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328 results for “Simulation results”
Simulation results for AGS paper
<p>Simulation data results for the DrugLogics AGS paper. See <a href="https://github.com/druglogics/ags-paper">https://github.com/druglogics/ags-paper</a></p>
Results of ISMIP6 CMIP6 forced simulations: a multi-model ensemble of the Greenland and Antarctic ice sheet evolution over the 21st century
<p>This archive provides the ice sheet model outputs produced as part of the publication "Payne et al. 2021 Future sea level change under CMIP5 and CMIP6 scenarios from the Greenland and Antarctic ice sheets", published in GRL</p> <p>Contact: Tony Payne a.j.payne@bristol.ac.uk, Sophie Nowicki sophien@buffalo.edu, ismip6@gmail.com </p> <p><br> Further information on ISMIP6 can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Greenland</p> <p>Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in the process producing this data set. Acknowledgements should have language similar to the below (if you only use CMIP5 forcing, remove CMIP6 and vice versa).</p> <p>“We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>For Greenland datasets </p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., Fettweis, X., and Nowicki, S.: Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution , The Cryosphere, 14, 985–1008, https://doi.org/10.5194/tc-14-985-2020, 2020.</p> <p>Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: <br> Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p>For Antarctica datasets</p> <p>Seroussi, H., Nowicki, S., Simon, E., Abe-Ouchi, A., Albrecht, T., Brondex, J., Cornford, S., Dumas, C., Gillet-Chaulet, F., Goelzer, H., Golledge, N. R., Gregory, J. M., Greve, R., Hoffman, M. J., Humbert, A., Huybrechts, P., Kleiner, T., Larour, E., Leguy, G., Lipscomb, W. H., Lowry, D., Mengel, M., Morlighem, M., Pattyn, F., Payne, A. J., Pollard, D., Price, S. F., Quiquet, A., Reerink, T. J., Reese, R., Rodehacke, C. B., Schlegel, N.-J., Shepherd, A., Sun, S., Sutter, J., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., and Zhang, T.: initMIP-Antarctica: an ice sheet model initialization experiment of ISMIP6, The Cryosphere, 13, 1441–1471, https://doi.org/10.5194/tc-13-1441-2019, 2019.</p> <p>Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H., Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134, https://doi.org/10.5194/tc-14-3111-2020, 2020.</p> <p><br> Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p> </p>
Scheduling Mechanisms to Control Spread of Covid-19 (Simulation Results)
<p><span>We study scheduling mechanisms that explore the trade-off between containing the spread of COVID-19 and performing in-person activity in organizations. </span><span>Our mechanisms, referred to as<span> </span></span><i>group scheduling</i><span>, are based on partitioning the population<span> </span></span><i>randomly</i><span><span> </span>into groups and scheduling each group on appropriate days with possible gaps (when no one is working and all are quarantined). Each group interacts with no other group and, importantly, any person who is symptomatic in a group is quarantined.</span><br> <br> <span>We show that our mechanisms effectively trade-off in-person activity for more effective control of the COVID-19 virus spread. In particular, we show that a mechanism which partitions the population into two groups that alternatively work in-person for five days each, flatlines the number of COVID-19 cases quite effectively, while still maintaining in-person activity at 70% of pre-COVID-19 level. Other mechanisms that partitions into two groups with less continuous work days or more spacing or three groups achieve even more aggressive control of the virus at the cost of a somewhat lower in-person activity (about 50%). We demonstrate the efficacy of our mechanisms by theoretical analysis and extensive experimental simulations on various epidemiological models based on real-world data.</span></p>
Simulation Results to Analyse the Benefits of Information Sharing - Dataset
<p>This is a data set used to obtain the results described in the paper "Shall We Collaborate? A Model to Analyse the Benefits of Information Sharing" published in the proceedings of the 3rd Workshop on Information Sharing and Collaborative Security (WISCS 2016), Vienna, Austria, 2016.</p>
Simulation Results Regarding Fairness, Dissatisfaction, Relative Time Loss and Inefficiency on 2+1 Roadways.
<p>Simulation results obtained by the Cooperative Lane Management and Traffic flow Optimisation (CoLMTO) platform (see https://doi.org/10.5281/zenodo.801531) regarding fairness, dissatisfaction, relative time loss and inefficiency on 2+1 roadways.</p>
Spidroin Martini Coarse grain and atomistic simulation results
<p>The pdb files contained in this zip archive are the outputs of our simulations of the pre-spun silk proteins (MaSp1 and MaSp2) found in Black Widow silk dope. We used a combination of Martini V2.6 Martini 3.0 coarse grain, Alphafold, and Charmm36 atomistic simulations. </p>
Experimental and simulation results of cure-induced strains for a cure study of an conventional epoxy resin
<p>Datasets of a number of case studies were investigated experimentally in an unconstrained setup. monitoring the temperature development and cure-induced shrinkage of an epoxy thermoset. Simulations using a 2D finite element thermomechanical model are used to elaborate on the thermal behaviour as well as the cure-induced strains seen experimentally for the cases provided. </p> <p>Please cite this folder of shared data and scripts alongside the original publication: <a href="https://doi.org/10.3390/polym16172435">doi.org/10.3390/polym16172435</a></p> <p>Cure cases published:</p> <p>C1 - [40_L]80_L</p> <p>C2 - [50_L]80_L</p> <p>C3 - [50_M]80_M</p> <p>C4 - [50_S70_S]80_M</p> <p>C5 - [50_S60_M]80_M</p> <p>C6 - [50_S60_L]80_M</p> <p>C7 - [50_S30_S]80_M</p> <p>C8 - [50_S30_M]80_M</p> <p> </p> <p>Model file: 1D_CASH.cae - This file contains the model with FE mesh and BC</p> <p>Python script: CASH_1D.py - This file contains the scripting, linking the cure profile with the model and other modifications necessary. The model calls the immediately important state-dependent variables (SDV) to the .odb file. The script then analyses the results from relevant nodal values and saves as an '_FEA_data.csv' file. </p> <p>CKP2_Submit.sh - Shell script for submitting jobs on a remote cluster if necessary. </p> <p> </p>
Simulation results of routing algorithms for multilayer networks
<p>Most current routing protocols are based on path computation algorithms in graphs (e.g., Dijkstra, Bellman-Ford, etc.). These algorithms have been studied for a long time and are very well understood, both in a centralized and distributed context, as long as they are applied to a network having a single communication protocol. The problem becomes more complex in the multi-protocol case, where there is a possibility of encapsulation of some network protocols into others, therefore inducing nested tunnels. The classic algorithms cited above no longer work in this case because they cannot manage the protocol encapsulations and the corresponding protocol stacks. In this work, we propose a highly parallelizable algorithm that takes into account protocol encapsulations as well as protocol conversions in order to compute shortest paths in a multi-protocol network. To achieve this computation efficiently, we study the transitive closure between subpaths (i.e., the concatenation of two subpaths to obtain a longer one) in the case where each subpath induces a protocol stack, and thus tunnels. Leveraging on Software-Defined Networks with a controller having a highly parallel architecture enables us to compute the routing tables of all nodes in a very efficient way. Experimentation results on both random and realistic topologies show that our algorithm outperforms the previous solutions proposed in the literature.</p>
Simulated results from an agent-based model examining inequality and innovation in social networks
<p>Theories of innovation often balance contrasting views that either smart people create smart things or smartly constructed institutions create smart things. While population models have shown factors including population size, connectivity, and agent behavior as crucial for innovation, few have taken the individual-central approach seriously by examining the role individuals play within their groups. To explore how network structures influence not only population-level innovation but also performance among individuals, we studied an agent-based model of the Potions Task, a paradigm developed to test how structure affects a group's ability to solve a difficult exploration task. We explore how size, connectivity, and rates of information sharing in a network influence innovation and how these have an impact on the emergence of inequality in terms of agent contributions. We find, in line with prior work, population size has a positive effect on innovation, but that large and small populations perform similarly per capita; that many small groups outperform fewer large groups; that random changes to structure have few effects on innovation; and that the highest performing agents tend to occupy more central network positions. Moreover, we show that every network factor which facilitates innovation leads to a proportional increase in inequality of performance, creating "genius effects" among otherwise "dumb" agents in both idealized and real-world networks.</p>
Photoactivation of the Orange Carotenoid Protein Requires Two Light-Driven Reactions Mediated by a Metastable Monomeric Intermediate – Absorption Spectra and Global Analysis Results, Molecular Dynamics Simulations
<p>Time-resolved absorption and molecular dynamics trajectory datasets associated with: Rose, J. B.; Gascón, J. A.; Sutter, M.; Sheppard, D. I.; Kerfeld, C. A.; Beck, W. F. Photoactivation of the Orange Carotenoid Protein Requires Two Light-Driven Reactions Mediated by a Metastable Monomeric Intermediate. <i>Phys. Chem. Chem. Phys.</i> <strong>2023</strong>, DOI: 10.1039/d3cp04484j.</p>
Model America - Arizona extract from ORNL's AutoBEM - (Archetypes with simulation results)
<p>Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (<a href="https://bit.ly/AutoBEM">bit.ly/AutoBEM</a>).</p> <p>Two sets of data are provided for 2,555,153 buildings located within the boundary of Arizona in the United States:</p> <ol> <li><strong>Data (420KB *.csv) - Arizona 527 building archetypes based on different Climate Zones (2B-136, 3B-133, 4B-128, 5B-130) with simulation results.</strong></li> <li><strong>Models (38.8MB *.zip by Climate Zone) – OpenStudio and EnergyPlus building energy models named according to ID.</strong></li> </ol> <p> </p> <p>This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).</p>
Model America - Summer 2020 Arizona Building Energy Simulation Results from ORNL's AutoBEM
<p>Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (<a href="https://bit.ly/AutoBEM">bit.ly/AutoBEM</a>).</p> <p>Data is provided for 2,555,152 buildings located within the boundary of Arizona in the United States:</p> <p><strong>Data (1.48GB *.csv) - Arizona 2,555,152 building information data with simulation results separated by county. (Simulation results are for June 1st-August 31st, 2020)<br></strong></p> <p><strong>Building Information Data Fields:</strong></p> <ul> <li>ID</li> <li>CZ</li> <li>Centroid</li> <li>State_Abbr</li> <li>Footprint2D</li> <li>Height,Area2D</li> <li>BuildingType</li> <li>NumFloors</li> <li>Area</li> <li>Standard</li> <li>NumWalls</li> <li>WWR_surfaces</li> </ul> <p><strong>Energy Simulation Data Fields:</strong></p> <ul> <li>Electricity_Facility[kBTU]</li> <li>NaturalGas_Facility[kBTU]</li> <li>Heating_Electricity[kBTU]</li> <li>Cooling_Electricity[kBTU]</li> <li>Heating_NaturalGas[kBTU]</li> <li>Heating_Total[kBTU]</li> <li>WaterSystems_Electricity[kBTU]</li> <li>Lighting_Electricity[kBTU]</li> <li>Equipment_Electricity[kBTU]</li> <li>Fans_Electricity[kBTU]</li> <li>Pumps_Electricity[kBTU]</li> <li>HeatRejection_Electricity[kBTU]</li> <li>HeatRecovery_Electricity[kBTU]</li> <li>Surface_Outside_Face_Heat_Emission[GJ]</li> <li>Zone_Exfiltration_Heat_Loss[GJ]</li> <li>Zone_Exhaust_Air_Heat_Loss[GJ]</li> <li>Heat_Rejection_Energy[GJ]</li> <li>Anthropogenic_Emissions[GJ]</li> </ul> <p>This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).</p>
MLPF results on the simulated CLIC dataset
<p>Updates over the previous version:</p> <ul> <li>updated validation outputs for the cluster-based model <ul> <li>fixed a bug with how the PF candidates were stored</li> <li>added single particle gun samples to validation</li> </ul> </li> <li>added new timing runs <ul> <li>for the baseline algo, included memory information</li> <li>run the GNN model up to ~10k inputs</li> </ul> </li> <li>added hypertuning summary tables</li> </ul> <p>Trained models and evaluation results for the upcoming paper "Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors", https://doi.org/10.48550/arXiv.2309.06782.</p> <p>The archive contains the following subfolders:</p> <ul> <li><strong>clusters_best_tuned_gnn_clic_v130</strong> <ul> <li>MLPF GNN model configs and weight files after hypertuning</li> <li>the inputs are reconstructed tracks and Pandora clusters</li> <li>the outputs are reconstructed PF candidates</li> <li>trained on tt and qq v1.3.0 (1M events each)</li> </ul> </li> <li><strong>hits</strong> <ul> <li>MLPF GNN model configs and weight files</li> <li>inputs are reconstructed tracks and calorimeter hits</li> <li>outputs are reconstructed PF candidates</li> <li>trained on tt, qq and gun samples (K0L, gamma, pi+-, pi0, neutron, ele, mu) v1.2.0</li> <li>training was restarted several times from previous checkpoints</li> </ul> </li> <li><strong>hypertuning</strong> <ul> <li>GNN and transformer model before and after hypertuning</li> <li>summary tables of the hypertuning runs</li> </ul> </li> <li><strong>timing</strong> <ul> <li>scaling study of baseline PF with number of gun particles on CPU</li> <li>scaling study of GNN model with number of input elements on GPU</li> </ul> </li> <li><strong>gpu_scaling</strong> <ul> <li>the scaling study of model training on multiple accelerator cards</li> </ul> </li> </ul> <p> </p> <p>The training dataset is available at</p> <ul> <li>Pata, Joosep, Wulff, Eric, Duarte, Javier, Mokhtar, Farouk, Zhang, Mengke, Girone, Maria, & Southwick, David. (2023). Simulated datasets for detector and particle flow reconstruction: CLIC detector (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8260741</li> </ul>
Discovery of Natural Dual Inhibitors from ZINC Database Targeting Thymic Stromal Lymphopoietin (TSLP) and Interleukin-33 (IL-33) as Potential Anti-Allergy Agents - MD Simulation XVG Files and Complex PDB Files (Docking Results)
<p>This repository contains input and output files of MD simulations along with MM/PBSA related files</p> <p>Manuscript Title: Discovery of Natural Dual Inhibitors from ZINC Database Targeting Thymic Stromal Lymphopoietin (TSLP) and Interleukin-33 (IL-33) as Potential Anti-Allergy Agents</p> <p> </p>
Raw results of the simulation experiments performed in the educacional study evaluating a guaranteed service
<p>This zip file contains the simulation results, gnuplot script and data files needed to generate the figures showing the simulation results presented in [1].</p> <p>[1] Suárez-González A., Rodríguez Rubio R., López-Ardao J.C., López-García C, Herrería-Alonso S. (2021). Educational case study: evaluation of guaranteed service on low earth orbit satellite networks. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 215-219. DOI: https://doi.org/10.46354/i3m.2021.emss.029</p>
Raw results of the simulation experiments performed to evaluate the Time-to-idle Control Variate ferformance in the single queue case
<p>This zip file contains the simulation results, gnuplot script and data files needed to generate the figures showing the simulation results presented in [1].</p> <p>[1] Suárez-González A., López-García C., López-Ardao J.C., Rodríguez Rubio R., Rodríguez Pérez M. (2021). "Time-to-idle Control Variate Performance in the Single Queue Case". Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 147-151. DOI: https://doi.org/10.46354/i3m.2021.emss.020</p>
Results from simulated drift trajectories of driting Fish Aggregating Devices used in Figures for paper Escalle et al., (2024) Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtles
<p>Escalle et al., (2024) Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtles<br><span><a href="../doi/10.5281/zenodo.10815559">https://zenodo.org/doi/10.5281/zenodo.10815559</a></span></p> <p>Results data description<br>The above doi contains simulation output data generated from passive drift simulations of drifting FADs in the Pacific ocean. We refer the reader to the main text of the paper for details and definitions of the different zones and simulation experiments.</p> <p>All data files are matrices stored in comma-delimited files (.csv), where the first provides the column names of the matrix, and the first column provides the row names. The first cell in row 1, column 1 is simply a placement value that describes the nature of the matrix. </p> <p>When this value is a hash symbol (#), it denotes that the matrix represents a spatial density of virtual particles under a particular drift scenario and over a particular period of time. Column names provide the latitude indices of each cell, and row names are the longitude indices. Values are the proportion of all particules in the domain that passed through this cell, during the drift-time window of this results file (see below).</p> <p>When the first cell value is a hash followed by a code (e.g. # EqZ), it denotes that the file represents a connectivity matrix between the zones given by the code (e.g. Equatorial Zones EqZ or Fishing Zones FZ) and defined in the row names of the matrix, and the turtle zones defined by the column names. Values are the proportion of particules beginning the zone defined by the row name at the start of the simulation, which are now present in the zone defined by the column name.</p> <p>Files name follow a convention describing the deployment and drift-time scenario they represent.</p> <p>For spatial density matrices:<br>[Origin Zones]_[Subset]_Density_[Drift Time]_....csv</p> <p>For connectivity/transition matrices:<br>[Origin Zones]_[Drift Time]_[Number of Simulations Run]_TransitionMatrix_....csv</p>
Simulations for convection during the intensive observation period of the TWP-ICE field campaign: results from cloud-resolving model and convective parameterization schemes
<p>Simulated convections from cloud-resolving model and convective parameterization schemes during the intensive observation period of the TWP-ICE field campaign, which are used to investigate the scale-awareness problem of convective parameterization schemes. The corresponded observations are also included in this dataset.</p>
PIC simulation results of an MMS southward IMF KH wave event
<p>Simulation data for Runs-A to D shown in the paper entitled "Multi-scale evolution of Kelvin-Helmholtz waves at the Earth's magnetopause during southward IMF periods" are uploaded. </p> <p>The simulations are performed using the VPIC code (githab.com/lanl/vpic).</p> <p>Values are normalized by B=2B_0, N=N_0 (magnetosheath component), the electron inertial length based on N_0 (d_e) and the speed of light c=2VAe=40VA.</p> <p>The ".gda" data are 2x smoothed, unformatted data written by fortran as "write(*) f".</p> <p>The ".dat" data are for the plots of time evolution curves shown in the paper.</p>
simulation results - article "The advantage of sex: reinserting fluctuating selection in the pluralist approach"
<p>Results of the simulations of sexual vs asexual fluctuating selection associated with Red Queen like effect (frequency dependent selection and fitness reduction due to mutations in asexuals. </p> <p>Two excell sheets labeled figure 1 and figure 2. </p> <p>Figure 1 : 18 groups of 5 columns with 18 corresponding graphs. </p> <p>Columns : </p> <p>SD_env: Generation wise Standard Deviation of the randomly fluctuating environmental conditions.</p> <p>m: inverse of the strength of the frequency dependent selection. The strength of selection is 1/m.</p> <p>rho: autocorrelation of the randomly fluctuating environmental conditions. Either 0 or 0.6.</p> <p>proba_sex: observed frequency of fixed sexual forms for a given Standard Deviation of randomly fluctuating environmental conditions</p> <p>n: number of runs</p> <p>Figure 2 : 25 groups of 5 columns with 25 corresponding graphs. Effect of a reduction of the twofold advantage of asexuals on the fluctuating selection.</p> <p>SD_env, proba_sex, n: same significance as in figure 1.</p> <p>mult_asex: reduction coefficient of the two fold advantage of asexuals due either to deleterious mutations accumulations or to intraspecific competition. If mult_asex=1 the advantage of asexuals over sexuals is 2. So the advantage of asexuals over sexuals is mult_asex*2</p> <p> </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.