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3,081 results for “array”
Above-ground biomass and NDVI for Sensor Node Array, 2017 - ongoing.
Spatial and temporal variability characterizes virtually all ecosystems, with resource supply changing over the course of growing season and across years due to climate variation. To better understand spatial heterogeneity in ecological response across landscape positions, we established a 16-node sensor array within a 45-hectare catchment landscape that measures temporal variability of important biogeochemical and hydrological controls on ecosystem processes. The array was established at the Niwot Saddle catchment in order to accompany long term water quality and discharge records taken at the top and bottom of this catchment. The region forms an important ecological linkage between the terrestrial areas of the Niwot Ridge LTER and the aquatic component in the Green Lakes Valley. Over two years (2017 – 2018), above-ground biomass data were sampled adjacent to each of the 16 sensor nodes to characterize plant productivity. Beginning in 2023, Normalized Difference Vegetation Index (NDVI) was measured at each node.
Plant species composition for sensor network array, 2017 - ongoing.
Above-ground plant species cover was recorded for vegetation plots in the sensor network, starting in 2017. Cover was measured annually at peak biomass using a 100 point-intercept method.
Infrasound array data recorded in July-August, 2019, at Mt. Etna (Italy) during the VOSSIA field experiment
<p>We present infrasound data recorded by two infrasound arrays installed at Mt. Etna (Italy) within the framework of the VOSSIA (Volcanic emissions analysis through Seismic and Infrasound Advanced monitoring) project. VOSSIA was supported by the Trans-National Access component of the EUROVOLC project (European Network of Observatories and Research Infrastructures for Volcanology, EU Horizon 2020 Research Infrastructure Project grant No 731070).</p> <p>This data repository includes continuous raw waveforms recorded by two 6-element, small-aperture, infrasound arrays during July-August, 2019. The arrays, ENEA and ENCR, were installed on Mt. Etna in proximity of the summit Nord East and South East craters, respectively. ENEA was equipped with Chaparral M60 sensors (<a href="http://chaparralphysics.com/specs/specs_model60UHP.pdf">http://chaparralphysics.com/specs/specs_model60UHP.pdf</a>), while IST2018 microphones (<a href="https://doi.org/10.1016/j.jvolgeores.2019.106668">https://doi.org/10.1016/j.jvolgeores.2019.106668</a>) were installed at ENCR. Data at both arrays were recorded with a sampling frequency of 100 Hz and 24-bit resolution using DiGOS Datacube<sup>3</sup> digitizers (<a href="https://digos.eu/seismology-and-cubes/">https://digos.eu/seismology-and-cubes</a>).</p> <p>Waveform data are provided as day-long files in MSEED format (<a href="https://ds.iris.edu/ds/nodes/dmc/data/formats/">https://ds.iris.edu/ds/nodes/dmc/data/formats/</a>).</p> <p>We also provide metadata including:</p> <p>1) Station coordinates (.csv file station_coords.csv);</p> <p>2) Instrument_response.rar: Instrument response information in different formats (individual RESP files, SEED DATALESS, xlm DATALESS)</p>
Lagrangian Decomposition of the Meridional Heat Transport at 26.5N - Water Parcel Crossings of the RAPID 26.5N Array
<p>This dataset contains the initial and final positions and properties of Lagrangian trajectories evaluated using 5-day mean velocity and tracer fields output from the ORCA0083-N06 ocean sea-ice model hindcast (1958-2015). Numerical water parcels are initialised to sample the full-depth southward transport across the RAPID 26.5N array every month during 2004-2015. Water parcels are advected backwards-in-time using a bespoke version of TRACMASS v7.1 Lagrangian particle tracking tool which enables users to specify a custom domain using a mask netCDF file.</p><p>Particles are initialised on the first-available day of each month (based on the centre of the model 5-day mean field windows) between 2004 and 2015 (inclusive) before being advected backwards-in-time within the North Atlantic Ocean until any one of four termination conditions are met: (1) water parcels reach the RAPID 26.5N array, (2) water parcels reach the OSNAP (West or East) arrays in the subpolar North Atlantic, (3) water parcels reach either the English Channel or Gibraltar Strait, or (4) particles reach the maximum advection time of 25-years. The 25-year maximum advection time ensures that we adequately resolve the subtropical gyre circulation north to the RAPID 26.5N array. The pathway transporting dense North Atlantic Deep Water from the OSNAP arrays to RAPID at 26.5N is not fully resolved in this Lagrangian experiment since these water parcels transit on multi-decadal timescales.</p><p>The number of water parcels initialised in each model-grid cell scales with the total northward transport through that cell, such that the maximum possible transport conveyed by any single particle is 5.0 mSv (mSv == 10-3 Sv), enabling the calculation of robust Lagrangian statistics. In reality, the average. water parcel has an associated volume transport of 3.3 mSv which is conserved throughout its circulation.</p><p>Water parcel locations (converted to geographical coordinates) and properties (conservative temperature, absolute salinity, potential density [TEOS-10]) are output on every model-grid cell crossing. TRACMASS determines particle properties on grid-cell crossings by taking the average of the properties stored at the nearest two T-grid points. Here, we provide the initial and final locations and properties of all water parcels initialised from RAPID 26.5N.</p><p>All Lagrangian experiments were completed using the JASMIN High-Performance Computing facility (<a href="https://jasmin.ac.uk">https://jasmin.ac.uk</a>).</p><p><strong>For a complete description of the ORCA0083-N06 hindcast configuration see:</strong> Moat et al. (2016).</p><p><strong>For a complete description of TRACMASS v7.1 see</strong>: <a href="https://www.tracmass.org">https://www.tracmass.org</a></p>
Wind power plants layouts according to arbitrary reference points, Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array
<p><strong>If this dataset helps your research, please cite it and the papers further below (according to which OWPP you study).</strong></p> <p>This dataset contains the layouts of the Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array offshore wind power plants (OWPPs), which can be utilized in a variety of studies. </p> <p>The X and Y coordinates, in kilometers, were written according to arbitrary reference points. The positions of wind turbine generators (WTGs) and substations (SS) for the OWPPs came from the sources below.</p> <p><strong>Thanet</strong>: WTGs from [1] (page 7), SS based on [3] (page 9).<br><strong>West of Duddon Sands</strong>: WTGs from [2] (page 5), SS based on [3] (page 9).<br><strong>Ormonde</strong>: WTGs and SS from [4] (page 2).<br><strong>Westermost Rough</strong>: WTGs and SS from [5] (page 4).<br><strong>Horns Rev 1</strong>: WTGs and SS from [6] (page 6).<br><strong>Horns Rev 2</strong>: WTGs and SS from [7] (page 5).<br><strong>Anholt</strong>: WTGs and SS from [8] (page 3).<br><strong>London Array</strong>: WTGs from [9] (page 15), SS based on [10] (page 2).</p> <p>From the OWPPs' layout figures [1-9], I used Graph Grabber 2.0.2* to extract the data points. Then, based on visual inspection of layouts in [1-9], I utilized 2D projections to align WTGs that should be aligned. References [1], [2], and [9] do not provide the SS positions. Thus, I carefully overlapped the layouts with other layouts from [3] and [10] to approximate the SS locations.</p> <p>Regarding the arbitrary reference points for the coordinates, although the values in the X and Y axes differ from [1-9], note that the distances among the WTGs are the same from [1-9]. Furthermore, there are no axes in [1,5]. Instead, distances are given, which are enough to obtain the layout. Values in meters were converted to kilometers.</p> <p>As seen in the attachments, the coordinates can be obtained via either "h5" or "csv" files, which can be easily read by Matlab, Julia, and Python, among others. <strong>The name of the datasets in the "h5" file are</strong>: "Thanet", "WDS", "Ormonde", "WMR", "HornsRev1", "HornsRev2", "Anholt", and "LondonArray".</p> <p><strong>Except for the London Array OWPP, the first row in all data matrices represents the SS coordinates, whereas the subsequent rows represent the WTGs. London Array has 2 substations, thus the first and second rows represent their coordinates. In all matrices, the first and second columns are the X and Y coordinates, respectively.</strong></p> <p>*<a href="https://www.quintessa.org/software/downloads-and-demos/graph-grabber-2.0.2">Graph Grabber 2.0.2 | Downloads And Demos | Software | Quintessa Limited | Scientific and Mathematical Consultancy</a></p>
Driving a low critical current Josephson junction array with a mode-locked laser
<p>Data for article "Driving a low critical current Josephson junction array with a mode-locked laser".</p>
Evidence of dual Shapiro steps in a Josephson junction array
<p>QCodes type databases containing raw data associated with the paper "Evidence of dual Shapiro steps in a Josephson junctions array" by N. Crescini, S. Cailleaux et al. acquired in the Institut Neel, CNRS, Grenoble, France between January 2022 and June 2022.</p> <p>There are two databases: one contains the characterization of the sample without microwave pump and the other one contains the study of the sample under microwave irradiation.</p> <p>Two Jupyter notebooks (python 3.8.11) are provided to analyze the datasets contained in the databases and reproduce the results of the article.</p> <p>For any additional information please contact: nicolo.crescini@neel.cnrs.fr or samuel.cailleaux@neel.cnrs.fr</p> <p> </p>
The 2001 Hawaiian Ocean Mixing Experiment (HOME): Internal-tide Mode-1 Amplitude Data from the Southern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for mode-1 internal-waves derived<br>from the six paths of the southern HOME tomography array.</p>
The 2001 Hawaiian Ocean Mixing Experiment (HOME): Internal-tide Mode-1 Amplitude Data from the Northern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for mode-1 internal-waves derived<br>from the six paths of the northern HOME tomography array.</p>
The 2001 Hawaiian Ocean Mixing Experiment (HOME): High-frequency (>1cpd) Barotropic Current Data from the Northern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for barotropic currents derived from<br>three of the six paths of the northern HOME tomography array.</p>
The 2001 Hawaiian Ocean Mixing Experiment (HOME): High-frequency (>1cpd) Barotropic Current Data from the Southern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for barotropic currents derived from<br>the six paths of the southern HOME tomography array.</p>
Dataset for Clock drift corrections for large aperture ocean bottom seismometer arrays: application to the UPFLOW array in the mid-Atlantic Ocean
<p>Dataset from Clock drift corrections for large aperture ocean bottom seismometer arrays: application to the UPFLOW array in the mid-Atlantic Ocean DOI: 10.1093/gji/ggae354.</p> <p>This dataset includes the clock drift polynoms refered to jthe deployment date (jul day from 2022) and consecutive days up to the recovery date. Two types of formats.</p> <ol> <li>Txt files</li> <li>Python Pickle files with a Dictionary containing the NumPY polynom1D and additional information.</li> </ol>
Dataset related to the publication "Lasing in an Assembled Array of Silver Nanocubes"
<p>This archive contains the raw data used to draw the graphs published in the paper "Lasing in an Assembled Array of Silver Nanocubes". </p>
West Texas Lightning Mapping Array - KTaL 2015-2016
<p>West Texas Lightning Mapping Array data collected near Lubbock, Texas as part of the Kinematic Texture and Lightning (KTaL) field campaign in 2015-2016. Includes VHF source locations and clustered flashes and their physical properties. Data are a subset of the full record, corresponding to the storms studied in a forthcoming publication.</p>
The Thousand-Pulsar-Array program on MeerKAT -- IX. The time-averaged properties of the observed pulsar population: data set
<p>This archive contains pulsar data presented as part of the MNRAS paper: <em>"The Thousand-Pulsar-Array program on MeerKAT -- IX. The time-averaged properties of the observed pulsar population"</em>.</p> <p>Folded, time-averaged pulse profiles (4 Stokes parameters, 8 frequency channels, 1024 time bins across the period) of the 1271 pulsars listed in Table 1 of the MNRAS paper are included in the ar_files.zip. Ephemerides of these pulsars (as used in the MNRAS paper) are included in the eph_files.zip. The pulsar data are readable by the PSRCHIVE package, see e.g. van Straten et al., Astronomical Research and Technology 9, 237 (2012).</p> <p>Tables 1, 5, and 6 from the MNRAS paper are included in tables_files.zip as .csv files. The file column_descriptions.txt describes the quantities in columns of these tables.<br> </p>
Test experiments with distributed acoustic sensing and hydrophone arrays for locating underwater sounds.
<p>Whales and dolphins rely on sound for navigation and communication, making them an intriguing subject for studying language evolution. Traditional hydrophone arrays have been used to record their acoustic behavior, but optical fibers have emerged as a promising alternative. This study explores the use of distributed acoustic sensing (DAS), a technique that detects local stress in optical fibers, for underwater sound recording. An experiment was conducted in Lake Zurich, where a fiber-optic cable and a self-made hydrophone array were deployed. A test signal was broadcasted at various locations, and the resulting data was synchronized and consolidated into files. Analysis revealed distinct frequency responses in the DAS channels and provided insights into sound propagation in the lake. Challenges related to cable sensitivity, sample rate, and broadcast fidelity were identified. This dataset serves as a valuable resource for advancing acoustic sensing techniques in underwater environments, especially for studying marine mammal vocal behavior.</p>
CGGA mRNA-array_301 Expression Data
<p><strong>Abstract:</strong></p> <p>The Chinese Glioma Datasets (CGGA) are comprehensive and valuable collections of data related to glioma, a type of brain tumor, originating from Chinese patients. The CGGA is a data portal for the storage and interactive exploration of cross-omics data, including nearly 2000 primary and recurrent glioma samples. This dataset's gene expression profile was measured experimentally using Agilent-014850 Whole Human Genome Microarray. The Sample IDs serve as unique identifiers for each sample. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>z-normalization was performed on all the samples </p> <p><strong>Acknowledgments:</strong></p> <p>Zhao, Z., Zhang, KN., Wang, QW., et al. Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients (2021). Genomics, Proteomics & Bioinformatics 19(1):1-12.</p> <p>Fang, S., Liang, J., Qian, T., et al. (2017). Anatomic Location of Tumor Predicts the Accuracy of Motor Function Localization in Diffuse Lower-Grade Gliomas Involving the Hand Knob Area. AMERICAN JOURNAL OF NEURORADIOLOGY. 38(10): 1990-1997</p> <p>Wang, Y., Wang, Y., Fan, X., et al. (2017). Putamen involvement and survival outcomes in patients with insular low-grade gliomas. JOURNAL OF NEUROSURGERY. 126(6): 1788-1794.</p> <p> </p> <p><strong>U-BRITE last update: </strong>07/27/2023</p>
Above and below ground phenology for the Niwot Ridge sensor node array, 2021 - 2022.
The below-ground growing season often extends beyond the above-ground growing season in tundra ecosystems. However, we do not yet know where and when this occurs and whether these phenological asynchronies are driven by variation in local vegetation communities or by spatial variation in microclimate. We deployed root in-growth cores in 4 locations in Niwot Ridge’s sensor node array as part of a multi-site study of above- and below-ground tundra phenology.
Dataset for "InGaN Nanohole Arrays Coated by Lead Halide Perovskite Nanocrystals for Solid-State Lighting"
<p>In this work, we demonstrate efficient light downconversion via FRET in InGaN/GaN multiple quantum well (MQW) nanohole arrays, coated with green-emitting CsPbBr3 and FAPbBr3 nanocrystals (NCs) and near-infrared (IR) FAPbI3 NC overlayers for solid-state lighting. Patterning the InGaN MQW into nanohole arrays allows a minimum nitride−NC separation while increasing the heterointerfacial area, thus improving simultaneously the nonradiative and radiative transfer efficiencies. Detailed spectroscopic studies of steady-state and time-resolved photoluminescence indicate a significant reduction in the quantum well photoluminescent decay time in the presence of NCs, accompanied by a significant concurrent increase of the NC integrated emission, providing evidence of efficient light down-conversion mediated by FRET with efficiencies as high as ∼83 ± 6% in the green and ∼74 ± 5% in the near-IR.</p>
Associated dataset for "Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"
<p>The conducted instrumental and perceptual evaluation utilize the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. However, the provided execution configurations (see below) are probably not exactly in accordance with the latest ReTiSAR code base. Hence, the at the time employed code state should be used in order to exactly reproduce the rendering results in this data set. The frozen code state for this data set is available at:<br> <a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.ICASSP">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.ICASSP</a></p> <p>Download the rendering pipeline and follow the setup instructions! Use the here included Conda environment file when setting up the Python environment. In this way you should obtain exactly the same Python setup as utilized in the instrumental and perceptual evaluation in the publication:</p> <pre><code>conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code>source activate ReTiSAR_ICASSP_freeze</code></pre> <p>Directory "SNR":</p> <ul> <li>Tools for instrumental evaluation (Section 4)</li> <li>Shell script to capture input and output signals of rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all specified configurations</li> <li>Matlab script to analyse captured signal and generate system transfer plots (Figure 1 to Figure 3 and further configurations)</li> </ul> <p>Directory "Relative Output Levels":</p> <ul> <li>Tools for preparation of perceptual evaluation (Section 5)</li> <li>Shell script to capture rendered uniformly contributing noise signals for all specified configurations</li> <li>Matlab script to analyse and level align captured signals and generate plot result plot (Figure 4)</li> </ul> <p>Directory "Absolute Output Levels":</p> <ul> <li>Tools for specification of perceptual evaluation (Section 5)</li> <li>Shell script to capture reproduced uniformly contributing noise signals for all specified configurations</li> <li>Matlab script to analyse the calibrated captured signals yielding the average level in the ear signals of 58.2 dBSPL (Section 5.1)</li> </ul> <p>Files in base directory and directory "Study Results":</p> <ul> <li>Tools for perceptual evaluation / user study (Section 5)</li> <li>Matlab GUI to conduct perceptual user study (employ by executing "ICASSP_gui.m", respective ReTiSAR instances are started and remote controlled by the GUI, raw study results will be stored in "results" directory)</li> <li>Matlab script to "calculate_conclusion.m" to analyse the raw study results and generate individual and conclusive result plots (Figure 5, Figure 6 and more)</li> </ul>
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.