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3,081 results for “array”
Loudspeaker array recordings in small anechoic chamber with two reflectors
<p>No description provided.</p>
Data for "A New Method of Three-Dimensional Location for Low-frequency Electric Field Detection Array"
<p>In a manuscript entitled “A New Method of Three-Dimensional Location for Low-frequency Electric Field Detection Array”, the lightning location data obtained by the low-frequency electric field detection array (LFEDA) were analyzed. The data of our results are including in the Data_for_Fig_0x.ogw. These files can be opened by Origin 9.0(or later). The data supports the aforementioned manuscript and can be used freely for scientific purposes with appropriate citation.</p>
Radiation Generation from an Array of Magnetized Anharmonic Carbon Nanotubes
Open the record for dataset details and reuse information.
Spike recordings from micro-electrocorticographic arrays
<p><strong>General description:</strong> This is the sample micro-ECoG recording for a sample animal (rat) for a sample session. Electrode dimensions are 30x30μm, with center-to-center pitch of 400 μm. The array was microfabricated on a flexible, ultra-thin parylene C substrate (4 μm). The array was placed over the rat's right M1 cortex (2✕2 cm craniotomy, with lower left corner 1 mm anterior and lateral to the bregma).</p> <p>The rat performs spontaneous navigation within a rectangular test arena, with simultaneous recording through the micro-ECoG array.</p> <p><strong>File type: </strong>Pickle file containing a <em>Python</em> dictionary object.</p> <p><strong>Variable names: </strong><em>N: </em>number of sorted units, <em>k:</em> number of samples, <em>t:</em> number of time steps</p> <ul> <li><strong>unit identifier</strong>: <em>uN (e.g., u0,</em> u1, ... u58) <ul> <li><strong>spike_timing_ms</strong> - <em>k</em> x 1: spike timing in milliseconds (with respect to spike troughs)</li> <li><strong>spike_waveform_mean </strong>- <em>t</em> x 1: mean spike waveform</li> <li><strong>wave_form </strong>-<strong> </strong><em>t</em> x <em>k</em>: individual spike waveforms for each spike, units in microvolts. 100ms per time step (-1 ms and +3 ms around spike trough)</li> <li><strong>rat</strong>: animal identifier</li> <li><strong>ch</strong>: electrode channel</li> <li><strong>unit</strong>: unit identifier</li> </ul> </li> </ul> <p><strong>Possible uses: </strong>This can be used to compare with other micro-ECoG recordings. The spikes likely arise from layer I neurons, which lie in proximity to the surface recording microelectrodes.</p> <p><strong>Contact information: </strong>For any queries, please direct to Yu Tung Lo (Jack) <jacklo@nus.edu.sg>.</p>
The phantom array experiment at the CSTB feat. Xiangzhen Kong and Anders Thorseth, supervised by Christophe Martinsons during the MetTLM project.
<p>This video shows the phantom array experiment carried out at the CSTB in Grenoble, France, featuring Xiangzhen Kong (the experimenter) and Anders Thorseth (the test subject), supervised by Christophe Martinsons (CSTB researcher) during the EMPIR MetTLM project funded by EURAMET.</p> <p>This experiment was designed to measure the sensitivity of people to the phantom array effect. The phantom array effect is a undesirable visual effect caused by temporal light modulation (TLM) occuring when high modulation frequencies are present in the light source while the observer moves their eyes. It is more visible in the dark than in bright ambient conditions. The highest visibility happens at around 600 Hz (peak of the sensitivity curve) but the phenomenon is visible from 80 Hz up to several kHz depending on the visual acuity of the observer.</p> <p>In this experiment, the light source was a fine slit. Its modulation frequency was slowed down to a few hertz in order to be seen easily on the video. The observer had to move their eyes back and forth between fixed points. Two sucessive stimuli were presented in a random order, a modulated stimulus and a steady-state stimulus (no TLM). The protocol was based on randomized, counterbalanced, repeated measures using a two-interval forced choice (2IFC) procedure optimized with an adaptive procedure called QUEST+ developed by Andrew Watson for psychovisual experiments. </p> <p>The experiment used an eye-tracing device to monitor the saccade speed and amplitude during the experiment.</p>
Carbon Nanotube-Based Chemiresistive Sensor Array for Dissolved Gases
<p>Abstract: Dissolved gases such as oxygen (DO) and ammonia (dNH3) are among the most consequential parameters for water quality assessment. Since the concentrations of DO and dNH3 are interdependent through the nitrogen cycle, simultaneous monitoring can be useful in many applications. For example, in wastewater treatment, aeration baths are used to adjust the rate of ammonia removal by the bioactive sludge. Here, we have developed a sensing array which can monitor dissolved molecular oxygen (DO) and dissolved un-ionized ammonia (dNH3) continuously and simultaneously. This was achieved by functionalizing two sensors made from single- walled carbon nanotube (SWCNT) films with two different molecules; phenyl-capped aniline tetramer (PCAT), and iron phthalocyanine (II) (FePc). It was found that the FePc-doped SWCNT (FePc@SWCNT) sensor demonstrated good sensitivity and selectivity to DO, compared to dNH3. Conversely, we found that the PCAT-doped SWCNT (PCAT@SWCNT) demonstrates greater sensitivity to ammonia. Investigating the effect of different PCAT salts as dopant, we describe the following series of sensor responses to ammonia: Chloride < Crotonate < Fumarate. Additionally, we coated our sensors with PDMS, which is selectively permeable to gases, over ionic species. Finally, using principal component analysis (PCA) and partial least square discriminant analysis, it was possible to discriminate between responses to DO and dNH3, with 100% accuracy. As a result, here, we have developed a compelling proof-of-concept for the use of a single sensing substrate, doped with molecules with distinct mechanisms of interaction with two different analytes, to simultaneously monitor concentrations of two dissolved gases, namely DO and dNH3. </p> <p> </p> <p>This repository contains dataset and code used in chemometric analysis.</p> <p>Dataset contained in dataset folder. Within the array_exp.csv file, "sensor366" represents the current data for a PCAT@SWCNT Fumarate sensor; "sensor353" represents a FePc@SWCNT sensor. The dO meter is a polarographic probe. Times for the ammonia additions and concentrations can be found in the Rmarkdown script.</p> <p>In the root directory, the renv.lock file can be found with required packages. Text outputs from code can also be found in the root directory.</p>
Enhancing Regional Quasi-Geoid Refinement Precision: An Analytical Approach Employing ADS80 Tri-linear Array Stereoscopic Imageryand GNSS Gravity-Potential Leveling
Open the record for dataset details and reuse information.
TUT Sound Events 2018 - Circular array, Reverberant and Synthetic Impulse Response Dataset
<p><strong>Tampere University of Technology (TUT) Sound Events 2018 - Circular array, Reverberant and Synthetic Impulse Response Dataset</strong></p> <p>This dataset consists of simulated, reverberant, and circular-array format recordings with stationary point sources each associated with a spatial coordinate. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), spatial location in azimuth and elevation angles (in degrees), and distance from the microphone (in meters). The sound events are spatially placed within a room using the image source method. The room size chosen was 10x8x4 meter with reverberation time per octave band of [1.0, 0.8, 0.7, 0.6, 0.5, 0.4] s and 125 Hz–4 kHz band center frequencies.</p> <p>The isolated sound events were taken from the <a href="https://archive.org/details/dcase2016_task2_train_dev">DCASE 2016 task 2 dataset.</a> This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. The sound events are randomly placed in a spatial grid with 10-degree resolution in full azimuth and [-60 60) degree elevation angles. Additionally, the sound events are placed at a random distance of at least 1 meter away from the microphone.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work.</p>
TUT Sound Events 2018 - Circular array, Anechoic and Synthetic Impulse Response Dataset
<p><strong>Tampere University of Technology (TUT) Sound Events 2018 - Circular array, Anechoic and Synthetic Impulse Response Dataset</strong></p> <p>This dataset consists of simulated anechoic circular-array format recordings with stationary point sources each associated with a spatial coordinate. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), spatial location in azimuth and elevation angles (in degrees), and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the <a href="https://archive.org/details/dcase2016_task2_train_dev">DCASE 2016 task 2 dataset.</a> This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. The sound events are randomly placed in a spatial grid with 10-degree resolution in full azimuth and [-60 60) degree elevation angles. Additionally, the sound events are placed at a random distance of [1 10] meters from the microphone.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work.</p>
Sequencing data for "Long read sequencing reveals poxvirus evolution through rapid homogenization of gene arrays"
<p>Illumina and Oxford Nanopore sequencing datasets (in FASTQ format) generated for the manuscript "Long read sequencing reveals poxvirus evolution through rapid homogenization of gene arrays." Paired-end Illumina MiSeq data are uploaded as two separate files ("r1" and "r2") for each passaged population. BAM files, filtered to exclude reads that did not align to K3L, are provided for passaged populations analyzed in the manuscript.</p>
W_b_arrays
<p>W and b arrays of the proposed ANN - eqs. (30)-(33)</p> <p> </p> <p> </p> <p><strong>Check reference below</strong> (to be added when the paper is published)</p> <p><a href="https://www.researchgate.net/publication/328870831_12_Neural_Networks_-_Structural_Damage_-_Localization">https://www.researchgate.net/publication/328870831_12_Neural_Networks_-_Structural_Damage_-_Localization</a></p>
W and b arrays
<p>Check reference below (to be added when the paper is published)</p> <p><a href="https://www.researchgate.net/publication/329336392_15_Neural_Networks_-_Buckling_-_Cellular_Steel_Beams">https://www.researchgate.net/publication/329336392_15_Neural_Networks_-_Buckling_-_Cellular_Steel_Beams</a></p>
W and b arrays
<p><strong>Check reference below</strong> (to be added when the paper is published)<br> <a href="https://www.researchgate.net/publication/329282738_14_Neural_Networks_-_Shear_Capacity_-_One-Way_RC_Slabs">https://www.researchgate.net/publication/329282738_14_Neural_Networks_-_Shear_Capacity_-_One-Way_RC_Slabs</a></p>
W and b arrays
<p>W and b arrays of the proposed ANN - eq. (4)</p> <p> </p> <p> </p> <p><strong>Check reference below</strong> (to be added when the paper is published)</p>
TAU Spatial Sound Events 2019 - Ambisonic and Microphone Array, Development Datasets
<p>This package consists of two development datasets, <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong>. These datasets contain recordings from an identical scene, with <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> providing four-channel First-Order Ambisonic (FOA) recordings while <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> provides four-channel directional microphone recordings from a tetrahedral array configuration. Both formats are extracted from the same microphone array. The recordings in the two datasets consist of stationary point sources from multiple sound classes each associated with a temporal onset and offset time, and DOA coordinate represented using azimuth and elevation angle. These development datasets are part of the <a href="https://github.com/sharathadavanne/seld-dcase2019">DCASE 2019 Sound Event Localization and Detection Task</a>.</p> <p>Both the development set consists of 400, one minute long recordings sampled at 48000 Hz, and divided into four cross-validation splits of 100 recordings each. These recordings were synthesized using spatial room impulse response (IRs) collected from five indoor locations, at 504 unique combinations of azimuth-elevation-distance. Furthermore, in order to synthesize the recordings, the collected IRs were convolved with <a href="http://www.cs.tut.fi/sgn/arg/dcase2016/task-sound-event-detection-in-synthetic-audio#audio-dataset">isolated sound events dataset from DCASE 2016 task 2</a>. Finally, to create a realistic sound scene recording, natural ambient noise collected in the IR recording locations was added to the synthesized recordings such that the average SNR of the sound events was 30 dB.</p> <p>The IRs were collected in Finland by Tampere University between 12/2017 - 06/2018. The data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND.</p> <p><strong>Download instructions</strong></p> <p>The three files, <strong><em>foa_dev.z01</em></strong>,<strong><em> foa_dev.z02</em></strong> and <strong><em>foa_dev.zip</em></strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> development dataset.<br> The two files, <strong><em>mic_dev.z01</em></strong> and, <strong><em>mic_dev.zip</em></strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> development dataset.<br> The <strong><em>metadata_dev.zip</em></strong> is the common metadata for both <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> development datasets.</p> <p>Download the zip files corresponding to the dataset of interest and use your favorite compression tool to unzip these split zip files.<br> </p>
Data for An Optical Tweezer Array of Ultracold Molecules
<p>Data for figures</p>
Facial Features of Air Gun Array Wavelets in the Time-Frequency Domain under Practical Conditions
<p>The data in folder (a) include mass of air in the bubble, variation in the temperature of the gas inside the bubble, bubble radius, bubble wall velocity, notional signature and far-field signature simulated by Van der Waals air-gun model. The actual data in folder (b) are the far-field wavelets at different locations measured by China Ocean University on the northern slope of the South China Sea in April 2017. The simulated data are the far-field wavelets corresponding to the actual locations simulated by Van der Waals air-gun model. The data in the folder (c) includes simulated three dimensional acoustic field and facial evaluation data for long array, square array, simultaneously fired vertical array, and time-delayed vertical array.</p>
Simulation of single cell read and parallel read for a 1T1R and a pseudo-crossbar memristor array
<p>Dataset: simulation of single cell read and parallel read for a 1T1R and a pseudo-crossbar memristor array (Deliverable D4.2 "Initial memristor crossbar based logic/arithmetic and memory designs and models", Fig.13, H2020 Project MNEMOSENE)</p>
Seconds-scale coherence on an optical clock transition in a tweezer array (publication data)
<p>Accompanying data for<em> Seconds-scale coherence on an optical clock transition in a tweezer array.</em></p>
W and b arrays
<p>W and b arrays of the proposed ANN - eq. (4)</p> <p> </p> <p><strong>Check reference below</strong> (to be added when the paper is published)</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.