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2,650 results for “waves”
IODP Expedition 378 P-wave velocity caliper (section/discrete)
P-wave velocity data were measured on undisturbed section halves (JRSO-defined x-axis) and/or discrete cube and cylinder samples (x, y, or z-axis) using pairs of piezoelectric transducers mounted on a caliper system. Report includes P-wave velocity in x, y, and/or z-direction, caliper separation, traveltime between transucers, and first arrival picks.
IODP Expedition 378 P-wave velocity logger (whole round)
P-wave velocity data were measured on whole-round sections on the Whole-Round Multisensor Logger (WRMSL) using pairs of piezoelectric transducers mounted on a caliper system. Measurements may be affected by degassing of pore fluid and microfracturing during core recovery. Report includes P-wave velocity in x-y plane and distance and traveltime between transducers.
Figures datasets for "Wave momentum shaping for moving objects in heterogeneous and dynamic media"
<p>Source data for Figures used in the manuscript "Wave momentum shaping for moving objects in heterogeneous and dynamic media".</p>
Dataset for wave-by-wave particle tracking in the surf zone
<p>This dataset comprises 49 trajectories with 3D positions of buoyant tracers reconstructed from stereo camera imaging using two cameras and a standard triangulation process. The data is extracted from stereo image frames of the sea surface, captured at a rate of 30 frames per second. These images were collected between 15:13:00 and 17:18:59 UTC on September 7, 2019, near the island of Sylt, Germany.</p> <div>An appropriate coordinate system was used to better represent the tracer position time series for the analysis of tracers position, velocity and acceleration. </div> <div>Details about the coordinate system are provided in the associated manuscript and supporting information as well as in </div> <div><a title="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL095722" href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL095722" target="_blank" rel="noopener noreferrer">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL095722</a></div> <div> </div> <div>The dataset is organized into four columns: the frame time [µs]; the X coordinate [m], defining the horizontal position with the origin at the base of Pole 2 (see above referenced paper) and oriented shoreward; the Y coordinate [m], denoting the transverse position perpendicular to the direction of wave propagation ; </div> <div>and the Z coordinate [m], specifying the vertical position with the axis oriented upward.</div> <p>These coordinates were obtained using a triangulation algorithm and adjusted using a coordinate system transformation to yield a precise, physically meaningful representation of the trajectories. The data is provided in .mat (MATLAB) format</p>
IODP Expedition 367 P-wave velocity logger (whole round)
P-wave velocity data were measured on whole-round sections on the Whole-Round Multisensor Logger (WRMSL) using pairs of piezoelectric transducers mounted on a caliper system. Measurements may be affected by degassing of pore fluid and microfracturing during core recovery. Report includes P-wave velocity in x-y plane and distance and traveltime between transducers.
IODP Expedition 367 P-wave velocity caliper (section)
P-wave velocity data were measured on undisturbed section halves (JRSO-defined x-axis) and/or discrete cube and cylinder samples (x, y, or z-axis) using pairs of piezoelectric transducers mounted on a caliper system. Report includes P-wave velocity in x, y, and/or z-direction, caliper separation, traveltime between transucers, and first arrival picks.
IODP Expedition 367 P-wave velocity caliper (discrete)
P-wave velocity data were measured on undisturbed section halves (JRSO-defined x-axis) and/or discrete cube and cylinder samples (x, y, or z-axis) using pairs of piezoelectric transducers mounted on a caliper system. Report includes P-wave velocity in x, y, and/or z-direction, caliper separation, traveltime between transucers, and first arrival picks.
IODP Expedition 367 P-wave velocity bayonet (section)
P-wave velocity data were measured on undisturbed section halves using pairs of piezoelectric transducers mounted in bayonets that are inserted into soft sediment along the JRSO-defined y-axis and/or z-axis. Report includes P-wave velocity in y and/or z direction, bayonet separation, traveltime between transducers, and first arrival picks.
COQTEL dataset: Corrosion Quantification Through Extended use of Lamb waves
<p><span>Corrosion is a major threat in the aeronautic industry, both in terms of safety and cost. Ultrasonic Lamb Waves (LW) appear to be very efficient for corrosion monitoring and can be made cost effective and versatile when emitted and received by a sparse array of piezoelectric elements (PZT). A LW solution relying on a sparse PZT array and allowing to monitor corrosion pit growth on stainless 316L grade steel plate is here used to collect data during a controlled corrosion experiment. Experimentally, the corrosion pit size is electrochemically controlled by both the imposed electrical potential and the injection of a corrosive NaCl solution through a capillary located at the desired pit location. In parallel, the corrosion pit growth is monitored in-situ every 10 seconds by sending and measuring LW using a sparse array of 4 PZTs bonded to the back of the steel plate enduring corrosion. Two independent experiments were achieved in order to assess the repeatability of the proposed approach. If embedded in aeronautical structure, such an approach could be a versatile and cost-effective alternative to actual non-destructive maintenance procedures that are time and manpower consuming. This dataset can thus ease the development of associated SHM algorithms and methodologies and help filling the gap actually existing between research and industry in that domain.</span> This dataset has been used for the article "<span>In-situ monitoring of µm-sized electrochemically generated corrosion pits using Lamb Waves managed by a sparse array of piezoelectric transducers" published in open access in the "Ultrasonics" peer reviewed journal by the same authors as the dataset.<br></span></p>
Data used in the manuscript: "Influence of coastal vegetation on the 2004 tsunami wave impact in west Aceh"
<p>The data set presented accompanies the study by Laso Bayas et al. (2011) “Influence of coastal vegetation on the 2004 tsunami wave impact in west Aceh”. The data set contains all the observed (not transformed) variables used in the above mentioned study. The accompanying text file describes each of the variables included. A total of 180 transects were employed for the Laso Bayas et al (2011) study. The variables were further standardized and simplified to use them into the statistical models described in the paper.</p>
Adriatic Sea wind-wave climate years 1981-2010 and 2021-2050 (RCP4.5 and RCP8.5)
<p>Adriatic Sea mean annual 50th, 90th, 95th and 99th percentiles of the significant wave height (Hs) from WAVEWATCH III v6.07 (2 km) forced with 1-hour ERA5 wind fields statistically scaled to QQ-match COSMO-CLM fields (available at https://doi.org/10.5281/zenodo.6021380).</p> <p>Reference periods:</p> <p>1) Historical climate: years 1981-2010</p> <p>2) Future climate: years 2021-2050 (IPCC scenario RCP4.5 and RCP8.5)</p>
Himawari 8 band 8 derived product for Lamb wave analysis
<p>Data from geostationary satellite Himawari 8 are processed for the analysis of Lamb waves that were generated by the eruption of Hunga Tonga-Hunga Haʻapai in Tonga on 15 January 2022. Himawari 8/9 gridded data are distributed by the Center for Environmental Remote Sensing (CEReS), Chiba University, Japan. The second time derivatives of band 8 thermal infrared images are stored. The used band was changed from version 1.</p> <p>The data format is NetCDF. The file name represents the date of the middle image used to generate each file (changed from version 2).</p>
Data for: Probing electron and hole co-localization by resonant four-wave mixing spectroscopy in the extreme-ultraviolet
<p>Data for: Probing electron and hole co-localization by resonant four-wave mixing spectroscopy in the extreme-ultraviolet</p>
Adriatic Sea wind and wave time series years 1981-2010 and 2021-2050 (RCP4.5 and RCP8.5)
<p>Wind and wave time series for 27 stations in the Adriatic Sea.</p> <p>Variables:</p> <p>- 10-m height wind speed (wnd) and wind direction (wnddir) from 1-hour ERA5 fields (25 km) statistically scaled to QQ-match COSMO-CLM fields (8 km)</p> <p>- significant wave height (hs) and peak wave period (tp) from WAVEWATCH III v6.07 (2 km) forced with the scaled ERA5 wind fields</p> <p>Reference periods:</p> <p>- Historical climate: years 1981-2010</p> <p>- Future climate: years 2021-2050 (IPCC scenario RCP4.5 and RCP8.5)</p>
Statistical characterization of Andalusian wave climate for several combinations of Global Climate Models and Regional Climate Models and periods 2026 - 2045 and 2081 - 2100.
<p>The following text is an extract of the extended abstract entitled "<strong>Parametric Characterization of Wave Climate along the Andalusian Coast for Non-Stationary Stochastic Simulation</strong>" whose authors are Manuel Cobos, Pedro Magaña, Pedro Otiñar and Asunción Baquerizo, and that was included in proceedings of <em>39th IAHR World Congress</em> where this dataset is included.</p> <p><em>Processed data comes from PIMA Adapta Costas project (Ramírez et al., 2019), in particular, from projections of maritime climate for 2026-2045 and 2081-2100. Sea climate contains, among other information, time series of the significant wave height (H<sub>s</sub>) obtained for several combinations of GCM-RCM projections of EUR-11 for the RCP 8.5. GCM-RCM combinations ACCE, CMCC, CNRM, GFDL, HADG, IPSL, MIRO with a 0.1 degrees grid were used for the Atlantic facade while CNRM, HADG, IPSL, MIRO, MEDC, MPIE, ESM2, EART models with 1/11 degrees were used for the Mediterranean one. A total of 210 locations were analyzed, 54 at the Atlantic facade and 156 at the Mediterranean one (Figure 1). The data was bias adjusted using the Empirical Quantile Mapping (Déqué et al., 2007; Michelangeli et al., 2009). Information of the significant wave height and the dependence between the values at a given time with previous values with a VAR(q) model is already available. </em></p> <p><em>At each location, the methodology of Lira-Loarca et al. (2021) was applied, using the software described in Cobos et al. (2022a). More precisely, for every GCM-RCM (hereinafter, model n for n = 1, .., N where N = 7 for Atlantic data and N = 8 for the Mediterranean data), a non-stationary marginal distribution of H<sub>s</sub>, , assuming that the year was the largest periodicity of the climate, was fitted to data using a lognormal model for the central part and two generalized Pareto distribution for the lower and upper tails, as in Solari and Losada (2011). The non- stationarity is considered by assuming a decomposition of the parameters of the distribution and of the percentiles of the common end points of the interval into a trigonometric truncated expansion.</em></p> <p><em>In addition, the coefficients of the matrix, C<sub>n</sub>, of a VAR(q) model with q up to 92 hours were estimated. The ensemble multi-model characteristics of the data were obtained from the compound distributions and the weighted averaged matrix coefficients. </em></p> <p><em>Soon, the results of the peak period (T<sub>p</sub>) and mean incoming wave direction (ϑ<sub>m</sub>) and the coefficients of the multivariate VAR model will also be included.</em></p> <p> </p> <p> </p>
Datasets for "The Venturia inaequalis effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins "
<p>Datasets for preprint entitled "The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi"</p> <p><strong>1) ViAnnotation.gff3</strong><br> Gene annotation of <em>Venturia inaequalis</em> MNH120 (<a href="https://genome.jgi.doe.gov/Venin1/Venin1.home.html">https://genome.jgi.doe.gov/Venin1/Venin1.home.html</a>) generated as part of the study "The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi". </p> <p>Gene reannotation was performed to include genes that would have been missed in the previous annotation by Deng et al. (2017), especially those genes encoding putative effector proteins, which are difficult to predict. For this purpose, we used a three-step approach. In the first step, coding sequences (CDSs) from <em>V. inaequalis</em> isolate 05/172, which were predicted as part of a previous study by Passey et al. (2018) (<a href="https://journals.asm.org/doi/full/10.1128/MRA.01062-18">https://journals.asm.org/doi/full/10.1128/MRA.01062-18</a>), were downloaded from the National Center for Biotechnology Information (<a href="https://www.ncbi.nlm.nih.gov/nuccore/QFBF00000000.1/">https://www.ncbi.nlm.nih.gov/nuccore/QFBF00000000.1/</a>) and mapped to the MNH120 genome using GMAP v2021-02-22. In the second step, RNA-seq reads from one biological replicate representing each <em>in planta</em> time point of <em>Malus domestica</em> infection by <em>V. inaequalis </em>(12 hour post-inoculation [hpi], 24 hpi, 2 days post-inoculation [dpi], 3 dpi, 5 dpi, 7 dpi), as well as one time point representing growth of the fungus in culture, were mapped to the MNH120 genome using HISAT2 v2.2.1. Then, a genome-guided <em>de novo</em> transcriptome assembly was performed using Trinity v2.12.0 and likely CDSs were identified using Transdecoder v5.5.0 (<a href="https://github.com/TransDecoder/TransDecoder">https://github.com/TransDecoder/TransDecoder</a>) in conjunction with a minimum open frame (ORF) length of 50 amino acids. Finally, in the third step, all annotations were visualized in Geneious v9.05, together with the previous annotation from Deng et al. (2017), and a manual curation was performed to create a consensus prediction. Note: this reannotation was generated with the aim of identifying as many genes as possible, and as a result, it contains many spurious genes. </p> <p><strong>2) Protein_sequences_ViAnnotation.fasta</strong></p> <p><strong>3) ECs_Families_AlphaFold.zip</strong></p> <p>This dataset is made up of predicted protein tertiary structures representing the main member of each up-regulated <em>V. inaequalis</em> effector candidate family. Structures were predicted using Alphafold with the ColabFold server (<a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n">https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n</a>). In cases where the effector candidate had less than 30 proteins with amino acid sequence similarity in the NCBI database, a custom multiple sequence alignment (MSA) was generated and used as input for AlphaFold2. Here, mature protein sequences were used.</p> <p><strong>4) singletons_AlphaFold_OpenSourceCASP14.zip</strong></p> <p>This dataset set is made up of predicted protein tertiary structures representing up-regulated<em> V. inaequalis</em> singleton effector candidates. Structures were predicted using AlphaFold (<a href="https://github.com/deepmind/alphafold">https://github.com/deepmind/alphafold</a>) open source code v2.0.1 and v2.1.0, with pre-set casp14, max_template_date: 2020-05-14. Mature protein sequences were used as input. </p> <p><strong>5) ECs_Avrs_phytopathogens_AlphaFold.zip</strong></p> <p>Predicted tertiary structures of avirulence (Avr) proteins or candidate Avr proteins from other fungal pathogens included in the "The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi" study. These structures were predicted using Alphafold with the ColabFold server (<a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n">https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n</a>). Mature protein sequences were used as input. </p> <p>If you have any questions about the datasets, please contact us.<br> Mercedes Rocafort: <a href="mailto:m.rocafort.ferrer@massey.ac.nz">m.rocafort.ferrer@massey.ac.nz</a><br> Carl Mesarich: <a href="mailto:c.mesarich@massey.ac.nz">c.mesarich@massey.ac.nz</a></p>
Data and code used in "Satellite magnetic data reveal interannual waves in Earth's core"
<p>Eigen mode solutions and code to obtain them for the results presented in <a href="https://doi.org/10.1073/pnas.2115258119">Satellite magnetic data reveal interannual waves in Earth's core</a>. The package uses the freely available code <a href="https://github.com/fgerick/Mire.jl">Mire.jl</a>.</p> <p><strong>Prerequisites</strong></p> <p>Installed python3 with matplotlib ≥v2.1, cmocean and cartopy. A working Julia ≥v1.7.</p> <p><strong>Run</strong></p> <p>In the project folder run</p> <pre><code>julia --project=.</code></pre> <p><br> Then, from within the Julia REPL run</p> <pre><code>]instantiate</code></pre> <p>at first time, to install all dependencies.</p> <p>After that, to compute all plots, run</p> <pre><code>using QGMCSat allfigs()</code></pre> <p>They're automatically saved in the "figs" subfolder of the repository.</p> <p>If loading QGMCSat fails, due to a missing cartopy or cmocean in the python version. Run (within Julia)<br> </p> <pre><code>ENV["PYTHON"] = "python" #this should point to the python version that has cartopy installed ]build PyCall</code></pre> <p><br> To calculate all data, run</p> <pre><code>using QGMCSat calculate_data()</code></pre> <p>This will take several hours/days depending on the machine (needs enough memory).</p> <p>Individual data can be accessed directly through the .jld2 files from Julia. You can check out the individual figure functions to get an idea where which data is stored.</p> <p>If there are any issues or questions, please don't hesitate to get in touch!</p>
Dataset for "Effects of Fracture Connectivity on Rayleigh Wave Dispersion"
<p>The scripts on the main directory reproduce figures 3 to 11 in the paper. The dataset is separated into two folders which should be extracted to the directory of the scripts, frac_dist and parrot_output. frac_dist contains .mat files with the fracture distribution of samples and parrot_output contains the results from the upscaling procedure (Favino et al., 2020). A summary of each code is provided in the readme.</p>
Model results and observation data in Zhang et al. modeling of wave interference at Ocean Beach, CA
<p>The dataset contains modeling results and observation data supporting the manuscript of Phase-resolved modeling of wave interference and its effects on nearshore circulation in a large ebb shoal-beach system by Yu Zhang, Fengyan Shi, Jim Kirby, Xi Feng.</p>
Metadata on EUbOPEN multiplex chemogenomic compound screen, wave 1
<p>This is the metadata about EUbOPEN multiplex chemogenomic compound screen, wave 1. The corresponding image data is found at <a href="https://www.ebi.ac.uk/biostudies/studies/S-BIAD145">https://www.ebi.ac.uk/biostudies/studies/S-BIAD145</a>.</p> <p>To compile the metadata Excel file into filelists, please use the Python scripts at: <a href="https://doi.org/10.5281/zenodo.6325622">https://doi.org/10.5281/zenodo.6325622</a>.</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.