Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,081
datasets available to search
ShareScore release 0.9.0
Dataset results
3,081 results for “array”
Experimental data for validation of a variational RANS level III flow model: water waves over an array of obstacles and Ogee weir flows
<p>Experimental dataset for the validation of a variational RANS level III flow model. The experimental data correspond to experiments on unsteady of water waves over an array of obstacles and steady curved flows over an Ogee weir. The experiments were conducted at the Hydraulics Laboratory at the Univeristy of Córdoba. </p>
Rye600K SNP Array 'Lo7' Map
<p>Mapping position of rye600K SNP array markers developed by Bauer., et al. 2017, on the reference chromosomal-scale rye reference genome 'Lo7'. Shared in the hope that it provides a valuable and easily-accessible resource for further studies in rye genomics.</p> <p><strong>Method:</strong></p> <p>Positional data of 600K SNP markers was obtained by mapping each of the 600843 SNP marker sequences to the rye reference genome ‘Lo7’ using NCBI blastn (v. 2.9.0+) function. Mapping position of SNPs were hereafter stringently filtered for I) complete SNP sequence alignment, and II) maximum of 1 mismatch to ensure an accurate positioning. </p> <p> </p> <p> </p>
DATASET: A low elevation imaging radar using a non-uniform coplanar receiver array for E~region observations
<p>Ionospheric Continuous-wave E region Bistatic Experimental Auroral Radar 3-Dimensional (ICEBEAR-3D) dataset for validation of the receiver antenna array reconfiguration, Suppressed-Spherical Wave Harmonic Transform (Suppressed-SWHT), and proper geometry for vertical interferometry using the geocentral angle.</p>
Dataset of image processing - High-throughput characterization of cortical microtubule arrays response to anisotropic tensile stress
<p>The data set contains the analysis data files from the image analysis workflow developed to quantify cortical microtubules rearrangements in the case of tensile stress (<a href="https://github.com/VergerLab/MT_Angle2Ablation_Workflow">https://github.com/VergerLab/MT_Angle2Ablation_Workflow</a>), generated form a specific dataset (https://doi.org/10.5878/17te-jg54). The files include the intermediary images processed at each step of the image analysis workflow in imageJ, the log files produced by the imageJ macro describing the input and the output images and the text files containing the quantified values. </p>
[Dataset] One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors, located in Graz, Austria
<p><strong>Highlights:</strong></p> <ul> <li>High-precision measurement data acquired within a scientific research project, using high-quality measurement equipment and implementing extensive data quality assurance measures.</li> <li>The dataset includes data from one full operational year in a 1-minute sampling rate, covering all seasons.</li> <li>Measured data channels include global, beam and diffuse irradiances in horizontal and collector plane. Heat transfer fluid properties were determined in a dedicated laboratory test.</li> <li>In addition to the measured data channels, calculated data channels, such as thermal power output, mass flow, fluid properties, solar incidence angle and shadowing masks are provided to facilitate further analysis.</li> <li>Uncertainties of data channels are provided based on data sheet specifications and GUM error propagation.</li> <li>The dataset refers to a real-scale application which is representative of typical large-scale solar thermal plant designs (flat plate collectors, common hydraulic layout).</li> <li>Additional information is provided in a "Data in Brief" journal article: <a href="https://doi.org/10.1016/j.dib.2023.109224">https://doi.org/10.1016/j.dib.2023.109224</a></li> </ul> <p> </p> <p><strong>Collector array description: </strong>The data is from a flat plate collector array with a total gross collector area of 516 m<sup>2</sup> (361 kW nominal thermal power). The array consists of four parallel collector rows with a common inlet and outlet manifold. Large-area flat-plate collectors from Arcon-Sunmark A/S are used in the plant. Collectors are all oriented towards the south (180°), have a tilt angle of 30° and a row spacing of 3.1 m. The collector array is part of a large-scale solar thermal plant located at Fernheizwerk Graz, Austria (latitude: 47.047294 N, longitude: 15.436366 E). The plant feeds into the local district heating network and is one of the largest Solar District Heating installations in Central Europe.</p> <p> </p> <p><strong>Data files:</strong></p> <ul> <li><strong>FHW_ArcS__main__2017.csv</strong> – This is the main dataset. It is advised to use this file for further analysis. The file contains the full time series of all measured and all calculated data channels and their (propagated) measurement uncertainty (53 data channels in total). Calculated data channels are derived from measured channels (see script make_data.py below) and have the suffix __calc in their channel names. Uncertainty information is given in terms of standard deviation of a normal distribution (suffix __std); some data channels are assumed to have no uncertainty (e.g., sun azimuth or shadowing).</li> <li><strong>FHW_ArcS__main__2017.parquet</strong> – Same as FHW_ArcS__main__2017.csv, but in parquet file format for smaller file size and improved performance when loading the dataset in software.</li> <li><strong>FHW_ArcS__parameters.json</strong> – Contains various metadata about the dataset, in both human and machine-readable format. Includes plant parameters, data channel descriptions, physical units, etc.</li> <li><strong>FHW_ArcS__raw__2017.csv </strong>– Dataset with time series of all measured data channels and their measurement uncertainty. The main dataset FHW_ArcS__main__2017.csv, which includes all calculated data channels, is a superset of this file.</li> </ul> <p> </p> <p><strong>Scripts: </strong></p> <ul> <li><strong>make_data.py</strong> – This Python script exposes the calculation process of the calculated data channels (suffix __calc), including error propagation. The main calculations are defined as functions in the module utils_data.py.</li> <li><strong>make_plots.py</strong> – This Python script, together with utils_plots.py, generates several figures based on the main dataset.</li> </ul> <p> </p> <p><strong>Data collection and preparation</strong>: AEE — Institute for Sustainable Technologies (AEE INTEC), Feldgasse 19, 8200 Gleisdorf, Austria; and SOLID Solar Energy Systems GmbH (SOLID), Am Pfangberg 117, 8045 Graz, Austria</p> <p> </p> <p><strong>Data owner</strong>: solar.nahwaerme.at Energiecontracting GmbH, Puchstrasse 85, 8020 Graz, Austria</p> <p> </p> <p><strong>Additional information</strong> is provided in a journal article in "Data in Brief", titled <a href="https://doi.org/10.1016/j.dib.2023.109224">"One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors in Graz, Austria"</a>.</p> <p> </p> <p><strong>Note: </strong>A Gitlab repository is associated with this dataset, intended as a companion to facilitate maintenance of the Python code that is provided along with the data. If you want to use or contribute to the code, please do so using the Gitlab project: <a href="https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017">https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017</a></p> <p> </p>
'Wind theft' from onshore wind turbine arrays: Sensitivity to wind farm parameterization and resolution
<p>Data and namelists from Pryor S.C., Shepherd T.J., Volker P., Hahmann A.N. and Barthelmie R.J.: ‘Wind theft’ from onshore wind turbine arrays: Sensitivity to wind farm parameterization and resolution. <em>Journal of Applied Meteorology and Climatology (doi:10.1175/JAMC-D-19-0235.1)</em></p>
JRC Phased Array Weather Radar Observational Data
<p>These datasets were observed by Phased Array Weather Radar from 0755 to 0810 JST on October 12, 2019. Data type is NetCDF. Data variables are radar reflectivity and Doppler velocity.</p>
DATA of "Resolving the 2D temporal evolution of subglacial water flow with dense seismic array observations."
<p>The data set contains all data presented in the paper: <strong>Observing the subglacial hydrology network and its dynamics with a dense seismic array</strong> published in PNAS ( <a href="https://doi.org/10.1073/pnas.2023757118">https://doi.org/10.1073/pnas.2023757118</a> )</p> <p>See our online presentation of this dataset: https://meetingorganizer.copernicus.org/EGU2020/EGU2020-10710.html.</p> <p>The present data and code concerns the source location obtained with matched-field-processing analysis and the hydraulic potential calculation (Shreve, R. L. Movement of Water in Glaciers. <em>J. Glaciol.</em> <strong>11</strong>, 205–214 (1972)).</p> <p>We perform source location over 1-sec long signal segment of the vertical component only. We filter the signal within the [3-7] Hz frequency range and coherently apply the MFP each 0.1 Hz within this range. To maximize our algorithm efficiency and minimize computational costs we use a gradient-based minimization algorithm (Nelder-Mead optimization) to converge to the best match between the trial and the observed phase delays rather than an exhaustive grid-search exploration. The convergence criterion is reached when the variance of values obtained over the last 5 iterations of the optimization is smaller than 1e<sup>-2</sup> with a maximum of 3000 iterations. Our 29 different starting points used for optimization are located 250 m below the glacier surface and they uniformly cover an area of 800 x 800 m<sup>2</sup> centered on the array. We set the initial velocity to 1800 m.sec <sup>-1</sup>. The 29 punctual locations found per signal segment (1 sec) after convergence are located all in the same place if a clear global convergence exists (i.e. high MFP output) or at up to 29 different locations if up to 29 local minima exist (i.e. low MFP output).</p> <p>Timeseries of physical quantities can be found here <a href="https://doi.org/10.5281/zenodo.3701520">https://doi.org/10.5281/zenodo.3701520</a></p> <p>Spatial observations acquired during the same period can be found here <a href="https://doi.org/10.5281/zenodo.3971815">https://doi.org/10.5281/zenodo.3971815</a></p> <p> </p> <p>The RESOLVE project has been supported by a grant from LabEx OSUG@2020 (Investissement d’avenir – ANR10LABX56) and by the IDEX Université Grenoble Alpes. Most of the computations presented in this paper were performed using the GRICAD infrastructure (https://gricad.univ-grenoble-alpes.fr), which is supported by Grenoble research communities, and with the CiGri tool (https://github.com/oar-team/cigri) developed by Gricad, Grid5000 (https://www.grid5000.fr) and LIG (<a href="https://www.liglab.fr/">https://www.liglab.fr/</a>).</p> <p> </p> <p>You can find more information on the method and seismic dataset used in this paper here: <a href="https://zenodo.org/deposit/5645545">https://zenodo.org/deposit/5645545</a></p>
Genome wide Illumina 450K array in AML patients with or without CEBPA mutation
<ul> <li>Num: Row Number</li> <li>Hybridization REF: CpG Reference ID</li> <li>Position: Position in hg19</li> <li>Gene: Entrez Gene Symbol</li> <li>Chrome: Chromosome Number</li> <li>p_vals: P value for t-test between patients with CEBPA and without CEBPA mutation</li> <li>mean_cebpa_mut: mean methylation score for patients with CEBPA mutation</li> <li>mean_no_cebpa_mut: mean methylation score for patients with CEBPA mutation </li> <li> <p>site_status: prediction status for CEBPA sites </p> </li> <li> <p>fdr: fdr p_value </p> </li> </ul>
Binaural room scanning files for a 56-channel circular loudspeaker array
<p>Binaural room scanning (BRS) files for the simulation of a 56-channel circular loudspeaker array with a diameter of 3m. The loudspeaker array setup is also suited to reproduce stereo or surround recordings.</p> <p>The BRS files allow for a dynamic binaural simulation of the actual loudspeaker array using the SoundScape Renderer [1]. With this you can listen to binaural simulations of stereo, surround, or even wave field synthesis (WFS) productions. For example, you can find music productions that can be used with the 56-channel array at [2].</p> <p>[1] http://spatialaudio.net/ssr/</p> <p>[2] http://dx.doi.org/10.14279/depositonce-5173</p>
Head-related impulse responses of a loudspeaker array
<p>Head-related impulse responses measured with a KEMAR dummy head of a loudspeaker array consisting of 13 loudspeakers. The dummy head was placed at three different positions, which allows to combine the measurements to an loudspeaker array consisting of 35 loudspeakers.</p> <p>The measurement equipment was exactly the same as in http://dx.doi.org/10.5281/zenodo.55418</p>
Binaural recordings of a real and binaural simulated circular loudspeaker array
<p>A KEMAR dummy head did binaural recordings of a noise signal played back over a circular loudspeaker array. The loudspeaker array had a diameter of 3m and consists of 56 loudspeakers. The real loudspeakers or a binaural simulation of them were used. For the latter a BRIR dataset had been recorded before with another dummy head. The KEMAR dummy head was wearing headphones all the time in order to allow for a direct comparison between the simulation and real setup.</p> <p>This dataset is used to create Fig. 4.9 in H. Wierstorf, Perceptual Assessment of of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014. See also https://github.com/hagenw/phd-thesis/tree/master/04_binaural_synthesis/fig4_09</p>
Schematic of the MoEDAL Timepix detector array (2012)
<p>A schematic of the MoEDAL Timepix Detector Array as of 2012 (not to scale), showing: a) Timepix detector F04-W0098 (sensor perpendicular to the beam); b) the LHCb (IP8) cavern wall; c) the LHC beam pipe; d) the LHCb VELO subdetector casing; e) Timepix detector F03-W0098 (sensor parallel to the beam).</p> <p>CERN@school DRN: CAS-PUB-MDL-000009-v1.0</p>
Supporting data for "Quantum simulation of a Fermi-Hubbard model using a semiconductor quantum dot array"
<p>Supporting data and analysis scripts for Fig. 3b of "Quantum simulation of a Fermi-Hubbard model using a semiconductor quantum dot array", ArXiv:1702.07511 (preprint) and 10.1038/nature23022 (publication)</p> <p>This dataset contains a readme file as well as three zipped folders that contain (1) raw data sets of all relevant measurements, as well as (2) matlab files to plot fitted data and the extracted parameters and (3) the code that uses the extracted parameters to plot the fan diagram.</p>
Testing strengths, limitations and biases of current Pulsar Timing Arrays detection analyses on realistic data
<p>In this project, we carried out an extensive investigation of the performance of current Pulsar Timing Arrays (PTA) analyses on simulated PTA datasets where we modeled the gravitational waves (GW) signal as the incoherent superposition of sinusoidal signals from a cosmic population of super-massive black hole binaries (SMBHBs). Here we publish the dataset referred to in the paper as the <em><strong>SMBHB_set</strong></em>: 100 realisations of PTA datasets with 25 pulsars and a GWB signal of nominal amplitude 2.4e-15. For each realisation, listed from 1001 to 1100, this repository contains the pulsars .par and .tim files, the mcmc chain (sampling over 66 parameters: 60 for pulsars intrinsic noise parameters and log amplitude and slope of a common red noise process), and the SMBHB details for that specific realisation of the GWB. The columns of the <em>MBHB_list.dat</em> files contain (from left to right): log10 MBH chirp mass (in solar masses in the source frame), mass ratio, source redshift, log10 of GW fundamental mode (n=2) in the observer frame, phi and theta angles in the sky, source inclination angle, source polarisation angle, source initial orbital phase, source initial direction of periastron and source initial eccentricity.</p> <p>If you make use of any of this data, please cite:<br><br></p> <div>@article{ refId0,</div> <div> author = {{Valtolina, Serena} and {Shaifullah, Golam} and {Samajdar, Anuradha} and {Sesana, Alberto}},</div> <div> title = {Testing strengths, limitations, and biases of current pulsar timing arrays’ detection analyses on realistic data},</div> <div> DOI= "10.1051/0004-6361/202348084",</div> <div> url= "https://doi.org/10.1051/0004-6361/202348084",</div> <div> journal = {A&A},</div> <div> year = 2024,</div> <div> volume = 683,</div> <div> pages = "A201",</div> <div>}</div>
STO crystal supported Pt nanodot arrays
<div> <div>Scanning Electron Microscopy (SEM) imaging of STO crystal supported Pt nanodot arrays performed with the high-resolution field-emission ThermoFisher FEI Nova Nano SEM 450 instrument operated at 2 kV using the through-lens detector (TLD) in secondary electron imaging mode.</div> <div> </div> </div>
Seismic source location with a match field processing approach during the RESOLVE dense seismic array experiment on the Glacier d'Argentiere
<p>This deposit contains the data set we used in our paper ‘<em>Dynamic imaging of glacier structures at high-resolution using source localization with a dense seismic array</em>’. The paper is in review for GRL and a preprint can be found here: <a href="http://dx.doi.org/10.1002/essoar.10507953.1">10.1002/essoar.10507953.1</a>.</p> <p>The dataset present here contains 34 files named ‘<strong>beam_15423_jd***.h5</strong>’. These files correspond to the output of the matched field processing for each day. They are in .h5 format and we provide a matlab code (<strong>read_MFP_data.m</strong>) to read these files. These files can be read with any other language since they are in . h5.</p> <p>In linux you can use <strong>h5dump –A filename.h5</strong> and you can see the content of each files.</p> <p> </p> <p>More information on how the MFP process is conducted can be found in on the <a href="https://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">website </a>dedicated to this aspect or on our paper. The whole procedure and associated codes is provided on the <a href="http://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/</a>.</p> <p>We also deliver with this deposit one day of seismic data <strong><a href="https://zenodo.org/api/files/873ccbe8-814d-4202-90ae-e115aab1942d/ZO_2018_121.h5?versionId=c59d2014-a6e1-43c5-96a6-91ee9a6b89ce">ZO_2018_121.h5 </a></strong>that can be used to test our MFP process. The data corresponds to the signal measured for 24 hours at each of the 98 sensors with a sampling rate of 500 Hz. More information on these seimsic signals can be found on our <a href="https://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">website </a>and the whole seimsic dataset can be found here <a href="https://seismology.resif.fr/networks/#/ZO__2018">https://seismology.resif.fr/networks/#/ZO__2018</a>. Detailed for downloading the dataset should be search on our website.</p> <p> </p> <p>Other dataset linked to this project are:</p> <ul> <li>Nanni, Ugo, Gimbert, Florent, Roux, Phillipe, & Lecointre, Albanne. (2020). DATA of "Resolving the 2D temporal evolution of subglacial water flow with dense seismic array observations." [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4024660">https://doi.org/10.5281/zenodo.4024660 </a></li> <li>Nanni, Gimbert, Roux, Helmstetter, Garambois, Lecointre, Walpersdorf, Jourdain, Langlais, Laarman, Lindner, Sergenat, Vincent, & Walter. (2020). DATA of the RESOLVE Project (https://resolve.osug.fr/) [Data set]. In Seismological Research Letters (Version v0). Zenodo. <a href="https://doi.org/10.5281/zenodo.3971815">https://doi.org/10.5281/zenodo.3971815 </a></li> </ul> <p>This dataset is also linked to two other study:</p> <p><em>Observing the subglacial hydrology network and its dynamics with a dense seismic array: </em></p> <p><a href="https://doi.org/10.1073/pnas.2023757118">https://doi.org/10.1073/pnas.2023757118</a></p> <p><em>A Multi‐Physics Experiment with a Temporary Dense Seismic Array on the Argentière Glacier, French Alps: The RESOLVE Project</em></p> <p><a href="https://doi.org/10.1785/0220200280">https://doi.org/10.1785/0220200280</a></p> <p> </p> <p> </p> <p> </p> <p> </p> <p>Do not hesitate to contact us if you would like to try this approach an another dataset.</p>
Remnants of the amalgamation of the East and West Cathaysia blocks revealed by a short-period dense seismic array
<p>This is the data used to support relevant research, including the data of the teleseismic receiver function with the Gaussian factor of 10.0, which can be used to facilitate further relevant research.</p>
Microphone array data for basic examples in Acoular
<p>This record contains microphone array data for use with <a href="http://acoular.org">Acoular </a>software.</p> <p>example_data.h5 contains a 1 second time history from a windtunnel measurement with 56 microphones. array_56.xml contains the microphone coordinates, and example_calib.xml the calibration factors.</p> <p>three_sources.h5 and two_sources.h5 contain synthetic measurement for a three and a two source scenario, respectively. array_64 contains the microphone coordinates for this case.</p> <p>three_sources.csv, three_sourcesv7.mat, and three_sourcesv73.mat contain the same data, but in CSV, MATLAB old and MATLAB new format.</p>
Reproducing nonlinear seismic response from in-situ soil dynamic parameters: application to the Delaney Park Downhole Array, Alaska
<p>The zip file named 'seismic data at DPDA.zip' contains all the seismic data used in our study.</p> <p>The DEEPSOIL file named 'ground response analysis at DPDA.dp' contains the numerical models used in our study.</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.