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197 results for “beat”
Invaders break assembly rules to beat the natives: How cheatgrass cheats
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Temporal relationship between dancer’s body movements and music beats in classical ballet
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Beat the heat: Movements of a cold-adapted ungulate during a record-breaking heat wave
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Beating Naive Bayes at Taxonomic Classification
<p><a href="https://qiime2.org/">QIIME 2</a> Artifacts containing microbiome samples, organised by EMPO 3 classification.</p> <p>Downloaded from <a href="https://qiita.ucsd.edu/">Qiita</a>.</p>
Data from: Effects of the selective stretch-activated channel blocker GsMtx4 on stretch-induced changes in refractoriness in isolated rat hearts and on ventricular premature beats and arrhythmias after coronary occlusion in swine
Mechanical factors may contribute to ischemic ventricular arrhythmias. GsMtx4 peptide, a selective stretch-activated channel blocker, inhibits stretch-induced atrial arrhythmias. We aimed to assess whether GsMtx4 protects against ventricular ectopy and arrhythmias following coronary occlusion in swine. First, the effects of 170-nM GsMtx4 on the changes in the effective refractory period (ERP) induced by left ventricular (LV) dilatation were assessed in 8 isolated rat hearts. Then, 44 anesthetized, open-chest pigs subjected to 50-min left anterior descending artery occlusion and 2-h reperfusion were blindly allocated to GsMtx4 (57 μg/kg iv. bolus and 3.8 μg/kg/min infusion, calculated to attain the above concentration in plasma) or saline, starting 5-min before occlusion and continuing until after reflow. In rat hearts, LV distension induced progressive reductions in ERP (35±2, 32±2, and 29±2 ms at 0, 20, and 40 mmHg of LV end-diastolic pressure, respectively, P<0.001) that were prevented by GsMTx4 (33±2, 33±2, and 32±2 ms, respectively, P=0.002 for the interaction with LV end-diastolic pressure). Pigs receiving GsMtx4 had similar number of ventricular premature beats during the ischemic period as control pigs (110±28 vs. 103±21, respectively, P=0.842). There were not significant differences among treated and untreated animals in the incidence of ventricular fibrillation (13.6 vs. 22.7%, respectively, P=0.696) or tachycardia (36.4 vs. 50.0%, P=0.361) or in the number of ventricular tachycardia episodes during the occlusion period (1.8±0.7 vs. 5.5±2.6, P=0.323). Thus, GsMtx4 administered under these conditions does not suppress ventricular ectopy following coronary occlusion in swine. Whether it might protect against malignant arrhythmias should be tested in studies powered for these outcomes.
Twist - torsion coupling in beating axonemes
<p>The dataset published here was used to measure a<strong> high resolution 3D wavefom </strong>of isolated and <strong>reactivated axonemes from <em>Chlamydomonas reinhardtii</em></strong>.</p> <p><a href="https://doi.org/10.1101/2024.03.18.585533"><span><span>doi:</span> https://doi.org/10.1101/2024.03.18.585533 </span></a></p> <p>It was further used to show <strong>twist-torsion coupling </strong>in these axonemes.</p> <p>The data is organized in six folders:</p> <p><strong>1) high resoluton 3D averaged waveform of isolated and reactivated axonemes from <em>Chlamydomonas Reinhardtii</em>.</strong><br>Data files (MATLAB and txt format) contain the 3D coordinates (along the 3D arc-length) of 32 axonemal shapes that comprise one beat cycle. <br>A corresponding txt file describes the details of the dataset. </p> <p><strong>2) 3D waveforms of single isolated and reactivated axonemes from <em>Chlamydomonas Reinhardtii</em>.</strong><br>Data files (MATLAB and txt format) contain the 3D shapes of 17 individual axonemes obtained from defocused darkfield-microsopy images. <br>A corresponding txt file describes the details of the dataset.</p> <p><strong>3) Image Raw Data of single isolated and reactivated axonemes used to reconstruct the 3D waveform<br></strong>Movie files (multi-layer tif) of reactivated axonemes imaged with defocused-darkfield-microscopy. <br>A corresponding txt file describes the details of the dataset.<strong><br></strong></p> <p><strong>4) Calibration of defocused darkfield-microscopy. <br></strong>Data file (MATLAB) contains the relationship between the z-position relative to the focal plane and the full-width-at-half-maximum (FWHM) of the axoneme signal, measured normal to the centerline as well as the z-stack of imges (multi-layer tif) used to extract this relation. <br>A corresponding txt file describes the details of the dataset.</p> <p><strong>5)</strong> <strong>Distance between gold nano paricle (GNP) and the axonemal centerline as a function of the beat cycle</strong><br>Data file (MATLAB) contains 20 measurements of d_C (where d_C is the normal distance between the center position of the GNP and the axoneme centerline in 2D images) as a function of time. A corresponding txt file describes the details of the dataset.</p> <p><strong>6) Image Raw Data of single isolated and reactivated axonemes with attached GNPs used to measure d_C. <br></strong>Movie files (multi-layer tif) of reactivated axonemes with attached gold nano particles (GNPs) imaged with darkfield-microscopy. <br>A corresponding txt file describes the details of the dataset.</p>
Data for spin quantum beats in pump push spectroscopy
<p>Spin quantum beats prove the quantum nature of reactions involving radical pairs, the key species of spin chemistry. However, such quantum beats remain hidden to transient-absorption-based optical observation because the spin hardly affects the radical pairs' absorption properties. We succeeded in demonstrating such quantum beats in the photo-induced charge separated state (CSS) of an electron-donor-acceptor dyad by using two laser pulses, one for pumping the sample, and another one, with variable delay, for further exciting the CSS to a higher electronic state, wherein ultrafast recombination to distinct, optically detectable products of singlet or triplet multiplicity occurs. This represents a spin quantum measurement of the spin state of the CSS at the time instant of the second (push) pulse.</p>
user English reviews of Beat Saber on Steam
<p>user English reviews of Beat Saber on Steam</p>
Beat This! Spectrograms for Beat and Downbeat Tracking
<p>This collection contains mel spectrograms and annotations of 16 datasets for beat and downbeat tracking. All datasets have been used in "<a href="https://arxiv.org/abs/2407.21658">Beat This! Accurate beat tracking without DBN postprocessing</a>" (Foscarin/Schlüter/Widmer, ISMIR 2024) and prior publications by other authors, but for many of these datasets, audio data is not publicly available. By publishing the spectrograms, we invite other researchers to improve the state of the art in beat and downbeat tracking.</p> <h3>Datasets</h3> <p>Spectrograms for the following datasets are included in the collection:</p> <ul> <li><a href="https://github.com/fosfrancesco/asap-dataset">asap</a>: "ASAP: a dataset of aligned scores and performances for piano transcription" (Foscarin et al., ISMIR 2020)</li> <li>ballroom: "An experimental comparison of audio tempo induction algorithms" (Gouyon et al., TASLP 2006) for the audio and "Rhythmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio" (Krebs/Böck/Widmer, ISMIR 2013) for the annotations</li> <li>beatles: "Evaluation methods for musical audio beat tracking algorithms" (Davies/Degara/Plumbley, Tech. Rep., QMU, 2019)</li> <li><a href="https://www.eumus.edu.uy/candombe/datasets/ISMIR2015/dataset.html">candombe</a>: "Beat and Downbeat Tracking Based on Rhythmic Patterns Applied to the Uruguayan Candombe Drumming" (Nunes et al., ISMIR 2015) </li> <li>filosax: "Filosax: A dataset of annotated jazz saxophone recordings" (Foster/Dixon, ISMIR 2021)</li> <li><a href="https://magenta.tensorflow.org/datasets/groove" rel="nofollow">groove_midi</a>: "Learning to groove with inverse sequence transformations" (Gillick et al., ICML 2019)</li> <li>gtzan: "Musical genre classification of audio signals" (Tzanetakis/Cook, TSAP 2002) for the audio and "Swing ratio estimation" (Marchand/Peters, DAFx 2015) for the annotations</li> <li><a href="https://zenodo.org/records/3371780">guitarset</a>: "GuitarSet: A dataset for guitar transcription" (Xi et al., ISMIR 2018)</li> <li>hainsworth: "Particle filtering applied to musical tempo tracking" (Hainsworth/Macleod, JASP 2004)</li> <li>harmonix: "The Harmonix set: Beats, downbeats, and functional segment annotations of western popular music" (Nieto et al., ISMIR 2019) for the original and "Modeling Beats and Downbeats with a Time-Frequency Transformer" (Hung et al., ICASSP 2022) for the version included here</li> <li>hjdb: "One in the jungle: Downbeat detection in hardcore, jungle, and drum and bass" (Hockman/Davies/Fujinaga, ISMIR 2012)</li> <li>jaah: "Audio-aligned jazz harmony dataset for automatic chord transcription and corpus-based research" (Eremenko et al., ISMIR 2018)</li> <li><a href="https://staff.aist.go.jp/m.goto/RWC-MDB/" rel="nofollow">rwc</a>: "RWC music database: Popular, classical and jazz music databases" (Goto et al., ISMIR 2002) for the audio and "AIST annotation for the RWC music<br>database" (Goto, ISMIR 2006) for the annotations</li> <li>simac: "A computational approach to rhythm description — Audio features for the computation of rhythm periodicity functions and their use in tempo induction and music content processing" (Gouyon, PhD thesis, UPF, 2005)</li> <li><a href="https://joserzapata.github.io/publication/selective-sampling-beat-tracking/">smc</a>: "Selective sampling for beat tracking evaluation" (Holzapfel et al., TASLP 2012)</li> <li>tapcorrect: "Towards Automatically Correcting Tapped Beat Annotations for Music Recordings" (Driedger et al., ISMIR 2019)</li> </ul> <p>If given, links in the above list point to locations for obtaining the original audio.</p> <h3>Annotations</h3> <p>The corresponding annotations are available on <a href="https://github.com/CPJKU/beat_this_annotations">https://github.com/CPJKU/beat_this_annotations</a>. A snapshot of v1.0 is included in this collection as <code>beat_this_annotations.zip</code>, but you may want to use a later release.</p> <h3>Spectrograms</h3> <p>Spectrograms are computed from monophonic audio at a sample rate of 22050 Hz with a window size of 1024 and hop size of 441 samples (yielding 50 frames per second), processed with a mel filterbank of 128 bands from 30 Hz to 11 kHz, and magnitudes scaled with ln(1+1000x). They are provided in half-precision floating-point format. Spectrograms can be reproduced with torchaudio 2.3.1 from a 22050 Hz waveform tensor (resampled with <code><a href="https://python-soxr.readthedocs.io/en/latest/soxr.html#soxr.resample">soxr.resample()</a></code>, if needed) via:</p> <pre><code>melspect = torchaudio.transforms.MelSpectrogram(sample_rate=22050, n_fft=1024, hop_length=441, f_min=30, f_max=11000, n_mels=128, mel_scale='slaney', normalized='frame_length', power=1)(waveform).mul(1000).log1p()</code></pre> <h3>Format</h3> <p>For each dataset, a compressed .zip file is provided, which in turn holds an uncompressed .npz file. The .npz file holds a set of numpy arrays in subdirectories named after the annotations. Each subdirectory contains a spectrogram of the original audio file ("track.npy"), 11 pitch-shifted versions from -5 to +6 semitones ("track_ps-5.npy" to "track_ps6.npy") and 10 time-stretched versions from -20% to +20% ("track_ts-20.npy" to "track_ts20.npy"), except for <code>gtzan.npz</code>, which is designated for testing and only holds the original audio files. The .npz files can be loaded in numpy via <code><a href="https://numpy.org/doc/2.0/reference/generated/numpy.load.html">np.load()</a></code>, or unzipped into a set of .npy files that can again be loaded via <a href="https://numpy.org/doc/2.0/reference/generated/numpy.load.html"><code>np.load()</code></a>. We also provide <a href="https://github.com/CPJKU/beat_this/blob/main/beat_this/dataset/mmnpz.py">code to load .npz files as memory maps</a> for more efficiency.</p>
Beating classical heuristics for the binary paint shop problem with the quantum approximate optimization algorithm
<p>Data repository for the paper "Beating classical heuristics for the binary paint shop problem with the quantum" by Michael Streif, Sheir Yarkoni, Andrea Skolik, Florian Neukart, and Martin Leib.</p> <p> </p>
Supplementary material 1 from: Neagu AC, Manolache S, Rozylowicz L (2022) The drums of war are beating louder: Media coverage of brown bears in Romania. Nature Conservation 50: 65-84. https://doi.org/10.3897/natureconservation.50.86019
Appendix S1. Coding categories (adapted from Hughes et al. 2020) and descriptive statistics of analyzed media articles
Shensong Yangxin Capsule in the Treatment of Heart Failure Complicated With Ventricular Premature Beat
ClinicalTrials.gov study NCT01612260. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Beat the Blues: Treating Depression in African American Elders
ClinicalTrials.gov study NCT00511680. IPD Sharing: Not stated. Countries: 1. Publications: 6.
OBEAT - Beating Obesity
ClinicalTrials.gov study NCT03854331. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Use of Binaural Beats for Colonoscopy
ClinicalTrials.gov study NCT04372511. IPD Sharing: NO. Countries: 1. Publications: 6.
Efficacy and Safety of memanTine in the Treatment Of Frequently symPtomatic Atrial Premature Beats
ClinicalTrials.gov study NCT06501638. IPD Sharing: NO. Countries: 1. Publications: 0.
Noninvasive Beat-to-Beat ClearSight Blood Pressure Monitoring
ClinicalTrials.gov study NCT02872896. IPD Sharing: Not stated. Countries: 1. Publications: 2.
CRM and Fusion Beats: Effects of Progressive Fusion on Intra-left Ventricular Mechanical Function
ClinicalTrials.gov study NCT00610896. IPD Sharing: Not stated. Countries: 1. Publications: 22.
Ground-Breaking Electroporation-based Intervention for PAROXysmal Atrial Fibrillation Treatment (BEAT PAROX-AF)
ClinicalTrials.gov study NCT05159492. IPD Sharing: UNDECIDED. Countries: 5. Publications: 0.
Health eHeart BEAT-AFib - Health eHeart Biomarkers of Early Atrial Transformation in Atrial Fibrillation
ClinicalTrials.gov study NCT04404465. IPD Sharing: NO. Countries: 1. Publications: 10.
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.