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1,028 results for “simulation model”
CCSM4 LR and HR Model Simulations for GRL paper "The Influence of a Resolved Gulf Stream on the Decadal Variability of Southeast US Rainfall"
<p>This archive contains model simulations of precipitation used in the GRL paper "The Influence of a Resolved Gulf Stream on the Decadal Variability of Southeast US Rainfall" (Zhang et al., 2021). These model simulations are standard control simulations based on the Community Climate System Model Version 4.0 (CCSM4) using eddying (HR) and eddy-parameterizing (LR) ocean component models. </p> <p><em><strong>In order to properly acknowledge those who have worked hard to create those data sets we do require that if this data is used in a publication that coauthorship be offered to those involved in the data creation. Please contact the author Dr. Wei Zhang (email: wz19@princeton.edu) for any further questions or potential collaboration. </strong></em></p>
Simulation Results Data for 'Modelling new insecticide-treated bed nets for malaria-vector control: How to strategically manage resistance?'
<p>GENERAL INFORMATION</p> <p>1. Title of Dataset: Simulation Results Data for 'Modelling new insecticide-treated bed-nets for malaria-vector control: How to strategically manage resistance?'</p> <p>2. Author Information<br> A. Investigator Contact Information<br> Name: Philip G. Madgwick<br> Institution: Syngenta <br> Address: Jealott’s Hill International Research Centre, Bracknell, RG42 6EY, UK<br> Email: philip.madgwick@syngenta.com</p> <p> B. Investigator Contact Information<br> Name: Ricardo Kanitz<br> Institution: Syngenta <br> Address: Syngenta Crop Protection, Rosentalstrasse 67, CH-4058 Basel, Switzerland<br> Email: ricardo.kanitz@syngenta.com</p> <p><br> 3. Date of data collection (single date, range, approximate date): 2021-01-13 to 2021-02-01 </p> <p>4. Geographic location of data collection: UK </p> <p>5. Information about funding sources that supported the collection of the data: </p> <p>This work was conducted during a postdoctoral research position for PGM funded by the Innovative Vector Control Consortium (IVCC).</p> <p><br> SHARING/ACCESS INFORMATION</p> <p>1. Licenses/restrictions placed on the data: NA</p> <p>2. Links to publications that cite or use the data: [UPDATE]</p> <p>3. Links to other publicly accessible locations of the data: NA</p> <p>4. Links/relationships to ancillary data sets: NA</p> <p>5. Was data derived from another source? No</p> <p>6. Recommended citation for this dataset: [UPDATE]</p> <p><br> DATA & FILE OVERVIEW</p> <p>1. File List: <br> PSData_random6.csv - 10^6 random samples of each of the 17 parameters in the model, where rows are samples and columns are parameters (with column names corresponding to the parameters identified in the rows of Table 1 of the manuscript; see also DATA-SPECIFIC INFORMATION)<br> Data_random6_maxpsMixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_maxpsMixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_maxpsMixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Mixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mosaic_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Mosaic_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mosaic_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Rotation_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_revmm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Rotation_Fixed_revmn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_revnn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_SoloA_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloA_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloA_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_SoloB_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloB_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloB_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance </p> <p>2. Relationship between files, if important: </p> <p>Files are named in accordance with the variables that describe each simulation setup, as described above. Each simulation dataset has 10^6 runs that correspond to the 10^6 random samples of each of the 17 parameters in the model in 'PSData_random6.csv'.</p> <p>3. Additional related data collected that was not included in the current data package: NA</p> <p>4. Are there multiple versions of the dataset? No</p> <p><br> METHODOLOGICAL INFORMATION</p> <p>1. Description of methods used for collection/generation of data: Data were collected using Simulator.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript. </p> <p>2. Methods for processing the data: Data were processed using Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript. </p> <p>3. Instrument- or software-specific information needed to interpret the data: Analysis was conducted in R version 4.0.3 (2020-10-10), using R packages identified in Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript. </p> <p>4. Standards and calibration information, if appropriate: NA</p> <p>5. Environmental/experimental conditions: NA</p> <p>6. Describe any quality-assurance procedures performed on the data: NA</p> <p>7. People involved with sample collection, processing, analysis and/or submission: NA </p> <p><br> DATA-SPECIFIC INFORMATION FOR: PSData_random6.csv</p> <p>1. Number of variables: </p> <p>17 variables with column names that have the following parameter meanings (see Table 1 in the manuscript): <br> Population Size = N = starting population size (and carrying capacity in logistic model); random sample range on log-scale: 10^2 - 10^9<br> Intrinsic Birth Rate = b = % population growth rate (in logistic model); random sample following a standard log-normal distribution with mean=0 and sd=1<br> Intrinsic Death Rate = d = % breeding mosquitoes that die into next generation; random sample range: 0 - 1<br> Female Exposure = x_[female-symbol] = % female mosquitoes that receive a dose; random sample range: 0 - 1<br> Male Exposure x_[male-symbol] = % male mosquitoes that receive a dose; random sample range: 0 - 1<br> Initial Frequency A = f_0,A = starting frequency of allele A; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size<br> Effectiveness 1 = m_1 = % dosed mosquitoes that die from insecticide 1; random sample range: 0 - 1<br> Resistance Restoration A = r_A = % return to baseline fitness with resistance allele A; random sample range: 0 - 1<br> Dominance of Resistance Restoration A = h^r_A = % resistance restoration in heterozygote with allele A; random sample range: 0 - 1<br> Resistance Cost A = c_A = % non-dosed mosquitoes that die from carrying allele A; random sample range on log-scale: 10^-3 - 10^-0.5<br> Dominance of Resistance Cost A = h^c_A = % resistance cost in heterozygote with allele A; random sample range: 0 - 1<br> Initial Frequency B = f_0,B = starting frequency of allele B; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size<br> Effectiveness 2 = m_2 = % dosed mosquitoes that die from insecticide 2; random sample range: 0 - 1<br> Resistance Restoration B = r_B = % return to baseline fitness with resistance allele B; random sample range: 0 - 1<br> Dominance of Resistance Restoration B = h^r_B = % resistance restoration in heterozygote with allele B; random sample range: 0 - 1<br> Resistance Cost B = c_B = % non-dosed mosquitoes that die from carrying allele B; random sample range on log-scale: 10^-3 - 10^-0.5<br> Dominance of Resistance Cost B = h^c_B = % resistance cost in heterozygote with allele B; random sample range: 0 - 1</p> <p>2. Number of cases/rows: </p> <p>10^6, corresponding to the number of random samples </p> <p>3. Variable List: NA </p> <p>4. Missing data codes: NA</p> <p>5. Specialized formats or other abbreviations used: NA</p> <p><br> DATA-SPECIFIC INFORMATION FOR: all other dataset files (e.g. Data_random6_maxpsMixture_Fixed_mm.csv) </p> <p>1. Number of variables: </p> <p>10 variables with column names that have the following meanings:<br> A_t_50% = the recorded number of generations that it takes for resistance allele A to reach >50% frequency; 0 means that resistance allele A never reaches >50% frequency <br> A_f_250 = the frequency of resistance allele A at the 250th generation <br> A_f_bar = the mean frequency of resistance allele A over the first 250 generations <br> B_t_50% = the recorded number of generations that it takes for resistance allele B to reach >50% frequency; 0 means that resistance allele B never reaches >50% frequency <br> B_f_250 = the frequency of resistance allele B at the 250th generation <br> B_f_bar = the mean frequency of resistance allele B over the first 250 generations <br> nf_80% = the recorded number of generations that it takes for the female population size to recover to >80% of its original size in the 0th generation; 0 means that the female population size never reaches >80% recovery; 1 means that the female population size never drops below 80% of its original size in the 1st generation<br> nf_250 = the female population size at the 250th generation<br> nf_bar = the mean female population size over the first 250 generations <br> nf_ext = the recorded number of generations that it takes for the female population size to drop below 1 (i.e. population extinction); 0 means that female population size never reaches <1</p> <p>2. Number of cases/rows: </p> <p>10^6, corresponding to the number of random samples </p> <p>3. Variable List: NA</p> <p>4. Missing data codes: all missing data is recorded as 0 </p> <p>5. Specialized formats or other abbreviations used: NA</p>
Simulated pp collisions at 13 TeV with 2 leptons + 1 b jet final state and selected benchmark Beyond the Standard Model signals
<p>This data-set is comprised of simulated events of pp collisions at 13 TeV with 2 leptons + 1 bottom jet sinal state, with HT > 500 GeV. It includes the following samples</p> <ul> <li>Standard-Model background (bkg), generated at leading order includes the sub-samples Z+Jets, ttbar, WW, WZ, and ZZ. <ul> <li>The processes were generated in kinematic regions to ensure good statistics across the whole phase space. The sampling was carried out using event generation filters at parton level as follows <ul> <li>ttbar: pT <100 GeV; pT in [100, 250] GeV; pT > 250 GeV</li> <li>The scalar sum of the pT of outgoing particles for Z+Jet: ST < 250 Gev; ST in [250, 500] GeV; ST > 500 GeV</li> <li>W/Z pT for dibosons: pT < 250 GeV; pT in [250, 500] GeV; pT > 500 GeV</li> </ul> </li> </ul> </li> <li>Vector-like T-quarks with masses 1.0, 1.2, 1.4 TeV (hq1000, hq1200, hq14000) pair produced either through the Standard-Model gluon (wohg) or through a BSM 3TeV heavy gluon (hg3000)</li> <li>tZ production through a Flavour Changing Neutral Current (fcnc) vertex</li> </ul> <p>The samples are provided with both a full set of features, or with a sanitised set of features. The sanitised features remove some accumulation at zeros from non-reconstructed objects (i.e. missing values). All samples were generated using MadGraph5 2.6.5 and the detector was simulated using Delphes 3 with the default CMS card. For the Standard-Model background, both Pythia 8.2 (with CMS CUETP8M1 underlying event tune and NNPDF 2.3 parton distribution functions) (pythia) and Herwig 7 (herwig) hadronisations are provided to compare the background simulation. For the BSM signals only Pythia is provided.</p> <p>For the details of the generation and on the differences between the two feature sets please refer to <a href="https://link.springer.com/article/10.1140%2Fepjc%2Fs10052-020-08807-w">Finding new physics without learning about it: anomaly detection as a tool for searches at colliders</a> for more details. Each file provides a train:validation:split with the ratios 1:1:1 to ensure equal statistical description of the events at each step of the machine learning workflow.</p>
A case study: evaluation of a single column model with advection to simulate fog/stratus during C-FOG experiment
<p>Those datasets are observed by C-FOG (<em>Toward Improving Coastal Fog Prediction</em>) campaign, which was organized as a field experiment combined with modelling and theoretical initiatives. The objective of C-FOG was to advance our understanding and ability to observe, simulate, and predict fog, with a particular focus on warm fog formation, development and dissipation over coastal environments.</p> <p>The uploaded observation data contains liquid water content, droplet number concentration, temperature, SST, wind, visibility, backscatter collected by ceilometer, and atmospheric profile. The details can be found in the dataset.</p> <p>Thanks for the intense observation by the C-FOG project, which collected valuable data for detailed fog research.</p>
Statistical analysis code for output from a model used to simulate foot-and-mouth disease dynamics in the United Kingdom
<p>Epidemics can sometimes be managed through reductions of host density, such as social distancing for human diseases, reducing plant density through cultural and genetic means, and host culling for epizootics. These approaches allow for a certain density of hosts to remain within a targeted area. By contrast, total ring depopulation is often used as a management strategy for emerging infectious diseases in livestock. In this study, we explore the trade-offs of a density-based culling strategy to determine if fewer livestock farms can be culled within rings while maintaining a decrease in disease transmission. To do so, we evaluated a farm-density-based ring culling strategy to control foot-and-mouth disease (FMD) in the United Kingdom. This strategy may allow for some farms within rings around infected premises (IPs) to escape depopulation, with the aim to prevent over-culling during outbreaks. Using a spatially-explicit, stochastic, state-transition simulation algorithm originally developed by Keeling et al. 2001 to model FMD spread in the United Kingdom, we simulated this reduced-farm-density, or "target density" strategy. We modeled FMD disease spread in four counties in the UK (Aberdeenshire, Cumbria, Devon, and North Yorkshire) that have different farm demographies. We ran 740,000 simulations in a full-factorial analysis of epidemic impact measurements (i.e. culled animals, culled farms, epidemic length) and cull strategy parameters (i.e. target farm density, daily farm cull capacity, cull radius). We found that all of the cull strategy parameters were drivers of epidemic impact. We found that outbreaks in Cumbria had higher epidemic impacts and were more likely to take off compared with other counties with more outbreaks being likely to take off in Cumbria. Most importantly, in all counties, our proposed target density strategy was more effective at combatting FMD compared with traditional 'total ring depopulation' when considering average culled animals and culled farms. The differences in epidemic impact between the counties are likely driven by farm demography, especially differences in cattle and farm density. This target density strategy can be applied to many different systems, including other livestock and agricultural systems, to reduce host density as opposed to over-culling hosts.</p>
Climate-ecosystem modelling made easy: the Land Sites Platform - simulation results
<p>Model data to reproduce the experiments and figures presented in "Climate-ecosystem modelling made easy: the Land Sites Platform" (Keetz & Lieungh et al., accepted; publication details to be added).</p> <p>The repository includes model input data for the BOR1 site, and model output for two cases (simulations) executed for that site. Case "bor1-1000y-allpfts" was run with default settings, whereas case "bor1-1000y-grasspfts" was run with C3 grass and Arctic C3 grass as the only plant functional types.<br> Concatenated versions of the model output (made by combining monthly history files) for each case are stored in the top folder as NetCDF files (.nc). The full case folders, including monthly output under /archive/lnd/hist, are stored under "cases/" as zipped directories.<br> The "data/" folder contains model input data for the site, and is identical for the two cases.</p> <p>Jupyter notebooks to analyse the data and create the plots in Figure 4 of the article are stored under "notebooks/technical_paper_results/" in the NorESM-LSP GitHub repository, which is accessible in the same version as the submitted manuscript here: 10.5281/zenodo.7310652</p> <p>Description: Model data to reproduce the experiments and figures presented in "Climate-ecosystem modelling made easy: the Land Sites Platform".<br> Coverage: Geographical coordinates 9.07876, 61.0355. Forcing data from the closest 0.5-degree model grid cell.<br> Format: NetCDF (.nc), zipped directories (.zip), shell scripts (.sh), XML files (.xml), text files (.txt), and others<br> Language: English<br> Relation: 10.5281/zenodo.7310652, <br> Source: GSWP3 for input data (Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T. and Hanasaki, N. (2006) GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land Surface. Bulletin of the American Meteorological Society, 87(10), 1381–98.)</p>
Simulations for pre-industrial climate using EC-Earth3-LR model — selected data for a study on AMOC
<p>A long-term control simulation of pre-industrial period (1850 CE) climates were performed by the EC-Earth3-LR climate model with a horizontal resolution of ~1.125°. The dataset contains selected output data from the simulations.</p> <p>In total, a 2000-year long control simulation was made, which has pre-industrial orbital boundary conditions, initialized by a pre-run steady restart file (the output of approximately 500-year pre-industrial control simulation). This dataset is used to investigate internal climate variability without external forcing changes under pre-industrial climate conditions.</p> <p>The dataset contains Earth system model results from EC-Earth3 presented in the study by Cao et al. (2022).</p> <p><strong>Model configuration</strong><br> Time periods: Pre-Industrial (2000-year time slice)<br> ESM configuration: EC-Earth3-LR<br> Horizontal resolution: ~1.125° (~125 km)</p> <p><strong>Available data</strong><br> Annual mean data for standard oceanographic and meteorological variables.</p>
Replication Package: Model-Driven Engineering for the Interoperability of Simulation Modeling Languages: a Case Study in the Space Industry
<p>Replication package "Architectural Support for Software Performance in Continuous Software Engineering: a Systematic Mapping Study".</p>
Test-particle simulations for "Quantifying the influence of bars on action-based dynamical modelling of disc galaxies"
<p>This contains the test-particle simulations of barred galaxies with varying bar properties (bar strength, pattern speed) which have been used in the manuscript "Quantifying the influence of bars on action-based dynamical modelling of disc galaxies" by Ghosh, S., Trick, W. H., Green G. M. (2022).</p> <p>For details for the datasets, please see the README.md file.</p> <p>For any further information, please feel free to contact Ghosh, Soumavo (ghosh@mpia.de).</p> <p> </p>
Online appendix and simulated data sets for assesment of Birth-Death Exposed-Infectious (BDEI) phylodynamic model estimators
<p>The birth-death exposed-infectious (BDEI) phylodynamic model describes the transmission of pathogens featuring an incubation period (when there is a delay between the moment of infection and becoming infectious, as for Ebola and SARS-CoV-2), and permits its estimation along with other parameters, from time-scaled phylogenetic trees.</p> <p>We implemented a highly parallelizable estimator for the BDEI model in a maximum likelihood framework (<a href="https://github.com/evolbioinfo/bdei">PyBDEI</a>) using a combination of numerical analysis methods for efficient equation resolution. This dataset contains the assessment of PyBDEI in comparison with a Bayesian implementation in <a href="http://www.beast2.org/">BEAST2</a> (mtbd package) and a deep learning estimator <a href="https://github.com/evolbioinfo/phylodeep">PhyloDeep</a>: the parameter values estimated by the 3 tools.<br><br>The PyBDEI and the theoretical findings behind it are described in A Zhukova, F Hecht, Y Maday, and O Gascuel. Fast and Accurate Maximum-Likelihood Estimation of Multi-Type Birth-Death Epidemiological Models from Phylogenetic Trees Syst Biol 2023. This dataset contains the online Appendix (Fig S1-S3 and Table S1).</p>
Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1- MD simulations
<p>Topology, parameter and coordinates files of the Molecular dynamics (MD) simulations of OR5K1 3D models from AlphaFold 2 (AF2) and Homology Modeling (HM). We used ACEMD3 (v3.5.1) as a molecular engine, CHARMM36 as force field. Three replicas of 100 ns (dcd files) for both systems are reported. Water molecules, ions, and membrane atoms (POPC: phosphatidylcholine) atoms were removed from the original trajectories before the upload.</p>
Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios
<p><strong>Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios</strong></p> <p>This dataset contains output ice sheet model runs forced by climate boundary conditions provided by CMIP6 climate model output. Each experiment set is archived in separate compressed tar.gz files. </p> <p>Description of the experiment sets, including the model setup, key parameters, climate forcings, and their main objectives are documented in Table 1 of Li, DeConto, Pollard (2023) Climate model differences contribute deep uncertainty in future Antarctic ice loss, Science Advances.</p> <p>Two kinds of output are included in each ice sheet run: fort.22 files contain time series of several key variables for the Antarctic Ice Sheet (area, volume, sea-level equivalent, etc.); fort.92.nc files contain 2D and 3D fields such as ice thickness and velocity at specific time slices.</p> <p> </p> <p> </p>
Simulations of stratocumulus cloud fields using RAMS model
<p>The simulations of the stratocumulus are performed with a domain size of km ( bin points) for 3 hours. The horizontal resolution is fixed at 100 m, and the vertical bin spacing is 50 m. The initial state of the simulations (DYCOMS-II case) is based on vertical profiles of potential temperature, moisture, and horizontal winds, that were adapted from Stevens et al. (2003). From these initial fields, 4 additional simulations are carried out by slightly modifying the temperature profiles to check their effects on the stratocumulus field and notably on entrainment rates. In addition, one extra simulation is realized by modifying the humidity profile. In summary, these 6 simulations are as follows: case 1) ‘Control’ is the basic simulation with the unmodified fields; case 2) ‘Control + layer 150 m’ is as ‘Control’ but the temperature inversion is 150 m above that of ’Control’ (less brutal than ‘Control’), expecting more entrainment; case 3) ‘Control + layer 300 m’ is as ‘Control’ but the temperature inversion is 300 m above that of ’Control’; case 4) ‘Control - 4K’ is as ‘Control’ but with a smaller temperature inversion, expecting more mixing; case 5) ‘Control + 4K’ is as ‘Control’ but with a stronger temperature inversion; and case 6) ‘Extra’ is as ‘Control’ but initialized using a slightly modified water vapor profile. Detailed application of the datasets is described in our under reviewing paper (Shang et al 2022).</p> <p>Reference:</p> <p>Shang, H., Hioki, S., Penide, G., Cornet, C., Letu, H., and Riedi, J.: Establishment of an analytical model for remote sensing of typical stratocumulus cloud profiles under various precipitation and entrainment conditions, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-674, in review, 2022.</p> <p>Stevens, B., Lenschow, D. H., Vali, G., Gerber, H., Bandy, A., Blomquist, B., Brenguier, J.-L., Bretherton, C. S., Burnet, F., Campos, T., Chai, S., Faloona, I., Friesen, D., Haimov, S., Laursen, K., Lilly, D. K., Loehrer, S. M., Malinowski, S. P., Morley, B., Petters, M. D., Rogers, D. C., Russell, L., Savic-Jovcic, V., Snider, J. R., Straub, D., Szumowski, M. J., Takagi, H., Thornton, D. C., Tschudi, M., Twohy, C., Wetzel, M., and van Zanten, M. C.: Dynamics and Chemistry of Marine Stratocumulus—DYCOMS-II, Bulletin of the American Meteorological Society, 84, 579-594, 10.1175/BAMS-84-5-579 %J Bulletin of the American Meteorological Society, 2003.</p>
Idealized large eddy model simulations
<p>This dataset contains matlab and grads output of four large eddy model simulations of the atmospheric boundary layer in Matlab data format. They are used to create figures presented in a paper titled "Large eddy simulations of diurnal entrainment/detrainment of the shallow cumulus clouds in subtropical Western Pacific Ocean". The matlabscripts and figures are in the les_scripts_figs.tar.gz file. Install the *.mat files and dropsonde netcdf files in the following "data" directory tree structure, then the figures can be recreated by running the matlabscripts. The *.ctl and *.dat files are the grads output that contains a 10 min interval statistics of model variables, i.e. stats_sw.dat for 3D variables and stats_sfc.dat for 2D surface variables of these LES experiments. The name of the variables are listed in their corresponding *.ctl files. </p> <p> >ls data<br> CAMP2EX_AVAPS_RD41_v1_20190927_212721.nc L2<br> CAMP2EX_AVAPS_RD41_v1_20190927_222459.nc L2sst<br> CAMP2EX_AVAPS_RD41_v1_20190928_004304.nc les_grads_L1sst.tar.gz<br> cld_iso.mat les_grads_L1.tar.gz<br> cld_mat.tar.gz les_grads_L2sst.tar.gz<br> cldtb_ent_det.mat les_grads_L2.tar.gz<br> L1 mse_cld_entr.mat<br> L1sst tke_cld_all.mat</p> <p>The L1, L1sst, L2, and L2sst are the subdirectories under data. Install the following files in their corresponding experiment subdirectory. </p> <p>>ls data/L1<br> L1_2721.mat L1_qc_w_tke.mat stats_sm.ctl stats_sw.dat<br> L1_cld_depth.mat stats_sfc.ctl stats_sm.dat<br> L1_mse_cld_entr.mat stats_sfc.dat stats_sw.ctl</p> <p>>ls data/L1sst<br> L1sst_2721.mat stats_sfc.ctl stats_sm.dat<br> L1sst_cld_depth.mat stats_sfc.dat stats_sw.ctl<br> L1sst_mse_cld_entr.mat stats_sm.ctl stats_sw.dat</p> <p>>ls data/L2<br> L2_2723.mat stats_sfc.ctl stats_sm.dat<br> L2_cld_depth.mat stats_sfc.dat stats_sw.ctl<br> L2_mse_cld_entr.mat stats_sm.ctl stats_sw.dat</p> <p>>ls data/L2sst<br> L2sst_2723.mat stats_sfc.ctl stats_sm.dat<br> L2sst_cld_depth.mat stats_sfc.dat stats_sw.ctl<br> L2sst_mse_cld_entr.mat stats_sm.ctl stats_sw.dat<br> </p>
Model setup code and input for internal tide-eddy simulation
<div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <p>The dataset includes the setup code and input files for simulations of internal tide-eddy interactions using MITgcm. Due to the large size of the model output data, it is not included but can be made available upon request at <a target="_new">yangwangow@gmail.com</a>. If you have any questions about using or testing these files, please feel free to reach out. Cheers!</p> </div> </div> </div> </div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Regional climate model simulations (CCLM 15km) of profiles for the MOSAiC period
<p>The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic. The data set includes simulation data of profiles and derived data for the MOSAiC period (Oct. 2019-Sept.2020). The regional climate model CCLM was used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data (AMSR2) with around 6 km resolution, and sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead data with 1 km resolution for the winter period (Nov. 2019-April 2020). Model output is available every 1h. In the vertical, the model extends up to 22 km with 60 vertical levels. On data below 10km are used. In addition to profiles, integrated water vapour and temperature for the lowest 2km were calculated. Values are grid-box averages at the ship position. Geostrophic wind was computed from the pressure gradient of the four surrounding grid points.</p> <p>Reference: Heinemann, G., Schefczyk, L., Willmes, S., Shupe, M., 2022: Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period. Elem. Sci. Anth., 10 (1). DOI: 10.1525/elementa.2022.00033.</p> <p><strong>Project: </strong> Modelling the impact of sea-ice leads on the atmospheric boundary layer during MOSAiC (MISLAM)</p> <p><strong>Funding: </strong>Federal Ministry of Education and Research (BMBF), grant 03F0887A</p>
Challenges simulating the AMOC in climate models
<p>This is a data set for the article:</p> <p>Jackson, Laura C, Helene T. Hewitt, Diego Bruciaferri, Daley Calvert, Tim Graham, Catherine Guiavarc’h, Matthew B. Menary, Adrian L. New, Malcolm Roberts and David Storkey, 2023: Challenges simulating the AMOC in climate models, submitted to Philosophical Transactions of the Royal Society A.</p> <p>Fig 1 calculated correlations between AMOC strength or trend in 1% CO2 experiment (values given in CMIP6_AMOC.txt) and sea surface salinity fields in the controls (fields for each model given in CMIP6_SSS.nc)</p> <p>We also include data of the AMOC streamfunction (*amoc.nc), March mixed layer depth (MarchMLD.nc) and temperature and salinity fields (TS.nc) for the mean of years 20-30 of models HadGEM3-GC3-1ML, MM and MH. These models are documented here <a href="https://doi.org/10.5194%2fgmd-2019-148">doi:10.5194/gmd-2019-148</a>. This data was used for plotting figures 2-4. </p> <p>Data for Fig 5 is in HadGEM3-GC3-1MM-noGM-av-20-40_MarchMLD.nc and HadGEM3-GC3-1MM-withGM-av-20-40_MarchMLD.nc</p> <p>Data for Fig 6 is in HadGEM3-GC1_control_20390301_20780330_MLD.nc and HadGEM3-GC1_relax_20390301_20780330_MLD.nc</p> <p>Data for Fig 7 is included in <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F7764695&data=05%7C01%7Claura.jackson%40metoffice.gov.uk%7Cceeee8cf4c9d4401060e08db2bcc93f2%7C17f1816120d7474687fd50fe3e3b6619%7C0%7C0%7C638151929867577211%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=nkL88P4lDCDB5E4qPxvL6%2B6pu6nU5kDtCofZg5%2FAoDs%3D&reserved=0">https://zenodo.org/record/7764695</a> </p> <p> </p>
Swiss Dwellings: A large dataset of apartment models including aggregated geolocation-based simulation results covering viewshed, natural light, traffic noise, centrality and geometric analysis
<p><strong>Introduction</strong></p> <p>This dataset contains detailed data on over 45,000 apartments (370,000 rooms) in ~3,100 buildings including their geometries, room typology as well as their visual, acoustical, topological, and daylight characteristics. Additionally, we have included location-specific characteristics for the buildings, including climatic data and points of interest within walking distance.</p> <p><strong>Changelog</strong></p> <ul> <li><strong>v3.0.0 (2023-03-31):</strong> <ul> <li>Updated the dataset increasing the total number of apartments to 45176 and incorporating fixes to some of the sites. The update includes re-digitized apartments and thus alters some ID values.</li> </ul> </li> <li><strong>v2.2.1 (2023-03-10):</strong> <ul> <li>A file, <code>location_ratings.csv</code>, has been included to provide ratings of the locations in which the buildings are situated. The ratings, provided by <a href="https://en.fpre.ch/">Fahrländer Partner AG</a>, give insights into the living situation at the buildings' addresses. Details for the different dimensions are provided below.</li> <li>The file <code>location.csv</code> has been updated to include the minimum and maximum temperatures for the locations in which the buildings are situated.</li> </ul> </li> <li><strong>v2.1.0 (2022-12-23)</strong>: <ul> <li>A file, <code>locations.csv</code>, has been included to provide information on the climatic and infrastructural characteristics of the locations in which each building is situated</li> </ul> </li> <li><strong>v2.0.0 (2022-10-17):</strong> <ul> <li>Additional to the residential units, we also include the commercial and public parts (such as staircases) of the models. The field <code>unit_usage</code> describes whether an area belongs to a commercial, residential, janitor or public part of the building</li> <li>Added the fields <code>elevation</code> and <code>height</code> to <em>geometries.csv</em> to describe the elevation above the terrain surface and the height of objects.</li> <li>Added the field <code>plan_id</code> which allows identifying which floors are based on the same floor plan (in some cases multiple floors of a building share the same floor plan</li> <li>Improved the ordering of fields in the CSV files (instead of alphabetic order)</li> <li>Minor changes to individual sites</li> </ul> </li> </ul> <p><strong>Procurement</strong></p> <p>The data is sourced from commercial clients of <a href="https://www.archilyse.com/">Archilyse AG</a> specializing on the digitization and analysis of buildings. The existing building plans of clients are converted into a geo-referenced, semantically annotated representation and undergo a manual Q/A process to ensure the accuracy of the data and to ensure a maximum 5%-deviation in the apartments' areas (validated with a median deviation of 1.2%).</p> <p><strong>Geometries</strong></p> <p>The dataset contains a file <code>geometries.csv</code> which contains the geometries of all areas, walls, railings, columns, windows, doors and features (sinks, bathtubs, etc.) of an apartment.</p> <p>In total, the datasets contain the 2D geometry of ~1.7 million separators (walls, railings), ~715,000 openings (windows, doors), ca. 520,000 areas (rooms, bathrooms, kitchens, etc.), and ~315,000 features (sinks, toilets, bathtubs, etc.).</p> <p>Each row contains:</p> <ul> <li><code>apartment_id</code>: The ID of the apartment (for features, areas), <em>note</em>: an apartment id is only unique per site</li> <li><code>site_id</code>: The ID of the site</li> <li><code>building_id</code>: The ID of the building</li> <li><code>floor_id</code>: The ID of the floor</li> <li><code>plan_id</code>: The ID of the plan on which the floor is based, multiple floors of a building might be based on the same plan</li> <li><code>unit_id</code>: The ID of the unit in which the element is spatially contained (for features, areas)</li> <li><code>area_id</code>: The ID of the area in which the element is spatially contained (for features)</li> <li><code>unit_usage</code>: The usage of the unit, possible values are: RESIDENTIAL, COMMERCIAL, PUBLIC, JANITOR</li> <li><code>entity_type</code>: The entity type (<em>area, separator, opening, feature</em>)</li> <li><code>entity_subtype</code>: The entity’s sub-type (e.g. <em>WALL</em>)</li> <li><code>geometry</code>: The element’s geometry as a <a href="https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry">WKT</a> geometry in meters. The geometry is given in the site’s local coordinate system. I.e. the position between elements of the same site are correct in respect to each other. The +y direction points northwards, the +x direction points eastwards.</li> <li><code>elevation</code>: The object's elevation above the terrain surface in meters. We assume one terrain baseline per building, thus all walls in a given floor share the same elevation value. However, windows in particular might start at different elevations and have differing heights.</li> <li><code>height</code>: The height of the entity in meters, <em>note</em>: In many cases, a default height is assumed</li> </ul> <p>An example:</p> <table> <thead> <tr> <th scope="col">column</th> <th scope="col"> </th> </tr> </thead> <tbody> <tr> <td>apartment_id</td> <td> <p>d4438f2129b30290845ce7eef98a5ba7</p> </td> </tr> <tr> <td>site_id</td> <td>127</td> </tr> <tr> <td>building_id</td> <td>164</td> </tr> <tr> <td>plan_id</td> <td>492</td> </tr> <tr> <td>floor_id</td> <td>861</td> </tr> <tr> <td>unit_id</td> <td>63777</td> </tr> <tr> <td>area_id</td> <td>767676</td> </tr> <tr> <td>unit_usage</td> <td>RESIDENTIAL</td> </tr> <tr> <td>entity_type</td> <td>area</td> </tr> <tr> <td>entity_subtype</td> <td>LIVING_ROOM</td> </tr> <tr> <td>geometry</td> <td> <p>POLYGON ((-6.1501158933490139 -4.8490786654693...</p> </td> </tr> <tr> <td>elevation</td> <td>0</td> </tr> <tr> <td>height</td> <td>2.6</td> </tr> </tbody> </table> <p><strong>Simulations</strong></p> <p>Besides the geometrical model, we also provide simulation data on the visual, acoustic, solar, layout, and connectivity-related characteristics of the apartments. The file <code>simulations.csv</code> contains the simulation data aggregated on a per-area basis. Each row contains the identifier columns <code>area_id</code>, <code>unit_id</code>, <code>apartment_id</code>, <code>floor_id</code>, <code>building_id</code>, <code>site_id</code> as defined above as well as 367 simulation columns. Each simulation column is formatted as:</p> <pre><code><simulation_category>_<simulation_dimensions>_<aggregation_function></code></pre> <p>For instance. the column <code>view_buildings_median</code> describes the amount of building surface that can be seen from any point in a given room. The aggregation methods vary per simulation category and are described in detail below.</p> <p><strong>Layout</strong></p> <p>The <em>layout</em> features represent simple features based on the geometry and composition of a room, the dataset provides the following information in an unaggregated form.</p> <p>Area Basics / Geometry</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_area_type</td> <td>The area’s area type</td> </tr> <tr> <td>layout_net_area</td> <td>The area’s share of the apartment’s net area (e.g. 0 for a balcony)</td> </tr> <tr> <td>layout_area</td> <td>The area’s actual area</td> </tr> <tr> <td>layout_perimeter</td> <td>The area’s perimeter</td> </tr> <tr> <td>layout_compactness</td> <td>The area’s compactness (the Polsby–Popper score)</td> </tr> <tr> <td>layout_room_count</td> <td>The area’s share to the apartment’s room count</td> </tr> <tr> <td>layout_is_navigable</td> <td>True if the area is navigable by a wheelchair</td> </tr> </tbody> </table> <p>Area Features</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_has_sink</td> <td>True if the area has a sink</td> </tr> <tr> <td>layout_has_shower</td> <td>True if the area has a shower</td> </tr> <tr> <td>layout_has_bathtub</td> <td>True if the area has a bathtub</td> </tr> <tr> <td>layout_has_toilet</td> <td>True if the area has a toilet</td> </tr> <tr> <td>layout_has_stairs</td> <td>True if the area has stairs</td> </tr> <tr> <td>layout_has_entrance_door</td> <td>True if the area is directly leading to an exit of the apartment</td> </tr> </tbody> </table> <p>Area Windows / Doors</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_number_of_doors</td> <td>The number of doors directly leading to the area</td> </tr> <tr> <td>layout_number_of_windows</td> <td>The number of windows of the area</td> </tr> <tr> <td>layout_door_perimeter</td> <td>The sum of all door lengths directly leading to the area</td> </tr> <tr> <td>layout_window_perimeter</td> <td>The sum of all window lengths of the area</td> </tr> </tbody> </table> <p>Area Walls / Railings</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_open_perimeter</td> <td>The sum of all of the boundaries of the area that are neither walls nor railings</td> </tr> <tr> <td>layout_railing_perimeter</td> <td>The sum of all of the boundaries of the area that are railings</td> </tr> <tr> <td>layout_mean_walllengths</td> <td>The mean length of the area’s sides</td> </tr> <tr> <td>layout_std_walllengths</td> <td>The standard deviation of the lengths of the area’s sides</td> </tr> </tbody> </table> <p>Area Adjacency</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_connects_to_bathroom</td> <td>True if the area connects to a bathroom</td> </tr> <tr> <td>layout_connects_to_private_outdoor</td> <td>True if the area connects to an outside area that is private to the apartment</td> </tr> </tbody> </table> <p><strong>View</strong></p> <p>The views from an object help to understand the impact of the surroundings on the object. The view simulation calculates the visible amount of buildings, greenery, water, etc. on each individual hexagon from the analyzed object. The values are expressed in steradians (sr) and represent the amount a particular object category occupies in the spherical field of view.</p> <p>Each of the following dimensions is provided using the room-wise aggregations' <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, the column <code>view_greenery_p20</code> describes the amount of greenery that can be seen from at least 20% of the positions in the area.</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>view_buildings</td> <td>The amount of visible buildings</td> </tr> <tr> <td>view_greenery</td> <td>The amount of visible greenery</td> </tr> <tr> <td>view_ground</td> <td>The amount of visible ground</td> </tr> <tr> <td>view_isovist</td> <td>The amount of visible isovist</td> </tr> <tr> <td>view_mountains_class_2</td> <td>The amount of visible mountains of UN mountain class 2</td> </tr> <tr> <td>view_mountains_class_3</td> <td>The amount of visible mountains of UN mountain class 3</td> </tr> <tr> <td>view_mountains_class_4</td> <td>The amount of visible mountains of UN mountain class 4</td> </tr> <tr> <td>view_mountains_class_5</td> <td>The amount of visible mountains of UN mountain class 5</td> </tr> <tr> <td>view_mountains_class_6</td> <td>The amount of visible mountains of UN mountain class 6</td> </tr> <tr> <td>view_railway_tracks</td> <td>The amount of visible railway_tracks</td> </tr> <tr> <td>view_site</td> <td>The amount of visible site</td> </tr> <tr> <td>view_sky</td> <td>The amount of visible sky</td> </tr> <tr> <td>view_tertiary_streets</td> <td>The amount of visible tertiary_streets</td> </tr> <tr> <td>view_secondary_streets</td> <td>The amount of visible secondary_streets</td> </tr> <tr> <td>view_primary_streets</td> <td>The amount of visible primary_streets</td> </tr> <tr> <td>view_pedestrians</td> <td>The amount of visible pedestrians</td> </tr> <tr> <td>view_highways</td> <td>The amount of visible highways</td> </tr> <tr> <td>view_water</td> <td>The amount of visible water</td> </tr> </tbody> </table> <p><strong>Sun</strong></p> <p>Sun simulations help to understand the impact of solar radiation on the object. The outcome of the sun simulations helps to identify surfaces that have great solar potential. Sun simulations are defined by the amount of solar radiation on each individual hexagon from the analyzed object. The sun simulation not only includes direct sun but also considers scattered light. The sun simulation values are given in Kilolux (klx). Simulations are performed for the days of the summer solstice, winter solstice, and the vernal equinox.</p> <p>Each of the following dimensions is provided using the room-wise aggregations' <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, column <code>sun_201806211200_median</code> describes the median amount of direct daylight received on the positions in the area.</p> <p>Vernal Equinox</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>sun_201803210800</td> <td>Daylight at 08:00 on 21st of March</td> </tr> <tr> <td>sun_201803211000</td> <td>Daylight at 10:00 on 21st of March</td> </tr> <tr> <td>sun_201803211200</td> <td>Daylight at 12:00 on 21st of March</td> </tr> <tr> <td>sun_201803211400</td> <td>Daylight at 14:00 on 21st of March</td> </tr> <tr> <td>sun_201803211600</td> <td>Daylight at 16:00 on 21st of March</td> </tr> <tr> <td>sun_201803211800</td> <td>Daylight at 18:00 on 21st of March</td> </tr> </tbody> </table> <p>Summer Solstice</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>sun_201806210600</td> <td>Daylight at 06:00 on 21st of June</td> </tr> <tr> <td>sun_201806210800</td> <td>Daylight at 08:00 on 21st of June</td> </tr> <tr> <td>sun_201806211000</td> <td>Daylight at 10:00 on 21st of June</td> </tr> <tr> <td>sun_201806211200</td> <td>Daylight at 12:00 on 21st of June</td> </tr> <tr> <td>sun_201806211400</td> <td>Daylight at 14:00 on 21st of June</td> </tr> <tr> <td>sun_201806211600</td> <td>Daylight at 16:00 on 21st of June</td> </tr> <tr> <td>sun_201806211800</td> <td>Daylight at 18:00 on 21st of June</td> </tr> <tr> <td>sun_201806212000</td> <td>Daylight at 20:00 on 21st of June</td> </tr> </tbody> </table> <p>Winter Solstice</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>sun_201812211000</td> <td>Daylight at 10:00 on 21st of December</td> </tr> <tr> <td>sun_201812211200</td> <td>Daylight at 12:00 on 21st of December</td> </tr> <tr> <td>sun_201812211400</td> <td>Daylight at 14:00 on 21st of December</td> </tr> <tr> <td>sun_201812211600</td> <td>Daylight at 16:00 on 21st of December</td> </tr> </tbody> </table> <p><strong>Noise / Window Noise</strong></p> <p>Noise level and the distribution of elements from an area help to understand how an object is exposed to the acoustics of this area. The acoustic simulation calculates the noise intensity on each individual hexagon from the analyzed object considering traffic and train noise datasets. Adjacent buildings are considered noise-blocking elements. The values are expressed in dBA (decibels).</p> <p>Window Noise</p> <p>The noise per window of a given area is aggregated via <code>min</code> and <code>max</code>. For instance, <code>window_noise_train_day_max</code> represents the maximum amount of noise received on any window of the area.</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>window_noise_traffic_day</td> <td>The amount of noise received on the area’s windows from daytime car traffic</td> </tr> <tr> <td>window_noise_traffic_night</td> <td>The amount of noise received on the area’s windows from night-time car traffic</td> </tr> <tr> <td>window_noise_train_day</td> <td>The amount of noise received on the area’s windows from daytime train traffic</td> </tr> <tr> <td>window_noise_train_night</td> <td>The amount of noise received on the area’s windows from night-time train traffic</td> </tr> </tbody> </table> <p>Area-Wise Noise</p> <p>The area-wise noise describes the amount of noise received from a noise source aggregated over the whole area in an unaggregated form. For instance, <code>noise_traffic_night</code> describes the dBA of noise received in the area from car traffic at night when propagating noise from all windows.</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>noise_traffic_day</td> <td>The amount of noise received in the area from daytime car traffic</td> </tr> <tr> <td>noise_traffic_night</td> <td>The amount of noise received in the area from night-time car traffic</td> </tr> <tr> <td>noise_train_day</td> <td>The amount of noise received in the area from daytime train traffic</td> </tr> <tr> <td>noise_train_night</td> <td>The amount of noise received in the area from night-time train traffic</td> </tr> </tbody> </table> <p><br> <strong>Connectivity</strong></p> <p>Centrality simulations help to analyze a floor plan, whether it’s a shopping mall and you want to identify prominent areas in order to select the most prominent spot or it’s an interior design circulation path and you want to determine open floor plan areas. Centrality simulations are done using topological measures that score grid cells by their importance as a part of a grid cell network.</p> <p>The distances and centralities are aggregated via <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, <code>connectivity_balcony_distance_min</code> describes the shortest distance to the next balcony from the point closest to the balcony in the area.</p> <p>Distances</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>connectivity_room_distance</td> <td>Distance to the next area of type ROOM</td> </tr> <tr> <td>connectivity_living_dining_distance</td> <td>Distance to the next area of type LIVING_DINING</td> </tr> <tr> <td>connectivity_bathroom_distance</td> <td>Distance to the next area of type BATHROOM</td> </tr> <tr> <td>connectivity_kitchen_distance</td> <td>Distance to the next area of type KITCHEN</td> </tr> <tr> <td>connectivity_balcony_distance</td> <td>Distance to the next area of type BALCONY</td> </tr> <tr> <td>connectivity_loggia_distance</td> <td>Distance to the next area of type LOGGIA</td> </tr> <tr> <td>connectivity_entrance_door_distance</td> <td>Distance to the next apartment exit</td> </tr> </tbody> </table> <p>Centralities</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>connectivity_eigen_centrality</td> <td>The Eigen-Centrality value</td> </tr> <tr> <td>connectivity_betweenness_centrality</td> <td>The Betweenness-Centrality value</td> </tr> <tr> <td>connectivity_closeness_centrality</td> <td>The Closeness-Centrality value</td> </tr> </tbody> </table> <p><strong>Location Properties</strong></p> <p>In addition to the apartment-related data, we also provide simulation data on the climatic, and infrastructural characteristics of the locations. The file <code>locations.csv</code> contains the simulation data aggregated on a per-building basis. Each row contains the identifier <code>building_id</code> corresponding to the building ids referenced in <code>geometries.csv</code> and <code>simulations.csv</code>.</p> <p><strong>Climate</strong></p> <p>The climate features represent 39 simple features based on the spatial climate analysis of Meteo Swiss as derived from <a href="https://www.meteoswiss.admin.ch/climate/the-climate-of-switzerland/spatial-climate-analyses.html.">MeteoSwiss</a>. Each column is formatted as <code>climate_<category>_<period>. </code>For instance, the column <code>climate_tnorm_january</code> describes the monthly mean temperature in degrees Celsius (from the norm period of 1991-2020) at the location of the building. The aggregation methods vary per simulation category and are described in detail below.</p> <p>Temperature Normals</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>climate_tnorm_year</td> <td>The yearly mean temperature in degrees Celsius of the current norm period from 1991 to 2020 (TnormY9120)</td> </tr> <tr> <td>climate_tnorm_january</td> <td>The monthly mean temperature in January in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>climate_tnorm_februry</td> <td>The monthly mean temperature in February in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>climate_tnorm_december</td> <td>The monthly mean temperature in December in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>climate_tminnorm_january</td> <td>The monthly minimum temperature in January in degrees Celsius of the current norm period from 1991 to 2020 (TminnormM9120)</td> </tr> <tr> <td>...</td> <td> </td> </tr> <tr> <td>climate_tminnorm_december</td> <td>The monthly minimum temperature in December in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>climate_tmaxnorm_january</td> <td>The monthly maximum temperature in January in degrees Celcius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>...</td> <td> </td> </tr> <tr> <td>climate_tmaxnorm_december</td> <td>The monthly maximum temperature in December in degrees Celcius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> </tbody> </table> <p>Sunshine Duration Normals</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>climate_snorm_year</td> <td>The yearly mean relative sunshine duration in percent of the current norm period from 1991 to 2020 (SnormY9120). Relative sunshine duration (RSD) is the ratio between the effective sunshine duration and the duration maximally possible if no clouds were covering the sun. A period with sunshine is defined as a period when the direct solar irradiance exceeds 200 W/m²</td> </tr> <tr> <td>climate_snorm_january</td> <td>The monthly mean relative sunshine duration for January in percent of the current norm period</td> </tr> <tr> <td>climate_snorm_februry</td> <td>The monthly mean relative sunshine duration for February in percent of the current norm period</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>climate_snorm_december</td> <td>The monthly mean relative sunshine duration for December in percent of the current norm period</td> </tr> </tbody> </table> <p>Precipitation Normals</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>climate_rnorm_year</td> <td>The yearly mean precipitation in mm of the current norm period (RnormY9120)</td> </tr> <tr> <td>climate_rnorm_january</td> <td>The monthly mean precipitation for January in mm of the current norm period (RnormM9120)</td> </tr> <tr> <td>climate_rnorm_februry</td> <td>The monthly mean precipitation for February in mm of the current norm period (RnormM9120)</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>climate_rnorm_december</td> <td>The monthly mean precipitation for December mm of the current norm period (RnormM9120)</td> </tr> </tbody> </table> <p><strong>10-Minute Walkshed Infrastructure</strong></p> <p>Based on OpenStreetMap data and its tagging system we counted all 465 tags (key and value tuples as listed here: https://wiki.openstreetmap.org/wiki/Map_features) which can be reached within a 10-minute walk from the location of the building. Each column is formatted as <code>walkshed_<poi_category>_<poi_type>. </code>For instance, the column <code>walkshed_shop_coffee</code> describes the number of coffee shops located within 10 minutes of walking from the building.</p> <p>The following is an excerpt of support categories and their corresponding types.</p> <ul> <li><code>shop: antique, art, ...</code></li> <li><code>amenity: art, atm, ...</code></li> <li><code>tourism: alpine, attraction, ...</code></li> <li><code>leisure: amusement, beach, ...</code></li> <li><code>healthcare: clinic, dentist, ...</code></li> <li><code>historic: archaeological, battlefield, ...</code></li> <li><code>ariaelway: station</code></li> </ul> <p><strong>Location Ratings</strong></p> <p>The location ratings, provided by <a href="https://en.fpre.ch/">Fahrländer Partner AG</a>, give insights into the living situation at locations in which the buildings are situated. The file location_ratings.csv provides the following information:</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>location_rating_MIKRAT_W</td> <td>Living situation - Overall (1.0=worst, 5.0=best)</td> </tr> <tr> <td>location_rating_IMAGE_W</td> <td>Living situation - Image (1.0=worst, 5.0=best)</td> </tr> <tr> <td>location_rating_DL_W</td> <td>Living situation - Service Quality (1.0=worst, 5.0=best)</td> </tr> <tr> <td>location_rating_FZ_W</td> <td> <p>Living situation - Leisure Quality (1.0=worst, 5.0=best)</p> </td> </tr> <tr> <td>location_rating_NASE_W_DOM</td> <td>The most dominant segment of demand:<br> <br> 1 Rural-traditional<br> 2 Modern worker<br> 3 Transitional-alternative<br> 4 Traditional middle class<br> 5 Liberal middle class<br> 6 Established alternative<br> 7 Upper middle class<br> 8 Professional elite<br> 9 Urban elite<br> 10 Unknown<br> <br> <a href="https://en.fpre.ch/marktdaten/nachfragersegmente/nachfragersegmente-im-wohnungsmarkt/">More Information</a></td> </tr> <tr> <td>location_rating_FGFRQZ</td> <td> <p>The mean number of pedestrians per hour throughout a day between 7 am and 8 pm of an average working day.<br> <br> 1 <50<br> 2 50-100<br> 3 100-200<br> 4 200-500<br> 5 >500</p> </td> </tr> </tbody> </table>
Dataset to Report on skill of CMIP6 models to simulate alkalinity and improved parameterizations for large scale alkalinity distribution
<p>Six sensitivity experiments were conducted with the ocean-sea ice-biochemistry model FESOM2.1-REcoM3 to investigate the sensitivity of total alkalinity to changes in parametrizations. The results are reported in part two of the report:</p> <p>Hinrichs, C., & Hauck, J. (2022). OceanNETS Task 4.4 – Report on skill of CMIP6 models to simulate alkalinity and improved parameterizations for large scale alkalinity distribution. <a href="https://oceanrep.geomar.de/id/eprint/56871/1/Deliverable_4.4_vfinal.pdf">https://oceanrep.geomar.de/id/eprint/56871/1/Deliverable_4.4_vfinal.pdf</a></p> <p>This item contains the data and plotting routine to reproduce Figure 14 in the report. The model data has been post-processed into global mean depth profiles.</p>
Deepwater Horizon Oil Spill Simulations using COAWST Modeling System
<p>There are four netcdf files which are 6-hr model outputs from the Coupled Ocean-Atmosphere-Wave-Sediment-Transport (COAWST) modeling system. The simulations cover the period 21-04-2010 12:00:00:00 to 05-05-2010 00:00:00 UTC. The datasets are described below:</p> <p>DWH_No-oil_romsout.nc: surface temperature from ROMS for no oil simulation</p> <p>DWH_No-oil_wrfout.nc: selected variables from WRF for no oil simulation</p> <p>DWH_Oil_romsout.nc: surface temperature from ROMS for oil simulation</p> <p>DWH_Oil_wrfout.nc: selected variables from WRF for oil simulation</p> <p>These netcdf files can be read by matlab, ferret, and any other softwares which can read netcdf datasets.</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.