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12 results for “configurational space”
Research data for "Exploring the configurational space of amorphous graphene with machine-learned atomic energies"
<p>This dataset supports the paper: "Exploring the configurational space of amorphous graphene with machine-learned atomic energies" (<a href="https://doi.org/10.1039/D2SC04326B">https://doi.org/10.1039/D2SC04326B</a>).</p> <p>Trajectory data for the 200-atom structures (Fig. 3) and the final configurations for the 612-atom structures as well as the GAP-17-optimised 610-atom structure from Toh et al are provided (Fig. 4). Additionally, the structures used for data analysis in Fig. 5 are given.</p> <p>The files are in extended xyz (.xyz) format and contain the raw data for coordinates, forces, and atomic energies (labelled 'c_1'). The files also contain the atomic energies relative to pristine graphene, labelled "Energy_per_atom", and the locally averaged energy relative to pristine graphene, labelled "NN_Energy_per_atom". Topological information is included at the end of the .xyz file for the 612-atom structures ('fig_4'/) and for the structures in 'fig_5/'.</p> <p>All raw atomic energies were computed using LAMMPS default settings and were output with six significant figures, with the exception of the Toh et al. structure (for which ASE was used, outputting a higher number of significant figures). </p> <p>The data can be read using, for example, the Atomic Simulation Environment (ASE), or visualised using Ovito.</p> <p> </p>
Can we manage a future with more fire? Effectiveness of defensible space treatment depends on housing amount and configuration
Context: Fire in forested wildland urban interface (WUI) landscapes is increasing throughout the western United States. Spatial patterns of fuels treatments affect fire behavior, but it is unclear how fire risk and fuel treatment effectiveness will change under future conditions. Objectives: (1) How do area burned, forest and fuel characteristics, and fire risk change over time under 21st-century climate? (2) When defensible space fuels treatments are applied around all houses, which scenarios of WUI housing amount and configuration minimize fire risk? Methods: In generic 10,000-ha US Northern Rocky Mountain subalpine forest landscapes, we simulated 21 scenarios differing in fuels treatment, housing amount and configuration (neutral landscape models), and projected future climate using the process-based model iLand. We compared fire risk at three scales: 1-ha home ignition zone (HIZ), 9-ha safe suppression zone (SSZ), and landscape. Results: Under warm-dry climate, annual area burned increased, but area burned at high fire intensity peaked in the 2060s and then declined sharply; fire risk followed similar trends. Defensible space treatments maintained low flame lengths in HIZs. Clustered housing was more effective at reducing SSZ risk compared to dispersed housing. At landscape scales, treating more of the landscape reduced fire risk but configuration was unimportant. Conclusions: The most effective strategy for reducing fire risk depends on the scale at which risk is assessed. Clustering WUI developments and treating between 10 to 30% of the landscape every 10 years can reduce fire risk across multiple scales.
The Life Cycle of Features in Highly-Configurable Software Systems Evolving in Space and Time
<p>Dataset covering the entire development history of four open-source systems from different domains, covering a total of 37500 commits from up to 20 years of development, which can serve as a source of information for new studies on the evolution of systems in space and time.</p>
The Impact of Tool Configuration Spaces on the Evaluation of Configurable Taint Analysis for Android
<p>The data accompanying our ISSTA'2021 submission, <em>Rethinking Android Taint Analysis Evaluations: A Study of the Impact of Tool Configuration Spaces</em></p> <p> </p> <p>Structure:</p> <p><em>results</em>: contains the raw results output by AQL for the runs on Fossdroid and Google Play. Note that we wrote DroidBench results directly to CSV, so there are no "raw" results for them. Instead, see the summaries package. The collection of APKs for both datasets are also in this package.</p> <p><em>summaries</em>: contains CSV summaries of the three replications of our experiments on all three datasets (including examples of Amandroid's nondetermism on non-default configurations).</p> <p><em>datasets</em>: contains our FossDroid classified results and justifications. Please see the README.md in that package for more information.</p> <p><em>diagrams: </em>contains the graphs detailing the FlowDroid and DroidSafe configuration spaces, including their partial orders and disablement relationships.</p> <p><em>violations</em>: contains the records of violations of our partial orders.</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>
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>
DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space
<p>We present <em>Deep learning for Collective Variables</em> (DeepCV), a computer code that provides an efficient and customizable implementation of the deep autoencoder neural network (DAENN) algorithm that has been developed in our group for computing collective variables (CVs) and can be used with enhanced sampling methods to reconstruct free energy surfaces of chemical reactions. DeepCV can be used to conveniently calculate molecular features, train models, generate CVs, validate rare events from sampling, and analyze a trajectory for chemical reactions of interest. We use DeepCV in an example study of the conformational transition of cyclohexene, where metadynamics simulations are performed using DAENN-generated CVs. The results show that the adopted CVs give free energies in line with those obtained by previously developed CVs and experimental results. DeepCV is open-source software written in Python/C++ object-oriented languages, based on the TensorFlow framework and distributed free of charge for noncommercial purposes, which can be incorporated into general molecular dynamics software. DeepCV also comes with several additional tools, i.e., an application program interface (API), documentation, and tutorials.</p>
Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials
<p>This dataset contains a vertical slice of the data used to generate the results found in the publication "Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials"<br> It contains nested sampling input files and trajectory files for each potential studied, as well as the xml files and training data for the new potential, GAP-20U+gr.</p>
Dataset and source code for the paper "Finding Near-Optimal Configurations in Colossal Product Spaces with Statistical Confidence"
<p>Dataset and source code for the paper "Finding Near-Optimal Configurations in Colossal Product Spaces with Statistical Confidence"</p>
Reconsidering Android Taint Analysis Evaluations: A Study of the Impact of Tool Configuration Spaces
<p>The data accompanying our ISSTA'2021 submission, <em>Reconsidering Android Taint Analysis Evaluations: A Study of the Impact of Tool Configuration Spaces</em></p> <p> </p> <p>Structure:</p> <p><em>results</em>: contains the raw results output by AQL for the runs on Fossdroid and Google Play. Note that we wrote DroidBench results directly to CSV, so there are no "raw" results for them. Instead, see the summaries package. The collection of APKs for both datasets are also in this package.</p> <p><em>summaries</em>: contains CSV summaries of the three replications of our experiments on all three datasets.</p> <p><em>datasets</em>: contains our FossDroid classified results and justifications. Please see the README.md in that package for more information.</p> <p><em>diagrams: </em>contains the graphs detailing the FlowDroid and DroidSafe configuration spaces, including their partial orders and disablement relationships.</p>
Full Configuration Interaction Excited-State Energies in Large Active Spaces from Subspace Iteration with Repeated Random Sparsification: Active Space Orbitals
<p>The orbitals (in Molden format) used to perform FCI-FRI calculations on the oxo-Mn(salen) complex described in our manuscript, Full Configuration Interaction Excited-State Energies in Large Active Spaces from Subspace Iteration with Repeated Random Sparsification, available on <a href="https://arxiv.org/abs/2201.12164">arxiv</a>.</p>
Efficient Configuration Tuning for Big-Data Software Systems via Configuration Space Reduction
<p>Efficient Configuration Tuning for Big-Data Software Systems via Configuration Space Reduction (Supplementary Material)</p>
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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)
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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.