Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
328
datasets available to search
ShareScore release 0.9.0
Dataset results
328 results for “Simulation results”
Conduction thermography - experimental data and simulations - preliminary results
Open the record for dataset details and reuse information.
ECMWF IFS potential salinity and temperature data interpolated to ALAMO float positions. Results of tropical cyclone tracking algorithm for TC Irma, Florence, Teddy and Ida simulations with ECMWF IFS.
<p>ECMWF IFS potential salinity and temperature data interpolated to ALAMO float positions. The data is from forecasts of tropical cyclone Irma, Florence, Teddy and Ida performed at horizontal atmosphere resolutions of TCo1279, TCo2559, TCo3999, TCo7999 and ocean resolutions of eORCA025 and eORCA025.</p> <p> </p> <p>Data also contains the results of tracking these tropical cyclones in the ECMWF IFS simulations. </p>
Results from simulation of Dynamic Precision Medicine 2-Window Trial
<p>Results from the simulation adapted from Beckman et al. PNAS 2012. For details see the preprint "A Generalized Evolutionary Classifier for Evolutionary Guided Precision Medicine" https://www.medrxiv.org/content/10.1101/2020.09.24.20201111v2</p> <p> </p> <p>The simualtion code that generated these results can be found at: https://github.com/GU-DPM/EvolutionaryClassifier</p> <p>------------------------------------------------------------------</p> <p>Annotations of simulation outcome files.</p> <p>Simulation outcomes are stored in multiple files.</p> <p>param_ALLDRUG_xxx.txt: parameter configuration values.<br>stopt_ALLDRUG_xxx.txt: survival times for each parameter configuration and each strategy.<br>dosage_ALLDRUG_xxx.txt: dosage combination sequences for each parameter configuration and each strategy.<br>pop_ALLDRUG_xxx.txt: population composition dynamics for each parameter configuration and each strategy.</p> <p>xxx denotes the index of the job when I ran simulations in my cluster. You can simply concatenate the files over all indices.</p> <p><br>param_ALLDRUG_xxx.txt format:</p> <p>Each line denotes a parameter configuration with the following entries:</p> <p>(1)parameter configuration index (one entry).<br>(2)initial population compositions x0 (four entries, x0(S), x0(R1), x0(R2), x0(R12)).<br>(3)growth rate g0 (one entry).<br>(4)drug sensitivity matrix Sa (eight entries, Sa(S,drug1), Sa(S,drug2), Sa(R1,drug1), Sa(R1,drug2), ...).<br>(5)transition rate matrix T (sixteen entries, T(S->S), T(S->R1), T(S->R2), T(S->R12), T(R1->S), T(R1->R1), T(R1->R2), T(R1->R12), ....</p> <p><br>stopt_ALLDRUG_xxx.txt format:</p> <p>Each line denotes the survival times (stopping times) of 10 strategies for each parameter configuration.</p> <p>strategy 0: strategy 0 in the PNAS paper.<br>strategy 1: strategy 2.2 in the PNAS paper.<br>strategy 2: strategy 2.2 in the PNAS paper for first two treatment decisions, followed by strategy 0 in the PNAS paper</p> <p>Each line has 11 entries: parameter configuration index and the survival times of the 10 strategies.</p> <p>The time horizon of the therapy is 1800 days. Each period is 45 days. If the patient is cured (the total tumor population = 0), then the survival time is reported as 1845 days. If the survival time is 1800 days, then the tumor population size is greater than 0 and smaller than the mortal level (1e13).</p> <p><br>dosage_ALLDRUG_xxx.txt format:</p> <p>Each line denotes the dosage combination sequence of each strategy for each parameter configuration.</p> <p>(1)parameter configuration index.<br>(2)strategy index (0-2).<br>(3)(drug1 dosage,drug2 dosage) at t=0.<br>(4)(drug1 dosage,drug2 dosage) at t=45.<br>...<br>(42)(drug1 dosage,drug2 dosage) at t=1755.</p> <p>If t exceeds the survival time of the strategy, then the drug dosage is set to -1.</p> <p><br>pop_ALLDRUG_xxx.txt format:</p> <p>Each line denotes the population composition dynamics of each strategy for each parameter configuration.</p> <p>(1)parameter configuration index.<br>(2)strategy index (0-9).<br>(3)(S,R1,R1,R12) population size at t=45.<br>(4)(S,R1,R1,R12) at t=90.<br>...<br>(42)(S,R1,R1,R12) at t=1800.</p> <p>If t exceeds the survival time of the strategy, then the population size is set to -1.</p> <p>Notice the dosage combination is reported at the beginning of each period, and the population composition is reported at the end of each period.</p> <p>When the best strategy over strategies 0-8 cures the patient and yields survival time 1800 days, I did not bother running strategy 9 (since it's time consuming). Thus the dosage combination and population composition should be ignored.</p> <p> </p>
The simulation results of runoff components and estimated surface water demands for the Yarkant River basin
<p>This dataset contains two directories as below. </p> <p>1. Hydrological_Simulation</p> <p>Historical_runoff_simulation.csv : the four runoff components at monthly scale (km^3/month) aggregated from daily runoff simulation results for the historical period</p> <p>The subdirectory "SSP126" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP1-2.6 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "SSP245" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP2-4.5 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "SSP585" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP5-8.5 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "48 experiments" contains the four runoff components at monthly scale (km^3/month) simulated for 48 climate scenarios, with the file name indicating the scenario settings:<br>t[+x]_p[+/-yy]_runoff_simulation.csv means the scenario with temperature increase at x C degree and precipitation change at +/- yy%.</p> <p>2. Water_Demand_Estimation</p> <p>Historical_water_demand_estimation.csv : the monthly water demands (km^3/month) from irrigation (including wheat, cotton, and corn) and other sectors for the historical period</p> <p>The subdirectory "SSP126" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP1-2.6 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "SSP245" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP2-4.5 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "SSP585" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP5-8.5 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "48 experiments" contains the monthly water demands (km^3/month) estimated for 48 climate scenarios, with the file name indicating the scenario settings:<br>t[+x]_p[+/-yy]_runoff_simulation.csv means the scenario with temperature increase at x C degree and precipitation change at +/- yy%.</p>
OpenGGCM simulation result during Substorm 2014-10-10.
<p>This dataset presents simulation results from the OpenGGCM model during a substorm event occurring on October 10, 2014. Detailed information regarding the data format can be accessed <a href="https://openggcm.sr.unh.edu/">[here] </a>. </p>
GITM Simulation Results for Extreme Events
<p>GITM Simulation Results for Extreme Events. This data files are prepared for the paper titled 'Possible Influence of Extreme Magnetic Storms on the Thermosphere in the High Latitudes' by Y. Deng et al (2017).</p> <p>In the filenames, 'Run1' represents a standard GITM run, 'Run2' represents a GITM run with both strong solar and geomagnetic forcing (see manuscript for details), and 'Run3' represents a GITM run with strong geomagnetic forcing.</p>
Simulation results of the agent-based model of urban insurgence with the effect of gathering sites and Koopman mode analysis
<p>The data set contains the simulation results of the agent-based model of urban insurgence with the effect of gathering sites and Koopman mode analysis. Some details on the agent-based model (without gathering sites) can be found in [Maria Fonoberova, Vladimir A. Fonoberov, Igor Mezic, Jadranka Mezic and P. Jeffrey Brantingham, Nonlinear Dynamics of Crime and Violence in Urban Settings, Journal of Artificial Societies and Social Simulation, 15(1), 2, http://jasss.soc.surrey.ac.uk/15/1/2.html, DOI: 10.18564/jasss.1921].</p> <p>Files in folder "0bar" are related to the case with 0 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "1bar" are related to the case with 1 preferential gathering site.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and average distance from the preferential gathering site. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "2bars" are related to the case with 2 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars2.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "3bars" are related to the case with 3 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars3.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "4bars" are related to the case with 4 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars4.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "5bars" are related to the case with 5 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "10bars" are related to the case with 10 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars10.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "20bars" are related to the case with 20 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars20.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "30bars" are related to the case with 30 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars30.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "prob25" are related to the case with 5 preferential gathering sites and 25% probability of agents moving towards them.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5_prob0.25.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "prob50" are related to the case with 5 preferential gathering sites and 50% probability of agents moving towards them.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5_prob0.50.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "prob75" are related to the case with 5 preferential gathering sites and 75% probability of agents moving towards them.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5_prob0.75.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "KMD" have detailed information for the case with 3 preferetial gathering sites and lattice size 200x200.</p> <p>File "2016_Actives_LD200_seed111_bars3.txt" has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens.</p> <p>Each file with name starting with Day for each active / intimidated (jailed) / non-intimidated (notjailed) agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship. Each file with name starting with Day for each LEO (cop) has his/her x-coordinate, y-coordinate, agent's state and -1, -1, -1. Last 3 entrees are not used. These files are provided for each time step of the simulation.</p>
Simulation Results for PhD Thesis
<p>These simulations are to be analysed with the code available from: https://bitbucket.org/MBradbury/slp-algorithms-tinyos</p>
Technical Report and Simulation Results for the Paper "It's About Time: On Optimal Virtual Network Embeddings under Temporal Flexibilities"
<p>Archiving the data and the technical report corresponding to our publication "It’s About Time: On Optimal Virtual Network Embeddings under Temporal Flexibilities".<br> The data was previously publicly available at https://net.t-labs.tu-berlin.de/~stefan/tvnep.html.</p>
jEPlus project files and results of simulation of apartment room model with NURBS lined shading
<p>This archive consists of two sets of jEPlus project files for simulation of apartment room model, based on PNNL high-rise apartment building, with a window shading lined by NURBS curves. The difference between the two sets of project files is that in one control points of NURBS can be set independently of each other, while in the other control points are divided in six predetermined groups with the same depth within each group.</p> <p>The archive also contains results of genetic optimisation with jEPlus+EA for locations representing main USA climate zones. For each climate zone there is an obj file, containing generated population of shading variants, and a csv file, containing values of shading parameters and the respective heating and cooling loads.</p> <p>Further, the archive contains two directories, one with Java code and another with gnuplot code, that had been used to process simulations results and create diagrams for the manuscript.</p>
Simulation results of two agent-based models of logistics systems
<p>The data set contains the simulation results of the two different agent-based models of logistics systems: <br> 1. A medical treatment facility (MTF) model, consisting of agents representing wounded soldiers and fixed sites representing medical facilities.<br> 2. A ship fueling (SF) simulation, consisting of agents representing fuel transport ships and fixed sites representing fuel-using bases.</p> <p>Folders in folder "MTF" are related to the MTF model.</p> <p>Files in folder "casualty_rateX" are related to the case with casualty rate of X new casualties per time step, where X = 30, 50, 70, . . . , 330.</p> <p>Each file "RunN_casualtyX_fullness.txt" has the "fullness" (defined as the ratio of the number of patients at a site to the total patient capacity of that site) of each site for each time step, where the run number N = 1, 2, 3, . . . , 100.</p> <p>Each file "RunN_casualtyX_dow.txt" has the total number of Dead Of Wounds that occur in all sites in each time step, where the run number N = 1, 2, 3, . . . , 100.</p> <p>Folders in folder "SF" are related to the SF model.</p> <p>Files in folder "siteMaxX" are related to the case with an initial (and maximum) site fuel value of X units, where X = 25, 50, 75, . . . , 200.</p> <p>Each file "assetTowedFuelHistory_siteMaxX_N.txt" has the number of towed fuel units for each asset for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p> <p>Each file "assetUseFuelHistory_siteMaxX_N.txt" has the number of onboard fuel units for each asset for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p> <p>Each file "siteHistory_siteMaxX_N.txt" has the number of fuel units at each site for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p>
Simulation data output used in the plasma nature of lightning channels and the resulting nonlinear resistance
<p>Simulation data shown in "The plasma nature of lightning channels and the resulting nonlinear resistance", a paper submitted for publication in JGR by da Silva et al. [2019].</p> <p>Each folder contains corresponds to a simulation case, as described below. Each file, e.g., s_1.m, corresponds to a different value of current input (all other parameters constant).</p> <p>Use the Matlab script load_and_plot_data.m to load and plot the data. A sample figure output is provided (Sample_Figure_Rss_king_s_3.pdf). The eight panels in the figure show the temporal evolution of all (important) quantities inside the lightning plasma channel, allowing for a detailed inspection of the results shown in da Silva et al. [JGR, 2019].</p> <p>See the file README.txt for further instructions.</p>
Path Planning for Autonomous Bus Driving in Urban Environments - Simulation Results
<pre>This video contains simulation results for the scientific article "Path Planning for Autonomous Bus Driving in Urban Environments".</pre>
Path Planning for Autonomous Bus Driving in Highly Constrained Environments - Simulation Results
<pre>This video contains simulation results for the scientific article "Path Planning for Autonomous Bus Driving in Highly Constrained Environments".</pre>
Finite element analysis results from simulation of fusion energy heat exchange component: hybrid CAD/IBSim model including a graphite foam interlayer
<p>Temperature profile data from a finite element analysis of a conceptual design for a fusion energy heat exchange component (monoblock). The mesh is a hybrid from a computer aided design (CAD) drawing for the pipe and armour and IBSim for the interlayer. The IBSim interlayer is generated directly from a 3D volumetric image of a graphite foam block (KFoam). The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1). Conversion of the data to FE mesh was achieved using ScanIP, part of the Simpleware suite of programmes, version 7 (Synopsys Inc., Mountain View, CA, USA).</p> <p>The mesh used for the analysis is available as a separate dataset:</p> <p><a href="https://doi.org/10.5281/zenodo.3522319">https://doi.org/10.5281/zenodo.3522319</a></p> <p>This data was used originally for the following publications (please cite if re-using the data):</p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Improving modelling of complex geometries in novel materials using 3D imaging”, Proceedings of NEA International Workshop on Structural Materials for Innovative Nuclear Systems, Manchester, UK, July 2016. <a href="https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf">https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf</a></p>
The AF2 predicted structures and MD simulation results for the paper "In-situ structural insights into activity regulation of mammalian pyruvate dehydrogenase complex"
<p>Thank you for your interest in our work. You can discover content that interests you within the respective compressed packages, accompanied by “readme” files.</p>
Simulated data ground truth and results for CALDER
<p>Supporting data for CALDER 2019 publication</p> <ul> <li>branchsim_results.ipynb: code used to load simulated results and generate figures used in paper</li> <li>exome_sim3_groundtruth.pickle: ground truth for simulated datasets, see notebook for usage example</li> <li>exome3_s.pickle: results from methods used for benchmarking on simulated data, see notebook for usage example</li> <li>calder_util.py: supporting functions used in notebook</li> <li>BsimExomeResults.zip: raw result files generated by CALDER, CALDER+pyclone, PhyloWGS, and CITUP on simulated datasets</li> </ul>
Reproduction of PANDA: results for simulations (parameter sensitivity)
<p>These data are the results obtained from applying PANDA to parameter sensitivity analysis and can be used to reproduce the analysis in the paper "Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA".</p> <p>To ensure that the file paths match those used in the code, please place the files in the following directories within your working directory before extracting them:</p> <p>"Results/simulations/parameter_sensitivity/paired/PANDA_results/parameter_sensitivity_paired_PANDA_results_sc_results.zip"</p> <p>"Results/simulations/parameter_sensitivity/paired/PANDA_results/st_results/parameter_sensitivity_paired_PANDA_results_st_results_*.zip"</p> <p>"Results/simulations/parameter_sensitivity/unpaired/PANDA_results/parameter_sensitivity_unpaired_PANDA_results_sc_results.zip"</p> <p>"Results/simulations/parameter_sensitivity/unpaired/PANDA_results/st_results/parameter_sensitivity_unpaired_PANDA_results_st_results_*.zip"</p>
Reproduction of PANDA: results for simulations
<p>These data are the results obtained from applying PANDA to simulations and can be used to reproduce the analysis in the paper "Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA".</p> <p>To ensure that the file paths match those used in the code, please place the files in the following directories within your working directory before extracting them:</p> <p>"Results/simulations/paired_scenario/paired_scenario_comparison_methods.zip"</p> <p>"Results/simulations/paired_scenario/PANDA_results/paired_scenario_PANDA_results_sc_results.zip"</p> <p>"Results/simulations/paired_scenario/PANDA_results/st_results/paired_scenario_PANDA_results_st_results_uniform_ST.zip"</p> <p>"Results/simulations/paired_scenario/PANDA_results/st_results/paired_scenario_PANDA_results_st_results_boundary_ST.zip"</p> <p>"Results/simulations/paired_scenario/PANDA_results/st_results/paired_scenario_PANDA_results_st_results_uniform_Visium.zip"</p> <p>"Results/simulations/paired_scenario/PANDA_results/st_results/paired_scenario_PANDA_results_st_results_boundary_Visium.zip"</p> <p>"Results/simulations/unpaired_scenario/unpaired_scenario_comparison_methods.zip"</p> <p>"Results/simulations/unpaired_scenario/PANDA_results/unpaired_scenario_PANDA_results_sc_results.zip"</p> <p>"Results/simulations/unpaired_scenario/PANDA_results/st_results/unpaired_scenario_PANDA_results_st_results_uniform_ST.zip"</p> <p>"Results/simulations/unpaired_scenario/PANDA_results/st_results/unpaired_scenario_PANDA_results_st_results_boundary_ST.zip"</p> <p>"Results/simulations/unpaired_scenario/PANDA_results/st_results/unpaired_scenario_PANDA_results_st_results_uniform_Visium.zip"</p> <p>"Results/simulations/unpaired_scenario/PANDA_results/st_results/unpaired_scenario_PANDA_results_st_results_boundary_Visium.zip"</p> <p>"Results/simulations/merfish.zip"</p> <p>"Results/simulations/reference_choice.zip"</p> <p>"Results/simulations/time_memory.zip"</p>
Wave Simulation Results Using XBeach and Application Results at Kansai International Airport (KIX)
<p>The dataset includes input files for the numerical simulations (grids, topography, boundary conditions, and parameter settings), XBeach simulation results for a wind speed of 30 m/s, and the application results for Kansai International Airport (KIX).</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.