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864 results for “numerative”
A Numerical Study of Tropical Cyclone and Ocean Responses to Air-sea Momentum Flux at High Winds
<p>The simulation data output from FIO-AOW for tropical cyclone study</p>
Identifying conceptual neural responses to symbolic numerals
<p>The goal of measuring conceptual processing in numerical cognition is distanced by the possibility that neural responses to symbolic numerals are influenced by physical stimulus confounds. Here, we targeted conceptual responses to parity (even <em>vs. </em>odd), using electroencephalographic (EEG) frequency-tagging with a symmetry/asymmetry design. Arabic numerals (2–9) were presented at 7.5 Hz in 50-s sequences; odd and even numbers were alternated to target differential, "asymmetry" responses to parity at 3.75 Hz (7.5 Hz/2). Parity responses were probed with four different stimulus sets, increasing in intra-numeral stimulus variability, and with two control conditions comprised of non-conceptual numeral alternations. Significant asymmetry responses were found over the occipitotemporal cortex to all conditions, even for the arbitrary controls. The large physical-differences control condition elicited the largest response in the stimulus set with the lowest level of variability (1 font). Only in the stimulus set with the highest level of variability (20 drawn, colored exemplars/numeral) did the response to parity surpass both control conditions. These findings show that physical differences across small sets of Arabic numerals can strongly influence, and even account for, automatic brain responses. However, carefully designed control conditions and highly variable stimulus sets may be used towards identifying truly conceptual neural responses.</p>
numerical data to accompany "Strong asymmetry in near-fault ground velocity during an oblique strike-slip earthquake revealed by waveform particle motions and dynamic rupture simulations"
<p>This is the numerical data to accompany the paper "Strong asymmetry in near-fault ground velocity during an oblique strike-slip earthquake revealed by waveform particle motions and dynamic rupture simulations". Please refer to the README.txt file for information about the individual datasets and archive files. </p>
Numerical methods for neurobiological networks - Accompanying Data
<p>Data supporting the following tutorial</p> <p>https://github.com/danieleavitabile/numerical-analysis-mathematical-neuroscience</p>
Mixed Precision Optimization Method acceleration time and numerical simulation results
<p>The stored data are the speedup ratios of the MPFlow method and other acceleration algorithms, as well as the elastic wave response data obtained by numerical simulation using the MPFlow method.</p>
Raw numerical data and cleavage assay blot for Figures found in the article: "The ion channel Anoctamin 10/TMEM16K coordinates organ morphogenesis across scales in the urochordate notochord"
<p><span>In relation to our publication: "The ion channel Anoctamin 10/TMEM16K coordinates organ morphogenesis across scales in the urochordate notochord" we provide all the individual quantitative observations that underlie the data summarized in the figures and results of our paper. More specifically we provide the data for <span><span>Fig. 1G, H, I, P, Q; Fig. 2D-G, R-U; Fig. 3E-H; Fig. 4D-F, Q-S; Fig. 5D-G, K-N; R-U; Fig. 6D-F, S-U; Fig. 7G, H, J-T; Fig. 8G-I, M, N, S, X, Y; Fig. 9D-G, R, S; Fig. 10D-Q, K-R; Fig. 11F, G; Fig. S2T-X; Fig. S3G; Fig. S4K, O, P and Fig. S6E-J</span></span></span>.</p> <p>In addition we provide a non-annotated and an annotated version of the original, uncropped gel picture shown in Fig. S4A</p>
FIGURE 3 in Numerical taxonomy and genus-species identification of Czekanowskiales in China based on machine learning
FIGURE 3. Dendrogram of 35 species of the Czekanowskiales from China.
Numerical model-informed testbed for surface PM2.5 concentration over China and its estimates during 2013-2021
<p>This is an updated version of https://doi.org/10.5281/zenodo.11122294, the data provided in NetCDF format.</p> <p>In addition to the long-term PM2.5 dataset created from Li et al (2024), which can be used for health assessments and studying air pollution influences, here we also provide testbed data crucial for evaluating machine learning-based retrieval methods, especially in scenarios where no ground-truth data is available.<br>The testbed dataset includes all inputs and outputs following the physical model simulation, which naturally correlates with physical laws such as emissions, diffusion, advection, and deposition, representing typical conditions that any prediction method should meet. This data can be used to evaluate and compare methods using the same dataset, allowing for continuous improvement. Besides traditional cross-validation, the proposed testbed validation is highly recommended to examine a method’s predictive ability. We will continue updating the testbed data for other pollutants and with different resolutions and regions in future studies.</p>
Fig. 3 in Marine and freshwater taxa: some numerical trends
Fig. 3 - The interdependence of marine, amphibiotic and freshwater fauna at different taxa levels.
Experimental raw data and numerical model
<p>Experimental raw data and numerical models for the work of Siyuan Qiang et, al., (2023), which focuses on the relationship between the saturation exponents and pore water distribution.</p> <p>Run the main.m file for numerical simulation<br>The simulation schemes could be easily changed by updating the SIG1.txt file.</p> <p>.mat file:<br>variable 'SIP':measured SIP frequency magnitude and phase (in degree) in experiments<br>variable 'Sw':measured water saturation in experiments</p>
A trait database for Phytoplankton of temperate lakes: numerical file
<p>Phytoplankton is a choice ecological indicator of lake ecological quality. Microalgae composing this biological compartment display a large diversity of morphological, physiological and ecological features, which are useful for understanding the interactions they have with other biotic and abiotic elements. Such kind of information together with classical Utermöhl phytoplankton analyses are useful for lake management and basic science. We present here a reference database of morphological (cell and colony sizes, biovolumes, mobility apparatus, colony formation), functional (functional groups of Reynolds, Padisak and Kruk, zooplankton edibility groups), physiological (trophic capacities) and ecological traits (Brettum index values) of more than 1200 taxa often observed in temperate lakes. This database, maintained since the 70’s until nowadays is now realized open access. A scientific paper associated to this database presents an overview of its content.</p>
Observation data and numerical model files for river plumes in Pallanza Bay of Lake Maggiore, Italy
<p>Data includes observation data collected from three deployments in Pallanza Bay of Lake Maggiore, Italy. Observation data includes velocity and thermistor chain data from deployments in 2012 and 2014, which captured a series of river plume discharges from the Toce River. Also included are numerical model source files and scenario files for simulations of river plumes into Pallanza Bay. </p>
Lithosphere removal (delamination) following continental collision: shape and complexity of observables predicted from 3D numerical models
<p>Upload contains most essential data files from the manuscript. The raw data amount to several TB and cannot be uploaded.</p> <p><strong>1 Model data</strong></p> <p><em>1.1 Models covered</em></p> <p>Relatively frequent output is available for the reference model (<em>R</em> in paper, see Table S2), prefixed <em>dp10</em>. Limited output is uploaded for models with geometric variations (<em>OSM -> dp19, TSM->dp26</em>).</p> <p><em>1.2 Types of data</em></p> <p>Most files are visualisation files (ending *.<em>vtr</em>). These are intended for loading into the visualisation software Paraview. Files starting with <em>comp_</em> contain the lithological composition field; other visualisation files hold the fields of selected physical parameters. Note that as of the design of the study, the maximum file size allowed for loading into Paraview is ca. 2 GB. This limits the number of fields available according to model size - larger models (geometrical variations) hold therefore less fields.</p> <p>Binary files with ending *.<em>prn</em> are saved model states, from which programs can be rerun.</p> <p>Time information mapping step numbers to years is provided in <em>times...txt</em>.</p> <p> </p> <p><strong>2 Software for reproduction</strong></p> <p>The numerical code is not in the public domain, and its use is restricted. A pre-compiled binary is provided and allows reproduction of the main/reference model. We present the input files; however, these cannot be changed freely (equivalent to software distribution), and changes will lead to termination of the program.</p> <p><em>2.1 Requirements, preparation and use</em></p> <p>The code is compiled on CentOS with gcc 4.8.2. There is no library dependency, apart from the intrinsic OpenMP and glibc (>= 2.17). The following requirements <em>must</em> be satisfied in order to run it:</p> <ul> <li>Linux OS (tested on CentOS and Fedora)</li> <li>> 160 GB shared memory</li> </ul> <p>The following provisions are recommended:</p> <ul> <li>16 cores</li> <li>high bandwidth storage</li> <li>storage space <em>O</em>(TB)</li> </ul> <p>To prepare, simply unzip the provided snapshot <em>clsd_reproduce_gcc_CentOS.zip</em> in an appropriate directory (cluster). The input file <em>init.t3c</em> sets the initial conditions; the input file <em>mode.t3c </em>controls solver and file output. <em>file.t3c</em> is the pointer to the last output step; if a model snapshot is available as binary dump (*.prn file), setting the pointer to its number allows restarting.</p> <p>For normal usage, create initial condition with executable <em>in3mg</em>, and subsequently run <em>i3mg</em>. This will create snapshots and *.vtr visualisation files.</p> <p> </p> <p>High-performance computing facilities, runtime (months), proper software environment, and training in use of the code, or in the use of the visualisation software, are not provided.</p> <p> </p>
The effect of numerical aperture on quantitative use-wear studies and its implication on reproducibility [complement to Supplementary Material 2]
<p>3D micro surface data processed in ConfoMap v7.4.8633 (a derivative of MountainsMap Imaging Topography developed by Digital Surf, Besançon, France).</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
The effect of numerical aperture on quantitative use-wear studies and its implication on reproducibility [complement to Supplementary Material 4]
<p>Python script and results of the Bayesian Multi-factor ANOVA.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Model output: Unraveling the mechanisms that cause cyclic channel-shoal dynamics of ebb-tidal deltas: a numerical modeling study
<p>Output from model runs for Lenstra et al., “Unraveling the mechanisms that cause cyclic channel-shoal dynamics of ebb-tidal deltas: a modeling study”. The dataset contains two types of model output, namely (1) the default model runs and (2) the sensitity runs with waves+tides, waves only, and tides only.</p> <p>The files covering the default model runs contain:</p> <p>ModeledDays - time vector for the depth series [days]</p> <p>XT - east-west location of grid points high resolution domains [m]</p> <p>YT - north-south location of grid points high resolution domains [m]</p> <p>depth - depth series for the high resolution domains [m]</p> <p>XSea - east-west location of grid points outer sea domain [m]</p> <p>YSea - north-south location of grid points outer sea domain [m]</p> <p>depthSea - depth series for the outer sea domain [m]</p> <p>XBasin - east-west location of grid points basin domain [m]</p> <p>YBasin - north-south location of grid points basin domain [m]</p> <p>depthBasin - depth series for the basin domain [m]</p> <p>ST_Days - time vector for the sediment transport series [days]</p> <p>ST_Inlet - tidally-averaged total sediment transport through the inlet (positive seaward) [10<sup>6</sup> m<sup>3</sup>/year]</p> <p>ST_Up - tidally-averaged total sediment transport through the cross-section at the updrift coast (positive eastward) [10<sup>6</sup> m<sup>3</sup>/year]</p> <p>ST_Down - tidally-averaged total sediment transport through the cross-section at the downdrif coast (positive eastward) [10<sup>6</sup> m<sup>3</sup>/year]</p> <p>The files covering the sensitivity runs contain for the high resolution domains:</p> <p>XT - east-west location of grid points [m]</p> <p>YT - north-south location of grid points [m]</p> <p>TA_STX - east-west component of the tidally-averaged total sediment transport [m<sup>3</sup>/s/m]</p> <p>TA_STY - north-south component of the tidally-averaged total sediment transport [m<sup>3</sup>/s/m]</p> <p>TA_U - east-west component of the tidally-averaged flow velocities [m/s]</p> <p>TA_V - north-south component of the tidally-averagedf low velocities [m/s]</p> <p>M2 - amplitude of the semi-diurnal tidal flow velocities (not for wave only runs) [m/s] </p>
Numerical model code, input files and output data for publication "Rapid mixing and exchange of deep-ocean waters in an abyssal boundary current"
<p>Contains numerical model data (code, input files, selected output, matlab diagnostic routines) to supplement publication ``Rapid mixing and exchange of deep-ocean waters in an abyssal boundary current'', by Naveiro Garabato and co-authors. All numerical model data, including any errors, is the responsibility of Sonya Legg. This data set will allow reproduction of simulations, and reproduction of diagnostics shown in plots in the above-referenced paper.</p>
Non-spherical particles in optical tweezers: a numerical solution
<p>We present numerical methods for modeling the dynamics of arbitrarily shaped particles trapped within optical tweezers, which improve the predictive power of numerical simulations for practical use. We study the dependence of trapping on the shape and size of particles in a single continuous wave beam setup. We also consider the implications of different particle compositions, beam types and media. The major result of the study is that for different irregular particle shapes, a range of beam powers generally leads to trapping. The trapping power range depends on whether the particle can be characterized as elongated or flattened, and the range is also limited by Brownian forces.</p> <p>This dataset supplements the publication and contains inputs and processing scripts for usage of scadyn (https://www.github.com/jherrane/scadyn) software for solving dynamical response of arbitrarily shaped particles in electromagnetic fields. Also is contained the minimal dataset (per PLos ONE standards) to reproduce the results presented.</p>
Outputs of numerical simulation of irregular waves propagating over an idealized shoal under non-breaking conditions
<p>This dataset contains bathymetry files (.txt) and outputs files (.mat) of numerical simulation of irregular waves propagating over an idealized shoal under non-breaking conditions, conducted with SWASH model. The spatial and temporal resolution are 0.5 m and 0.1 s, respectively.</p>
Lower ionospheric disturbances due to geomagnetic storms of March & April 2023: inferred from VLF navigational signals and numerical simulations
<p>This is VLF data used in the research paper as per title</p>
ScienceDex guides
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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.