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9,882 results for “Scale”
NOAA Open Data Dissemination: Petabyte scale Earth System Data in the Cloud
<p>NOAA Open Data Dissemination (NODD) makes NOAA environmental data publicly and freely available on Amazon Web Services (AWS), Microsoft Azure (Azure), and Google Cloud Platform (GCP). These data can be accessed by anyone with an internet connection and span key datasets across the Earth system including satellite imagery, radar, weather models and observations, ocean databases, and climate data records. NODD works closely with NOAA stakeholders to make these data available consistently and performantly, often with near-real-time latency. Since its inception, NODD has grown to provide public access to more than 24PB of NOAA data and can support billions of requests and petabytes of access daily. NODD growth is driven by use; stake- holders routinely access more than 5PB of NODD data every month. NODD continues to grow to support open petabyte-scale Earth system data science in the cloud by investing in onboarding additional NOAA data, performant data formats, and streaming data platforms. Here, we document how this pro- gram works with a focus on data provenance, discuss the key datasets available through NODD, show how these data are being used, and highlight ways these data can be accessed with the goal of accelerating use of NOAA resources in the cloud.</p>
Data and codes for An atomic-scale multi-qubit platform
<p>All the source data, codes and raw images used in main text and supplementary information of "<strong>An atomic-scale multi-qubit platform</strong>" are supplied in the file. </p>
Open Data for publication Influence of the Time Scale on the Reaction Mechanism of CO Oxidation over a Au/TiO2 Catalyst
<p>The file contains the origianl raw data for publication:</p> <p>Influence of the Time Scale on the Reaction Mechanism of CO Oxidation over a Au/TiO2 Catalyst, Angewandte Chemie, 2023</p> <p>The data contains the IR spectra of the CO2 signal, the IR spectra at different temepratures (modulation experiments) and the IR spectra for different modulation periods at 50 deg C (modulation experiments), as described in the publication.</p>
Control of the electron dynamics in solid-state high harmonic generation on ultrafast time scales by a polarization-skewed laser pulse
<p>Dataset of the publication “Control of the electron dynamics in solid-state high harmonic generation on ultrafast time scales by a polarization-skewed laser pulse”, by X. Song, S. Yang, G. Wang, J. Lin, L. Wang, T. Meier, and W. Yang, published in Optics Express 31, 18862 (2023) , <a href="https://doi.org/10.1364/OE.491418">https://doi.org/10.1364/OE.491418</a> .<br> The zip file includes a brief description, the data on which the plot of figures 1 – 3 are based, and matlab figure files.</p>
PTVD: A Large-Scale Plot-Oriented Multimodal Dataset Based on Television Dramas
<p>Art forms such as movies and television (TV) dramas are reflections of the real world, which have attracted much attention from the multimodal learning community recently. However, existing corpora in this domain share three limitations: <strong>i.</strong> annotated in a scene-oriented fashion, they ignore the coherence within plots; <strong>ii.</strong> their text lacks empathy and seldom mentions situational context; <strong>iii.</strong> their video clips fail to cover long-form relationship due to short duration. To address these fundamental issues, using 1,106 TV drama episodes and 24,875 informative plot-focused sentences written by professionals, with the help of 449 human annotators, we constructed PTVD, the first plot-oriented multimodal dataset in the cinema domain.</p>
Oxonium ion scanning mass spectrometry for large-scale plasma glycoproteomics
<p>Protein glycosylation — a complex and heterogeneous post-translational modification frequently dysregulated in disease — has been difficult to analyse at scale. Here we report a data-independent-acquisition technique for the large-scale mass-spectrometric quantification of glycopeptides in plasma samples. The technique, which we named ‘OxoScan-MS’, identifies oxonium ions as glycopeptide fragments and exploits a sliding-quadrupole dimension to generate comprehensive, untargeted oxonium ion maps of precursor masses assigned to fragment ions from non-enriched plasma samples. By applying OxoScan-MS to quantify 1,002 glycopeptide features in the plasma glycoproteomes from COVID-19 patients and healthy controls, we found that severe COVID-19 induces differential glycosylation in IgA, haptoglobin, transferrin and other disease-relevant plasma glycoproteins. OxoScan-MS may allow for the quantitative mapping of glycoproteomes at the scale of hundreds to thousands of samples.</p>
Supporting data for: Whole genome sequencing reveals fine-scale environment associated divergence near the range limits of a temperate reef fish
<p>Environmental variation is increasingly recognized as an important driver of diversity in marine species despite the lack of physical barriers to dispersal and the presence of pelagic stages in many taxa. A robust understanding of the genomic and ecological processes involved in structuring populations is lacking for most marine species, often hindering management and conservation action. Cunner (<em>Tautogolabrus adspersus</em>), is a temperate reef fish with both pelagic early life history stages and strong site-associated homing as adults; the species is also of interest for use as a cleaner fish in salmonid aquaculture in Atlantic Canada. We aimed to characterize genomic and geographic differentiation of cunner in the Northwest Atlantic. To achieve this, a chromosome-level genome assembly for cunner was produced and used to characterize spatial population structure throughout Atlantic Canada using whole genome resequencing. The genome assembly spanned 0.72 Gbp and 24 chromosomes; whole genome resequencing of 803 individuals from 20 locations from Newfoundland to New Jersey identified approximately 11 million genetic variants. Principal component analysis revealed four regional Atlantic Canadian groups. Pairwise F<sub>ST</sub> and selection scans revealed signals of differentiation and selection at discrete genomic regions, including adjacent peaks on chromosome 10 across multiple pairwise comparisons (<em>i.e.</em>, F<sub>ST</sub> 0.5–0.75). Redundancy analysis suggested association of environmental variables related to benthic temperature and oxygen range with genomic structure. Results suggest regional scale diversity in this temperate reef fish and can directly inform the collection and translocation of cunner for aquaculture applications and the conservation of wild populations throughout the Northwest Atlantic.</p>
When 'good' is not good enough: a retrospective Rasch analysis study of the Berg Balance Scale for persons with Multiple Sclerosis
<p>Raw data associated with the scientific publication "When ‘good’ is not good enough: a retrospective Rasch analysis study of the Berg Balance Scale for persons with Multiple Sclerosis".</p>
The appendix for evaluating the impact of large-scale nucleic acid testing and home quarantine on COVID-19 prevention and control: a dynamic modeling approach
<p><strong>Appendix I</strong>: The program of evaluating the impact of large-scale nucleic acid testing and home quarantine on COVID-19 prevention and control: a dynamic modeling approach</p> <p><strong>Appendix II</strong>: The sensitivity analysis of evaluating the impact of large-scale nucleic acid testing and home quarantine on COVID-19 prevention and control: a dynamic modeling approach</p>
Data produced by the study "Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality in Africa".
<p>This is the processed data used in the manuscript entitled "Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality in Africa".</p> <p> </p>
Data for: Sub-zero temperatures and large-scale weather patterns induce tooth damage in Icelandic arctic foxes
<p><span>Tooth damage in carnivores can reflect shifts in both diet and feeding habits, and in large carnivores it is associated with increased bone consumption. Variation in tooth condition in Icelandic arctic foxes, a mesocarnivore, was recorded from 854 individual foxes spanning 29 years. We hypothesized that annual climatic variations, which can influence food abundance and accessibility, will influence tooth condition by causing dietary shifts toward less edible prey. We examined tooth condition in relation to four climatic predictors: mean annual winter temperature, indices of both the El Ni</span><span>ñ</span><span>o anomaly and North Atlantic subpolar gyre (SPG), and the number of rain-on-snow days (ROS). We found</span> unequivocal evidence for a strong effect of annual climate on tooth condition. <span>Teeth of Icelandic foxes were in better condition</span> <span>when </span>winter temperature<span>s were higher</span>, <span>when the </span><span>SPG</span><span> was </span>more positive, and <span>when the </span>number of <span>ROS was low. We also found </span>a substantial subregional effect with foxes from northeastern Iceland having lower tooth <span>damage</span> than those <span>from</span> <span>two</span> western sites. <span>Contradicting our original hypothesis that foxes</span> from northeastern Iceland<span>, </span>where <span>foxes are known to scavenge on </span>large mammal remains<span> (e.g., sheep and horses),</span> would show the highest tooth <span>damage, we suggest that</span> western <span>coastal </span>sites exhibited greater tooth <span>damage because</span> cold <span>winter</span> <span>temperatures </span>lower<span>ed</span> the availability of seabirds<span>,</span> causing a shift in diet toward <span>abrasive </span>marine subsidies <span>(e.g., bivalves) and </span>frozen beach wrack. <span>O</span>ur study shows that monitoring tooth breakage and wear can be a useful tool <span>for</span> evaluat<span>ing</span> the impact of climate on carnivore populations <span>and that climate change may influence the condition and fitness of carnivores in complex and potentially conflicting ways</span>.</p>
Supplementary material 3 from: Mas H, Santoiemma G, Lencina JL, Gallego D, Pérez-Laorga E, Ruzzier E, Rassati D (2023) Investigating beetle communities in and around entry points can improve surveillance at national and international scale. NeoBiota 85: 145-165. https://doi.org/10.3897/neobiota.85.103904
Area of Spain and position where baited cross-traps were set up both inside and outside the selected ports of entry
Supplementary material 1 from: Mas H, Santoiemma G, Lencina JL, Gallego D, Pérez-Laorga E, Ruzzier E, Rassati D (2023) Investigating beetle communities in and around entry points can improve surveillance at national and international scale. NeoBiota 85: 145-165. https://doi.org/10.3897/neobiota.85.103904
Number, position (port area vs. surrounding area), city, and geographic coordinates for each of the 14 traps used during this study
FIGURE 9. Diplazium zanzibaricum.—A. Young shoot showing scales.— B in Synoptic Revision of Diplazium (Athyriaceae) in Rwanda with description of Diplazium cyamudongoense sp. nov.
FIGURE 9. Diplazium zanzibaricum.—A. Young shoot showing scales.— B. Detail of stipes with scales.—C. Pinnules showing sori. Photographs: E.Fischer, Gahinga. Scale bars: A. 5 cm; B, C. 1 cm.
Small-scale Layered Structures at ICB
<p>This data set, published for the manuscript titled "Small-scale Layered Structures at the Inner Core Boundary," comprises PcP and PKiKP observations obtained from the China National Seismic Network (CNSN). These observations were utilized in this study to constrain the structure of the Inner Core Boundary (ICB) beneath Northeast Asia.</p>
Properties of whistler waves' ducting in plasmas with systems of small-scale density depletions. Simulation results.
<p><strong>Properties of whistler waves' ducting in plasmas with systems of small-scale density depletions. </strong><strong>Simulation results.</strong></p> <p>The results of numerical FDTD simulations for the paper entitled "Properties of Whistler Waves’ Ducting in Plasmas With Systems of Small-scale Density Depletions" by Zudin I.Yu. et al.</p> <p>All files stored here are vectors or matrices coefficients presented in the text format and space symbol separated. "loadtxt" function from "numpy" library can be used for the data loading by a Python user. Each folder corresponds to a figure with the same number. The data stored in "data.zip" archive сontaining the following folders.</p> <p><strong>Fig2 folder</strong></p> <p>All files are the solutions of the dispersion equations wrote in the paper. Each of file consist in two columns. First column is the density depletion width</p> <p>(in meters), and second column is wavenumber <span class="math-tex">\(p\)</span></p> <ul> <li>"even_0.dat", "even_1.dat" ... are the fist, the second etc. even modes of the plain depletion.</li> <li>"odd_0.dat", "odd_1.dat" ... are the fist, the second etc. odd modes of the plain depletion.</li> <li>"m=0_0.dat", "m=0_1.dat" ... are the fist, the second etc. brunches of the modes of the cylindrical depletion with azimuth number m=0.</li> <li>"m=+1_0.dat", "m=+1_1.dat" ... are the fist, second the etc. modes of the cylindrical depletion with azimuth number m=1.</li> <li> "m=-1_0.dat", "m=-1_1.dat" ... are the fist, second the etc. modes of the cylindrical depletion with azimuth number m=-1.</li> </ul> <p><strong>Fig3 folder</strong></p> <p>All files are the dependences of normalized localization scale <span class="math-tex">\(\delta / d\)</span> from <span class="math-tex">\(d\)</span>. Each of file consist in two columns. The first one is the density depletion width <span class="math-tex">\(d\)</span> (in meters), and the second column is <span class="math-tex">\(p\)</span>.The files is named in such a manner as in Figure2 folder.</p> <p><strong>Fig5 folder</strong></p> <p>"Ex.dat", "Ey.dat" and so on are the amplitudes of electric and magnetic field components presented in Figure #5 of the paper. "Ne.dat" is simulated density profile at z=60 km, as in above the file consists in two columns. The first is the</p> <p>coordinate and the second is the density. "xgrid.dat" and "zgrid.dat" are Cartesian coordinates of the grid's nodes used for field representation. So Ex(x[i],z[j])=Ex[j,i]</p> <ul> <li>units of plasma density is "m(-3)"</li> <li>units of electrical field components is "mkV/m"</li> <li>units of magnetic field components is "pT"</li> <li>units of Cartesian coordinates is "km"</li> </ul> <p>The grid used for the data presentation is less detailed than that used for actual simulation.</p> <p><strong>Fig6 folder</strong></p> <p>The same description as for Fig5 folder.</p> <p><strong>Fig7 folder</strong></p> <p>"subdecameter.Sz.dat", "subdecameter.Sx.dat", "subhectometer.Sz.dat", and "subhectometer.Sz.dat" are the Pointing vector projections calculated in the cases of subdecameter and subhectometer depletions. "xgrid.dat", "zgrid.dat" are the coordinates of the grid's nodes. "subdecameter.Ne.dat" and "subhectometer.Ne.dat" is is simulated density profiles of systems of subdecameter and subhectometer depletions. The files ogranized in such mannar as in Figure5 folder. The grid used for the data presentation is less detailed than that used for actual simulation.</p> <ul> <li>units of plasma density is "m(-3)"</li> <li>units of energy flux is "mkW/m(2)"</li> <li>units of Cartesian coordinates is "km"</li> </ul> <p><strong>Fig8 folder</strong></p> <p>"subdecameter.Sz.dat", "subdecameter.Sz.dat" are the Pointing vector projections calculated in the cases of subdecameter and subhectometer depletions. "subhectometer.Ex.dat", and "subhectometer.Ex.dat" are the amplitudes of the electric field component. "xgrid.dat", "zgrid.dat" are the coordinates of the grid's nodes. "subdecameter.Ne.dat" and "subhectometer.Ne.dat" is is simulated density profiles of systems of subdecameter and subhectometer depletions. The files organized in such manner as in Figure5 folder. The grid used for the data presentation is less detailed than that used for actual simulation.</p> <ul> <li>units of electrical field components is "mkV/m"</li> <li>units of plasma density is "m(-3)"</li> <li>units of energy flux is "mkW/m(-2)"</li> <li>units of Cartesian coordinates is "km"</li> </ul> <p><strong>Fig9 folder</strong></p> <p>"subhectometer.Ex.dat", "subhectometer.Ex.dat" etc. are the profiles along <span class="math-tex">\(x\)</span> of the components of the electric field and the plasma density. The files organized in such a manner. First column is coordinates values, the second one is the field component or density value.</p> <ul> <li>units of plasma density is "m(-3)"</li> <li>units of electrical field components is "mkV/m"</li> <li>units of Cartesian coordinates is "km"</li> </ul> <p><strong>Fig10 folder</strong></p> <p>"subhectometer.Ex.dat", "subhectometer.Ex.dat" are the 2d-spectrums of the <span class="math-tex">\(E_x\)</span> complex amplitude in the cases of subhectometer and subdecameter depletions. "kx_grid.dat" and "kz_grid.dat" are the Cartesian coordinates of the grid's nodes used for spectrums representation.</p> <ul> <li>units of electrical field components is "mkV/m km(2)"</li> <li>units of Cartesian coordinates is "km(-1)"</li> </ul> <p><strong>Fig11 folders</strong></p> <p>"subhectometer.Ex.dat", "subhectometer.Ex.dat", and "smoothed.Ex.dat" are the 1d spectrums presented on the Figure #11 the cases of subhectometer, subdecameter and smoothed depletions.</p> <ul> <li>units of electrical field components is "mkV/m km"</li> <li>units of Cartesian coordinates is "km(-1)"</li> </ul> <p><strong>Fig12 folders</strong></p> <p>"subhectometer.Ex.dat", "subhectometer.Ex.dat", and "smoothed.Ex.dat" are the 1d spectrums presented on the Figure #12 the cases of subhectometer, subdecameter and smoothed depletions.</p> <ul> <li>units of electrical field components is "mkV/m km"</li> <li>units of Cartesian coordinates is "km(-1)"</li> </ul> <p> </p>
Regenerating tropical forests are consistently affected by biological invasions across spatial scales
<p>Here we present our raw data (obtained via fieldwork) and all our analysis from the manuscript entitled “Regenerating tropical forests are consistently affected by biological invasions across spatial scales”. This manuscript is available as a preprint (<a href="https://doi.org/10.32942/X2SC76">https://doi.org/10.32942/X2SC76</a>) and as a published paper in Biological Invasions (https://doi.org/10.1007/s10530-024-03257-x). </p> <p>We investigated the effects of white-popinac (<em>Leucaena leucocephala</em> (Lam.) de Wit) invasions on regenerating areas of Atlantic Forest, considering local and regional scales, and beta-diversity.</p> <p>To reproduce the results, please see instructions in the READ_ME.txt file. </p>
Interpreting Cis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics
<p>Results and code to replicate analysis in "Interpreting Cis-Regulatory Interactions from<br> Large-Scale Deep Neural Networks for Genomics" by Toneyan and Koo.</p>
Using a Web Map Service to map Little Ice Age glacier extents at regional scales
<p>Extending the record of glacier area changes into the past improves our understanding of climate change impacts. Although analogue maps showing historic glacier extents are abundant, digital outlines from before the satellite era are sparse as the digitisation of moraines and trimlines on freely available satellite images is challenging. With the now available very high-resolution images provided by Web Map Services (WMS), new doors are open for the precise digitisation. Here, we used the ESRI WMS to digitise Little Ice Age (LIA) glacier extents and present area changes since the LIA in four selected regions along with a detailed uncertainty analysis. We used modern glacier outlines as a starting point and additionally consulted Sentinel-2 images, the ArcticDEM and historic maps for interpretation. Dating records from the literature allowed calculating area change rates. In total, 493 LIA glaciers (4640 km2, now 891 ice bodies with 3590 km2) were digitised, yielding relative area changes of −20% (−0.14% a−1), −15% (−0.10% a−1), −26% (−0.16% a−1) and −61% (−0.19% a−1) for Alaska, Baffin Island, Novaya Zemlya and the tropics, respectively. The ESRI WMS images are a great asset to precisely map moraines and trimlines, but information about the timing of the related extents requires further sources.</p>
1D-1V Vlasov-Poisson Simulations of Mutual Impedance Experiments in the presence of small-scale and large-scale plasma inhomogeneities - PART 1
<p>=====================================================================================================<br> Author : L. Bucciantini<br> Date : 05/07/2023<br> Laboratory : CNRS-LPC2E, Orléans (France)<br> =====================================================================================================</p> <p>Dear reader, we thank you for your interest in our dataset. In this document, we describe what you will<br> find in it.</p> <p>In case you need help with using the dataset, or if you are interested in mutual impedance experiments,<br> do not hesitate to contact our team in Orléans (pierre.henri@cnrs-orleans.fr, pierre.henri@oca.eu)</p> <p>=====================================================================================================<br> Topic:<br> This dataset contains the outputs of numerical simulations performed to assess<br> the impact of plasma inhomogeneities on the diagnostic performance<br> of mutual impedance experiments.</p> <p><br> Numerical model:<br> The outputs are obtained from a numerical model based on the solution of the 1D-1V Vlasov-Poisson<br> system of equations. The scheme used to solve the model is the one developed<br> by Mangeney et al. (2002), A Numerical Scheme for the Integration of the Vlasov-Maxwell System of Equations. Journal of Computational Physics, (doi: https://doi.org/10.1006/jcph.2002.7071).<br> The 1D-1V Vlasov-Poisson version of this model is described in Henri, et al. (2010), Vlasov-Poisson<br> simulations of electrostatic parametric instability for localized Langmuir wave packets in the solar wind,<br> Journal of Geophysical Research (Space Physics), 115, 6106 (2010)<br> -----------------------------------------------------------------------------------------------------<br> What is inside the dataset:</p> <p>The dataset is composed of 7 different mutual impedance measurements, each corresponding to one<br> directory (see below). The measurements (i.e. directory) correspond to small-scale plasma inhomogeneities at different<br> positions with respect to the mutual impedance antennas, or to a large-scale plasma inhomogeneity.</p> <p>Each directory contains a number of folders. Each folder corresponds to the emission of a signal at<br> different frequency.<br> -----------------------------------------------------------------------------------------------------<br> List of directories:</p> <p>(Note : each directory corresponds to one mutual impedance measurement)</p> <p>L : These outputs correspond to one mutual impedance measurement in<br> small antenna emission amplitude, which corresponds to a linear<br> plasma response to the emission.</p> <p>s_xx : Each of these outputs corresponds to one mutual impedance measurement in correspondance of<br> a small-scale plasma inhomogeneity at xx Debye lengths of distance from the emitting antenna<br> </p> <p>-----------------------------------------------------------------------------------------------------<br> List of folders inside the directories:</p> <p>Folders begin with the name "000" and have increasing index. Inside the same directory, each folder<br> represents a different simulation used to build the same mutual impedance measurement.</p> <p>------------------------------------------------------------------------------------------------------<br> List of files inside the folders:</p> <p>density_e.npz : electron density inside the box, in function of time (tempo)</p> <p>density_p.npz : ion density inside the box, in function of time (tempo)</p> <p>E.npz : electric field in the box, in function of time (tempo)</p> <p>qrho.npz : electric potential in the box, in function of time (tempo)</p> <p>qrho_imposed.npz : electric charge imposed at the emitting antennas, in function of time (tempo)</p> <p>tempo.npz : time-vector for density, electric field, electric potential and charge vectors</p> <p>TEST_Luca.dat : parameters describing the characteristics of the simulated plasma box</p> <p><br> [<br> Note : the previous files can be opened as follows</p> <p>import numpy as np</p> <p>vector_file_name = np.load('file_name.npz') # Use these for the .npz files<br> characteristics_of_the_box = np.genfromtxt('TEST_Luca.dat',skip_header=1)</p> <p>]</p> <p><br> -------------------------------------------------------------------------------------------------------<br> Characteristics of the simulated plasma box (TEST_Luca.dat)</p> <p>nx : amount of spatial grid points</p> <p>xl : physical size of the spatial box, expressed in Debye length</p> <p>tt_w : time resolution for ion and electron density, electric field, electric potential and charge</p> <p>rap_m : ion-to-electron mass ratio</p> <p>R_p : ion-to-electron temperature ratio</p> <p>dt : time step used to evolve in time the numerical simulation</p> <p>emission : emission frequency</p> <p>power : amplitude of the electric charge imposed at the emitting antennas</p> <p><br> (Note : all parameters not listed here but present inside the TEST_Luca.dat file correspond to additional<br> functionalities of the model. For the use of this dataset, they can be discarded.)</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </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.