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3,081 results for “array”
Dataset for "Performance Enhancement of EAD Thrusters with Non-Uniform Emitters Array"
<p>Dataset to reproduce figures of "Performance Enhancement of EAD Thrusters with Non-Uniform Emitters Array"</p>
Mechanical Metamaterial: Square Array of Circular Holes Under Deformation
<p>This repository contains data for a research project involving graph neural networks (GNNs) applied to mechanical metamaterials and their deformations. </p> <p>The mechanical metamaterials of interest consist of a flexible rubber-like material with a square grid of almost-circular holes in it, of various diameters. The holes are not quite circular, because they were made slightly elliptic to avoid the bifurcation point.</p> <p>This data was used to obtain the results in the paper 'Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterials'.</p> <p>The data includes PyTorch Geometric Graph objects, raw data from MATLAB simulations, and files describing the finite element mesh. The Jupyter notebook 'data_create_graphs.ipynb' in the GitHub repository can be used to turn the raw data and mesh information into the graph objects.</p> <p>GitHub repository with the code: <a title="https://github.com/FHendriks11/SimEGNN" href="https://github.com/FHendriks11/SimEGNN">https://github.com/FHendriks11/SimEGNN</a></p> <p>Link to the corresponding paper <em>Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterials</em>: <a href="https://www.sciencedirect.com/science/article/pii/S0045782525001392">https://www.sciencedirect.com/science/article/pii/S0045782525001392</a> (also on ArXiv: <a title="https://arxiv.org/abs/2404.17365" href="https://arxiv.org/abs/2404.17365">https://arxiv.org/abs/2404.17365</a>)</p> <p>The .pkl files are pickle files and can be opened in Python using the standard pickle library. The mesh files, which have the extension .mat, are MatLab files and can be opened either in MatLab or in Python using scipy.io.loadmat from the scipy library.</p>
Tampere University Rotated Circular Array Impulse Response Dataset(TUNI-RCAIR)
<p>The TUNI-RCAIR dataset consists of impulse responses collected from different spaces in the Tampere university campus, Finland. </p> <p>Each folder represents a data collection environment, and it consists of impulse responses and recorded data in the form of wav files. Each space has its own three alphabet identifier, for example- Anechoic chamber is represented as AEC. The folder names indicate the space and corresponding identifier</p> <p>There are seven different data collection environments, and the distance between the source and receiver is varied thrice (0.7m, 1.4m, and 2.1m), which can be found in the names of the sub folders.</p> <p>Further details about the dataset, the characteristis of the space used for data collection, can be found in the page - <strong>https://tuni-rcair.github.io/</strong></p> <p>This dataset is accepted as a part of EUSIPCO 2021, the name of the corresponding paper is, "Tampere University Rotated Circular Array Dataset". The link to the paper will be updated.</p> <p>EUSIPCO paper DOI - https://doi.org/10.23919/EUSIPCO54536.2021.9616072</p> <p>For citation please use - A. V. Venkatakrishnan, P. Pertilä and M. Parviainen, "Tampere University Rotated Circular Array Dataset," 2021 29th European Signal Processing Conference (EUSIPCO), 2021, pp. 201-205, doi: 10.23919/EUSIPCO54536.2021.9616072.</p> <p> </p>
Data from: Coherent mechanical noise cancellation and cooperativity competition in optomechanical arrays
<p>Source data for figures.</p>
Cross-correlated ambient data recorded on a distributed acoustic sensing array
<p>Distributed acoustic sensing (DAS) is a relatively new technology used in many geophysical applications. The versatility and high temporal-spatial resolution make DAS ideal for rapid deployment surveys such as earthquake-aftershock monitoring and hazard assessment. However, these applications often rely on trenched cable installations that are time consuming and cost prohibitive, or on existing telecom fibers that are limited in spatial coverage. We deploy a DAS array composed of six parallel linear subsections directly on ground surfaces with different conditions. We apply ambient interferometry and adopt a simplified spectral-analysis-of-surface waves (SASW) method to determine the average shear-wave velocity of the top 30 m (VS30). Our methodology results in robust VS30 estimates for each surface deployment subsection that are consistent with collocated 1 m-depth trenched cables. The implications of these findings support DAS as a viable method for non-invasive rapid deployment surface surveys for earthquake hazard assessment.</p>
Dataset supporting the article "Megapixel camera arrays for high-resolution animal tracking in multiwell plates"
<p>IB and LF contributed equally.</p> <p> </p> <p>This deposition contains the supporting dataset for the article:</p> <p><strong>Megapixel camera arrays for high-resolution animal tracking in multiwell plates</strong></p> <p>Ida Barlow, Luigi Feriani, Eleni Minga, Adam McDermott-Rouse, Thomas J O'Brien, Ziwei Liu, Maximilian Hofbauer, John R Stowers, Erik C Andersen, Siyu S Ding, André EX Brown</p> <p> </p> <p> </p> <p><strong>Acknowledgements</strong>:</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 714853) and was supported by the Medical Research Council through grant MC-A658-5TY30. This work was supported by a Research Grant from HFSP (Ref.-No: RGP0001/2019). AMR was supported by a BBSRC CASE studentship part-funded by Syngenta.</p>
Associated dataset for "Effects of Additive Noise in Binaural Rendering of Spherical Microphone Array Signals"
<p>The instrumental evaluation utilized the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. The employed code state at that time should be used to reproduce the rendering results in this data set exactly. The frozen code state for this data set is available at:<br><a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2021.TASLP">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2021.TASLP</a></p> <p>Download the rendering pipeline and follow the setup instructions! Use the Conda environment file included here when setting up the Python environment. In this way, you will obtain the exact Python setup as utilized in the instrumental evaluation in the publication:</p> <pre><code>conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code>source activate ReTiSAR_TASLP_freeze</code></pre> <p>Matlab script "generate_norm_levels.m":</p> <ul> <li>The level contributions used in the publication are contained in "record_CLL_levels.sh", therefore this needs to be executed only in case other level distributions should be generated</li> <li>Generate the string for a ReTiSAR configuration (as used in "record_CLL_levels.sh") to emulate normally contributing EM32 self-noise based on Forum Acusticum publication data</li> <li>Generate the string for aReTiSAR configuration (as used in "record_CLL_levels.sh") to emulate normally contributing GL162 self-noise based on Gaussian normal distribution</li> </ul> <p>Matlab script "prepare_MagLS_HRIRs.m":</p> <ul> <li>Apply Magnitude Least Squares pre-processing to HRIRs (as used in "record_CLL_levels.sh")</li> </ul> <p>Shell script "record_CLL_levels.sh":</p> <ul> <li>Record the output ear signals of the rendering pipeline at multiple head orientations for all investigated configurations (according to Table 1)</li> <li>All captured signals are contained in the respective configuration directory, e.g. "rec_25ch_Fliege_sh4" to "rec_338ch_Gauss_sh12"</li> </ul> <p>Matlab script "calculate_CLL_levels.m":</p> <ul> <li>Read the individual rendering pipeline output recordings for arbitrary configurations</li> <li>Visualize the raw captured signals per configuration</li> <li>Visualize the RMS signal level variations over all head orientations per configuration</li> <li>Visualize the Interaural Level Difference variations over all head orientations per configuration</li> <li>Visualize the Composite Loudness Level variations over all head orientations per configuration (like Figure 2)</li> <li>Gather the above determined RMS, ILD, CLL, etc. metrics in a Matlab dataset per configuration (will be utilized in "plot_gathered_CLL_levels.m")</li> <li>All generated plots and Matlab datasets are contained in the respective configuration directory, e.g. "rec_25ch_Fliege_sh4" to "rec_338ch_Gauss_sh12"</li> </ul> <p>Matlab script "plot_gathered_CLL_levels.m":</p> <ul> <li>Visualize the resulting Composite Loudness Level gradient detections over all head orientations for arbitrary combinations of configurations (like Figure 3 to Figure 14)</li> <li>All generated plots are contained in the "CLL_results" directory</li> </ul>
Stanford fiber-optic DAS array: Earthquake detection dataset
<p>Repurposing the fiber-optic cables from the existing telecommunication infrastructure makes it possible to record dense continuous seismic data in urban areas at low cost. From 2016 to 2019, we connected a disctributed acoustic sensing (DAS) interrogator unit to the fiber-optic cables in telecommunication conduits under Stanford University campus, recording years of continuous seismic data.</p> <p>This repository contains processed TensorFlow Record data files containing examples of earthquake and background noise signals recorded by the Stanford fiber-optic DAS array. These data were used for training, evaluation, and testing of a convolutional neural network for earthquake detection. </p> <p> </p> <p> </p>
Software and data release for Fermi Pulsar Timing Array
<p>The archive include photon data from the Fermi Large Area Telescope, processed to enable its use for pulsar timing. It includes Python code for performing pulsar timing using Poisson likelihood and for producing pulse time-of-arrival measurements. It further includes code and scripts for constraining noise processes in the data, including the a signal from the nanohertz gravitational wave background.</p> <p> </p> <p>See README for more information.</p>
Memory array locations, delay times, and participant response
<p>Deliberative decisions based on an accumulation of evidence over time depend on working-memory, and working memory has limitations, but how these limitations affect deliberative decision-making is not understood. We used human psychophysics to assess the impact of working-memory limitations on the fidelity of a continuous decision variable. Participants decided the average location of multiple visual targets. This computed, continuous decision variable degraded with time and capacity in a manner that depended critically on the strategy used to form the decision variable. This dependence reflected whether the decision variable was computed either: 1) immediately upon observing the evidence, and thus stored as a single value in memory; or 2) at the time of the report, and thus stored as multiple values in memory. These results provide important constraints on how the brain computes and maintains temporally dynamic decision variables. <br><br>The following data is the responses and trial information collected from each subject in each condition: Simultaneous Perceived, Simultaneous Computed, Sequential Perceived, and Sequential Computed.</p>
Complex feline disease mapping using a dense genotyping array
<p>The current feline genotyping array of 63k single nucleotide polymorphisms has proven its utility within breeds, and its use has led to the identification of variants associated with Mendelian traits in purebred cats. However, compared to single gene disorders, association studies of complex diseases, especially with the inclusion of random bred cats with relatively low linkage disequilibrium, require a denser genotyping array and an increased sample size to provide statistically significant associations. Here, we undertook a multi-breed study of 1,122 cats, most of which were admitted and phenotyped for nine common complex feline diseases at the Cornell University Hospital for Animals. Using a proprietary 340k single nucleotide polymorphism mapping array, we identified significant genome-wide associations with hyperthyroidism, diabetes mellitus, and eosinophilic keratoconjunctivitis. These results provide genomic locations for variant discovery and candidate gene screening for these important complex feline diseases, which are relevant not only to feline health, but also to the development of disease models for comparative studies.</p>
Aedes aegypti in North America (Microsatellite and SNP array)
<p>The <em>Aedes aegypti</em> mosquito first invaded the Americas about 500 years ago and today is a widely distributed invasive species and the primary vector for viruses causing dengue, chikungunya, Zika, and yellow fever. Here we test the hypothesis that the North American colonization by <em>Ae. aegypti</em> occurred via a series of founder events. We present findings on genetic diversity, structure, and demographic history using data from 70 <em>Ae. aegypti</em> populations in North America genotyped at 12 microsatellite loci and/or ~20,000 single nucleotide polymorphisms (SNPs), the largest genetic study of the region to date. We find evidence consistent with a colonization driven by serial founder effect (SFE), with Florida as the putative source for a series of westward invasions. This scenario was supported by 1) a decrease in the genetic diversity of <em>Ae. aegypti </em>populations moving west, 2) a correlation between pairwise genetic and geographic distances, and 3) demographic analysis based on allele frequencies. A few <em>Ae. aegypti</em> populations on the west coast do not follow the general trend, likely due to a recent and distinct invasion history. We argue that SFE provides a helpful albeit simplified model for the movement of <em>Ae. aegypti </em>across North America, with outlier populations warranting further investigation.</p>
An improved earthquake catalog during the 2018 Kilauea eruption from combined onshore and offshore seismic arrays
<p>The Island of Hawai'i was formed by repeated eruptions of basalts at an oceanic hotspot. Kilauea, the youngest among the subaerial volcanoes of the island, erupted intensely in 2018. The eruption provided an opportunity to look into the mechanisms that operate at the volcano and associated earthquake activities, as it was recorded simultaneously, for the first time, by onshore and offshore seismometers. We used most of the publicly available seismic data during the eruption period, including temporary arrays, to build a more complete earthquake catalog during the eruption than that provided by the Hawaiian Volcano Observatory (HVO). We used a short-time-average/long-time-average (STA/LTA) method to identify potential earthquakes. The detections were associated into events and automatically picked with P- and S-wave arrivals, which were used to locate the events in a three-dimensional velocity model. After re-examining these earthquake events, their coda/duration magnitudes were determined. The resulting half-year catalog contains 375,736 events with one of the highest daily earthquake numbers ever reported (6,128 on June 21st, 2018). A great number of events were recorded during the caldera collapses, from its beginning untill its rapid ending. The catalog also contains abundant events near the Pu'u'o'o vent and in the lower East Rift Zone, where an increase of seismicity in the mid-July and August indicated a step-up in magma intrusion after the eruption.</p>
Interferometric array data for UK-Canada seafloor cable
<p>Dataset for interferometric data showing in Marra et al, "Optical interferometry–based array of seafloor environmental sensors using a transoceanic submarine cable".<br> Data in .MAT file format. Sampling rate and start time showing in the filename. Data is frequency deviation (Hz).</p>
Design and performance of an ecosystem-scale forest soil warming experiment with infrared heater arrays
<p><span>How forest ecosystems respond to climate warming will determine forest trajectories over the next 100 years. However, the potential effects of elevated temperature on forests remain unclear, </span><span>primarily because of the absence of long-term and large-size field warming experiments in forests, especially in Asia.</span></p> <p><span>Here, we present the design and performance of an ecosystem-scale warming experiment</span><span> using an infrared (IR) heater array in a 60-year-old temperate mixed forest at Qingyuan Forest CERN in northeastern China.</span></p> <p><span>In paired </span><span>108 m<sup>2</sup> plots (n = 3),</span><span> the surface soils were constantly elevated 2 degrees</span><span> above control plots with a feedback control system over four years (2018-2021). Subsoils down to 60 cm depth were warmed 1.2-2 degrees</span><span>. Soil warming did not affect soil moisture either in surface soils or subsoils. Turn-off time due to weather extremes (heavy rains, snow) and power outages only accounted for 2.5% of the total warming period.</span></p> <p><span>In conclusion, we provide a proof-of-principle setup that allows long-term analysis of forest response to warming temperatures in large-size field plots. Importantly, our warming experiment demonstrated the feasibility of IR heater arrays for soil warming in tall-statured forest ecosystems.</span></p>
dataset for "Long-lived Bell states in an array of optical clock qubits"
<p>Dataset for main figures in "Long-lived Bell states in an array of optical clock qubits".</p>
GNSS-A data obtained at the SGO-A sites "TOS2", "KUM2", "FUKU", and "MYGI" for the identical four transponder-array
<p>Contains the GNSS-A data obtained by the Japan Coast Guard at the SGO-A sites "TOS2", "KUM2", "FUKU", and "MYGI" for the identical four transponder-array.</p> <p>The datasets are stored in the following directories and files for analyses after array determination:<br> -[initcfg]: contains initial site-parameter files<br> -[obsdata]: contains initial acoustic observation files<br> -[single-default]: contains the prep. results (without array-geometry constraint)<br> -[cfgfix]: contains site-parameter files for array-geometry constraint analysis<br> -[fix4sta-abic]: contains the empirical Bayes' results obtained with array-geometry constraint</p> <p>These data can be/were processed with <a href="https://doi.org/10.5281/zenodo.6414642">GARPOS v1.0.1</a>, <a href="https://doi.org/10.5281/zenodo.6825238">GARPOS-MCMC v1.0.0</a>.</p>
SolCAP 8K array genotyping of population 15143
<p><span></span></p> <p>The dataset is a tab-deliminted text file with genotyping information for a diploid potato progeny population derived from a cross of diploid potato clones 12120-03 X 07506-01. The SolCap 8303 Infinium Chip was used for SNP genotyping. It was originally generated for study on genetic mapping of Verticillium wilt resistance. The data was used again for analysis of segregation distortion in the most recent manuscript. </p>
Illumina HD genotypes for 3,092 cattle from Burkina Faso, Ghana, Nigeria and Tanzania for: "Assessment of genotyping array performance for genome-wide association studies and imputation in African cattle"
<p>Raw HD data for Riggio et al. 2022: Assessment of genotyping array performance for genome-wide association studies and imputation in African cattle</p> <p>This repository contains the raw Illumina HD genotypes (i.e., 777,962 SNPs) mapped to the bovine UMD3.1 genome assembly for 3,092 animals from four African countries (namely Burkina Faso, Ghana, Nigeria and Tanzania). </p>
Code and data for N Le et. al "Scalable and robust quantum computing on qubit arrays with fixed coupling"
<p>Simulation code and data used in N Le et. al "Scalable and robust quantum computing on qubit arrays with fixed coupling."</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.