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1,028 results for “modelling & simulation”
An SI-traceable protocol for the validation of radiative transfer model-based reflectance simulation: datasets
<p>This data record contains datasets used in the study "An SI-traceable protocol for the validation of radiative transfer model-based reflectance simulation":</p> <ul> <li>The <code><span>final_design.ply</span></code> file contains the mesh corresponding to the final artefact design.</li> <li>The <code><span>material_measurements.nc</span></code> file contains goniophotometer records for the material reflectance.</li> <li>The <code><span>artefact_measurements.nc</span></code> file contains goniophotometer records for the artefact reflectance.</li> </ul>
Evaluation datasets and results of the paper "A Framework for Measuring the Quality of Business Process Simulation Models"
<p>Datasets and files used in the evaluation of the publication entitled "A Framework for Measuring the Quality of Business Process Simulation Models", where:</p> <ul> <li><strong><em>BPS-models/</em></strong>: folder containing the BPS models used in the evaluation (the BPS models discovered by ServiceMiner are not included due to privacy reasons). <ul> <li>The BPS models discovered by SIMOD are composed of <em>i)</em> a BPMN file with the process model structure, and <em>ii)</em> a JSON file with the parameters of the simulation. These files correspond to the format of Prosimos simulation engine (<a href="https://prosimos.cloud.ut.ee/">https://prosimos.cloud.ut.ee/</a>).</li> <li>The BPS models of the Loan Application and Procure to Pay processes are composed of a BPMN file with both the process model structure and parameters, corresponding to the format of the BIMP simulator used in APROMORE (<a href="https://apromore.com/">https://apromore.com/</a>).</li> </ul> </li> <li><em><strong>measures/</strong></em>: folder containing the distance values of each measure reported in the paper.</li> <li><em><strong>original-event-logs/</strong></em>: folder containing the (train and test) event logs used in the evaluation.</li> <li><em><strong>simulated-logs/</strong></em>: folder containing the simulated logs evaluated in the paper (synthetic, SIMOD, and ServiceMiner).</li> <li><em><strong>ComputeLogDistance.py</strong></em>: script to compute the distance measures proposed in the paper.</li> </ul> <p> </p> <p>To evaluate the distance measures of a set of simulated event logs in the folder <em>simulated_logs/</em> against the test log <em>test_event_log.csv.gz</em>, run:<br><em> python ComputeLogDistance.py -cfld test_event_log.csv.gz simulated_logs/</em></p> <p>*The flag <em>-cfld</em> is optional, due to the high computational complexity of the CFLD measure.</p> <p><strong>WARNING</strong>: set the column names of each log accordingly (where <em>log_1_ids</em> are the IDs of the test log, and <em>log_2_ids</em> the IDs of the simulated logs). Examples:</p> <pre><code># Column IDs for the (train/test) real-life logs, and the SIMOD simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='start_time', end_time='end_time', resource='resource' ) # Column IDs for the Loan Application and Procure to Pay simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='Start_Time', end_time='End_Time', resource='resource' ) # Column IDs for the ServiceMiner simulated logs. EventLogIDs( case='case_id', activity='Activity', start_time='start_time', end_time='end_time', resource='Resource' )</code></pre> <p> </p>
Monthly accumulated sublimation and yearly accumulated surface mass balance (SMB) components RACMO model simulations for Antarctica on 27km grid for 2000-2012
<p>Monthly accumulated (denoted monthlyS) sublimation components and yearly accumulated (denoted yearlyS) surface mass balance (SMB) components for Antarctica (ANT) on 27 km horizontal grid produced by RACMO model are presented in this dataset for the year 2000-2012. The dataset consists of data from three simulations named, NODRIFT, Rp3, and RpNew. NODRIFT represents the run with no blowing snow sublimation, Rp3 corresponds to version of the blowing snow model with simplifications, RpNew corresponds to the advanced version with new updates to the blowing snow model in RACMO. Details of the simulations can be found in the associated paper : <a title="Contribution of blowing snow sublimation to the surface mass balance of Antarctica" href="https://doi.org/10.5194/egusphere-2024-116" target="_blank" rel="noopener">https://doi.org/10.5194/egusphere-2024-116</a>. The data includes yealy accumulated SMB components including SMB, snow melt, refreezing, precipiation, runoff, blowing snow erosion, surface sublimation, and blowing snow sublimation, the data also includes yearly averaged (denoted yearlyA) . Furthermore, the data includes monthly accumulated sublimation components of surface sublimation (subl), and blowing snow sublimation (suds). </p>
Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
<p>Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash (<em>Fraxinus pennsylvanica</em>) using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross-validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic-genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially-explicit model. Approximate Bayesian Computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross-validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.</p>
Figure 4. CFD simulation results for Models A in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 4. CFD simulation results for Models A (panels A and C) and B (panels B and D) in steady 5.6 m/s flow. The blue, yellow, and green regions are areas of reduced flow speed that generate forces on the tags. The upper panels show the flow speed over a horizontal cross-section at the tag midline. The lower panels show flow speed over a vertical cross-section at the centerline of the tag. The improved flow around Model B is evident in the smaller magnitude of blue coloration in the wake behind the tag.
Fig. 7 in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 7. The average Eldana saccharina larval infestation with the passage of time simulated for the SIT/IS pilot site near the Eston area of KwaZulu-Natal, South Africa with weekly releases commencing only in fields of age at most 6 mo at the start of the release. Time, t, is measured in days. The density, e/100s, is the number of borers, e, per 100 stalks of sugarcane. The graph shows that the average infestation level in the second yr of the control program is substantially reduced.
Fig. 5 in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 5. The discretization of the spatial domain. On the lef a typical sugarcane field layout is illustrated with different colors representing crop age, and on the right the discretized domain corresponding to the area within the red square. This was done by transforming the spatial information obtained from the shapefiles to a matrix data structure in Matlab containing the entries '0', '1' and '2' denoting non-sugarcane patches, patches inside a field and edge patches, respectively. In the right half of the figure these data are represented by white, blue and green, respectively.
Fig. 4. A in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 4. A model representing sugarcane dynamics as currently implemented in the simulation tool for the field application of a SIT/IS strategy against Eldana saccharina in sugarcane.
Fig. 3 in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 3. The Eldana saccharina module developed in this study. The module describes the dynamics of all E. saccharina life stages under the influence of the SIT/IS technique. Other control measures may also be included, and are currently under investigation.
Fig. 6 in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 6. The graphical user interface designed for the SIT/IS simulation tool for the field application of a SIT/IS strategy against Eldana saccharina in sugarcane. The initial infestation, e/100s, is the number of borers per 100 stalks of sugarcane.
Fig. 2 in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 2. The pest species subsystem which may include all the important pest species in South African sugarcane.The total damage caused by the various pest species may be estimated by such a system. Currently, only the Eldana saccharina module has been developed.
Fig. 9. A in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 9. A spatial overview of the Eldana saccharina larval infestation at the end of a 24 mo simulation of a SIT/IS program at the pilot site near the Eston area of KwaZulu-Natal, South Africa. The colors indicate infestation levels measured in e/100 stalks, i.e., number of borers per 100 stalks of sugarcane. The fields colored in dark blue in the top right corner are aged 0, 1 and 2 mo, and they were harvested just before the end of the simulation; therefore the infestation levels are still low and this is before the commencement of releases of irradiated adult moths.
Figure 1. Markov Chain Model&Figure 2. Transition matrix-Study of a Random Navigation on the Web Using Software Simulation
<p>For a good simulation it is very important to find methods for<br> navigating through the web (Levene and Wheeldon, 2004). John Kemeny and Laurie Snell have<br> proposed the use of Markov models for web simulations (Kemeny and Snell, 1960). Cadez et al. (2000)<br> used Markov models for classifying the sessions into different categories for browsers. Some other<br> proposed techniques choose to combine different order Markov models for obtaining low state<br> complexity and improving accuracy, as Deshpande and Karypis (2004). Dongshan and Junyi (2002)<br> used for predicting the access providing good scalability and high coverage a hybrid-order tree-like<br> Markov model. As an alternative to the Markov model Pitkow proposed a longest subsequence model<br> (Pitkow and Pirolli, 1999), also for predicting the next page accessed by the user Sarukkai chose<br> Markov models (Sarukkai, 2000).<br> Transitions are simulated using the Markov Chain nodes, Google matrix and an arbitrary initial<br> probability distribution. Examples can be seen in Figure 1 and Figure 2.</p>
WV-TTL: water vapor mixing ratio from GEOSCCM and trajectory model simulations in tropical tropopause layer
<p>This dataset includes 100 hPa water vapor mixing ratio simulated from a trajectory transport model and a climate-chemistry model in the tropical tropopause layer from 2005 to 2016 in the format of netCDF. The data are monthly and have three dimensions as lon/lat/time in the unit of parts per million by volume.</p> <p>Also included the tropical average time series of indices for Brewer-Bobson circulation (BDC), tropospheric temperature and/or Quasi-biennial Oscillation (QBO) from ERAi/MERRA-2/GEOSCCM. These indices are used in a multivariate regression.</p>
Simulating the dispersal of Monochamus galloprovinciallis : R script of the dispersal model and video of the simulation
<p>This folder contains the R script to simulate the dispersal of Monochamus galloprovincialis from an individual-based model and the resulting video. This study was conducted in the frame of the FP7 project called "REPHRAME" and a working group of ANSES (French Agency for Food, Environmental and Occupational Health & Safety).</p> <p>This material complements the following publication:</p> <p>Robinet C, David G, Jactel H (2019) Modeling the distances traveled by flying insects based on the combination of flight mill and mark-release-recapture experiments. Ecological Modelling, 402: 85-92.<br> https://doi.org/10.1016/j.ecolmodel.2019.04.006</p>
Ocean-only simulation outputs based on the IPSL-CM5A-LR and IPSL-CM5A-MR models
<p><br> Name of the simulations<br> piControl2 : COUPLED-LR<br> piControlMR3 : COUPLED-MR<br> OR2L2E : CLIM-LR<br> OR2L2G : CLIM-MR<br> OR2L2M : TOTAL-LR member 1<br> OR2L2M2 : TOTAL-LR member 2<br> OR2L2M3 : TOTAL-LR member 3<br> OR2L2M4 : TOTAL-LR member 4<br> OR2L2M5 : TOTAL-LR member 5<br> OR2L2R : TOTAL-MR member 1<br> OR2L2R2 : TOTAL-MR member 2<br> OR2L2R3 : TOTAL-MR member 3<br> OR2L2R4 : TOTAL-MR member 4<br> OR2L2R5 : TOTAL-MR member 5<br> OR2L2Q : RANDOM-LR<br> OR2L2S : RANDOM-MR<br> piControl2_perm : like COUPLED-LR but with random permutation identical to those used to generate RANDOM-LR<br> piControlMR3_perm : like COUPLED-MR but with random permutation identical to those used to generate RANDOM-MR</p> <p>Variables :<br> zomsfatl : Atlantic meridional overturning streamfunction<br> thetao_700 : averaged potential temperature between 0m and 700m<br> dens : 3D seawater density<br> EOF-NA_SLP : PC1 of the North Atlantic monthly SLP</p> <p> </p>
Simulated Neutral Landscape Models and Scaling Results for Testing of Multi-Dimensional Grid-Point Scaling Algorithm
<p>The data package contains (1) simulations of neutral landscape models of categorical data for benchmark testing of scaling algorithms, and (2) scaling results for testing consistency and sensitivity of the newly developed Multi-Dimensional Grid-Point (MDGP) scaling algorithm.</p> <p>Neutral landscapes were generated using the "nlmpy" python module. The MDGP scaling algorithm and the test framework were implemented in R (https://github.com/gannd/landscapeScaling). </p>
Mechanisms for a record-breaking rainfall in the coastal metropolitan city of Guangzhou, China: observation analysis and nested very-large-eddy simulation with the WRF Model
<p>A video shows the processes of a record-breaking rainfall in the coastal metropolitan city of Guangzhou, China simulated by WRF nested very-large-eddy simulation.</p>
A structural model of the human serotonin transporter in an outward-occluded state: MD simulation data
<p>The uploads contain relevant data to supplement the study https://www.biorxiv.org/content/10.1101/637009v1, where the details of the methods are described.</p> <p>charmm_energy_minimization.inp is the input file that was used to run an energy minimization on structural models</p> <p>The two archives contain relevant MD simulation data in coordinate, parameter and trajectory files:</p> <p>hSERT_Ce.tar.gz outward-open X-ray structure PDB 5I71</p> <p>hSERT_Ceo.tar.gz outward-occluded structural model</p>
Webis Simulation Data Mining Bridge Models Corpus 2012 (Webis-SDMbridge-12)
<p>This corpus provides the simulation data mining community with a collection of 14641 bridge models and simulated behavior.</p> <p><strong>1. Folder "1-designs"</strong></p> <p>The text files in this directory should contain all information for the<br> independent variables any machine learning experiment. For reference, all 14641 IFC models are supplied in subfolders 001 to 147.</p> <p><strong>2. Folder "2-simulation"</strong></p> <p>This folder contains samples of the simulation output that may be viewed in Paraview (http://www.paraview.org). The original model contains the "Org" filename fragment, and the maximum and minimum behaviors are indicated with "Max" and "Min" filename fragments. Displacement, strain, and stress behaviors are all given. Only three of the 14641 models are given as the file sizes are<br> around 1.4 to 2.2 megabytes each. The complete data (approximately 81 gigabytes) can be regenerated and provided if necessary on request (email webis@medien.uni-weimar.de).</p> <p><strong>3. Folder "3-aggregation"</strong></p> <p>Maximum displacement, strain, and stress measurements are given in the text files individually, and together in the files with the "vtk" filename fragment. This data should be sufficient for the dependent variables of any machine learning experiment.</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.