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864 results for “numerative”
Numerical approaches for DC Hall coefficient of the Hubbard model
<p>This contains the data and the notebooks to generate the data and plots in the paper "Numerical approaches for estimating the DC Hall coefficient of the doped Hubbard model" (arXiv:2103.04998)</p> <p>DQMC code for the zero field data: <a href="https://github.com/wenowang96/3-current">https://github.com/wenowang96/3-current</a></p> <p>DQMC code for the finite field data: <a href="https://github.com/edwnh/dqmc">https://github.com/edwnh/dqmc</a></p> <p>proxy D1 in the code is proxy D in the paper.</p> <p>proxy D2 in the code is proxy D_gamma in the paper.</p>
3D HDF5 data from numerical relativity simulations
<p>3D HDF5 data from numerical relativity simulations. Test data for visualization purposes.</p>
Data-Two-dimensional numerical study on particle motion trajectories and deposition in a channel of partial diesel particulate filter
<p>A numerical investigation on the soot laden flow of gas in a PDPF (Partial Diesel Particulate Filter) is presented based on solving the momentum equations for continuous phase in the Euler frame and the motion equations for dispersed phase in the Lagrangian frame. The interaction between the gas and particles is treated as one-way coupling for extremely dilute particle concentration, while the interaction between particles and porous wall is implemented through user-defined-subroutines. To accurately track the motion of nanoscale particles, the drag force, the Brownian excitation, and the partial slip are included in the particle motion equation. Two methods are used to verify the gas flow model and reasonable agreement for both comparisons is observed. The effects of upstream velocity, wall permeability and particle size on the filtration efficiency and deposition distribution of the particles along the wall surface of inlet channel are quantitatively studied. The results show that (1) the wall permeability plays the most primary role in determining the filtration efficiency of PDPF; (2) high upstream velocity improves filtration efficiency and drives the deposition position of particles to the rear of inlet channel; (3) the dependence of the particle deposition distribution on its own size is mainly reflected in the initial deposition position; (4) the filtration efficiency of PDPF is not proportional markedly to the gas flow into inlet channel at a low wall permeability, implying an intense separation of particles from gas streamline at the flow entrance.</p>
A Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model
<p>Data used to generate the figures in "A Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model" 2021. The zip files contains the hybrid forecasts and regridded ERA5 data used to verify the forecasts as well as the SPEEDY-LLR and ML-only runs. </p>
Figure 2 in Population dynamics of Pseudocalanus minutus elongatus in the Gulf of Gdansk (southern Baltic Sea) - experimental and numerical results
Figure 2. Vertical profiles of temperature (◦C) (A, B) and salinity (PSU) (C, D) at station J23 in 2006 and 2007, respectively.
Figure 6 in Population dynamics of Pseudocalanus minutus elongatus in the Gulf of Gdansk (southern Baltic Sea) - experimental and numerical results
Figure 6. Mean abundance of Pseudocalanus minutus elongatus from all stations and station J23 in the Gulf of Gdańsk for 2006 and 2007.
Figure 5 in Population dynamics of Pseudocalanus minutus elongatus in the Gulf of Gdansk (southern Baltic Sea) - experimental and numerical results
Figure 5. Vertical profiles of abundance (ind. m–3) of nauplii, CI to CV and adults (females and males) at station J23 for 2006 and 2007.
Figure 9 in Population dynamics of Pseudocalanus minutus elongatus in the Gulf of Gdansk (southern Baltic Sea) - experimental and numerical results
Figure 9. Total biomass (mg C m–3) of Pseudocalanus minutus elongatus as vertical mean concentrations at three stations (1, 2, 3 = J23) in the Gulf of Gdańsk; numerical simulations.
Figure 4 in Population dynamics of Pseudocalanus minutus elongatus in the Gulf of Gdansk (southern Baltic Sea) - experimental and numerical results
Figure 4. Stage structure of Pseudocalanus minutus elongatus in the Gulf of Gdańsk at station J23 for 2006 and 2007.
Figure 3 in Population dynamics of Pseudocalanus minutus elongatus in the Gulf of Gdansk (southern Baltic Sea) - experimental and numerical results
Figure 3. Taxonomical structure of Copepoda in the Gulf of Gdańsk (southern Baltic Sea) including data from all stations for 2006 and 2007.
Figure 11 in Population dynamics of Pseudocalanus minutus elongatus in the Gulf of Gdansk (southern Baltic Sea) - experimental and numerical results
Figure 11. Observed and simulated (based on monthly averaged) biomasses (mgm–3) of w.w. Pseudocalanus minutus elongatus at station J23 = 3 in the Gulf of Gdańsk. Calculated average values of all stations are also presented.
Figure 7 in Population dynamics of Pseudocalanus minutus elongatus in the Gulf of Gdansk (southern Baltic Sea) - experimental and numerical results
Figure 7. Weighted mean depth (WMD) of nauplii (N), copepodites (C1–3, C4–5) and adult (F, M) stages of Pseudocalanus minutus elongatus at station J23 in the Gulf of Gdańsk.
Figure 8 in Population dynamics of Pseudocalanus minutus elongatus in the Gulf of Gdansk (southern Baltic Sea) - experimental and numerical results
Figure 8. Vertical mean biomasses (mg C m–3) of eggs (Egg–N2), nauplii (N3–N6), younger copepodites (C1–C3), older copepodites (C4–C5) and adults at three stations (1, 2, 3 = J23) in the Gulf of Gdańsk; numerical simulations.
Figure 1 in Population dynamics of Pseudocalanus minutus elongatus in the Gulf of Gdansk (southern Baltic Sea) - experimental and numerical results
Figure 1. Location of the sampling stations; full circles indicate sampling stations, crosses indicate numerical simulations.
Figure 10 in Population dynamics of Pseudocalanus minutus elongatus in the Gulf of Gdansk (southern Baltic Sea) - experimental and numerical results
Figure 10. Simulated annual biomass profiles of five Pseudocalanus minutus elongatus groups and total biomass (mg C m–3) at point 3 = J23 in 2007.
Dataset for Formation of Metamorphic Soles underlying Ophiolites during Subduction Initiation: A Systematic Numerical Study
<p>The numerical model results in the paper 'Formation of Metamorphic Soles underlying Ophiolites during Subduction Initiation: A Systematic Numerical Study'.</p>
Dataset from "Numerical gravitational backreaction on cosmic string loops from simulation"
<p>This file contains a data table representing the average power spectrum, P_n, of Nambu-Goto cosmic strings evolved under numerical gravitational backreaction. The power spectra and the methods used to produce them are reported on in "Numerical gravitational backreaction on cosmic string loops from simulation" [to appear], by the same authors as this dataset. See Fig. 5 of that paper for a visualization.</p> <p>The file is organized in three columns:</p> <ol> <li>The fraction of evaporation, chi. The range is from 0.0 to 0.7 in steps of 0.1.</li> <li>The mode number, n. The range is from 2^0 to 2^39 in multiplicative steps of 2.</li> <li>The logarithmically binned elements of the power spectrum, nP_n. Bin edges are 2^i to 2^(i+1)-1 for i from 0 to 39.</li> </ol>
Numerical modeling results for Compression at strike-slip fault is a favorable condition for subduction initiation
<p>Each compressed file contains the visualization data of one model with vtk files. </p> <p>Opensource software Paraview is required to open the vtk files. </p> <p>Put the 'pvsm' file into folds with time.pvd for visulization.</p>
Summary of results from numerical experiments described in research paper "A New Simheuristic Approach for Stochastic Runway Scheduling"
<p>This file provides a summary of results from the numerical experiments described in our research paper, "A New Simheuristic Approach for Stochastic Runway Scheduling".</p>
Postprocessed data for "Tracing the rain formation pathways in numerical simulations of deep convection"
<p>This dataset is associated with the journal paper "Tracing the rain formation pathways in numerical simulations of deep convection".</p> <p>There are six .npz files containing data for the 5 simulations (CTRL, CTRLrfix, K13, CTRL800, K13800)<br> and one npz file containing data for constructing the rain pdfs of simulation CTRL (Figure 6).</p> <p>This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52‐07NA27344 IM Release number LLNL-MI-843623</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.