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855 results for “model system”
Raw data to "Opioid sequestration by intravenous lipid emulsion – comparison of lipophilicity in a cell-free system and cellular model"
<p>Data that resulted from the conduction of the in vitro part of the project: Intravenous lipid emulsions as a treatment in acute opioid poisoning - pharmacokinetic and pharmacodynamic evaluation in the rabbit model. It served as raw data for the publication Opioid sequestration by intravenous lipid emulsion – comparison of lipophilicity in a cell-free system and cellular model (draft title). </p>
Antarctic surface climate and surface mass balance in the Community Earth System Model version 2 (1850-2100) - AWS data
<p>This Antarctica AWS temperature and wind speed dataset was compiled by Alexandra Gossart and Niels Souverijns (<a href="https://doi.org/10.1175/JCLI-D-19-0030.1">https://doi.org/10.1175/JCLI-D-19-0030.1</a>).</p>
Assimilation of NASA's Airborne Snow Observatory snow measurements for improved hydrological modeling: A case study enabled by the coupled LIS/WRF-Hydro system
<p>Data Analysis Scripts and Post-Processed Model Data for a case study using assimilation of ASO Snow Data into the NASA LIS/WRF-Hydro Model. </p> <p>Manuscript Citation:</p> <p>Lahmers T. M., S. V. Kumar, D. Rosen, A. L Dugger, D. Gochis, J. A. Santanello, C. Gangodagamage<sup>,</sup> and R. Dunlap,<strong> </strong>2020: Assimilation of NASA’s Airborne Snow Observatory snow measurements for improved hydrological modeling: A case study enabled by the coupled LIS/WRF-Hydro system,<em>Water Resour. Res.,</em></p>
Supplemental data files for: Evidence for the superposition of tectonic systems in the northern Songliao Block, NE China, revealed by a 3-D electrical resistivity model
<p>Data files for a 3-D electrical resistivity model in the northern Songliao Block, NE China, including the MT data observed there (note the data format is for 3-D inversion using ModEM), and the preferred resistivity model.</p> <p>The software EMdesk from Jilin Kingti Geoexploration Tech, Ltd (Changchun, China) can be used for data analysis and modeling (http://www.kingti.net).</p>
Auxiliary Euro-Calliope datasets: Spatial data to represent a European energy system model at several spatial resolutions
<p>Main output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://doi.org/10.5281/zenodo.3246302">https://doi.org/10.5281/zenodo.3246302</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with two key differences:</p> <ol> <li>The spatial extent has been expanded to include Iceland.</li> <li>Two new spatial resolutions have been added: `ehighways` and `ehighways_disaggregated`.</li> </ol> <p>`ehighways` defines 98 regions based on the result of work undertaken in the European Commission Seventh Framework Programme project e-HIGHWAY 2050 [1]. The regions cover 35 European countries; 19 are described at a national resolution and the rest at a subnational resolution. Those at a subnational resolution are aggregated from NUTS3-2006 statistical units. `ehighways_disaggregated` provides the data at the resolution of statistical units in Europe, which is then aggregated to produce the data at the `ehighways` resolution. The mapping from statistical units to ehighways regions is defined in `./ehighways/statistical_units_to_ehighways_regions.csv`. `./ehighways/units.png` shows a map of the resulting 98 `ehighways` regions. The region colours are used to help differentiate regions and have no other meaning.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p>[1] Anderski, T., Surmann, Y., Stemmer, S., Grisey, N., Momot, E., Leger, A.-C., Betraoui, B., and van Roy, P. (2014). European cluster model of the Pan-European transmission grid (e-HIGHWAY 2050)</p>
Systemic application of the TRPV4 antagonist GSK2193874 induces tail vasodilation in a mouse model of thermoregulation
<p>In humans, skin is a primary thermoregulatory organ, with vasodilation leading to rapid body cooling, whereas in Rodentia the tail performs an analogous function. Many thermodetection mechanisms are likely to be involved including transient receptor potential vanilloid-type 4 (TRPV4), an ion channel with thermosensitive properties. Previous studies have shown that TRPV4 is a vasodilator by local action in blood vessels, so here we investigated whether constitutive TRPV4 activity effects <em>Mus muscularis</em> tail vascular tone and thermoregulation. We measured tail blood flow by pressure plethysmography in lightly sedated Mus muscularis (CD1 strain) at a range of ambient temperatures, with and without intraperitoneal administration of the blood brain barrier crossing TRPV4 antagonist GSK2193874. We also measured heart rate and blood pressure. As expected for a thermoregulatory organ, we found that tail blood flow increased with temperature. However, unexpectedly we found that GSK2193874 increased tail blood flow at all temperatures, and we observed changes in heart rate variability. Since local TRPV4 activation causes vasodilation that would increase tail blood-flow, these data suggest that increases in tail blood flow resulting from the TRPV4 antagonist may arise from a site other than the blood vessels themselves, perhaps in central cardiovascular control centres.</p>
Supplementary data - "Learning Reduced Models for Large-Scale Agent-Based Systems"
<p>This repository contains supplementary data on my PhD thesis "Learning Reduced Models for Large-Scale Agent-Based Systems". Chapters 1-3, 7 and A do not have supplementary data.</p> <p><strong>Chapter 4</strong></p> <ul> <li><em>Large_deviation_example.zip</em> contains the trajectory for Figure 4.8.</li> <li><em>mean_exit_time*</em> contains the raw data to compute the mean exit time and standard deviation for the ABM process (JP) and SDE process (CLE). It contains additionally a precomputed mean and standard deviation as well as the corresponding numbers of agents.</li> <li><em>transition_matrix*</em> contain the computed box discretizations as MATLAB and Numpy files as used for Figures 4.2-4.4, 4.6 and Tables 4.1 and 4.2.</li> </ul> <p><strong>Chapter 5</strong></p> <ul> <li><em>CVM_2021-07-09-15-53_training_data.npz</em> contains the training data for Figure 5.7 a and b.</li> <li><em>CVM_2021-09-29-07-13_distribution.npz </em>contains the raw data for Figure 5.7 c.</li> <li>The remaining data for Chapter 5 can be found in the related dataset <a href="https://doi.org/10.5281/zenodo.4522119">doi.org/10.5281/zenodo.4522119</a>.</li> </ul> <p><strong>Chapter 6</strong></p> <ul> <li><em>CVM_pareto_estimate</em> contains trajectory data required for Figure 6.6 b to estimate points in the Pareto Front using the civil violence model. </li> <li><em>CVM_training_data</em> contains the training data to construct the surrogate model. Each data set consists of <em>CVM_*_cops_train.npz</em> as training set, <em>CVM_*_cops_trajectory.npz</em> as sample trajectory and <em>CVM_*_cops.pkl</em> to compute the training data.</li> <li><em>CVM_covering_iterations_8.mat</em> Pareto set covering after 8 iterations for the civil violence model. Required for Figure 6.6 a.</li> <li><em>CVM_pareto_set+front.npz</em> is required for Figure 6.6 b. </li> <li><em>CVM_surrogate_model.mat</em> contains the surrogate model for the civil violence model</li> <li><em>Expl_iterations_*</em> contains Pareto set coverings after 8 and 12 iterations for Example 6.1.4 and Figure 6.1.</li> <li><em>VM_covering_iterations_12.mat</em> contains the Pareto set covering depicted in Figure 6.4 a.</li> <li><em>VM_ODE_covering_iterations_12_subset_front.mat</em> contains the Pareto set covering depicted in Figure 6.5 and 6.5 c.</li> <li><em>VM_ODE_covering_iterations_12_subset.mat</em> contains the Pareto set covering depicted in Figure 6.5 and 6.5 d.</li> <li><em>VM_ODE_covering_iterations_12.mat</em> contains the Pareto set covering depicted in Figure 6.4 b.</li> <li><em>VM_surrogate_model.mat</em> contains the surrogate model for the extended voter model.</li> <li><em>VM_test_points_non_pareto.npz</em> contains Non-Pareto points in Figure 6.5 and 6.5 d.</li> <li><em>VM_test_points_pareto.npz</em> contains Pareto points in Figure 6.5 and 6.5 c.</li> </ul>
Content Analysis on System Dynamics Modelling Application in Agriculture
<p>The data is a compilation of journal articles retrieved from three databases - Scopus, Web of Science, and Science Direct, using this Boolean search string: ("system dynamics" OR "systems thinking" OR "causal loop diagram") AND ("Agri*" OR "Food" OR "Crop" OR "Meat" OR "Animal" OR "Livestock")). </p>
SESMG scenario-files of the study "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution"
<p>This dataset contains model scenario-files belonging to the publication "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution".</p> <p>The individual scenarios can be executed and evaluated with the "Spreadsheet Energy System Model Generator" (<a href="https://github.com/chrklemm/SESMG">SESMG</a>) <a href="https://github.com/chrklemm/SESMG/tree/v0.4.0rc1">v0.4.0rc1</a></p> <p>The respective file names indicate to which model run mentioned in the main study the scenario-files belong. For model runs for which no sepparate scenario file exists, the scenario "reference.xlsx" with adjusted SESMG settings was used.</p> <p> </p>
The June 2012 North American Derecho: A testbed for evaluating regional and global climate modeling systems at cloud-resolving scales
<p>This is the companion data for the manuscript titled 'The June 2012 North American Derecho: A testbed for evaluating regional and global climate modeling systems at cloud-resolving scales', submitted to the Journal of Advances in Modeling Earth Systems in September 2022.</p> <p>derecho_simulation_result: this folder contains part of the SCREAM RRM outputs I ran on NERSC Cori in 2021-2022 corresponding to the simulation in Table 1 of the manuscript.</p> <p>wrf_simulation_result: this folder contains part of the WRF outputs run by Jianfeng Li from PNNL (jianfeng.li@pnnl.gov) in 2022 corresponding to the simulations in Table 4 of the manuscript.</p> <p>plot_script: this folder includes python scripts to plot figures shown in the manuscript.</p> <p>ASOS_station: this txt file contains the processed ASOS station wind speed used in the manuscript.</p> <p><br> Unfortunately, all model outputs are large (~ 3.8 TB for SCREAM RRM and 3.1 TB for WRF). Therefore, I only provide the variables (i.e., OLR, precipitation, composite radar reflectivity, and 10-m wind speed) used directly to generate figures in this repository. All model outputs are archived on tape at NERSC.</p> <p>For more details, refer to the manuscript, or contact me (wrliu@ucdavis.edu).</p>
Project files provided as supporting information to the manuscript "Fast, accurate, and system-specific variable-resolution modelling of proteins"
<p>Project files provided as supporting information to the manuscript "Fast, accurate, and system-specific variable-resolution modelling of proteins".</p> <p>The dataset contains the following files:</p> <p>- RMSD_adk: files containing the root-mean-square deviation computed on the C-alpha atoms/beads in the atomistic and CANVAS simulations of adk (Figure 6).</p> <p>- RMSF_adk: files containing the root-mean-square fluctuations of the C-alpha atoms/beads in the atomistic and CANVAS simulations of adk (Figure 6).</p> <p>- APBS_adk: PQR file of the system and DX file of the surface potential, for both the atomistic and CANVAS systems (Figure 7).</p> <p>- SASA_antibody: files containing the per-residue solvent accessible surface area of the atomistic region of the ake protein, in the atomistic and CANVAS simulations (Figure 8).</p> <p>- RMSF_antibody: files containing the root-mean-square fluctuations of the C-alpha atoms/beads in the atomistic and CANVAS simulations of antibody (Figure 9).</p> <p>- APBS_antibody: PQR file of the system and DX file of the surface potential, for both the atomistic and CANVAS systems (Figure 10).</p> <p>- SASA_antibody: files containing the per-residue solvent accessible surface area of the hinge region of the antibody for each conformational cluster, in the atomistic and CANVAS simulations (Figure 11).</p> <p>- RMSIP_antibody: RMSIP between the essential subspaces computed from the atomistic and CANVAS simulations (Figure S3).</p> <p>- rgyr_antibody: files containing the radii of gyration of the antibody for each conformational cluster, in the atomistic and CANVAS simulations (Figure S4).</p> <p>- ake_AA.mp4: video of the all-atom simulation of adenylate kinase</p> <p>- ake_canvas.mp4: video of the canvas simulation of adenylate kinase</p>
Supporting Information for the Journal Article "Self-Parametrizing System-Focused Atomistic Models"
<p>This dataset contains the supporting information published together with the article "Self-Parametrizing System-Focused Atomistic Models" (<a href="https://doi.org/10.1021/acs.jctc.9b00855"><em>J. Chem. Theory Comput.</em>, <strong>2020</strong>, <em>16</em>, 1646</a>).</p>
Technoeconomic dataset for long-term energy systems modelling in Ghana (2015-2065)
<div> <p>Technoeconomic data and assumptions for energy systems modelling in Ghana, including capital cost, fixed cost, variable cost, power plants' characteristics (e.g. list of existing power plants in Ghana, operational life, efficiency, capacity factors), fuels' prices and emission intensities, power demand/consumption/generation, residual capacity, fossil fuels' reserves, and renewable energy potentials in 2015-2065. This document is complementary to CCG Starter Data Kit for Ghana (Allington et al., 2023) as it updates it to ensure the OSeMOSYS models are closer to the Ghanaian context.</p> </div>
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>
Code and Data Supplement for Using feature importance as exploratory data analysis tool on earth system models
<p>This contains:</p> <ul> <li>Code for all analyses in</li> <li>E3SM data</li> </ul> <p>For the paper Using <em>feature importance as exploratory data analysis tool on earth system models.</em></p>
eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies (data)
<p>Dataset and results used for the simulations in following publication:</p> <p>Carsten Wegkamp, Henrik Wagner, Eike Niehs, Julien Essers, Marcel Lüdecke, Mattias Hadlak, Bernd Engel:<br>"<strong>eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies</strong>",<br>Open Source Modelling and Simulation of Energy Systems (OSMSES) 2024, Vienna, Austria, 2024</p> <p> </p> <p>This contains the input (scenario) files for the building & grid scenario and the results of the two simulations.<br>It uses the elenia Energy Library (eELib) with release version 1.0.0: https://gitlab.com/elenia1/elenia-energy-library</p>
Description of Catchment Systems Model (CSM)
<p>A flow diagram and an introductory document on the farm to landscape modelling framework for the assessment of the trade-offs and co-benefits for the mitigation of diffuse pollution in England were shared. They provide a short description of 5 interlinked modules, key references for the model and full list of mitigation measures available. </p>
Thermodynamic characterization of the (H2 + C3H8) system significant for the hydrogen economy: Experimental (p, rho, T) determination and equation of-state modelling
<p>File: 2023_IJHE_Manuscript_repository.docx</p> <p>This is an author-created, un-copyedited version of an article accepted for publication in the International Journal of Hydrogen Energy (2023, 48 (23), 8645-8667). The editor of the Journal is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher-authenticated, Open-Access version is available online at: https://doi.org/10.1016/j.ijhydene.2022.11.170<br><br>File: 2023_IJHE_Results_Repository.xlsx</p> <p>This is the MS Excel data file of the paper. </p> <p> </p> <p> </p>
Role of Troposphere-Convection-Land Coupling in the Southwestern Amazon Precipitation Bias of the Community Earth System Model version 1 (CESM1)
<p>Necessary outputs and scripts for recreating the figures for the journal article with the same title.</p>
Some atmospheric model fields shown in paper titled "E3SMv0-HiLAT: A Modified Climate System Model Targeted for the Study of High Latitude Processes
<p>These files contain 2-D fields of atmospheric T and P maps, as climatologies averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, and from the CESM Large Ensemble control simulation (LENS), the basis for two of the plots in our manuscript that is, as of January 2019, in review at JAMES. All of these data files are binary, written sequentially, 288 x 192. Also included are some PowerPoint-generated pdf images of the two fields.</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.