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5,805 results for “Data model”
Data set for reliability-based lift-to-power consumption optimization with an accelerated Kriging model for clapping-wing micro air vehicles
<p>Procedures of the reliability-based lift-to-power consumption optimization with an accelerated Kriging model</p> <p>Step 1: Run the file “LHS.m” to generate initial samples.</p> <p>Step 2: Modify the aerodynamic model according to initial samples (e.g. flapping1_Def.xml, flapping1.bat), and then run the “.bat file” to obtain the original force data.</p> <p>Step 3: Run the file “Kriging.m” to obtain the average lift using a filter.</p> <p>Step 4: Run the file “FW_2.m”, “FW_3.m” to obtain sub-optimal-result.</p> <p>Step 5: Find the new training sample and obtain the eigenvalue of the new training sample.</p> <p>Step 6: Rerun the file “FW_2.m”, “FW_3.m” to obtain sub-optimal-result by reloading the new “.mat” files (e.g. FW_2_41.mat, FW_2_P_20.mat).</p> <p>Step 7: Go to Step 4 until the convergence criteria are satisfied.</p> <p>Step 8: Obtain the optimal result. PS: Other files are function files.</p>
A predictive group-contribution framework for the thermodynamic modelling of CO2 absorption in cyclic amines, alkyl polyamines, alkanolamines and phase-change amines: new data and SAFT- gamma Mie parameters.FPE 2022
<p>All data in the figures in the publication. </p>
Supplemental data for "Patterns of West Nile virus in the Northeastern United States using negative binomial and mechanistic trait-based models"
<p>Supplemental data files for "Patterns of West Nile virus in the Northeastern United States using negative binomial and mechanistic trait-based models"</p> <p><strong>S1 Table File.</strong> A table containing the probability of obtaining a result of 0 WNV cases in 20 years based on the group-estimated negative binomial model for each county with no cases reported.</p> <p><strong>S2 Table File.</strong> A table containing the county temperature inputs used in this study, and the associated relative R0 predictions</p> <p><strong>S3. Table File.</strong> A table containing the negative binomial probabilities by county, including group identity and group populations.</p>
Southeastern Tibetan Plateau Dispersion data & velocity model
<p>We obtained a 3-D isotropic and azimuthal anisotropic model in the southeastern Tibetan Plateau. These datasets contain the dispersion data we picked, and the models we obtained.</p>
Model codes and data for ``A control volume finite element model for predicting the morphology of cohesive-frictional debris flow deposits"
<p>The code and the dataset can be read/run by using Matlab. The description is as follows:</p> <p>1. Dataset (field_data) includes transect data of three field debris flow deposits from Coussot et al. (1996). The data can be read and calibrated with the analytical solution by the code field_calibration.m.</p> <p>2. Dataset (data_T01-T04, T11-T15_DT) includes experimental fan topography data, calibrated parameters, and simulation outputs. </p> <p>3. Two calibration codes are used for the model parameter calibrations of the two sets of experiments.</p> <p>4. Function aggradation_DT.m is the CVFEM model for simulating fan morphology. Use CVFEM_exp_simulation.m code to run simulations for the experiments.</p>
Skabbholmen data from: Concurrent ordination: Simultaneous unconstrained and constrained latent variable modeling
<ol> <li>In community ecology, unconstrained ordination can be used to indirectly explore drivers of community composition, while constrained ordination can be used to directly relate predictors to an ecological community. However, existing constrained ordination methods do not explicitly account for community composition that cannot be explained by the predictors, so that they have the potential to misrepresent community composition if not all predictors are available in the data.</li> <li>We propose and develop a set of new methods for ordination and Joint Species Distribution Modelling (JSDM) as part of the Generalized Linear Latent Variable Model (GLLVM) framework, that incorporate predictors directly into an ordination. This includes a new ordination method that we refer to as concurrent ordination, as it simultaneously constructs unconstrained and constrained latent variables. Both unmeasured residual covariation and predictors are incorporated into the ordination by simultaneously imposing reduced rank structures on the residual covariance matrix and on fixed-effects.</li> <li>We evaluate the method with a simulation study, and show that the proposed developments outperform Canonical Correspondence Analysis (CCA) for Poisson and Bernoulli responses, and perform similar to Redundancy Analysis (RDA) for normally distributed responses, the two most popular methods for constrained ordination in community ecology. Two examples with real data further demonstrate the benefits of concurrent ordination, and the need to account for residual covariation in the analysis of multivariate data.</li> <li>This article contextualizes the role of constrained ordination in the GLLVM and JSDM frameworks, while developing a new ordination method that incorporates the best of unconstrained and constrained ordination, and which overcomes some of the deficiencies of existing classical ordination methods.</li> </ol>
Model code and output data for Tang et al. 2022 (doi:10.3389/fenvs.2022.1013875)
<p>flex_extract.tar: The code used to prepare inputdata for flexpart and flexdust.</p> <p>FLEXDUST_default_20210301-20210331.nc: The output of default FLEXDUST.</p> <p>FLEXDUST_updated_20210311-20210320.nc: The output of updated FLEXDUST.</p> <p>flexpart_1h_default.nc: The output of forward FLEXPART simulation using default FLEXDUST.</p> <p>flexpart_1h_erode_topoTc_true_moreveg0.1_76FVThresh_noblock_KOK_AN05.nc: The output of forward FLEXPART simulation using updated FLEXDUST.</p>
Data attached to Radio Science paper "Model to Scale Rain Attenuation Time Series with Link Elevation Angle for LEO Satellite Based Systems"
<p>Data attached to Radio Science paper "Model to Scale Rain Attenuation Time Series with Link Elevation Angle for LEO Satellite Based Systems"</p>
Model data for tropical nudging experiments
<p>© Crown Copyright, Met Office</p> <p>The accompanying data is made available under the terms of the Non-Commercial Government Licence (http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/).</p> <p>We use the Hadley Centre Global Environmental Model version 3 with the Global Coupled model 2.0 configuration (HadGEM3-GC2; Williams et al., 2015). The model has a horizontal resolution of 0.83x0.55degrees and 85 levels with a lid at 85km. It employs interactive sea ice and ocean. We initialise the model using the observational conditions from the 1<sup>st</sup> September 2000 in all cases to prevent any differences in initial conditions. Model simulations are run for 3 months through to the start of December.</p> <p>Model simulations are performed, nudging the tropics to the observationally-constrained reanalysis for the years 1993-2015 inclusive. For each year, a 20-member ensemble is produced using stochastic perturbations, giving 460 simulations in total. In each case, we nudge the tropical column at every timestep to ERA-Interim reanalysis (Dee et al., 2011) for the respective year. The tropical relaxation spans all vertical levels and extends +/- 19.5 degrees north/south of the equator, with an additional 8 degree latitude tapering. Temperature, along with the zonal and meridional components of wind are nudged at every timestep, with an e-folding timescale of 6 hours. Nudging moisture fields has little impact, so is not included. </p> <p>Data provided here is output for mean sea level pressure, geopotential height on the 200hPa pressure level, rainfall and sea ice.</p> <p> </p> <p>Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, D.P. and Bechtold, P., 2011. The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the royal meteorological society, 137(656), pp.553-597. https://doi.org/10.1002/qj.828</p> <p>Williams, K. et al. The Met office global coupled model 2.0 (GC2) configuration. Geosci. Model Dev. 88, 1509–1524 (2015). https://doi.org/10.5194/gmd-8-1509-2015</p> <p> </p>
Data for Typhoon Modelling with Observed Drag Reduction over Land as well as in-situ Observations
<p>No description provided.</p>
3D Vs model and phase velocity dispersion data of the Pearl River Delta onshore-offshore area
<p>3D Vs model and phase velocity dispersion data for PRD.</p>
Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-driven Storm Tides and Inundation: Model Data and Analysis Code
<p>This repository contains model data and analysis codes related to the manuscript entitled "Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-driven Storm Tides and Inundation", as follows:</p> <ol> <li>Model data are maximum water surface elevations of ensemble 48-hr forecast ADCIRC model simulations for three historical US landfalling hurricanes: 2017 Irma, 2018 Florence, and 2020 Laura. These are located in the "NameYYYY_Results.tar" archive files as "maxele.63.nc" files. Also included in the tar files are the hurricane forecast track files in Automated Tropical Cyclone Forecasting (ATCF) system format (*.22) and the error variable parameters (*.json) for each forecast. </li> <li>Model data of best-track runs for the 2017 Irma, 2018 Florence, and 2020 Laura hurricanes, and astronomical tide-only runs for the corresponding time periods are located in the "NameYYYY_besttrack+tides.tar" archive files. Both the maximum water surface elevations "maxele.63.nc" and the time series of water surface elevations "fort.63.nc" are included. </li> <li>ADCIRC input mesh (*.14) and mesh property files (*.13) are included in "ADCIRC_mesh_files.zip".</li> <li>Joint Karhunen-Loeve Polynomial Chaos (KL-PC) analysis python scripts with and without considering inundation are located in "klpc_analysis_scripts.zip". Requires <a href="https://github.com/noaa-ocs-modeling/EnsemblePerturbation">EnsemblePerturbation</a> python toolbox. </li> <li>Python scripts for analyzing and plotting the KL-PC results (Figures 6-14 and Table 1 in the manuscript) are located in "results_plotting_scripts.zip". Requires <a href="https://github.com/noaa-ocs-modeling/EnsemblePerturbation">EnsemblePerturbation</a> python toolbox. </li> </ol>
Data for paper Linear and Non-linear Modelling of Bromate Formation During Ozonation of Surface Water in Drinking Water Production
<p>Data for journal paper</p>
Models and data for coq-proof-difficulty
<p>Models and data for the coq-proof-difficulty project.</p>
Supplementary material 5 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
MaxEnt Supplementary Info
Supplementary material 4 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
Moran's I Correlograms
Supplementary material 3 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
Sampled PUD occurrence
Supplementary material 2 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
InVEST model configuration
Supplementary material 1 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
Sites used for validation
Data set from Fischertechnik Smart Factory Model at University of St.Gallen (Standard Fischertechnik Configuration)
<p>This is about 90 mins worth of data collected via the MQTT interface of the Fischertechnik Industry 9.0V smart factory model available at the University of St.Gallen. Each entry in the file corresponds to one message (as JSON object) received on a specific topic via MQTT.</p> <p>The description of the MQTT interface can be found here: <a href="https://github.com/fischertechnik/txt_training_factory/blob/master/TxtSmartFactoryLib/doc/MqttInterface.md">https://github.com/fischertechnik/txt_training_factory/blob/master/TxtSmartFactoryLib/doc/MqttInterface.md</a></p> <p>Check the following publications to learn more about our research using the model factory:</p> <p>Malburg, L., Seiger, R., Bergmann, R., & Weber, B. (2020). Using physical factory simulation models for business process management research. In <em>Business Process Management Workshops: BPM 2020 International Workshops, Seville, Spain, September 13–18, 2020, Revised Selected Papers 18</em> (pp. 95-107). Springer International Publishing.</p> <p>Seiger, R., Zerbato, F., Burattin, A., García-Bañuelos, L., & Weber, B. (2020, October). Towards iot-driven process event log generation for conformance checking in smart factories. In <em>2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW)</em> (pp. 20-26). IEEE.</p> <p>Seiger, R., Malburg, L., Weber, B., & Bergmann, R. (2022). Integrating process management and event processing in smart factories: A systems architecture and use cases. <em>Journal of Manufacturing Systems</em>, <em>63</em>, 575-592.</p> <p> </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.