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40 results for “Multi-channel”
Multi-channel seismic reflection profiles SALTFLU (Salt deformation and sub-salt fluid circulation in the Algero-Balearic abyssal plain) - Pre-Stack Kirchhoff Time & Depth Migration 2022
<p>This archive contains sections of reprocessed multi-channel seismic reflection profiles SALTFLU, acquired south of Ibiza (Spain) in 2012 with the OGS Explora (pre-stack Kirchhoff time and depth stacks, and migration velocities in SEG-Y format). It also contains the cruise report describing the survey acquisition in 2012. Connected articles describe the processing flow applied to this dataset and interpretations led by the first author. </p> <p>Field File Identification and Shot Numbers (FFID, SHOTNO) are linearly interpolated by matching the CMP numbers before and after migration. Bytes 73-76 and 77-80 are identical to bytes 181-184 and 185-188 and contain the CMP coordinates.</p> <p> </p> <p> </p> <p> </p>
M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research [V1.6]
<p>V1.6 release notes:</p> <ul> <li>The test subset is released, which contains T1w (6 repetitions/subject), T2w (6 repetitions/subject), and FLAIR (4 repetitions/subject) data from 25 new subjects. These data have passed motion inspection, but one should note that due to the doubled repetition numbers, the average inter-contrast motions are around twice larger than those in the training and validation subsets. To facilitate users, we release the ground truth images as well, but please do not use them during hyperparameter tuning.</li> </ul> <p>V1.5 release notes:</p> <ul> <li>T1w Gradient echo (GRE) data for all 183 subjects are released. Note that the phase encoding direction for GRE data is in the AP direction, different from other contrasts. These GRE data were not checked for motions.</li> <li>A few incorrect records of patient_id were corrected in the H5 files.</li> <li>Scans 2022062708 and 2022062709 were removed due to duplication. Two new scans were added to replace them.</li> </ul> <p>V1.1 release notes:</p> <ul> <li>Please refer to https://www.nature.com/articles/s41597-023-02181-4 for details.</li> </ul>
DEMAND: a collection of multi-channel recordings of acoustic noise in diverse environments
<p><strong>DEMAND: Diverse Environments Multichannel Acoustic Noise Database</strong></p> <p>A database of 16-channel environmental noise recordings</p> <p><strong>Introduction</strong></p> <p>Microphone arrays, a (typically regular) arrangement of several microphones, allow for a number of interesting signal processing techniques. The correlation of audio signals from microphones that are located in close proximity with each other can, for example, be used to determine the spatial location of sound source relative to the array, or to isolate or enhance a signal based on the direction from which the sound reaches the array.</p> <p>Typically, experiments with microphone arrays that consider acoustic background noise use controlled environments or simulated environments. Such artificial setups will in general be sparse in terms of noise sources. Other pre-existing real-world noise databases (e.g. the <a href="http://catalog.elra.info/product_info.php?products_id=693">AURORA-2</a> corpus, the <a href="http://spandh.dcs.shef.ac.uk/projects/chime/PCC/datasets.html">CHiME</a> background noise data, or the <a href="http://www.speech.cs.cmu.edu/comp.speech/Section1/Data/noisex.html">NOISEX-92</a> database) tend to provide only a very limited variety of environments and are limited to at most 2 channels.</p> <p>The DEMAND (Diverse Environments Multichannel Acoustic Noise Database) presented here provides a set of recordings that allow testing of algorithms using real-world noise in a variety of settings. This version provides 15 recordings. All recordings are made with a 16-channel array, with the smallest distance between microphones being 5 cm and the largest being 21.8 cm.</p> <p><strong>License</strong></p> <p>This work, the audio data and the document describing it, is licensed under a <a href="http://creativecommons.org/licenses/by-sa/3.0/deed.en_CA">Creative Commons Attribution-ShareAlike 3.0 Unported License</a>.</p> <p><strong>The data</strong></p> <p>A description of the data and the recording equipment is provided in the file <strong>DEMAND.pdf</strong>. All recordings are available as 16 single-channel WAV files in one directory at both 48 kHz and 16 kHz sampling rates. All files are compressed into "zip" files.</p> <p><strong>Other information</strong></p> <p>The MATLAB scripts listed in the documentation can be found in the file <strong>scripts.zip</strong>.</p> <p><strong>The Authors</strong></p> <p>This work was created by Joachim Thiemann (IRISA-CNRS), Nobutaka Ito (University of Tokyo), and Emmanuel Vincent (Inria Rennes - Bretagne Atlantique). It was supported by Inria under the Associate Team Program <a href="http://versamus.inria.fr">VERSAMUS</a>.</p>
DCASE 2018, Task 5: Monitoring of domestic activities based on multi-channel acoustics - Development dataset
<p>This repository contains the development data of task 5 of the DCASE 2018 challenge. The dataset is a derivative of the SINS database.</p> <p>The SINS database contains a continuous recording of one person living in a vacation home over a period of one week. The recordings were manually annotated on daily activity level: "Cooking", "Dishwashing", "Eating", "Social activity (visit, phone call)", "Vacuum cleaning", "Watching TV", "Working", "Presence" and "Absence". More information can be found on (please cite this papers when using the dataset):</p> <p>G. Dekkers, S. Lauwereins, B. Thoen, M. W. Adhana, H. Brouckxon, T. van Waterschoot, B. Vanrumste, M. Verhelst, and P. Karsmakers, “The SINS database for detection of daily activities in a home environment using an acoustic<br> sensor network,” in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), Munich, Germany, November 2017, pp. 32–36.</p> <p>G. Dekkers, L. Vuegen, T. van Waterschoot, B. Vanrumste, and P. Karsmakers, “DCASE 2018 Challenge - Task 5: Monitoring of domestic activities based on multi-channel acoustics,” KU Leuven, Tech. Rep., July 2018.</p> <p>The derivative of the SINS database, 'DCASE 2018 – Task 5 development dataset' consists of data collected by 4 microphone arrays in the combined living room and kitchen area. The continuous recordings were split into audio segments of 10s. These audio segments are provided as individual files along with the ground truth. In total 72984 segments are made available, leading to approximately 200 hours of data.</p> <p>More information about the challenge and the specific dataset can be found <a href="http://dcase.community/challenge2018/task-monitoring-domestic-activities">here</a>. Information solely related to the content of the dataset is available in 'DCASE2018-task5-dev.doc.zip'. <br> <br> <strong>By accessing or using this database, the user accepts the provided EULA (available in DCASE2018-task5-dev.doc.zip).</strong></p>
Multi-channel Surface EMG Dataset for Fatigue analysis
<p>This is the data used in paper "Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG" DOI: 10.1109/NILES50944.2020.9257909</p> <p>Data can be found as a txt files for each subject separately or can be found as .mat file with all subjects included.</p> <p>Data details:</p> <ul> <li>Sampling Frequency= 200 Hz </li> <li>8-Bit resolution</li> <li>15 Healthy Subjects </li> <li>6 Kg Load with elbow flexed to a 90 angle</li> <li>120 Seconds Duration</li> <li>8 channels sEMG </li> <li>50 Hz Notch Filtered</li> </ul> <p>For more details and citation:</p> <p>A. Ebied, A. M. Awadallah, M. A. Abbass and Y. El-Sharkawy, "Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG," 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2020, pp. 423-427, doi: 10.1109/NILES50944.2020.9257909.</p>
DCASE 2018, Task 5: Monitoring of domestic activities based on multi-channel acoustics - Evaluation dataset
<p>The dataset is a derivative of the SINS dataset and is meant to be used as an evaluation set for the <a href="http://dcase.community/challenge2018/task-monitoring-domestic-activities">DCASE2018 Task 5 challenge</a>. The development set to be used can be found <a href="https://zenodo.org/record/1247102#.WzIF_NUzZhE">here</a>. The dataset is a derivative of the SINS database.</p> <p>The SINS database contains a continuous recording of one person living in a vacation home over a period of one week. The recordings were manually annotated on daily activity level: "Cooking", "Dishwashing", "Eating", "Social activity (visit, phone call)", "Vacuum cleaning", "Watching TV", "Working", "Presence" and "Absence". More information can be found on (please cite this papers when using the dataset):</p> <p>G. Dekkers, S. Lauwereins, B. Thoen, M. W. Adhana, H. Brouckxon, T. van Waterschoot, B. Vanrumste, M. Verhelst, and P. Karsmakers, “The SINS database for detection of daily activities in a home environment using an acoustic<br> sensor network,” in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), Munich, Germany, November 2017, pp. 32–36.</p> <p>G. Dekkers, L. Vuegen, T. van Waterschoot, B. Vanrumste, and P. Karsmakers, “DCASE 2018 Challenge - Task 5: Monitoring of domestic activities based on multi-channel acoustics,” KU Leuven, Tech. Rep., July 2018.</p> <p>The derivative of the SINS database, 'DCASE 2018 – Task 5 evaluation dataset' consists of data collected by 7 microphone arrays in the combined living room and kitchen area. The continuous recordings were split into audio segments of 10s. These audio segments are provided as individual files. In total 72972 segments are made available, leading to approximately 200 hours of data with annotations.</p> <p>More information about the challenge and the specific dataset can be found here. Information solely related to the content of the dataset is available in 'DCASE2018-task5-eval.doc.zip'.</p> <p>By accessing or using this database, the user accepts the provided EULA (available in DCASE2018-task5-eval.doc.zip).</p>
WHISPER SET 1: a dataset for multi-channel, multi-device speech separation and speech enhancement
<p>This dataset is <code>WHISPER SET 1,</code> a dataset for speech enhancement and source separation recorded with a Wireless Acoustic Sensor Network (WASN) called WHISPER <a href="https://ieeexplore.ieee.org/abstract/document/8110202">Kiselev2018</a>. The dataset contains samples for up to 4 concurrent speakers and speech in noise. The dataset was recorded in a room with low reverberation (T_60 = 0.2 s) and using 16 microphones. In general, each track contains first a calibration phase where each of the speakers sequentially is active alone for 15 seconds. Followed by 15 seconds of all the speakers together (plus noise in some cases). </p> <p>If you use this dataset please cite:</p> <ul> <li><strong>E. Ceolini, I. Kiselev and S. Liu, "Evaluating multi-channel multi-device speech separation algorithms in the wild: a hardware-software solution," in <em>IEEE/ACM Transactions on Audio, Speech, and Language Processing</em>.</strong></li> </ul> <p>===</p> <p>Each sample is a 16-channel wav file in which the order of the channel follows the following logic:</p> <p>0 - module 5 mic 1 1 - module 5 mic 2 2 - module 5 mic 3 3 - module 5 mic 4 4 - module 6 mic 1 5 - module 6 mic 2 6 - module 6 mic 3 7 - module 6 mic 4 8 - module 7 mic 1 9 - module 7 mic 2 10 - module 7 mic 3 11 - module 7 mic 4 12 - module 8 mic 1 13 - module 8 mic 2 14 - module 8 mic 3 15 - module 8 mic 4</p> <p>Refer to the <a href="https://github.com/SensorsAudioINI/WHISPER_SET_1/blob/master/WHISPER4_floor_annotated.png">floor plan</a> for a visual illustration of the microphone arrangement.</p> <p>The files are divided into two subfolders, one for the samples of speech enhancement and one for the samples of speech separation.</p> <ul> <li>In the folder of speech separation, the files are divided into subfolders defining the number of speakers in the mixtures (2, 3, or 4)</li> <li>In the folder of speech enhancement, the files are divided into subfolders following the SNR of the mixture (0, -5, -10 dB)</li> </ul> <p>Samples are ordered in folders. Each sample folder contains a 15 seconds 16-channels <code>mixture.wav</code> file, plus the 15 seconds 16-channels <code>calibX.wav</code> files one for each speaker alone or noise alone in the mixture. That is a sample with a mixture with 4 speakers will have 4 calibration files (calib1.wav, calib2.wav, calib3.wav, calib4.wav) and a mixture of a speaker plus noise will have 2 calibration files one for speech (calib1.wav) and one for noise (calib2.wav).</p> <p>== </p> <p>A Jupyter notebook is included to show an example of how to use the data of this dataset for speech separation and speech enhancement using beamforming. The notebook is dependent on <a href="https://github.com/Enny1991/beamformers">this beamforming library</a> and <a href="https://github.com/Enny1991/sep_eval">this tool</a> to evaluate the quality of the separation.</p> <p>==</p> <p>Refer to the README.md in the dataset for more information.</p> <p>For any question please contact enea.ceolini@gmail.com</p>
Simultaneous two-photon voltage or calcium imaging and multi-channel LFP recordings in barrel cortex of awake and anesthetized mice
<p>Neuronal population activity, both spontaneous and sensory-evoked, generates propagating waves in cortex. However, high spatiotemporal-resolution mapping of these waves is difficult as calcium imaging, the work horse of current imaging, does not reveal subthreshold activity.</p> <p>Here, we present a platform combining voltage or calcium two-photon imaging with multi-channel local field potential (LFP) recordings in different layers of the barrel cortex from anesthetized and awake head-restrained mice. A chronic cranial window with access port allows injecting a viral vector expressing GCaMP6f or the voltage-sensitive dye (VSD) ANNINE-6plus, as well as entering the brain with a multi-channel neural probe. We present both average spontaneous activity and average evoked signals in response to multi-whisker air-puff stimulations.</p> <p>Time domain analysis shows the dependence of the evoked responses on the cortical layer and on the state of the animal, here separated into anesthetized, awake but resting, and running. The simultaneous data acquisition allows to compare the average membrane depolarization measured with ANNINE-6plus with the amplitude and shape of the LFP recordings. The calcium imaging data connects these data sets to the large existing database of this important second messenger. Interestingly, in the calcium imaging data, we found a few cells which showed a decrease in calcium concentration in response to vibrissa stimulation in awake mice.</p> <p>This system offers a multimodal technique to study the spatiotemporal dynamics of neuronal signals through a 3D architecture in vivo. It will provide novel insights on sensory coding, closing the gap between electrical and optical recordings.</p>
Building Object and Outdoor Scene Segmentation (BOOSS) - Multi-channel (RGB + Thermal) Aerial Imagery Datasets
<p>The dataset of <em>Building Object and Outdoor Scene Segmentation (BOOSS)</em> is based on multi-channel aerial imagery data. It covers </p> <p>- Ground Truth</p> <p>- RGB</p> <p>- Thermal</p> <p>The annotations in version 1.0 include roofs, facades, cars, roof equipment, and ground equipment</p> <p>Please cite as:</p> <p>Hou, Yu, Meida Chen, Rebekka Volk, and Lucio Soibelman. "An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets." <em>Remote Sensing</em> 13, no. 21 (2021): 4357.</p>
20230315 ImageJ bio-format importer multi-channel ND2 bug
<p>A bug wherein ND2 images taken with different camera settings are read incorrectly by ImageJ's bio-format importer</p> <p>imageJ_test.nd2 - The ND2 file produced by NIS Elements AR on a Nikon LV100 microscope. 11 time steps, 2 channels (images taken with different filter blocks).</p> <p>test_image_ImageJ.jpg - A screenshot of Fiji/ImageJ after opening the ND2 with bio-formats importer. The number of dimensions is correct (11 time steps, two channels) but each channel is mono rather than an rgb image and the images are badly jumbled.</p> <p>test_image_metadata_ImageJ.jpg - The OME metadata of the ND2.</p> <p>test_image_movie_imageJ.avi - The ImageJ output exported as an AVI.</p> <p>test_image_NIS.jpg - A screenshot of the ND2 opened in Nikon's NIS Elements Viewer.</p> <p> </p> <p> </p> <p> </p>
Kirchhoff pre-stack depth migration images of the multi-channel seismic data, SO190, RV. SONNE
<p>The dataset consists of four newly processed 2-D pre-stack depth migrated multi-channel seismic lines (BGR06_303, BGR06_305, BGR06_311 and BGR06_313) collected by GEOMAR and BGR in 2006. The dataset reveals the subducted oceanic reliefs and detailed accretionary wedge structure offshore eastern Java, Bali, Lombok, and Sumbawa islands, along the Sunda arc. The dataset is saved in standard SEGY format and could be loaded in open-source or commercial software. </p>
Dataset of stereo and multi-channel IRs for a 50-point Lebedev quadrature.
<p>We present a free dataset of Impulse Response measurements for all positions on a 50-point Lebedev arranged loudspeaker array for a variety of stereo microphone configurations, 32 Eigenmic capsules, and up to 4th Order Ambisonics. This dataset has particular relevance for those interested in training novel stereo to ambisonic upmix algorithms. </p>
A Study of Multi-electrode Circular Irreversible Electroporation (IRE) Catheter and Multi-Channel IRE Generator in Paroxysmal Atrial Fibrillation (AF)
ClinicalTrials.gov study NCT05552963. IPD Sharing: YES. Countries: 1. Publications: 0.
Simultaneous two-photon voltage or calcium imaging and multi-channel LFP recordings in barrel cortex of awake and anesthetized mice
Open the record for dataset details and reuse information.
Broadcasting Competitively against Adaptive Adversary in Multi-channel Radio Networks
Full video presentation of the paper: Broadcasting Competitively against Adaptive Adversary in Multi-channel Radio Networks.<br><br>Appears in Session 1 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Dataset underlying publication "Optical-comb-based frequency stability transfer across the spectrum with a multi-channel FPGA
<p>This archive contains datasets underlying plots of the publication "Optical-comb-based frequency stability transfer across the spectrum with a multi-channel FPGA".</p> <p>Datasets contain header, and a minimum script to reproduce figures is provided</p> <p>This work was supported by the European Metrology Program for Innovation and Research (EMPIR), Project 20FUN08 Nextlasers, which<br>received funding from the EMPIR programme cofinanced by the Participating States and from the European Union’s Horizon 2020 research and innovation program. </p>
Implementation of a Multi-Channel DASH7 IoT Communication System for Packet Investigation and Validation
<p>This repository contains three cabled <a href="https://www.dash7-alliance.org/" target="_blank" rel="noopener">DASH7</a> data sets. All data sets are formatted as sigmf-data and sigmf-meta pairs, which can be investigated using <a href="https://iqengine.org/" target="_blank" rel="noopener">IQEngine</a>, <a href="https://www.gnuradio.org/" target="_blank" rel="noopener">GNU Radio</a>, or <a href="https://www.mathworks.com/products/matlab.html" target="_blank" rel="noopener">MATLAB</a>. Below you can find a more extended description of the data sets.</p> <p><strong>CH0.zip, CH93.zip, CH186.zip:</strong></p> <ul> <li>Cabled data sets of 3 channels</li> <li>10 recordings per channel</li> <li>1 DASH7 packet per file pair (SigMF)</li> <li>Fc: 866.5 MHz</li> <li>Sample rate: 7.68 MHz</li> <li>Data type: ci16_le</li> <li>Length: 1 second</li> <li>Channel class: Lo-Rate</li> <li>Sync word: 0x0B67</li> <li>3 Lo-Rate channel recordings <ul> <li>channel 0 (Fc: 863.0125 MHz), </li> <li>channel 93 (Fc: 865.3375 MHz), </li> <li>channel 186 (Fc: 867.6625 MHz)</li> </ul> </li> <li>Payload: 3 bytes [counter_byte 0xAB 0xCD] <ul> <li>counter byte is always [0x00]</li> </ul> </li> </ul> <p><strong>logs.zip:</strong></p> <ul> <li>Contains all the DASH7 gateway logs per measured channel.</li> </ul>
AggMapNet: Enhanced and Explainable Low-Sample Omics Deep Learning with Feature-Aggregated Multi-Channel Networks
<p>This data contains the datasets used in the paper "AggMapNet: Enhanced and Explainable Low-Sample Omics Deep Learning with Feature-Aggregated Multi-Channel Networks", each folder is named by the dataset name in the paper</p>
Multi-channel auto-encoders for learning domain invariant representations enabling superior classification of histopathology images
<p>A partially synthetic histopathology dataset containing image patches of colon tissue from 3 staining and scanning conditions.</p> <p>This dataset can be used to develop novel histopathology image analysis algorithms that are better able to generalise to novel data domains.</p> <p>See repo for more information.</p>
Dataset for "Water Table and Permeability Estimation from Multi-Channel Seismoelectric Spectral Ratios"
<p>This dataset is for using multi-channel seismoelectric spectral ratios to estimate the water table depth and permeability of stratified materials. Readers may use the dataset by utilizing the main program entitled "Inv_BL_SESR.m" to excute the inversion procedure and reproduce the figures in the associated paper. For further guidance on accessing the necessary codes and data, please refer to the README file.</p> <p> </p>
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.