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5,805 results for “Data model”
Determinant Quantum Monte Carlo data for the Hubbard model on the half filled square lattice, on a (U,B)-grid
<p>Data generated with QUEST 1.4.9. For documentation see these two homepages:<br> Original homepage: http://quest.ucdavis.edu/<br> Newest version available at: https://code.google.com/archive/p/quest-qmc/</p> <p>The simulations are done at half filling on a square lattice, with the following parameters:</p> <ul> <li>Lattice sizes: 4x4, 6x6, 8x8, 10x10, 12x12, periodic boundary conditions</li> <li>Trotter discretizations: 0.1 and 0.2</li> <li>Inverse temperature beta = 10.0</li> <li>48 values for the on-site interaction U from 0.0 to 10.0</li> <li>48 values for the magnetic field (in z-direction) B from 0.0 to 4.0</li> <li>10000 warmup sweeps, 30000 measurement sweeps</li> </ul> <p>The following data from equal time measurements are available:</p> <ul> <li>Charge-Charge Correlation (next neighbors)</li> <li>Greens Function (n.n.)</li> <li>Magnetization</li> <li>Double Occupancy</li> <li>Kinetic Energy</li> <li>Total Energy</li> <li>Spin-Spin Correlation (n.n.)</li> <li>Spin-Spin Correlation (only ZZ) (n.n.)</li> <li>Ferromagnetic Structure Factor (ZZ)</li> <li>Antiferromagnetic Structure Factor (ZZ)</li> </ul> <p>The data are available as a hdf5 archive. The python script 'extract.py' illustrates the access with h5py. Relevant QUEST input parameters are provided in the group 'parameters' within the archive.</p> <p>All calculated quantities are averaged over multiple consecutive simulations, which is why the data is not presented in the usual QUEST output. This was necessary due to limited walltime on the used supercomputer.</p> <p>The authors acknowledge the North-German Supercomputing Alliance (HLRN) for providing computing resources via project number hbp00046 that have contributed to these results.</p>
Tutorial Weather Data Cutouts for PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a>.</p> <p><strong>It contains</strong> alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.</p> <p><strong>Not all data dependencies</strong> are shipped with the <a href="https://github.com/PyPSA/PyPSA-eur">code repository</a>, since git is not suited for handling large changing files. Instead we provide separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-eur.readthedocs.io/en/latest/installation.html">documentation</a>.</p> <p>The provided lightweight <strong>cutouts </strong>are spatiotemporal subsets of the German weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset for March 2013 to be used for the <a href="https://pypsa-eur.readthedocs.io/en/latest/tutorial.html">PyPSA-Eur tutorial</a>. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>).</p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul>
Pol III modeling data and scripts using the Integrative Modeling Platform
<p>This repository contains the data obtained running the Integrative modeling platform with crosslinks and cryoem data, on the RNA Pol III system.</p>
Logistics Transport Label Data - 'Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts'
<p>Example dataset described in ICMLA2019 Paper 'Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts' (Dörr, Brandt, Meyer, Pouls).</p>
Data and R-Scripts for: Value of crowd-based water level class observations for hydrological model calibration
<p>This dataset corresponds to the study<br> "Value of crowd-based water level class observations for hydrological model calibration"<br> submitted to Water Resources Research in August 2019.</p> <p>Please use the R-Scripts in ascending numbers and adapt the paths to where you stored the files.<br> The helpfunctions.R will be used by some of the scripts and you might<br> want to adapt a path in line 356 for it to be used correctly with the scripts 8a and 8b.</p> <p>The parameter ranges used for the HBV calibration can be found in the "Parameters and parameter ranges.pdf"</p> <p>If you do not wish to calibrate the model, and just perform some statistics<br> start with script 7 and use the<br> - CrossValidation_stats_all.txt in the LUT Tables folder which contains<br> all model performances.<br> - CrossValidation_stats_WP1.txt contains also results of the upper benchmark<br> (only those labelled with no error and hourly).<br> - RandomParamPerformance_Validation.txt contains the results of the random parameters<br> (lower benchmark).<br> - The folders Validation Results and Calibration Results contain the files in HBV-format after the model<br> calibration and validatin were completed. The results of the Calibration and Validation files are also summarized<br> in the aforementioned txt-files within script 6 -HBV CrossValidation.R.<br> Please be aware that for the study only the catchments Murg, Guerbe, Mentue, and Verzasca were used!</p> <p><br> If you run into trouble using the data please contact simon.etter[at]outlook.com.</p> <p>Co-authors are:<br> Prof. Dr. Jan Seibert - jan.seibert[at]geo.uzh.ch<br> Dr. Ilja (H.J.) van Meerveld - ilja.vanmeerveld[at]geo.uzh.ch<br> Barbara Strobl - barbara.strobl[at]geo.uzh.ch</p>
Language modeling data for Swahili
<p>The Swahili dataset developed specifically for language modeling task. The dataset contains 28,000 unique words with 6.84M, 970k, and 2M words for the train, valid and test partitions respectively which represent the ratio 80:10:10. The entire dataset is lowercased, has no punctuation marks and, the start and end of sentence markers have been incorporated to facilitate easy tokenization during language modeling. The train partition is the largest in order to support unsupervised learning of word representations while the hyper-parameters are adjusted based on the performance on the valid partition before evaluating the language model on the test partition.</p>
Evaluation data used in "An innovative STEM outreach model (OH-Kids) to foster the next generation of geoscientists, engineers, and technologists"
<p>This repository contains all data of the evaluation questionnaire used to assess modifications in pupils’ perceptions of same water resources concepts and science and scientist resulting from the application of OH-Kids outreach model in six Mexican primary schools (n=344 pupils).</p>
UAS-SfM data from Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA
<p>Data for:</p> <p>Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA<br>Sean Reilly 1, Matthew L. Clark 2, Lika Loechler 2, Jack Spillane 2, Melina Kozanitas 3, Paris Krause 4, David Ackerly 3, Lisa Patrick Bentley 4, and Imma Oliveras Menor 1,5</p> <p>1 Environmental Change Institute, University of Oxford, Oxford OX1 3QY, UK<br>2 Center for Interdisciplinary Geospatial Analysis, Department of Geography, Environment, and Planning, Sonoma State University, Rohnert Park, CA 94928, USA<br>3 Departments of Integrative Biology and Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA<br>4 Department of Biology, Sonoma State University, Rohnert Park, CA 94928, USA<br>5 AMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université de Montpellier, Montpellier, France</p> <p>Study abstract:</p> <p>There is a pressing need for well-informed management to reduce wildfire hazard and restore fire’s beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California.</p> <p>Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69 – 0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49 – 0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring.</p> <p>These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs. </p> <p>Published in Remote Sensing of Environment</p> <p><br>Contents:</p> <p>This repository contains multispectral UAS-SfM data from four sites around California, USA:<br>jcksn: Jackson Demonstration State Forest<br>ltr: LaTour Demonstration State Forest<br>ppwd: Pepperwood Preserve<br>sdlmtn: Saddle Mountain Open Space Preserve</p> <p>Data were collected during a series of campaigns:<br>c1: Pepperwood, 2019-09-01 to 2019-10-15<br>c3: Jackson, 2020-06-15 to 2020-07-02<br>c4: LaTour, 2020-07-07 to 2020-07-17<br>c6: Saddle Mountain, 2020-08-04 to 2020-08-09<br>c9: Jackson, 2021-07-08 to 2021-07-12</p> <p>Data are included in three formats:<br>raw: Raw outputs from Pix4D (spectral and las)<br>reg_grnd, reg_cnpy: Las files with merged multispectral data and classified ground, registered to ALS using either ground points (grnd) or, in cases with insufficient ground points for registration, to the canopy (cnpy)<br>hnrm: Height normalized las files, normalization performed using ALS terrain model</p> <p>File naming structure:<br>site_campaign_flightzone_uas_processedstate</p> <p>See accompanying paper for methods on data collection and processing</p> <p>Data are grouped into zipped folder by product type</p> <p>Funding:</p> <p>Funding for this research was supported by CAL FIRE Forest Health and Forest Legacy (8GG18806) and California State University, Agricultural Research Institute (20-01-106) awards to L.P.B and M.L.C. S.R. was funded by the Rhodes Trust and through the University of Oxford Environmental Change Institute Small Grant Scheme. Pepperwood ground data collection was supported by funding from the Gordon and Betty Moore Foundation and National Science Foundation grants 1754475 and 1835086.</p> <p>Citation:</p> <div> <div>Reilly, S., Clark, M.L., Loechler, L., Spillane, J., Kozanitas, M., Krause, P., Ackerly, D., Bentley, L.P., Menor, I.O., 2024. Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA. Remote Sensing of Environment 312, 114310. <a href="https://doi.org/10.1016/j.rse.2024.114310">https://doi.org/10.1016/j.rse.2024.114310</a></div> </div> <p> </p> <p> </p>
Figure 2. Maximum likelihood tree from 16S rRNA data under the best-fitting model T92 in Notes on the distribution and biology of northern brown shrimp Farfantepenaeus aztecus (Ives, 1891) in the eastern Mediterranean
Figure 2. Maximum likelihood tree from 16S rRNA data under the best-fitting model T92 + G. Numbers above branches indicate bootstrap values among 1000 replicates. Branches without bootstrap numbers mean that the bootstrap values are below 50%.
Ensemble Learning of Catchment-Wise Optimized LSTMs Enhances Regional Rainfall-Runoff modelling - Case Study: Basque Country, Spain - Data
<div> <div>This data and results are for paper: "Ensemble Learning of Catchment-Wise Optimized LSTMs Enhances Regional Rainfall-Runoff modelling - Case Study: Basque Country, Spain" by Hosseini et al. 2024 (Preprint - Under review J.Hydro 2024) Available at SSRN: <a href="https://ssrn.com/abstract=4918782" target="_blank" rel="noopener">https://ssrn.com/abstract=4918782</a></div> <div> </div> </div>
Time Variable Ionospheric Electric Field Model (TiVIE) light data v 1.0
<p>This is the TiVIE light data to accompany TiVIE model v 1.0 produced by Maria-Theresia Walach, Lancaster University for the publication Walach, M.-T., and Grocott, A. (submitted 2024). </p>
Data produced and used in the publication "Modelling chemical advection during magma ascent" by Dominguez et al., 2024.
<p>Data produced by the numerical models used for the study titled "Modelling chemical advection during magma ascent", by Dominguez et al., 2024. The code used to produce the data is available at https://zenodo.org/records/12624639.</p> <p>Each file contains the data of one numerical model store as HDF5, with 4 timesteps and the initial conditions.</p>
Supplementary data for "Collision-induced absorptions by pure CO2 in the infrared: New measurements in the 1150–4500 cm−1 spectral range and empirical modeling for applications" published in Icarus. https://doi.org/10.1016/j.icarus.2024.116265
<p>Supplementary data for the paper entitled ""Collision-induced absorptions by pure CO2 in the infrared: New measurements in the 1150–4500 cm−1 spectral range and empirical modeling for applications" published in <em>Icarus</em>. https://doi.org/10.1016/j.icarus.2024.116265</p> <p>These data files contain the coefficients needed to compute the shape of a pure CO2 CIA band at a given temperature (see Eq. (3)) of the paper https://doi.org/10.1016/j.icarus.2024.116265</p> <p>"CIA_shape_coeff.dat" involves a CIA band shape without dimer signatures </p> <p>"CIA_dimer_shape_coeff.dat" involves a CIA band shape with dimer signatures </p> <p>"CIA_Fermi_doublet_shape_coeff.dat" involves the Fermi doublet band shape</p> <p>First column is wavenumber in cm-1, the rest of the columns contain the coefficients.</p>
Data: Testing the mating system model of parasite complex life cycle evolution reveals demographically driven mixed mating
<p>Abstract: Many parasite species use multiple host species to complete development; however, empirical tests of models that seek to understand factors impacting evolutionary changes or maintenance of host number in parasite life cycles are scarce. Specifically, Brown et al.’s (2001) mating system model, which posits multi-host life cycles are an adaptation to prevent inbreeding in hermaphroditic parasites and thus, preclude inbreeding depression, remains untested. The model assumes loss of a host results in parasite inbreeding and predicts host loss can only evolve if there is no parasite inbreeding depression. <a name="_Hlk169780726"></a>We provide the first empirical tests of this model using a novel approach we developed for assessing inbreeding depression from field-collected, parasite samples. The method compares genetically-based, selfing-rate estimates to a demographic-based selfing rate, which was derived from the closed mating system experienced by endoparasites. Results from the hermaphroditic trematode <em>Alloglossidium renale</em>, which has a derived 2-host life cycle, supported both the assumption and prediction of the mating system model as this highly inbred species had no indication of inbreeding depression. Additionally, comparisons of genetic and demographic selfing rates revealed <a name="_Hlk169781073"></a>a mixed mating system that could be explained completely by the parasite’s demography, i.e., its infection intensities.</p>
Data from: Strategies of resource sharing in clonal plants: A conceptual model and an example of contrasting strategies in two closely related species
<p>These experimental data were collected to quantify amount of C and N translocated between mother and daughter ramets of two stoloniferous species. Data includes concentrations of 13-C and 15-N in plants samples originating from pulse-chase labelling, absolute amounts of the labels present, as well as dry mass of the samples. Details are described in the relevant paper.</p> <p> </p>
Surrogate waveform model data for black hole binary systems computed in point-particle black hole perturbation theory
<p>This repository contains all publicly available surrogate data for gravitational waveforms produced within the point-particle black hole perturbation theory framework and calibrated to numerical relativity simulations performed with the Spectral Einstein Code (SpEC). </p> <p>Several surrogate models are currently available in this catalog:</p> <ol> <li><strong>BHPTNRSur2dq1e3</strong>, for aligned spin black hole binary systems with mass-ratios varying from 3 to 1000 and spins from −0.8≤χ1≤0.8 on the larger black hole. This surrogate model is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT) with calibration to numerical relativity (NR) data. The waveforms include all spin-weighted spherical harmonic modes up to ℓ=4 except the (4,1) and m=0 modes. Model details can be found in <a href="https://arxiv.org/abs/2407.18319">Rink et al. 2024</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="https://bhptoolkit.org/BHPTNRSurrogate/">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/BHPTNRSurrogate/blob/main/tutorials/BHPTNRSur2dq1e3.ipynb">tutorial</a>) or the GWSurrogate Python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a> or <a href="https://anaconda.org/conda-forge/gwsurrogate">conda-forge</a>.</li> <li><strong>BHPTNRSur1dq1e4</strong>, an updated version of the <strong>EMRISur1dq1e4 </strong>model described below. The updated version includes better calibration to NR, a smoother transition to plunge model, and more harmonic modes. Model details can be found in <a href="https://arxiv.org/abs/2204.01972">Islam et al. 2022</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="https://bhptoolkit.org/BHPTNRSurrogate/">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/BHPTNRSurrogate/tree/main/tutorials/BHPTNRSur1dq1e4">tutorial</a>) or the GWSurrogate Python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a> or <a href="https://anaconda.org/conda-forge/gwsurrogate">conda-forge</a>.</li> <li><strong>EMRISur1dq1e4</strong>, for non-spinning black hole binary systems with mass-ratios varying from 3 to 10000. This surrogate model is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT), with the total mass rescaling parameter tuned to NR simulations. Available modes are [(2,2), (2,1), (3,3), (3,2), (3,1), (4,4), (4,3), (4,2), (5,5), (5,4), (5,3)]. The m<0 modes are deduced from the m>0 modes. Model details can be found in <a href="https://arxiv.org/abs/1910.10473">Rifat et al. 2019</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="http://github.com/BlackHolePerturbationToolkit/EMRISurrogate">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/EMRISurrogate/blob/master/EMRISur1dq1e4.ipynb">tutorial</a>) or the GWSurrogate Python package (Jupyter notebook <a href="https://github.com/sxs-collaboration/gwsurrogate/blob/master/tutorial/notebooks/nonspinning_nr_emri.ipynb">tutorial</a>), which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a>.</li> </ol>
SDUST2023BCO: a global seafloor model determined from multi-layer perceptron neural network using multi-source differential marine geodetic data
<div> <p>SDUST2023BCO.nc is the global marine bathymetric model covering 80°S~80°N and 0°~360°E on 1′×1′ grids. The dataset contains geospatial information (latitude, longitude), SDUST2023BCO bathymetric model and an attachment data.</p> </div>
Data of PK/PD model of Reserpine-Induced Myalgia (RIM) model in rats
<p><span>This study aimed to establish a model that relates the pharmacokinetic and pharmacodynamic aspects of the reserpine-induced myalgia (RIM) model. To do this, measurements of reserpine in plasma and dopamine, norepinephrine, and serotonin in nervous tissue were carried out. </span></p>
Spatiotemporal dysregulation of neuron-glia related genes and pro-/anti-inflammatory miRNAs in the 5xFAD mouse model of Alzheimer's disease - Supplementary data
<p><strong>Supplementary Table 1. </strong> Gene expression profile by RT-qPCR analysis revealed no significant differences when simultaneously considering the genotype (WT/5xFAD), age (6/9 months) and brain region (HPC, hippocampus/PFC, prefrontal cortex). </p> <p><strong>Supplementary Table 2.</strong> miRNA-target table for the Analyzed microRNAs and targets selected for this study. Obtained in the online platform https://www.mirnet.ca/</p> <p><strong>Supplementary Table 3. </strong> Node table for the analyzed microRNAs and targets selected for this study. We only considered miRNAs and/or targets with a node degree of at least 2. Obtained in the online platform https://www.mirnet.ca/</p> <p><strong>Supplementary Table 4.</strong> Bivariate Pearson’s correlation coefficients and respective p-values obtained between all miRNAs and genes.</p> <p><strong>Supplementary Table 5.</strong> List of microRNAs analyzed by RT-qPCR and their primer sequences.</p> <p><strong>Supplementary Table 6.</strong> List of genes and respective primer sequences used for mRNA analysis by RT-qPCR.</p> <p><strong>Supplementary Table 7.</strong> Raw data used for correlational analysis in hippocampus (HPC) and prefrontal cortex (PFC) using the cor function in RStudio software.</p>
Data-driven physics-based modeling of pedestrian dynamics
<p>Python package to create physics-based pedestrian models from crowd measurements</p> <p>Github: <a href="https://github.com/c-pouw/physics-based-pedestrian-modeling">https://github.com/c-pouw/physics-based-pedestrian-modeling</a></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.