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307 results for “Model combination”
Phase field modelling combined with data-driven approach to unravel the orientation influenced growth of interfacial Cu6Sn5 intermetallics under electric current stressing
<p><strong>Description:</strong></p> <p>The datasets are constituted by two folders, namely, (A) data_features_and_metric.zip and (B) grain_area_prediction.zip. </p> <p><strong>(A) data_features_and_metric.zip:</strong></p> <p>The following are the contents of this folder</p> <p>(i) <em>grainTheta.csv file</em> : The "grainTheta.csv" file consists the datasets generated from multiple phase field simulations. Name of the columns in the csv file are:</p> <p> <strong>gnid </strong>= grain id number "n", <strong>ntheta = </strong>orientation angle of n<sup>th</sup> grain (<sup>o</sup>); <strong>nltheta </strong>= orientation angle of grain to the left of n<sup>th</sup> grain (<sup>o</sup>); <strong>nrtheta </strong>= orientation angle of grain to the right of n<sup>th</sup> grain (<sup>o</sup>); <strong>j </strong>= current density (A/m<sup>2</sup>);<strong> t =</strong> time (s); <strong>area</strong> = area of n<sup>th</sup> grain (m<sup>2</sup>);<strong> tl</strong> = horizontal length of the top edge of grain "n" (m) ; <strong>bl </strong>= horizontal length of the bottom edge of grain "n" (m) </p> <p>The features gnid, ntheta, nltheta and nrtheta for a given observation are determined during the design of initial conditions of the corresponding phase field simulation. The value of "j" for the observation is determined via the boundary condition in the same numerical simulation. The result from the finite element method based phase field simulation has provided the numerical quantities for t, area, tl and bl attributes. The multiple observations in the data file have been obtained from multiple phase field simulations. </p> <p>(ii) <em>imc_theta.ipynb, imc_theta.py and imc_theta.html files</em>: These files contain the code to build the Pearson's Correlation Coefficient (PCC) heatmap analysis of the data contained in grainTheta.csv file. </p> <p>(iii) <em>comparison_mse.csv</em>: This data file includes the information about mean square error for training data (tmse) and mean square error for validation data (vmse) at Epoch = 199 resulting from 10 different artificial neural network (ANN) models distinguished by 10 different values of learning rates (lr) . Thus, the name of the columns in this csv file are <strong>modelno</strong>, <strong>lr</strong>, <strong>tmse</strong> and <strong>vmse</strong>. </p> <p>(iv) <em>mse_comparison.gnu</em>: This file consists the codes required to output a png image from the data provided in comparison_mse.csv<em>. </em></p> <p>(v) train_loss.csv and val_loss.csv: These files consist of the data of tmse and vmse at all points of Epochs for the ANN model with lr = 2.5E-4 . Thus, the first column in train_loss.csv file corresponds to tmse whereas the second column is Epochs number. Similarly, vmse and Epochs represent the two columns in val_loss.csv file. </p> <p> </p> <p>(vi) <em>mse_lr2p5e-4.gnu</em> : This file consists the codes required to output a png image from the data provided in train_loss.csv and val_loss.csv<em>. </em></p> <p><strong>(B) grain_area_prediction.zip:</strong></p> <p>Inside this folder, there is a folder named "prediction_of_grain_area" consisting of the following files:</p> <p><em>initial_area.csv file</em>: This file consists the value of the initial grain area of grain 4. It is a constant at all orientation angle.</p> <p><em>predicted_result_00_5e4.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 0<sup>o</sup> and 0<sup>o </sup>respectively, and for applied current density of 5.0E+4 J/m<sup>2</sup> .</p> <p><em>predicted_result_00_5e5.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 0<sup>o</sup> and 0<sup>o </sup>respectively, and for applied current density of 5.0E+5 J/m<sup>2</sup> .</p> <p><em>predicted_result_9090_5e4.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 90<sup>o</sup> and 90<sup>o </sup>respectively, and for applied current density of 5.0E+4 J/m<sup>2</sup> .</p> <p><em>predicted_result_9090_5e5.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 90<sup>o</sup> and 90<sup>o </sup>respectively, and for applied current density of 5.0E+5 J/m<sup>2</sup> .</p> <p><em>area_00_adj.gnu</em> : This gnu file contains the code to produce the png image from the data contained in <em>predicted_result_00_5e4.csv </em>and<em> predicted_result_00_5e5.csv </em>. The information about the initial area of grain 4 is obtained from <em>initial_area.csv</em> file by the code.</p> <p><em>area_9090_adj.gnu</em> : This gnu file contains the code to produce the png image from the data contained in <em>predicted_result_9090_5e4.csv </em>and<em> predicted_result_9090_5e5.csv </em>. The information about the initial area of grain 4 is obtained from <em>initial_area.csv</em> file by the code.</p> <p> </p>
Analgesic Effect of Different Combinations of Dexketoprofen Trometamol With Tramadol Hydrochloride in a Model of Moderate to Severe Pain
ClinicalTrials.gov study NCT01307020. IPD Sharing: Not stated. Countries: 6. Publications: 1.
A Study to Evaluate BMS-986141 Added on to Aspirin or Ticagrelor or the Combination, on Thrombus Formation in a Thrombosis Chamber Model in Participants With Stable Coronary Artery Disease and Healthy
ClinicalTrials.gov study NCT05093790. IPD Sharing: Not stated. Countries: 1. Publications: 1.
MoveStrong at Home: A Feasibility Study of a Model for Remote Delivery of Functional Strength and Balance Training Combined With Nutrition Education for Older Pre-frail Adults.
ClinicalTrials.gov study NCT04663685. IPD Sharing: NO. Countries: 1. Publications: 13.
Data from: Implementation of Option B and a fixed-dose combination antiretroviral regimen for prevention of mother-to-child transmission of HIV in South Africa: a model of uptake and adherence to care
Open the record for dataset details and reuse information.
Accounting for imperfect detection in data from museums and herbaria when modeling species distributions: Combining and contrasting data-level versus model-level bias correction
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Modelling data for: Short-course combination treatment for experimental chronic Chagas disease
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VCF datasets and analysis scripts for: The combination of genomic offset and niche modelling provides insights into climate change-driven vulnerability
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Data from: Combining mesocosms with models to unravel the effects of global warming and ocean acidification on a temperate marine ecosystem
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Data from: Combining geostatistical and biotic interactions modelling to predict amphibian refuges under crayfish invasion across dendritic stream networks
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Data from: Combined mechanistic modelling predicts changes in species distribution and increased co-occurrence of a tropical urchin herbivore and a habitat-forming temperate kelp
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Combining bioenergetics and movement models to improve understanding of the population consequences of disturbance
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Data from: Problems with combining modelling and social science approaches to understand artisanal fisheries bycatch
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Data from: Combining past and contemporary species occurrences with ordinal species distribution modeling to investigate responses to climate change
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2m Digital Terrain Model Combining LIDAR and Stereo-Enhanced Photogrammetric Data, Niwot Ridge LTER Project Area, Colorado
Citation: Manley, W.F., Parrish, E.G., and Lestak, L.R., 2009, High-Resolution Orthorectified Imagery and Digital Elevation Models for Study of Environmental Change at Niwot Ridge and Green Lakes Valley, Colorado: Niwot Ridge LTER, INSTAAR, University of Colorado at Boulder, digital media. This dataset is a Digital Terrain Model (DTM) for the Niwot Ridge Long Term Ecological Research (LTER) project area at 2 m resolution, and is well suited for hydrologic modeling and other analyses of bare-earth terrain. The DTM is derived from the first reflective surface or a Digital Surface Model (DSM) that was created from 12 micron digital stereo aerial photography. Elevation points were both automatically filtered and hand-adjusted to better represent bare earth conditions. Green Lakes Valley LiDAR (Light Detection and Ranging) point data were appended to the filtered and edited data. Breakline information (ie. ridge tops, lake edges, and streams) was added. A final 2 meter gridded DTM and shaded relief model was then generated. The DTM is useful for terrain analysis and derivation of layers such as slope angle, aspect, shaded relief images, and contours. The DTM and shaded relief model covers a total area of 98 km2 and is available in Environmental Systems Research Institute's (ESRI's) GRID format for a total dataset size of 125 MB. They share a UTM zone 13 projection, NAD83 horizontal datum and NAVD88 vertical datum, with FGDC-compliant metadata. The DTM is available through an unrestricted public license, and can be obtained online or on DVD by request (see Distributor contact information below). Imagery available in this series includes orthorectified aerial photography for 1953, 1972, 1985, 1990, 1999, 2000, 2002, 2004, 2006 and 2008. Together, the digital elevation models and imagery will be of interest to land managers, scientists, and others for observation and analysis of natural features and ecosystems. NOTE: This EML metadata file does not contain important geospat
2m Digital Terrain Shaded Relief Model Combining LIDAR and Stereo-Enhanced Photogrammetric Data, Niwot Ridge LTER Project Area, Colorado
Citation: Manley, W.F., Parrish, E.G., and Lestak, L.R., 2009, High-Resolution Orthorectified Imagery and Digital Elevation Models for Study of Environmental Change at Niwot Ridge and Green Lakes Valley, Colorado: Niwot Ridge LTER, INSTAAR, University of Colorado at Boulder, digital media. This dataset is a Digital Terrain Shaded Relief Model (DTM) for the Niwot Ridge Long Term Ecological Research (LTER) project area at 2 m resolution, and is well suited for visualization of bare-earth terrain. The DTM is derived from the first reflective surface or a Digital Surface Model (DSM) that was created from 12 micron digital stereo aerial photography. Elevation points were both automatically filtered and hand-adjusted to better represent bare earth conditions. Green Lakes Valley LiDAR (Light Detection and Ranging) point data were appended to the filtered and edited data. Breakline information (ie. ridge tops, lake edges, and streams) was added. A final 2 meter gridded DTM and shaded relief model was then generated. The DTM is useful for terrain analysis and derivation of layers such as slope angle, aspect, shaded relief images, and contours. The DTM and shaded relief model covers a total area of 98 km2 and is available in Environmental Systems Research Institute's (ESRI's) GRID format for a total dataset size of 125 MB. They share a UTM zone 13 projection, NAD83 horizontal datum and NAVD88 vertical datum, with FGDC-compliant metadata. The DTM is available through an unrestricted public license, and can be obtained online or on DVD by request (see Distributor contact information below). Imagery available in this series includes orthorectified aerial photography for 1953, 1972, 1985, 1990, 1999, 2000, 2002, 2004, 2006 and 2008. Together, the digital elevation models and imagery will be of interest to land managers, scientists, and others for observation and analysis of natural features and ecosystems. NOTE: This EML metadata file does not contain important geospatial data pr
GAP model parameter files for "Combining phonon accuracy with high transferability in Gaussian approximation potential models"
<p>GAP model parameter files to accompany the publication</p> <p>"Combining phonon accuracy with high transferability in Gaussian approximation potential models"</p> <p>by Janine George, Geoffroy Hautier, Albert P. Bartók, Gábor Csányi, and Volker L. Deringer</p> <p>All models are defined by the main parameter file "gp_iter6C.xml" and the associated file "gp_iter6C.xml.sparseX.GAP_00001", where "GAP_0000" is a placeholder for the unique identifier of the potential (also given in the XML header), and the trailing "1" indicates that only one set of descriptor (here, SOAP) parameters is given.</p> <p>The directories in this dataset follow the figures in the publication for which the respective potentials have been first used.</p> <p>Fig_2/only_random/M_1000<br> Fig_2/only_random/M_3000<br> Fig_2/only_random/M_5000<br> Fig_2/only_random/M_7000<br> Fig_2/only_random/M_9000</p> <p>Fig_2/only_individual/M_1000<br> Fig_2/only_individual/M_3000<br> Fig_2/only_individual/M_5000<br> Fig_2/only_individual/M_7000<br> Fig_2/only_individual/M_9000</p> <p>Fig_2/combined/M_1000<br> Fig_2/combined/M_3000<br> Fig_2/combined/M_5000<br> Fig_2/combined/M_7000<br> Fig_2/combined/M_9000</p> <p>Fig_4/only_individual/f_0.1000<br> Fig_4/only_individual/f_0.0100<br> Fig_4/only_individual/f_0.0010<br> Fig_4/only_individual/f_0.0001</p> <p>Fig_4/combined/f_0.1000<br> Fig_4/combined/f_0.0100<br> Fig_4/combined/f_0.0010<br> Fig_4/combined/f_0.0001</p> <p>Fig_5/GAP-18_plus_SCs_f0.01 <br> Fig_5/GAP-18_plus_SCs_f0.001</p> <p> </p>
Embeddings from "CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix-Space Model"
<p>This item holds the learned embeddings from the paper:</p> <p>Mai, Florian, Lukas Galke, and Ansgar Scherp. “CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model.” In <em>International Conference on Learning Representations</em>, 2019. <a href="https://openreview.net/forum?id=H1MgjoR9tQ">https://openreview.net/forum?id=H1MgjoR9tQ</a>.</p>
Data release for 'Ensemble Forecasting of Major Solar Flares: Methods for Combining Models'
<p>This is a release of the data that were used for validation in the paper 'Ensemble Forecasting of Major Solar Flares: Methods for Combining Models' by J. A. Guerra, S. A. Murray, D. S. Bloomfield, and P. T. Gallagher, that has been submitted to the Journal of Space Weather and Space Climate.</p> <p> </p> <ul> </ul> <p>The naming scheme for the files is in the format:</p> <pre><code>class_type_metric.dat</code></pre> <ul> <li>'class' denotes whether the forecast is for M- or X- class flares.</li> <li>'type' is what kind of forecast, i.e., the original ensemble members, an ensemble created from probabilistic validation metrics, or an ensemble created from categorical validation metrics.</li> <li>'metric' specifies the metric used to create the ensemble in the case of 'probabilistic' or 'categorical' types as above (see paper for further details), or in the case of the original ensemble members the name of the operational forecasting method.</li> </ul> <p> </p> <p>The data files are in the format:</p> <pre><code>obs,prob</code></pre> <ul> <li>'obs' denotes whether or not a flare was observed within 24 hours of the forecast issue time (1 for yes and 0 for no).</li> <li>'prob' gives the probabilistic forecast value (between 0.0 and 1.0).</li> </ul> <p> </p> <p>These data files can easily be read into the <a href="https://cran.r-project.org/web/packages/verification/verification.pdf">R verification package</a> to replicate the results presented in the paper.</p>
Data from: Combining correlative and mechanistic niche models with human activity data to elucidate the invasive potential of a sub-Antarctic insect
<p>Aim</p> <p>Correlative Species Distribution Models (SDMs) are subject to substantial spatio-temporal limitations when historical occurrence records of data-poor species provide incomplete and outdated information for niche modelling. Complementary mechanistic modelling techniques can, therefore, offer a valuable contribution to underpin more physiologically-informed predictions of biological invasions, the risk of which is often exacerbated by climate change. In this study we integrate physiological and human pressure data to address the uncertainties and limitations of correlative SDMs and to better understand, predict, and manage biological invasions.</p> <p>Location</p> <p>Western archipelagos of the Southern Ocean and martime Antarctica</p> <p>Taxon</p> <p>Eretmoptera murphyi (Chironomidae), invertebrates.</p> <p>Methods</p> <p>Mahalanobis Distances were used for correlative SDM construction for a species with few records. A mechanistic SDM was built around different fitness components (larval survival and life stage progression) as a function of temperature. SDM predictions were combined with human activity levels in Antarctica to generate a site vulnerability index to the colonization of E. murphyi. Future scenarios of ecophysiological suitability were built around the warming trends in the region.<br> Results Both SDMs converge to predict high environmental suitability in the species' native and introduced ranges. However, the mechanistic model indicates a slightly larger invasive potential based on larval performance at different temperatures. Human activity levels across the Antarctic Peninsula play a key role in discerning site vulnerabilities. Niche suitability in Antarctica grows considerably under long-term climate scenarios, leading to a substantially higher invasive threat to the Antarctic ecosystems. In turn changing conditions result on growing physiological mismatches with the environment in the native range on South Georgia.</p> <p>Main conclusions</p> <p>Long-term studies of invasion potential under climate benefit from integrating correlative predictions with physiological experiments, as the invasion potential varies depending on the area and the timescale examined. This study also highlights a conservation paradox whereby the accidental introduction of an insect represents a threat to the Antarctic ecoystems that contrasts with its endangered status at the native range.</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.