seven microphones enabled acoustic localization of the bats and source level estimation of their approach calls. Call duration and peak frequency did not change, but average call source levels (SL<sub>RMS</sub>, at 0.1 m as dB re. 20 μPa, root-mean-square) increased, from 112 dB in the no-noise treatment, to 118 dB (maximum 129 dB) at the maximum noise level of 94 dB. The magnitude of the Lombard effect was small (0.13 dB SL<sub>RMS</sub>/dB of noise), resulting in mean broadband and narrowband ENRs of -11 and 8 dB respectively at the highest noise level. Despite these poor ENRs, the bats still performed echo-guided landings, making us conclude that they are very resilient to masking even when they cannot avoid it spectrally, spatially or temporally.</p>
Fig. 3 in Joaquim José da Silva (c. 1755-1810): his life, natural history collecting activities, and involvement in the so-called first scientific expedition in the interior of Angola
Fig. 3. – Holotype of Cyphia stheno Webb at P. [Silva s.n., P00088662; © Muséum national d'Histoire naturelle, Paris]
AndroCT: Ten Years of App Call Traces in Android
<p>A large-scale dataset on the dynamic profiles based on function calls of 35,974 benign and malicious Android apps from 10 historical years (2010 through 2019). Function calls are a commonly used means to model program behaviors, which may contribute to various code analysis approaches to assuring software correctness, reliability, and security. In particular, our dataset includes dynamic profiles of each app resulting from the same-length of time (10 mins) of being exercised by randomly generated inputs on both emulator and real device, enabling interesting and useful app analysis that reason about app behaviors in an evolutionary perspective while informing the differences of app behaviors on different run-time hardware platforms. Since we have 20 yearly datasets associated with 35,974 unique Android apps across the 10 years, profiling these apps took 12,000 hours. Considering the costs of filtering out apps that were originally sampled but that we were unable to profile (due to various reasons such as broken APKs, not being executable because of incompatibility issues, not instrumentable, etc.), we took over two years to produce all these traces. We hope to save future researchers' time in producing such a set of dynamic data to enable their empirical and technical work. </p> <p>==================</p> <p>Thanks for your interest in our dataset. Collecting this dataset took tremendous computational and human effort. Thus, please observe the following restrictions in using our dataset: </p> <p> - Do not redistribute this dataset without our consent.<br> - Do not make commercial usage of this dataset.<br> - Get a faculty, or someone in a permanent position, to agree and commit to these conditions.<br> - When publishing your work that uses our dataset, please cite the following MSR 2021 data paper.</p> <p><br> @inproceedings{AndroidCT,<br> title = {AndroCT: Ten Years of App Call Traces in Android},<br> author = {<a href="https://2021.msrconf.org/profile/wenli">Wen Li</a>, <a href="https://2021.msrconf.org/profile/xiaoqinfu">Xiaoqin Fu</a>, and <a href="https://2021.msrconf.org/profile/haipengcai">Haipeng Cai</a>},<br> booktitle = {The 18th International Conference on Mining Software Repositories (MSR 2021), Data Showcase Track},<br> year = {2021},<br> }</p>
Nocturnal flight calls dataset: long-term acoustic monitoring of birds migrating at night
<p><strong>General Description:</strong></p> <p>This is a development set used in the experiments in the Ph.D. thesis: "Nowe metody akustycznej identyfikacji ptaków migrujących nocą" (<em>"Novel methods of acoustic identification of birds migrating at night"</em>) by Hanna Pamula. The project focuses on the detection (and - partially - classification) of passerine birds' calls from long-term audio recordings collected during bird autumn migration between 2016 and 2019. The dataset consists of >56,5 hours of recordings with annotations of nocturnal flight calls of passerine birds migrating along the Baltic Sea coast, Poland.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p>Development_Set_3.1.zip</p> <p>|_Development_Set_3.1/</p> <p> |__Training_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Validation_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Testing_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p>Training Set: 86 recordings</p> <p>Validation Set: 8 recordings</p> <p>Testing set: 18 recordings (BUT: uploaded 20 recordings, as in the previous version of the dataset - version 3, two additional recordings were used. Then, they were deleted in the final version of development set 3.1. Two additional recordings are: 'BUK5_20161101_002104a and BUK5_20161101_002104b)</p> <p>Names of waveforms and annotations are matching.</p> <p><strong>Waveforms:</strong></p> <p>The whole dataset consists of 114 recordings. One hundred thirteen recordings are about 30 minutes long (29min56s – 29min 59s), one recording is 1min20s. All data were recorded at 44,100 Hz sampling rate, one channel, with SM2 Wildlife Acoustics recorders + SMX-NFC microphone. The recording sessions were performed at night (starting time and date denoted in a file name) on the Baltic Sea coast in Poland (Dąbkowice, near Darłowo).</p> <p><strong>Annotations:</strong></p> <p>Transcriptions were produced using Audacity 2.4.1: https://www.audacityteam.org/ by an experienced birdwatcher, Hanna Pamula. While every effort has been made to ensure the quality and accuracy of the labels, some errors may occur, taking into account the difficulty of nocturnal call recognition and transcription tasks in general.</p> <p>Transcription format:</p> <p>[Starting time (sec)] [Ending time (sec)] [Label]</p> <p><strong>Meaning of the labels:</strong></p> <p>1. Positive classes – migrating passerine birds:</p> <ul> <li>'s' – song thrush call (Turdus philomelos)</li> <li>'k' – blackbird call (Turdus merula)</li> <li>'d' – redwing call (Turdus iliacus)</li> <li>'r' – robin call (Erithacus rubecula)</li> <li>‘kwiczol’ – fieldfare call (Turdus pilaris)</li> <li>‘skowronek’ – skylark call (Alauda arvensis)</li> <li>Each of the above labels could also have a question mark '?', e.g. 'r?', 'k?' – meaning that it's not a sure label. In a bird call detection task, they are regarded as positive chunks containing bird call(s).</li> <li>'ni' – non identified bird call (distant/quiet/not recognized)</li> </ul> <p>Only the supposed calls of migrating passerine birds were labeled; other sounds of species were ignored (e.g., robin's tik-calling, which can be often heard at dusk, and may be regarded as warning sounds).</p> <p>2. Negative classes – other marked sound events:</p> <ul> <li>'g' – other bird calls/songs/sounds. Sounds that could confuse the model; for example, sounds of migrating geese, cranes, plovers calls, etc.</li> <li>'gh' – human voices</li> <li>'t' – cracks, clicks, raindrops, other noise</li> <li>‘puszczyk’ – tawny owl voice (Strix aluco)</li> <li>'czapla' – grey heron voice (Ardea cinerea)</li> </ul> <p>Not all occurrences of the negative sounds were labeled – only some chosen examples to represent the possible noises/negative samples. Thus these annotations can't be used for entirely different detection / classification tasks than intended, e.g., detecting migrating cranes or human voices in long-term recordings.</p> <p>3. Labels to be excluded from analysis:</p> <ul> <li>'???', '??? mysz', '??? high freq' – unknown, not sure if the sound event is a birds' call or not. Uncertainty about belonging to a positive/negative class in the detection task.</li> </ul>
Fig. 11 in Differences in the male calling songs of two sibling species of Cicada (Hemiptera: Cicadoidea) in Greece
Fig. 11. Dendrogram of the relationships between 20 males of C. mordoganensis Boulard from Samos and Ikaria and 10 males of C. orni L. from Athens, revealed by UPGMA cluster analysis of Euclidean distances. Data standardized. IK – Ikaria; SA – Samos; AT –Athens; numbers refer to specimens.
Figs 1–2 in Differences in the male calling songs of two sibling species of Cicada (Hemiptera: Cicadoidea) in Greece
Figs 1–2: Left lateral view of the genital segments of a male. 1 – C. mordoganensis Boulard from Samos; 2 – C. lorni L. from Dionysos, Athens. Scale = 0.8 mm.
Figs 7–10 in Differences in the male calling songs of two sibling species of Cicada (Hemiptera: Cicadoidea) in Greece
Figs 7–10. Song of a male of C. orni L. (Dionysos, Athens). 7 – oscillogram over a period of 10 s; 8 – oscillogram with an extended time-base of 0.5 s; 9 – sonagram over a period of 1.0 s; 10 – spectrogram.
Figs 3–6 in Differences in the male calling songs of two sibling species of Cicada (Hemiptera: Cicadoidea) in Greece
Figs 3–6. Song of a male of C. mordoganensis Boulard (Samos). 3 – oscillogram over a period of 10 s; 4 – oscillogram with an extended time-base of 0.5 s; 5 – sonagram over a period of 1.55 s; 6 – spectrogram.
Acoustic data of calls of Manx shearwater on Lundy Island
<p>Vocalizations are widely used to signal behavioural intention in animal communication, but may also carry additional information encoded in the signal, in particular, vocalisations may carry acoustic signatures unique to the calling individual. Manx shearwater (<em>Puffinus puffinus</em>) are nocturnal seabirds that breed in dense colonies, where they must recognize and locate mates among thousands of conspecifics calling in the dark. There is evidence for individual vocal signatures in two shearwater species, but quantitative data on the vocalisations of Manx shearwater are lacking. We recorded calls of 13 Manx shearwaters on Lundy Island, UK, by eliciting vocal responses to playback of conspecific calls. We measured several spectral and temporal parameters of the calls, applied linear discriminate analysis with leave-one-out cross-validation, and have confirmed the individual vocal signatures. We then calculated among-individual repeatability of 34 features describing the vocalization to determine the extent to which these features may contribute to individual signature coding. We found that calls cluster by individual in both temporal and spectral characteristics, suggesting these are contributing to Manx shearwaters' unique call signatures.</p>
Fig. 1 in A Description Of The Male Drumming Call Of Besdolus Ventralis (Pictet, 1841) (Plecoptera: Perlodidae)
Fig. 1. Oscillograms at different time resolutions showing the amplitude modulation pattern of a Besdolus ventralis male call. A, an entire call; B, the first beat group of the call shown in A; C, the second beat group of the call shown in A; D, the third beat group of the call shown in A, red /\ symbols point toward individual beats. (Amplitude is normalised
Text-fig. 13. Macrophotographs of the fossil stems from Mhengere. a: large (90 cm diameter) palm tree trunk in situ; b: external view of the outer roots at the base of the trunk; average diameter of single root is 7 mm; c, d: cross-sections of a fragment of trunk showing the random distribution of equal-sized fibre vascular bundles throughout the trunk, the so-called Coccos-type. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 13. Macrophotographs of the fossil stems from Mhengere. a: large (90 cm diameter) palm tree trunk in situ; b: external view of the outer roots at the base of the trunk; average diameter of single root is 7 mm; c, d: cross-sections of a fragment of trunk showing the random distribution of equal-sized fibre vascular bundles throughout the trunk, the so-called Coccos-type.
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.