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5,805 results for “Data model”
Research data for "A machine-learned interatomic potential for silica and its relation to empirical models"
<p>This dataset supports the paper "A machine-learned interatomic potential for silica and its relation to empirical models". The paper is online here:</p> <p>The following files are provided:</p> <ul> <li>xyz-file containing all structures in the training database including forces and energies</li> <li>GAP file containing the corresponding parameters, which can be used for example for Lammps MD simulations</li> <li>Amorphous structure files for silica created by different interatomic potentials.</li> </ul>
Data and Codes of Characterizing Uncertainties of Earth System Modeling with Heterogeneous Many-core Architecture Computing
<p>These are the supporting information to verify the results in the paper, including input data, model outputs, the postprocessing scripts and the source codes.</p>
ROOOH modeling data — archive
<p>This archive contains ECHAM-HAMMOZ model output files used for the paper:</p> <p>Hydrotrioxide (ROOOH) formation in the atmosphere</p> <p>by Berndt et al.</p> <p> </p>
Data Supplement: Open-source model-based reconstruction in Julia (ISMRM 2022)
<p>Dataset to accompany the abstract presented at ISMRM 2022:</p> <p>Open-source model-based reconstruction in Julia: A pipeline for spiral diffusion imaging</p> <p>Instructions after download are found at the github link for the project.</p>
IMGT-KG data model
<p>This dataset contains the data model of IMGT-KG and the related imported files.</p>
3D Model data from the virtual asset marketplace Sketchfab.
<p>3D Model data from the virtual asset marketplace Sketchfab.</p> <p>Publication: <a href="https://www.mdpi.com/0718-1876/17/3/48">https://www.mdpi.com/0718-1876/17/3/48</a></p>
An Algebraic Architecture Model to Select PUSH/PULL Data Transfer
<p>The supplementary material for ASE 2022 Submission #21.</p> <p> </p> <p><strong>Detailed Proofs</strong></p> <p>The detailed proofs of Theorems 1, 2, 3 and other lemmas are contained in the following .PDF file.</p> <table> <tbody> <tr> <td>File Name </td> <td>Description</td> </tr> <tr> <td>ASE2022_proofs.pdf</td> <td>The detailed proofs of the correstness</td> </tr> </tbody> </table> <p> </p> <p><strong>Graph-based Refactoring Tool</strong></p> <p>The following .jar file is the current implementation of the graph-based tool for push/pull refactoring. </p> <table> <tbody> <tr> <td>File Name </td> <td>Description </td> </tr> <tr> <td>GraphBasedRefactor.jar</td> <td>graph-based refactoring tool</td> </tr> </tbody> </table> <p>It can work with Java 13 or later. The tool can be used in the following steps:</p> <ol> </ol> <ol> <li>Launch the tool from the command prompt.<br> <code>java -jar GraphBasedRefactor.jar</code></li> <li>Open a model file. (File -> Open)</li> <li>Select one data transfer method for each edge. (A pop-up menu is displayed by double clicking each edge.)</li> <li>Generate a plain Java prototype or JAX-RS prototype. (Generate -> Generate Plain Java Prototype, or Generate -> Generate JAX-RS Prototype)</li> </ol> <p> </p> <p><strong>Model Files</strong></p> <p>As case studies, we designed the architectures of the WOS (Weather Observation System), POS (Point Of Sales) system, twitter-like web service and jumping game. Their model files are compressed in the following .zip file.</p> <table> <tbody> <tr> <td>File Name </td> <td>Description </td> </tr> <tr> <td>models.zip</td> <td>Compressed model files</td> </tr> </tbody> </table> <p> </p> <p><strong>Generated Prototypes</strong></p> <p>We generated the prototypes from the above model files. The source files are compressed in the following .zip files.</p> <table> <tbody> <tr> <td>File Name </td> <td>Description</td> </tr> <tr> <td>prototypes.zip</td> <td>Compressed prototypes</td> </tr> </tbody> </table> <p> </p> <p><strong>Performance Evaluations</strong></p> <p>We measured the computation time of each API call on the JAX-RS prototypes of WOS, JAX-RS prototypes of twitter-like web service, and Java prototypes and the completed implementations of POS. The results are summarized in the following Excel file.</p> <table> <tbody> <tr> <td>File Name </td> <td>Description </td> </tr> <tr> <td>evaluations.xlsx</td> <td>Performance evaluations of API calls</td> </tr> </tbody> </table> <p> </p> <p><strong>Completed Implementations</strong></p> <p>We had completed the implementaions of the jumping game and POS system in Java. The source files and the executable .jar files are compressed in the following .zip files.</p> <table> <tbody> <tr> <td>File Name </td> <td>Description </td> </tr> <tr> <td>JumpingGame.zip</td> <td>A completed push-first implementation and the executable .jar file of the jumping game in Java</td> </tr> <tr> <td>PosSystem.zip</td> <td>A completed push-first implementation and the executable .jar file of the POS system in Java</td> </tr> </tbody> </table> <p>The .jar files can work with Java 13 or later. They can be launched by typing the following commands on the command prompt.</p> <p><code>java -jar JumpingGame.jar</code></p> <p><code>java -jar PosSystem.jar</code></p>
Data for paper "Graph Deep Learning Model for Mapping Mineral Prospectivity"
<p>Four prospecting information, namely, NE- and NW- trending faults, Agno Batholithic pluton margins, and porphyry intrusive contacts for mineral prospectivity mapping in Baguio district, Philippines.</p>
Development of a multivariable risk model integrating urinary peptide metabolites and Extracellular Vesicle RNA data to detect significant prostate cancer
<p>The aim of this study was to investigate whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples were analyzed by mass spectrometry and NanoString gene-expression analysis. As a result, four classifiers were generated: ‘MassSpec’ (CE-MS proteomics), ‘EV-RNA’, ‘SoC’ (standard of care) and ‘ExoSpec’. The best prediction for Gs³3+4 at initial biopsy (AUC=0.83, 95% CI:0.77-0.88) was achieved by applying ‘ExoSpec’ classifier and he outperformed other predictive classifiers. In addition, the results showed that the performance of ‘ExoSpec’ could reduce unnecessary biopsies by 30%.</p>
Validation Data used for manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model"
<p>those are the processed data that used for model-data comparison in the manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model", including Lake Surface Temperature and Lake Surface Ice Cover from Great Lakes Surface Environmental Analysis (GLSEA), Surface Air temperature and Precipitation from Climatic Research Unit (CRU). </p>
Underlying data AFTERLIFE model
<p>Set of simulation results using AFTERLIFE framework at different number of iterations</p>
Plant competition models data
<p>Dataset from the density and distance experiments. </p>
Shallow and deep subsurface sediment remobilization and intrusion in the Middle Jurassic to Lower Cretaceous Agardhfjellet Formation (Svalbard) [Supplementary material/digital model data]
<p>Supplementary model data for the publication Ogata et al. (in review):</p> <p>Most of the models suffer from incorrect location metadata, limiting their use to mostly qualitative interpretations.</p> <p>Shallow and deep subsurface sediment remobilization and intrusion in the Middle Jurassic to Lower Cretaceous Agardhfjellet Formation (Svalbard).</p>
Source data and codes for the paper "Inviting atomic mechanics to macro-continua: A study on monocrystalline Si using a spatial multilevel coarsening model"
<p>Source data and codes for the paper "Inviting atomic mechanics to macro-continua: A study on monocrystalline Si using a spatial multilevel coarsening model"</p> <p>This file includes </p> <p>- Source data for Figs 1-5 and Supplementary Materials</p> <p>- LAMMPS codes and raw log files used to produce the results of this study</p> <p> </p>
Dynamic implicit modeling of tunnel unfavorable geology based on multi-source data fusion using support vector machine
<p>This is the relevant data of the article "Dynamic implicit modeling of tunnel unfavorable geology based on multi-source data fusion using support vector machine"</p>
Data from: Computational Modeling of Gluteus Medius Muscle Moment Arm in Caviomorph Rodents Reveals Ecomorphological Specializations
<p>The data stored in this repository allow the reproduction of the study described in the following. Vertebrate musculoskeletal locomotion is realized through lever-arm systems. The instantaneous muscle moment arm (IMMA), which is expected to be under selective pressure and thus of interest for ecomorphological studies, is a key aspect of these systems. The IMMA changes with joint motion and its length change is technically difficult to acquire—usually, proxies such as osteological in-levers are used instead—and has not been compared in a larger phylogenetic ecomorphology framework, yet. We used 18 species of the ecologically diverse clade of caviomorph rodents to test whether its diversity is reflected in the IMMA of the hip extensor M. gluteus medius. A large IMMA is beneficial for torque generation; a small IMMA facilitates fast joint excursion. We expected large IMMAs in scansorial species, small IMMAs in fossorial species, and somewhat intermediate IMMAs in cursorial species, depending on the relative importance of acceleration and joint angular velocity. We modelled the IMMA over the entire range of possible hip extensions and applied macroevolutionary model comparison to selected joint poses. We also obtained the osteological in-lever of the M. gluteus medius to compare it to the IMMA. At small hip extension, the IMMA was largest on average in scansorial species, while the other two lifestyles were similar. We interpret this as an emphasized need for increased hip joint torque when climbing on inclines, especially in a crouched posture. Cursorial species might benefit from a fast joint excursion, but their similarity with the fossorial species is difficult to interpret and could hint at ecological similarities. At larger extension angles, cursorial species displayed the second-largest IMMAs after scansorial species. The larger IMMA optimum results in powerful hip extension which coincides with forward acceleration at late stance beneficial for climbing, jumping, and escaping predators. This might be less relevant for a fossorial lifestyle. The results of the in-lever only matched the IMMA results of larger hip extension angles, suggesting that the modelling of the IMMA provides more nuanced insights into adaptations of musculoskeletal lever arm systems than this osteological proxy.</p>
WRF model configuration and data used for the NHESS manuscript "Droughts in Germany: Performance of Regional Climate Models in reproducing observed characteristics"
<p>The file contains:</p> <ul> <li>the namelist.input document with the description of the WRF model configuration used in Warscher et al. (2019)</li> <li>WRF simulation outputs from the reanalysis run: monthly values for the time period 1980-2009 of precipitation, maximum and minimum temperature (needed for the SPEI calculation) from the innermost (5 km grid resolution) and second innermost (15 km) domain; from both domains the same section, relevant for the study, was taken; the data was bilineraily interpolated to 12.5 km horizontal grid resolution to match the EUR-11 CORDEX format</li> </ul> <p> </p>
Data and results from: "De novo spatiotemporal modelling of cell-type signatures in the developmental human heart"
<p>This repository contains data used and results produced in the manuscript:</p> <p>Sergio Marco Salas, Xiao Yuan, Christer Sylven, Mats Nilsson, Carolina Wählby, Gabriele Partel. "De novo spatiotemporal modelling of cell-type signatures in the developmental human heart"</p> <p>In situ sequencing [1] and scRNA-seq data [2] generated by Asp et al. [3] were downloaded and redistributed under CC BY 4.0 license.</p> <p>[1] Wu, Chenglin; Qian, Xiaoyan; Nilsson, Mats (2019): ISS data in "A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart". figshare. Dataset. https://doi.org/10.6084/m9.figshare.10058048.v1 </p> <p>[2] Asp, Michaela (2021), “Developmental heart - filtered and unfiltered count matrices and meta tables”, Mendeley Data, V2, doi: 10.17632/mbvhhf8m62.2</p> <p>[3] Asp, M., Giacomello, S., Larsson, L., Wu, C., Fürth, D., Qian, X., ... & Lundeberg, J. (2019). A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. <em>Cell</em>, <em>179</em>(7), 1647-1660.</p>
Model outputs and observation data for "Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)"
<p>Model outputs and observation data for paper "Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)".</p>
Model data and namelists for Sterzinger et al. (2022) - "Do arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations"
<p>Model data and namelists for "<a href="https://acp.copernicus.org/preprints/acp-2022-36/">Do arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations</a>"</p> <p>Horizontally averaged data is provided in NetCDF4 files (oliktok.nc, ascos.nc, summit.nc) for all output variables. Horizontally averaged vertical momentum flux is provided in a separate file for each simulation (*_vert_momentum_flux.nc files).</p> <p>Info on variables is provided by the RAMS model variable guide PDF <a href="https://vandenheever.atmos.colostate.edu/vdhpage/rams/docs/RAMS-VariableList.pdf">available here</a>.</p> <p>Model namelists are provided for each simulation (*_RAMSIN files). ASCOS initialization sounding info is provided within the ASCOS_RAMSIN file - initialization soundings are provided in SOUND_IN files.</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.