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619 results for “configuration”
Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration - model outputs
<p>These tar file are associated with an article submitted to Geoscientific Model Development under identification number gmd-2021-413 (https://www.geoscientific-model-development.net): Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration<br> By A. Voldoire, R. Roehrig, H. Giordani, R. Waldman, Y. Zhang, S. Xie, MN Bouin</p> <p>3 files correspond to code components that can be distributed freely</p> <p>- surfex.tgz for the surfex v8.0 distributed under a Cecill-C License</p> <p>- oasis-mct-3.0.tgz for oasis-mct3.0 distributed under a GNU General Public License</p> <p>- nemo_v3.6.tgz for the nemo, distibuted under a Cecill-C License</p> <p>These three components are mainly fortran codes.</p> <p>The last file "<a href="https://zenodo.org/api/files/e94922e7-22eb-445b-acc3-03e11ca1af6b/CNRM-CM6-1D_published_experiments.tgz?versionId=2460ec63-89f5-415f-9613-1954f048b238">CNRM-CM6-1D_published_experiments.tgz </a>" contains all model outputs that have been used in this article. These model outputs are in netcdf format and organized by experiment.</p>
Data from: Dominance and competition drive assemblage configuration in an Iberian steppe bird community
<p>Open scripts and data bases for Proceedings of the Royal Society B: Biological Sciences ( Barrero et al., Dominance and competition drive assemblage configuration in an Iberian steppe bird community).</p> <p>Four documents are provided: The two R Scripts needed to create and fix the models and two .xls files with the data described below.</p> <p>Scripts: a) HMSC_2022_FullModel_Run_GitHub.Rmd: script to run the full model. b) HMSC_2022_NullModel_Run_GitHub.Rmd: script to run the null model.</p> <p>Xlsx: a) Grid.xlsx: Coordinates of the species surveyed. b) Data2.xlsx: Habitat descriptor variables</p>
Analyzing the Impact of Undersampling on the Benchmarkingand Configuration of Evolutionary Algorithms - Dataset
<p>This repository contains the raw data and code nessecary to reproduce the results from the paper "Analyzing the Impact of Undersampling on the Benchmarkingand Configuration of Evolutionary Algorithms"</p> <p>The main file is the python-notebook 'reproducibility.ipynb', which details the full process for reproduction of the results shown in the paper. The two additional .py files are included for computation which takes longer and can be parallelized.</p> <p>The folder 'irace_conf_static_modcma.zip' contains the verification runs: 200 independent runs of each configuration. Indexes are according to 'Irace_confs_static_modcma_v2.csv'</p> <p>The folder 'logs_baseline_cs.zip' contains the raw irace files on which the analysis is based. This data is taken from the following repository:<br> de Nobel, Jacob, Vermetten, Diederick, Wang, Hao, Doerr, Carola, & Bäck, Thomas. (2021). Data and Code from: Tuning as a means of assessing the benefits of new ideas in interplay with existing algorithmic modules (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4524959</p>
GECCO2022 Automated Algorithm Selection for Radar Network Configuration
<p>This upload is a dataset companion to a paper in the Real World Application track of the GECCO 2022 conference.</p> <p>The dataset includes a binary container for the objective function used in the paper as well as the corresponding experimental logs.</p>
Configuration of magnetotail current sheet prior to magnetic reconnection onset
<p>Data repository for "Configuration of magnetotail current sheet prior to magnetic reconnection onset". This repository contains the following files: (1) "xyarray" is the main dataset; (2) "read_pritchett_pic_2d.py" is the python module that reads xyarray; (3) "simulation_params2.py" is the auxiliary python module that stores simulation parameters; (3) The python scripts with prefix "prod2_" plot the production figures; (4) "plt_style.py" is the plotting style sheet; (5) "movie-prod2_ratio-force.mp4" shows the evolution of different terms in the momentum equation prior to magnetic reconnection (The gray lines stand for the sum of all terms).</p>
Porting the WAVEWATCH III Wave Action Source Terms to GPU - WaveWatchIII configuration files
<p>This dataset contains the mesh and model configuration information for a WaveWatchIII run using a global ustructured grid.</p> <ul> <li>global_refined_59K.msh <ul> <li>Unstructured mesh file in gmsh format. An unstructured mesh of 1 degree global resolution and 0.25 degree in regions with depth less than 4km e.g. 1 degree at the equator and 0.25 degree at the coastal regions </li> </ul> </li> <li>global_refined_228K.msh <ul> <li>Unstructured mesh file in gmsh format. An unstructured mesh of 0.5 degree global resolution and 0.125 degree in regions with depth less than 4km.</li> </ul> </li> <li>ww3_grid.inp <ul> <li>The input file for the ww3_grid pre-processing program. This file contains many of the model configuration settings.</li> </ul> </li> <li>ww3_shel.inp <ul> <li>Input file for the ww3_shel program.</li> </ul> </li> </ul>
Identification of Performance Changes at Code Level (Measurement Configuration Dataset)
<p><strong>Measurement Configuration Dataset </strong></p> <p><em>This is the anonymous reviewing version; the source code repository will be added after the review.</em></p> <p>This dataset provides reproduction data for performance measurement configuration at source code level in Java. The measurement data can be obtained using the precision-experiments repository https://anonymous.4open.science/r/precision-experiments-C613/ (Examining Different Repetition Counts) yourself. These data conatained here are the data we obtained from execution on i7-4770 CPU @ 3.40GHz.</p> <p>The analysis was tested on Ubuntu 20.04 and gnuplot 5.2.8. It will not work with older gnuplot versions.</p> <p>To execute the analysis, extract the data by</p> <pre><code class="language-bash">tar -xvf basic-parameter-comparison.tar tar -xvf parallel-sequential-comparison.tar</code></pre> <p>and afterwards build the precision-experiments repo and execute the analysis by</p> <pre><code class="language-bash">cd precision-experiments/precision-analysis/ ../gradlew fatJar cd scripts/configuration-analysis/ ./executeCompleteAnalysis.sh ../../../../basic-parameter-comparison ../../../../parallel-sequential-comparison</code></pre> <p>Afterwards, the following files will be present:</p> <ul> <li><strong>precision-experiments/precision-analysis/scripts/configuration-analysis/repetitionHeatmaps/heatmap_all_en.pdf</strong> (Heatmaps for different repetition counts)</li> <li><strong>precision-experiments/precision-analysis/scripts/configuration-analysis/repetitionHeatmaps/heatmap_outlierRemoval_en.pdf </strong>(Heatmap with and without outlier removal for 1000 repetitions)</li> <li><strong>precision-experiments/precision-analysis/scripts/configuration-analysis/histogram_outliers_en.pdf</strong> (Histogram of the outliers)</li> <li><strong>precision-experiments/precision-analysis/scripts/configuration-analysis/heatmap_parallel_en.pdf </strong>(Heatmap with sequential and parallel execution)</li> </ul>
Effect of Guest Molecules on the Stacking Configuration of Covalent Organic Frameworks: A Periodic Energy Decomposition Analysis
<p>Illustrative videos that provide an in-depth understanding of the methodology and the result obtained from our recent study. A full description of the method is found in the paper. The potential energy surface was plotted using imshow Matplotlib and the 3d structures were rendered using povray. The videos are all free to use for educational purposes but please remember to cite the paper at anytime you use the videos. </p> <p> </p>
Project files provided as supporting information to the manuscript "Information-theoretical measures identify accurate low-resolution representations of protein configurational space"
<p>The dataset contains the following compressed folder:</p> <p>-Notebooks.zip:</p> <p>This folder contains:<br> -python_script:<br> -RESREL.py: script performing the clusterization and computing the relevance resolution curves<br> -random_curves.py: script generating the random value and computing the corresponding RES-REV curves_s<br> -Cluster_distance_matrix.py: script returning the distance among clusters for a given partition.<br> -python_notebook:<br> -Exploratory_analysis.ipynb: Analysis performed on the 12-protein_dataset<br> -DMAPS_ANTI.ipynb: Diffusion Map for the Antibody<br> -DMAPS_COV_1ake.ipynb: Diffusion Map + Inter-Intra state decomposition of covariance for 1ake</p> <p>Packages required for the usage of these python scripts/notebooks:<br> -numpy<br> -pandas<br> -matplotlib<br> -seaborn<br> -multiprocessing<br> -scipy</p> <p> </p> <p>========<br> RAW DATA<br> ========</p> <p>The raw data produced and employed in this study are available on a Google Drive folder at the following address:</p> <p>https://drive.google.com/drive/folders/1PasAUCgpR5-gdzUVEdyusgZIayQN0Le9</p> <p>In this folder, together with the compressed Notebooks.zip folder, one can fin the compressed folder Data.zip, within which the following data are present:</p> <p>-12-protein_dataset:<br> -md.mdp: the .mdp file used in the MD simulations<br> -PROTEIN_PDB_CODE:<br> -Hk_{sel}.npy & Hs_{sel}.npy: the Rel & Res curves, sel=[all, CA, CB]<br> -RMSD_{sel}.npy: the RMSD matrix, sel=[all, CA, CB]<br> -npt.gro:protein+water+ions structure @TEO the equilibration (NVT+NPT)<br> -MSR_df.csv: a dataset containing the following columns<br> 'area' : area behind the Relevance-Resolution curve;<br> 'selection': the atomic selection (['all', 'CA', 'CB']) used to compute the RMSD matrix used for the clusterization (and consequently the Relevance-Resolution curves)<br> 'method': the linkage measure used in the clustering procedure, an integer in [0,6];<br> 'method_name': the linkage measure used in the clustering procedure, a string in ['average','ward','complete','single','centroid','median','weighted'];<br> 'rmsd_mean': the mean value of the rmsd vector along the trajectory computed wrt the first frame;<br> 'rmsd_var': the variance of the rmsd vector along the trajectory computed wrt the first frame;<br> 'rgy_mean': the mean value of the radius of gyration along the trajectory;<br> 'rgy_var': the variance of the radius of gyration along the trajectory;<br> 'rmsf_mean': the mean value of the rmsf;<br> 'rmsf_var': the variance of the rmsf;<br> 'RMSD_M_mean': the mean value of the RMSD matrix.<br> 'RMSD_M_var': the variance of the RMSD matrix.<br> -Random:<br> -curves.npy= 100K Relevance-Resolution Random curves for M=40001<br> -curves_s.npy= 100K Relevance-Resolution Random curves for M=15000<br> -validation_dataset:<br> -antibody:<br> -Hk_CB.npy & Hs_CB.npy: the Rel & Res curves<br> -RMSD_CB.npy: the RMSD matrix<br> -DIFF_{M}.npy: the eigenvalue/vector of the 10-D diffusion space<br> -Label_{method}.npy: the label vector for n_clusters<br> -1ake:<br> -Hk_{sel}.npy & Hs_{sel}.npy: the Rel & Res curves<br> -RMSD_{sel}.npy: the RMSD matrix<br> -DIFF_{M}.npy: the eigenvalue/vector of the 10-D diffusion space<br> -Label_{method}.npy: the label vector for n_clusters<br> -intra_{m}.npy: the intra-cluster covariance matrix<br> -inter_cov_{m}.npy: the inter-cluster correlation matrix</p> <p> </p> <p>NOTE<br> =====</p> <p>The matrices of the cluster distances for adenylate kinase and antibody have been computed through the script Cluster_distance_matrix.py.</p> <p>These matrices have not been included in the dataset because of their large size; the raw data are however available upon request.<br> </p>
EXCEED-DMv1.0.0: Si and Ge Electronic Configurations
<p>This dataset contains the electronic configuration files for Si and Ge targets for use with EXCEED-DMv1.0.0. These electronic configuration files were used to compute the DM-electron interaction rates in the EXCEED-DM user manual: <a href="https://arxiv.org/abs/2210.14917">[2210.14917] EXCEED-DM: Extended Calculation of Electronic Excitations for Direct Detection of Dark Matter (arxiv.org)</a>. A description of the data in the files is given on the documentation website: https://tanner-trickle.github.io/EXCEED-DM/. We repeat it below for convenience (note that :math:, :cite: environments are rendered in the documentation).</p> <p> </p> <p>- <strong>File:</strong> <strong>Si/scatter/Si_scatter_elec_config.hdf5</strong><br> - <strong>Description:</strong> <br> - Electronic states assumed to be spin-degenerate, i.e., one-component wave functions. Used for binned scattering rate and dielectric calculations.<br> - <strong>Initial States: </strong><br> - Modelled with a combination of <strong>STO basis</strong> states and <strong>PW basis</strong> states. <strong>STO basis</strong> is used for the low energy, "core" states, while <strong>PW basis</strong> is used for the valence states.<br> - <strong>STO basis</strong><br> - 10 states (1 :math:`\mathbf{k}` point (:math:`\mathbf{k} = 0`), 10 bands). States are the electrons in the :math:`1s \rightarrow 2p` orbitals, for both Si in the unit cell. The sum over lattice vectors, :math:`\mathbf{r}` extends to cells :math:`\pm 1` away from the center, i.e., includes 27 cells in total. Each state is sampled on a :math:`128 \times 128 \times 128` uniform grid in the unit cell. STO basis coefficients, e.g., :math:`C_{j, l, n, \kappa}` are taken from the tabulated values `here <https://linkinghub.elsevier.com/retrieve/pii/S0092640X8371003X>`_. Energy of each state is taken from the Materials Project database, material ID mp-149.<br> - Notes: The reason for the small number of :math:`\mathbf{k}` points is due to runtime considerations, one has to choose between a larger sampling grid, i.e., sample the high momentum contributions in the unit cell, or more :math:`\mathbf{k}` points. Since the main features of these states are at high momentum, this is prioritized.<br> - <strong>PW basis</strong><br> - 4000 states (:math:`10 \times 10 \times 10 \, \mathbf{k}` points, 4 bands). Computed with DFT (VASP), see Ref. :cite:`Griffin:2021znd` for more details. Uniform sampling in the 1BZ. Each state was expanded to an :math:`E_\text{cut} = \text{keV}` and then all-electron reconstructed with :code:`pawpyseed` to :math:`E_\text{cut} = 2 \, \text{keV}`. Lowest energy state at :math:`-11.814 \, \text{eV}`.</p> <p><br> - <strong>Final States:</strong><br> - Modelled with a combination of **PW basis** and **single PW basis** states. **PW basis** is used for the lower energy "conduction" bands, **single PW basis** is used for higher energy states being approximated as "free".<br> - <strong>PW basis</strong><br> - 60000 states (:math:`10 \times 10 \times 10 \, \mathbf{k}` points, 60 bands). Computed with DFT (VASP), see Ref. :cite:`Griffin:2021znd` for more details. Uniform sampling in the 1BZ. Each state was expanded to an :math:`E_\text{cut} = \text{keV}` and then all-electron reconstructed with :code:`pawpyseed` to :math:`E_\text{cut} = 2 \, \text{keV}`. Lowest energy state at :math:`1.11 \, \text{eV}`. All bands which have an :math:`E_{i \mathbf{k}} < 60 \, \text{eV}`, for any :math:`\mathbf{k}`, are included.<br> - <strong>single PW basis</strong><br> - 40000 states (:math:`10 \times 10 \times 400` grid in :math:`(\theta, \phi, p)` space). Uniformly sampled on the sphere in :math:`(\theta, \phi)`, logarithmically sampled in :math:`E = p^2/2m_e` between :math:`E_\text{min} = 60 \, \text{eV}` and :math:`E_\text{max} = 400 \, \text{eV}`.</p> <p><br> - <strong>File: Si/abs/Si_abs_elec_config.hdf5</strong><br> - <strong>Description: </strong><br> - Electronic states assumed to be spin-degenerate, i.e., one-component wave functions. Used for absorption rate calculations.<br> - <strong>Initial States: </strong><br> - Modelled with a combination of <strong>STO basis</strong> states and <strong>PW basis</strong> states. <strong>STO basis</strong> is used for the low energy, "core" states, while <strong>PW basis</strong> is used for the valence states.<br> - <strong>STO basis</strong><br> - 10000 states (:math:`10 \times 10 \times 10 \, \mathbf{k}` points, 10 bands). States are the electrons in the :math:`1s \rightarrow 2p` orbitals, for both Si in the unit cell. :math:`\mathbf{k}` grid is uniformly sampled over the 1BZ. The sum over lattice vectors, :math:`\mathbf{r}` extends to cells :math:`\pm 1` away from the center, i.e., includes 27 cells in total. Each state is sampled on a :math:`128 \times 128 \times 128` uniform grid in the unit cell. STO basis coefficients, e.g., :math:`C_{j, l, n, \kappa}` are taken from the tabulated values `here <https://linkinghub.elsevier.com/retrieve/pii/S0092640X8371003X>`_. Energy of each state is taken from the Materials Project database, material ID mp-149.<br> - Notes: A larger number of :math:`\mathbf{k}` vectors can, and must be, used here is because transitions must be vertical. This limits the number of transitions, relative to a scattering rate calculation.<br> - <strong>PW basis</strong><br> - 4000 states (:math:`10 \times 10 \times 10 \, \mathbf{k}` points, 4 bands). Computed with DFT (VASP), see Ref. :cite:`Griffin:2021znd` for more details. Uniform sampling in the 1BZ. Each state was expanded to an :math:`E_\text{cut} = \text{keV}` and then all-electron reconstructed with :code:`pawpyseed` to :math:`E_\text{cut} = 2 \, \text{keV}`. Lowest energy state at :math:`-11.814 \, \text{eV}`.</p> <p><br> - <strong>Final States:</strong><br> - Modelled with a combination of <strong>PW basis</strong> and<strong> single PW basis</strong> states. <strong>PW basis</strong> is used for the lower energy "conduction" bands, <strong>single PW basis</strong> is used for higher energy states being approximated as "free".<br> - <strong>PW basis</strong><br> - 60000 states (:math:`10 \times 10 \times 10 \, \mathbf{k}` points, 60 bands). Computed with DFT (VASP), see Ref. :cite:`Griffin:2021znd` for more details. Uniform sampling in the 1BZ. Each state was expanded to an :math:`E_\text{cut} = \text{keV}` and then all-electron reconstructed with :code:`pawpyseed` to :math:`E_\text{cut} = 2 \, \text{keV}`. Lowest energy state at :math:`1.11 \, \text{eV}`. All bands which have an :math:`E_{i \mathbf{k}} < 60 \, \text{eV}`, for any :math:`\mathbf{k}`, are included.<br> - <strong>single PW basis</strong><br> - 2152000 states (:math:`10 \times 10 \times 10 \, \mathbf{k}` grid). :math:`\mathbf{k}` points are sampled uniformly in the 1BZ. For each :math:`\mathbf{k}`, all :math:`\mathbf{G}` were included such that :math:`60 \, \text{eV} < |\mathbf{k} + \mathbf{G}|^2/2m_e < \text{keV}`. Different :math:`\mathbf{G}` correspond to different bands when the parabolic dispersion relation gets folded in to the 1BZ.</p> <p> </p> <p><br> - <strong>File: Ge/scatter/Ge_scatter_elec_config.hdf5</strong><br> - <strong>Description: </strong><br> - Electronic states assumed to be spin-degenerate, i.e., one-component wave functions. Used for binned scattering rate and dielectric calculations.<br> - <strong>Initial States: </strong><br> - Modelled with a combination of <strong>STO basis</strong> states and <strong>PW basis</strong> states. <strong>STO basis</strong> is used for the low energy, "core" states, while <strong>PW basis</strong> is used for the valence states.<br> - <strong>STO basis</strong><br> - 28 states (1 :math:`\mathbf{k}` point (:math:`\mathbf{k} = 0`), 28 bands). States are the electrons in the :math:`1s \rightarrow 3d` orbitals, for both Ge in the unit cell. The sum over lattice vectors, :math:`\mathbf{r}` extends to cells :math:`\pm 1` away from the center, i.e., includes 27 cells in total. Each state is sampled on a :math:`128 \times 128 \times 128` uniform grid in the unit cell. STO basis coefficients, e.g., :math:`C_{j, l, n, \kappa}` are taken from the tabulated values `here <https://linkinghub.elsevier.com/retrieve/pii/S0092640X8371003X>`_. Energy of each state is taken from the Materials Project database, material ID mp-32.<br> - Notes: The reason for the small number of :math:`\mathbf{k}` points is due to runtime considerations, one has to choose between a larger sampling grid, i.e., sample the high momentum contributions in the unit cell, or more :math:`\mathbf{k}` points. Since the main features of these states are at high momentum, this is prioritized.<br> - <strong>PW basis</strong><br> - 4000 states (:math:`10 \times 10 \times 10 \, \mathbf{k}` points, 4 bands). Computed with DFT (VASP), see Ref. :cite:`Griffin:2021znd` for more details. Uniform sampling in the 1BZ. Each state was expanded to an :math:`E_\text{cut} = \text{keV}` and then all-electron reconstructed with :code:`pawpyseed` to :math:`E_\text{cut} = 2 \, \text{keV}`. Lowest energy state at :math:`-11.814 \, \text{eV}`.</p> <p><br> - <strong>Final States:</strong><br> - Modelled with a combination of <strong>PW basis</strong> and<strong> single PW basis</strong> states. <strong>PW basis</strong> is used for the lower energy "conduction" bands, <strong>single PW basis</strong> is used for higher energy states being approximated as "free".<br> - <strong>PW basis</strong><br> - 82000 states (:math:`10 \times 10 \times 10 \, \mathbf{k}` points, 82 bands). Computed with DFT (VASP), see Ref. :cite:`Griffin:2021znd` for more details. Uniform sampling in the 1BZ. Each state was expanded to an :math:`E_\text{cut} = \text{keV}` and then all-electron reconstructed with :code:`pawpyseed` to :math:`E_\text{cut} = 2 \, \text{keV}`. Lowest energy state at :math:`0.67 \, \text{eV}`. All bands which have an :math:`E_{i \mathbf{k}} < 60 \, \text{eV}`, for any :math:`\mathbf{k}`, are included.<br> - <strong>single PW basis</strong><br> - 40000 states (:math:`10 \times 10 \times 400` grid in :math:`(\theta, \phi, p)` space). Uniformly sampled on the sphere in :math:`(\theta, \phi)`, logarithmically sampled in :math:`E = p^2/2m_e` between :math:`E_\text{min} = 60 \, \text{eV}` and :math:`E_\text{max} = 400 \, \text{eV}`.</p> <p><br> - <strong>File: Ge/abs/Ge_abs_elec_config.hdf5</strong><br> - <strong>Description: </strong><br> - Electronic states assumed to be spin-degenerate, i.e., one-component wave functions. Used for absorption rate calculations.<br> - <strong>Initial States: </strong></p> <p> - Modelled with a combination of <strong>STO basis</strong> states and <strong>PW basis</strong> states. <strong>STO basis</strong> is used for the low energy, "core" states, while <strong>PW basis</strong> is used for the valence states.<br> - <strong>STO basis</strong><br> - 28000 states (:math:`10 \times 10 \times 10 \, \mathbf{k}` points, 28 bands). States are the electrons in the :math:`1s \rightarrow 3d` orbitals, for both Ge in the unit cell. :math:`\mathbf{k}` grid is uniformly sampled over the 1BZ. The sum over lattice vectors, :math:`\mathbf{r}` extends to cells :math:`\pm 1` away from the center, i.e., includes 27 cells in total. Each state is sampled on a :math:`128 \times 128 \times 128` uniform grid in the unit cell. STO basis coefficients, e.g., :math:`C_{j, l, n, \kappa}` are taken from the tabulated values `here <https://linkinghub.elsevier.com/retrieve/pii/S0092640X8371003X>`_. Energy of each state is taken from the Materials Project database, material ID mp-32.<br> - Notes: A larger number of :math:`\mathbf{k}` vectors can, and must be, used here is because transitions must be vertical. This limits the number of transitions, relative to a scattering rate calculation.<br> - <strong>PW basis</strong><br> - 4000 states (:math:`10 \times 10 \times 10 \, \mathbf{k}` points, 4 bands). Computed with DFT (VASP), see Ref. :cite:`Griffin:2021znd` for more details. Uniform sampling in the 1BZ. Each state was expanded to an :math:`E_\text{cut} = \text{keV}` and then all-electron reconstructed with :code:`pawpyseed` to :math:`E_\text{cut} = 2 \, \text{keV}`. Lowest energy state at :math:`-11.814 \, \text{eV}`.</p> <p> - <strong>Final States:</strong></p> <p> - Modelled with a combination of <strong>PW basis</strong> and<strong> single PW basis</strong> states. <strong>PW basis</strong> is used for the lower energy "conduction" bands, <strong>single PW basis</strong> is used for higher energy states being approximated as "free".<br> - <strong>PW basis</strong><br> - 82000 states (:math:`10 \times 10 \times 10 \, \mathbf{k}` points, 82 bands). Computed with DFT (VASP), see Ref. :cite:`Griffin:2021znd` for more details. Uniform sampling in the 1BZ. Each state was expanded to an :math:`E_\text{cut} = \text{keV}` and then all-electron reconstructed with :code:`pawpyseed` to :math:`E_\text{cut} = 2 \, \text{keV}`. Lowest energy state at :math:`0.67 \, \text{eV}`. All bands which have an :math:`E_{i \mathbf{k}} < 60 \, \text{eV}`, for any :math:`\mathbf{k}`, are included.<br> - <strong>single PW basis</strong><br> - 2586000 states (:math:`10 \times 10 \times 10 \, \mathbf{k}` grid). :math:`\mathbf{k}` points are sampled uniformly in the 1BZ. For each :math:`\mathbf{k}`, all :math:`\mathbf{G}` were included such that :math:`60 \, \text{eV} < |\mathbf{k} + \mathbf{G}|^2/2m_e < \text{keV}`. Different :math:`\mathbf{G}` correspond to different bands when the parabolic dispersion relation gets folded in to the 1BZ.</p> <p> </p>
Material for manuscript submitted to Earth and Space Science "Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin"
<p>Configuration files for AROME Indian Ocean, NEMO and OASIS which are necessary to reproduce the results in the publication :</p> <p>Corale, L; Malardel S. , Bielli S. and M-N Bouin (2022) Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin. <em>Earth and Space Science.</em></p>
Flags with configurator (parametric)
You can choose different flag/country by using configurator here: https://b2b.partcommunity.com/community/partcloud/index?route=part&name=Flags+configurator&model_id=60715&varsettransfer= Source: Objaverse 1.0 / Sketchfab
Neighborhood benthic configuration reveals hidden social diversity: classified benthic data
<p>Ecological interactions among benthic communities are crucial for shaping marine ecosystems. Understanding these interactions is essential for predicting how ecosystems will respond to environmental changes, invasive species, and conservation management. However, determining the prevalence of species interactions at the community scale is challenging. To overcome this challenge, we employ tools from social network analysis, specifically exponential random graph modeling (ERGM). Our approach explores the relationships among animal and plant organisms within their neighborhoods. Inspired by companion planting in agriculture, we use spatiotemporal co-occurrence as a measure of mixed species interaction. In other words, the variety of community interactions based on co-occurrence defines what we call "co-occurrence social diversity." Our objective is to use ERGM to quantify the proportion of interactions at both the simple paired level and the more complex triangle level, enabling us to measure and compare co-occurrence social diversity. Applying our approach to the Spanish coastal zone across 8 sites, 5 depths, and sunlit/shaded aspects, we discover that 80% of sessile communities, consisting of over a hundred species, exhibit co-occurrence social diversity, with 5% of species consistently forming associations with other species. These organism-level interactions likely have a significant impact on the overall character of the site.</p>
Prompts generated from ChatGPT3.5, ChatGPT4, LLama3-8B, and Mistral-7B with NYT and HC3 topics in different roles and parameters configurations
<h2>Description</h2> <p>Prompts generated from ChatGPT3.5, ChatGPT4, Llama3-8B, and Mistral-7B with <a href="https://www.nytimes.com/2023/07/19/learning/175-writing-prompts-to-spark-discussion-and-reflection.html">NYT</a> and <a href="https://huggingface.co/datasets/Hello-SimpleAI/HC3">HC3</a> topics in different roles and parameter configurations. </p> <p>The dataset is useful to study lexical aspects of LLMs with different parameters/roles configurations.</p> <ul> <li>The 0_Base_Topics.xlsx file lists the topics used for the dataset generation</li> <li>The rest of the files collect the answers of ChatGPT to these topics with different configurations of parameters/context: <ul> <li>Temperature (parameter): Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.</li> <li>Frequency penalty (parameter): Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.</li> <li>Top probability (parameter): An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass.</li> <li>Presence penalty (parameter): Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.</li> <li>Roles (context) <ul> <li>Default: No role is assigned to the LLM, the default role is used.</li> <li>Child: The LLM is requested to answer as a five-year-old child. </li> <li>Young adult male: The LLM is requested to answer as a young male adult. </li> <li>Young adult female: The LLM is requested to answer as a young female adult. </li> <li>Elderly adult male: The LLM is requested to answer as an elderly male adult. </li> <li>Elderly adult female: The LLM is requested to answer as an elderly female adult.</li> <li>Affluent adult male: The LLM is requested to answer as an affluent male adult. </li> <li>Affluent adult female: The LLM is requested to answer as an affluent female adult. </li> <li>Lower-class adult male: The LLM is requested to answer as a lower-class male adult. </li> <li>Lower-class adult female: The LLM is requested to answer as a lower-class female adult. </li> <li>Erudite: The LLM is requested to answer as an erudite who uses a rich vocabulary.</li> </ul> </li> </ul> </li> </ul> <h2>Paper</h2> <ul> <li>Paper: <a href="https://dl.acm.org/doi/10.1145/3696459">Beware of Words: Evaluating the Lexical Diversity of Conversational LLMs using ChatGPT as Case Study</a></li> <li>Cite:</li> </ul> <p><code>@article{10.1145/3696459,</code><br><code>author = {Mart\'{\i}nez, Gonzalo and Hern\'{a}ndez, Jos\'{e} Alberto and Conde, Javier and Reviriego, Pedro and Merino-G\'{o}mez, Elena},</code><br><code>title = {Beware of Words: Evaluating the Lexical Diversity of Conversational LLMs using ChatGPT as Case Study</code><code>},</code><br><code>year = {2024},</code><br><code>publisher = {Association for Computing Machinery},</code><br><code>address = {New York, NY, USA},</code><br><code>issn = {2157-6904},</code><br><code>url = {https://doi.org/10.1145/3696459},</code><br><code>doi = {10.1145/3696459},</code><br><code>abstract = ,</code><br><code>note = {Just Accepted},</code><br><code>journal = {ACM Trans. Intell. Syst. Technol.},</code><br><code>month = sep,</code><br><code>keywords = {LLM, Lexical diversity, ChatGPT, Evaluation}</code><br><code>}</code></p>
Systematic DFT Modeling van der Waals Heterostructures from a Complete Configurational Basis Applied to γ-PC/WS2
<p>See the paper:</p> <p> </p> <p>Celis, J.; Cao, W. <em>J. Chem. Theory Comput.</em> <strong>2024</strong>, 20, 6, 2377-2389</p>
Trajectories of backtracked passive particles for: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea"
<p>This collection hosts the trajectories of bactracked passive particles using Ocean Parcels v2.0 (The Parcels v2.0 Lagrangian framework: new field interpolation schemes. Delandmeter, P and E van Sebille (2019), <em>Geoscientific Model Development</em>, <em>12</em>, 3571–3584) and ocean surface velocity fields from the output of: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea". </p> <p>trajectories_2009.tar: trajectories for particles released between 2009-01-01 and 2009-12-31</p> <p>trajectories_2010.tar: trajectories for particles released between 2010-01-01 and 2010-12-31 (as shown in "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea")</p> <p>trajectories_2011.tar: trajectories for particles released between 2011-01-01 and 2011-12-31</p> <p>release_sites.csv: release sites for each release date.</p> <p>ocean_parcels_backtrack_VIB_carib12.py : python script to run ocean parcels to generate the trajectories published here.</p>
Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization (DataSet)
<p>Data from the article: <br>"Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization", L. Reyes, K. Campos, G. D. Avendaño, L. González-Paz, A. Vivas, Y. J. Alvarado, and S. Flores.</p> <p>Data to be used with some implementation of the forecasting method of reference:<br>Sugihara G. and May R. M., Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series, <em>Nature</em> <strong>344</strong>, 734–741 (1990).</p>
A Configurationally Stable Helical Indenofluorene
<p>Cartesian coordinates of the DFT optimized geometries from the molecules reported in the manuscript entitled "A Configurationally Stable Helical Indenofluorene"</p>
Electronic Supplement to Structural configuration of the Otates fault (southern Basin-and-Range Province) and its rupture in the 3 May 1887 MW = 7.5 Sonora, Mexico earthquake
<p>Electronic supplement to "Structural configuration of the Otates fault (southern Basin-and-Range Province) and its rupture in the 3 May 1887 MW = 7.5 Sonora, Mexico earthquake" (Seismological Society of America Bulletin, v. 98, no. 6, p. 2879-2893, 2008) with color-coded elevation model, satellite image of major Basin andRange normal faults in the study area, color version of geologic map, and additional photographs.</p> <p>High-resolution files of these figures are also available without restriction from </p> <p>http://www.seismosoc.org/Publications/BSSA_html/bssa_98-6/2008129-esupp/</p>
Planning as Optimization: Online Learning of Situations and Optimal Configurations - SASO 2019 - Accompanying material
<p>These files are accompanying material for our submission "" to SASO 19:</p> <p>Many approaches apply optimization techniques in SASs, mostly within the planning procedure, to generate new system configurations or adaptation plans. We performed an analysis of these techniques based on approaches published during the last ten years in conferences and journals related to self-adaptive systems (SASs), namely ACM Transactions on Autonomous and Adaptive System (TAAS), the International Conference on Autonomic Computing and Communications (ICAC), the International Conferences on Self-Adaptive and Self-Organizing Systems (SASO), the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), and the Symposium on the Foundations of Software Engineering (FSE). We identified the use of 29 different techniques in 51 publications. This list shows that a large set of techniques from different classes such as probabilistic, combinatorial, evolutionary, stochastic, mathematical, and meta-heuristic optimization are applied in SASs.</p> <p> </p> <p>We provide two files:</p> <p>- List of References (SASO - References - Planning_as_Optimization.pdf)</p> <p>- Dataset (SASO - Dataset - Planning_as_Optimization.xlsx)</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.