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1,028 results for “modelling & simulation”
Data for "Including ash in UKESM1 model simulations of the Raikoke volcanic eruption reveal improved agreement with observations" by Wells et al., 2023
<p>Data used for figures in "Including ash in UKESM1 model simulations of the Raikoke volcanic eruption reveal improved agreement with observations" by Wells et al., 2023</p> <p>See https://github.com/awells96/Raikoke for code to reproduce the figures.</p>
Simulation outputs required to generate figures for "Modeling Multi-Scale Deformation Cycles in Subduction Zones with a Continuum Visco-Elastic-Brittle Framework"
<p>This file contains all of the model simulation outputs necessary to produce the figures for the paper "Modeling Multi-Scale Deformation Cycles in Subduction Zones with a Continuum Visco-Elastic-Brittle Framework".</p> <p>Below are the details of which file is required to produce which figure:</p> <p> </p> <p><strong>Figure 5 (De_dam_fields.pdf) </strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/De_dam_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz">De_dam_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz</a></p> <p> </p> <p><strong>Figure 6 (convergence.pdf)</strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_0_001_th_10_10_alpha_4_ddam10.tar.gz">comp_dt_We_0_001_th_10_10_alpha_4_ddam10.tar.gz </a></p> <p>Contains 4 files, each one for a different temporal resolution (delta t).</p> <p> </p> <p><strong>Figure C1 (convergence2.pdf, appendix)</strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_0_1_th_10_9_alpha_4_ddam10.tar.gz">comp_dt_We_0_1_th_10_9_alpha_4_ddam10.tar.gz </a></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_10_th_10_8_alpha_4_ddam10.tar.gz">comp_dt_We_10_th_10_8_alpha_4_ddam10.tar.gz </a></p> <p>Each contains 4 files, one for each temporal resolution (delta t).</p> <p> </p> <p><strong>Figure 7 (CPU_time.pdf)</strong></p> <p>No simulation output file: all of the necessary information (CPU times) are included in the associated MATLAB code, available in the Github repository.</p> <p> </p> <p><strong>Figure 8 (T_h.pdf)</strong></p> <p><strong>Left panels, a, c, e</strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_0_001_dt_10_5_alpha_4_ddam_10.tar.gz">comp_th_We_0_001_dt_10_5_alpha_4_ddam_10.tar.gz </a></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_0_1_dt_10_4_alpha_4_ddam_10.tar.gz">comp_th_We_0_1_dt_10_4_alpha_4_ddam_10.tar.gz </a></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_10_dt_10_3_alpha_4_ddam_10.tar.gz">comp_th_We_10_dt_10_3_alpha_4_ddam_10.tar.gz </a></p> <p>Each contains 4 files, one for each healing time (T_h)</p> <p><strong>Right panels, b, d, f</strong></p> <p>comp_th_We_0_001_dt_10_5_th_10_11_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_001_dt_10_5_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_001_dt_10_5_th_10_8_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_11_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_10_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_8_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_11_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_10_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_8_alpha_4_ddam_10.tar.gz</p> <p>Each contains 5 files, for 5 different realisations of the model simulations (same parameters, different initial noise on cohesion)</p> <p> </p> <p><strong>Figure 9 (comp_ddam_We_0_001.pdf)</strong></p> <p>comp_ddam_We_0_001_dt_10_5_th_10_10.tar.gz</p> <p>One file for each alpha value (2, 3, 4, 6, 8), one file for each delta d value (0.1, 0.3, 0.5, 0.7, 0.9)</p> <p> </p> <p><strong>Figure 10 (comp_ddam_We_0_1.pdf)</strong></p> <p>comp_ddam_We_0_1_dt_10_4_th_10_9.tar.gz</p> <p>One file for each alpha value (2, 3, 4, 6, 8), one file for each delta d value (0.1, 0.3, 0.5, 0.7, 0.9)</p> <p> </p> <p><strong>Figure 11 (discussion.pdf)</strong></p> <p>u_sfc_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>u_sfc_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_50.tar.gz</p> <p> </p> <p><strong>SI movie</strong></p> <p>SI_movie.tar.gz</p>
Dynamic Vegetation Model Dynamic Organic Soil Terrestrial Ecosystem Model (DVM-DOS-TEM) simulations focused on Eight Mile Lake, Alaska and Imnavait Creek, Alaska [2000-2015]
<p>This set of files store model simulations using the biosphere model Dynamic Vegetation Model Dynamic Organic Soil Terrestrial Ecosystem Model (DVM-DOS-TEM), developed to simulate biophysical and biogeochemical interactions between the soil, vegetation and atmosphere. To improve predictions of net carbon releases from thawing permafrost, we tested the sensitivity of a suite of model parameters. We analyzed the responses of ecosystem carbon balances to permafrost thaw by running site-level simulations at two long-term tundra ecological monitoring sites in Alaska: Eight Mile Lake (EML) and Imnavait Creek watershed (IMN). These sites are characterized by similar tussock tundra vegetation but differing soil drainage conditions and climate, IMN consists of well-drained soils, and EML has historically well-drained soils, however permafrost thaw has altered drainage conditions to wetter soils. Simulations were conducted at a 1km resolution, over a 1,000 km2 area (10x10 km square) centered on two long term ecological research sites in Alaska: Eight Mile Lake located in Interior Alaska (63.8900° N, 149.2535° W), and Imnavait creek watershed located on the northern foothills of the Brooks range (68°37′ N, 149°18′ W).</p> <p>Historical simulations are spanning the 2000 to 2015, and forced using climate simulations from the Climate Research Unit, time series 4.0. We ran 1,000 site level simulations for each model variable. The variables that are produced are gross primary productivity (GPP, in gC.m-2.m-1), net ecosystem exchange (NEE, gC.m-2.m-1), ecosystem respiration (RECO, gC/m2/m-1), active layer thickness (ALT, m), soil temperature (TLAYER,°C) at 5, 10, 40 cm depths, soil moisture (LWCLAYER, m-3/m-3) at 5, 10 cm depths, and snow depth (SNOWDEPTH, m), evapotransipiration(EET, mm/m2/time), potential evapotransipiration (PET, mm/m2/time), leaf area index (LAI, m2/m2), organic layer thickness (OLT, m). The data are stored as compiled csv files, with time as the index, and each model sample output stored in the columns. In addition, there is a postprocessing python script to demonstrate the step and workflow used to generate the individual csv files post processed from the raw model outputs stored as netcdfs.</p>
CLUBB Single Column Model simulation scripts and data
<p>This archive contains run scripts for CLUBB single-column model (SCM) simulations and post-processed data and analysis scripts used in Zhang et al. (2023, JAMES) entitled "removing numerical pathologies in a turbulence parameterization through convergence testing". There are two compressed files included:</p> <p>1. <a href="https://zenodo.org/api/files/f1bd6537-5c11-40df-91c9-b562fa0debfe/Figure_scripts.tar.gz">Figure_scripts.tar.gz</a>: contains scripts and figures used in the paper </p> <p>2. <a href="https://zenodo.org/api/files/f1bd6537-5c11-40df-91c9-b562fa0debfe/run_scripts.tar.gz">run_scripts.tar.gz</a> : contains scripts to run CLUBB-SCM simulations. </p> <p>The versions of the CLUBB-SCM code used for the simulations to generate the model output for analysis in our study can be found on Zenodo under <a href="http://doi.org/10.5281/zenodo.7803749">10.5281/zenodo.7803749</a>. </p> <p> </p>
LiftWEC deliverable 3.6 - Part I: Dataset from 3D validation simulations of LiftWEC device using a high-fidelity RANS model
<p>This dataset contains numerical simulation results obtained from 3D-validation studies of the high-fidelity RANS model employed in the LiftWEC project. The case identifiers (ID) correspond to the case numbering employed in the experimental reference cases defined by École Centrale de Nantes. It is highly recommended to read the corresponding project reports on numerical modelling (D3.6) and on experimental modelling (D4.5, D4.6, D4.7, D4.8) which are also available in the LiftWEC community on zenodo (https://zenodo.org/communities/liftwec/).</p> <p>The cases comprise simulations of a rotor at constant velocity in calm water and regular waves in full 3D simulations. It further includes 2D simulation results of a rotor at constant rotational velocity in irregular waves and at variable velocity in monochromatic waves.</p> <p>All loads in the data set are given in force per unit span length (N/m), torque and power output is given as values per unit span as well. Wave elevation data up and down-wave of the rotor is given in (m).</p> <p> </p> <p> </p> <p> </p>
LiftWEC deliverable 3.6 - Part II: Dataset from numerical simulations of full-scale LiftWEC device using a high-fidelity RANS model
<p>This dataset contains the numerical simulation results for a full-scale LiftWEC device in regular and irregular wave conditions. The regular wave cases occur at static pitch and fixed rotational velocity. The parameters used for these cases are described in the corresponding *CaseParameters.csv file. These simulations were used to derive a first estimate of the maximum conversion efficiency of the rotor from wave power to shaft power. A detailed description of the employed numerical model and the case setup can be found in LiftWEC deliverable 3.6 Hydrodynamic Validation of Final Design, which was also uploaded to the LiftWEC community on zenodo. In accordance with the coordinate system definition used in the validation case, a relative phase angle of 270° degree corresponds to foil1 at 3 o'clock position while the wave crest passes over the rotor axis position. Rotation is clockwise, Rotor rotates in the direction of orbital wave particle velocities.</p> <p>The control reference case describes the first test case of a cyclorotor in irregular waves under active control of angular velocity and foil pitch. This case is also documented in deliverable D3.6. The case files can be used to recreate wave conditions and motion signal. The load-file can be used to validate the obtained tangential and radial forces on foil 1 as well as the total power output over time. More information on the control model can be found in the corresponding project deliverables D5.X.</p> <p>All loads given as values per unit span length (e.g. for forces [N/m]).</p>
Observing system simulation experiments to evaluate transport model error on CO2 flux estimates
<p>Atmospheric CO2 inversion using coarse-resolution transport model can cause large errors on surface carbon flux estimates. The transport model errors on flux estimates are isolated using observing system simulation experiments presented here.</p>
LiftWEC deliverable 3.3 - Dataset from 2D validation simulations of LiftWEC device using a high-fidelity RANS model
<p>This dataset contains results obtained from numerical simulations of the LiftWEC model scale device in a two-dimensional setting. The simulations were done based on the experimental validation campaign conducted in the scope of the LiftWEC project and documented in deliverables D4.2, D4.3 and D.4. The corresponding experimental datasets are also available within the LiftWEC community on zenodo.</p> <p>The numerical setup as well as a presentation and discussion of obtained results is available in LiftWEC deliverable D3.3 Tool Validation and Extension report, which also contains information on the potential flow model. All forces presented in this document are given as forces per unit span length. As the 2D RANS model was found to be rather sensitive to high fluctuations at this preliminary investigation stage, results are presented as mean forces and force fluctuations at rotation period, analysed by means of an FFT post-processing routine. The case identifiers correspond to the case numbering employed in the experimental model tests.</p>
Finite Element Analysis-Based Soft Robotic Modeling: Simulating a Soft Actuator in SOFA
<p>This document represent a step by step guide for a simulation in SOFA framework of a cable driven soft robot.</p>
Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution (data)
<p>Data for "Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution"</p>
Data accompanying "Diurnal variability of the upper ocean simulated by a climate model"
<p>Data used for creating figures in the draft article "Diurnal variability of the upper ocean simulated by a climate model". This includes:</p> <ul> <li>Multi-year, monthly mean diurnal cycle metrics at all model grid points.</li> <li>Monthly mean diurnal cycle data for individual years at selected locations.</li> </ul> <p>Code used to create these data files, and to create the plots, is in a Github repository (https://github.com/JackReevesEyre/cfs-analysis-gaea/). The repository is also archived on Zenodo (https://doi.org/10.5281/zenodo.7846095).</p>
Data used to simulations in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains input data of simulations by WRF-CMAQ, WRF-Chem and WRF-CHIMERE in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. WRF-CMAQ input data including emission, ICs and lateral BCs of meteorology and air quality:</p> <p>YYYYMM.zip represents the input data for each month for simulations. Due to the large size of the compressed file containing input data each month, there may be interruptions when uploading it to Zenodo. Therefore, we will split each compressed file into 50MB. If users want to browse the file, they can download the segmented files, and then merge them into the YYYYMM.zip file using the Linux command line "unzip 'YYYYMM.zip.*' -d combined"</p>
Output files from SPEEDY v.42 ensembles described in the paper: "Multi-decadal pacemaker simulations with an intermediate-complexity climate model" by F. Molteni, F. Kucharski and R. Farneti (part 1 of 2)
<p>The monthly-mean output from SPEEDY v.42 ensembles (either driven by prescribed sea-surface temperature (SST) or coupled to the TOM3 model) consists of a series of IEEE little-endian binary files and metadata files in text format.<br> For each year of integration (indicated by a 4-digit number YYYY) and ensemble member (indicated by a 3-digit number NNN), two binary files are present, named:<br> • attmNNN_YYYY.grd, including data on the 120x60 grid-point atmospheric grid;<br> • sftmNNN_YYYY.grd, including data on the 360x180 grid-point surface grid.<br> The metadata for these files are contained in the text files <strong>attmEEE.ctl</strong> and <strong>sftmEEE.ctl</strong> respectively, where EEE is a 3-digit ensemble identifier (usually, but not necessarily, equal to one of the ensemble-member number NNN).</p> <p><br> This repository contains data from:</p> <ul> <li>(part 1) a 41-year 5-member ensemble (653) run with prescribed SST</li> <li>(part 2) a 70-year 5-member ensemble (104) run with the coupled SPEEDY-TOM3 model.</li> </ul> <p>Integration years are 1980 to 2020 for ensemble 653 and 1951 to 2020 for ensemble 104.</p> <p><br> The structure of the binary data and metadata files follows the conventions for gridded datasets set by the GrADS diagnostic and plotting package (developed by the Center for Ocean-Land-Atmosphere Studies of George Mason University), as described here:<br> http://cola.gmu.edu/grads/gadoc/aboutgriddeddata.html</p> <p><br> In addition to the COLA-GMU web site, free version of the GrADS package for different platforms can be downloaded from the OpenGrADS web site:<br> http://opengrads.org/</p> <p><br> Specifically, the SPEEDY v.42 output consists of sequential-access files where each record contains a two-dimensional field. Three-dimensional fields are stored as a sequence of consecutive records, one for each of the 8 pressure levels where model-level data are interpolated by the post-processing routines. For each month of the year:</p> <p><br> the <strong>attmNNN_YYYY.grd</strong> files contain a sequence of <strong>9 3-D variables and 26 2-D variables</strong>;<br> the <strong>sftmNNN_YYYY.grd</strong> files contain a sequence of <strong>21 2-D variables</strong>.</p> <p>Within each record, grid-point data are stored as a NLONxNLAT array with longitude varying from west to east and latitude varying from south to north. The list of variables and levels is specified in the <strong>attmEEE.ctl</strong> and s<strong>ftmEEE.ctl</strong> files. These files contain descriptors which allow the data of each ensemble to be accessed as a single dataset by the GrADS package.</p> <p>Although the metadata files are specific to the GrADS package, the binary data can be read by different types of code. As example of fortran90 instructions to read the content of the <strong>attmNNN_YYY.grd</strong> and <strong>sftmNNN_YYY.grd</strong> files for one year/ens.member is as follows:</p> <p>integer, parameter :: nlon=120<br> integer, parameter :: nlat=60<br> integer, parameter :: nlev=8<br> integer, parameter :: nlon0=360<br> integer, parameter :: nlat0=180</p> <p>integer :: jmonth, jvar3d, jvar2d, jlev<br> real :: fld3d(nlon,nlat,nlev)<br> real :: fld2d(nlon,nlat), fld0(nlon0,nlat0)</p> <p>open (unit=1, file=”attmNNN_YYY.grd”, form=”formatted”, access=”sequential”)<br> open (unit=2, file=”sftmNNN_YYY.grd”, form=”formatted”, access=”sequential”)</p> <p>do jmonth=1,12</p> <p> do jvar3d=1,9<br> do jlev=1,nlev<br> read (1) fld3d(:,:,jlev)<br> …………<br> enddo<br> enddo</p> <p> do jvar2d=1,26<br> read (1) fld2d(:,:)<br> ………<br> enddo</p> <p> do jvar2d=1,21<br> read (2) fld0(:,:)<br> ………<br> enddo</p> <p>enddo</p> <p>close (1)<br> close (2)</p> <p> </p> <p> </p>
Output files from SPEEDY v.42 ensembles described in the paper: "Multi-decadal pacemaker simulations with an intermediate-complexity climate model" by F. Molteni, F. Kucharski and R. Farneti (part 2 of 2)
<p>The monthly-mean output from SPEEDY v.42 ensembles (either driven by prescribed sea-surface temperature (SST) or coupled to the TOM3 model) consists of a series of IEEE little-endian binary files and metadata files in text format.<br> For each year of integration (indicated by a 4-digit number YYYY) and ensemble member (indicated by a 3-digit number NNN), two binary files are present, named:<br> • attmNNN_YYYY.grd, including data on the 120x60 grid-point atmospheric grid;<br> • sftmNNN_YYYY.grd, including data on the 360x180 grid-point surface grid.<br> The metadata for these files are contained in the text files <strong>attmEEE.ctl</strong> and <strong>sftmEEE.ctl</strong> respectively, where EEE is a 3-digit ensemble identifier (usually, but not necessarily, equal to one of the ensemble-member number NNN).</p> <p><br> This repository contains data from:</p> <ul> <li>(part 1) a 41-year 5-member ensemble (653) run with prescribed SST</li> <li>(part 2) a 70-year 5-member ensemble (104) run with the coupled SPEEDY-TOM3 model.</li> </ul> <p>Integration years are 1980 to 2020 for ensemble 653 and 1951 to 2020 for ensemble 104.</p> <p><br> The structure of the binary data and metadata files follows the conventions for gridded datasets set by the GrADS diagnostic and plotting package (developed by the Center for Ocean-Land-Atmosphere Studies of George Mason University), as described here:<br> http://cola.gmu.edu/grads/gadoc/aboutgriddeddata.html</p> <p><br> In addition to the COLA-GMU web site, free version of the GrADS package for different platforms can be downloaded from the OpenGrADS web site:<br> http://opengrads.org/</p> <p><br> Specifically, the SPEEDY v.42 output consists of sequential-access files where each record contains a two-dimensional field. Three-dimensional fields are stored as a sequence of consecutive records, one for each of the 8 pressure levels where model-level data are interpolated by the post-processing routines. For each month of the year:</p> <p><br> the <strong>attmNNN_YYYY.grd</strong> files contain a sequence of <strong>9 3-D variables and 26 2-D variables</strong>;<br> the <strong>sftmNNN_YYYY.grd</strong> files contain a sequence of <strong>21 2-D variables</strong>.</p> <p>Within each record, grid-point data are stored as a NLONxNLAT array with longitude varying from west to east and latitude varying from south to north. The list of variables and levels is specified in the <strong>attmEEE.ctl</strong> and s<strong>ftmEEE.ctl</strong> files. These files contain descriptors which allow the data of each ensemble to be accessed as a single dataset by the GrADS package.</p> <p>Although the metadata files are specific to the GrADS package, the binary data can be read by different types of code. As example of fortran90 instructions to read the content of the <strong>attmNNN_YYY.grd</strong> and <strong>sftmNNN_YYY.grd</strong> files for one year/ens.member is as follows:</p> <p>integer, parameter :: nlon=120<br> integer, parameter :: nlat=60<br> integer, parameter :: nlev=8<br> integer, parameter :: nlon0=360<br> integer, parameter :: nlat0=180</p> <p>integer :: jmonth, jvar3d, jvar2d, jlev<br> real :: fld3d(nlon,nlat,nlev)<br> real :: fld2d(nlon,nlat), fld0(nlon0,nlat0)</p> <p>open (unit=1, file=”attmNNN_YYY.grd”, form=”formatted”, access=”sequential”)<br> open (unit=2, file=”sftmNNN_YYY.grd”, form=”formatted”, access=”sequential”)</p> <p>do jmonth=1,12</p> <p> do jvar3d=1,9<br> do jlev=1,nlev<br> read (1) fld3d(:,:,jlev)<br> …………<br> enddo<br> enddo</p> <p> do jvar2d=1,26<br> read (1) fld2d(:,:)<br> ………<br> enddo</p> <p> do jvar2d=1,21<br> read (2) fld0(:,:)<br> ………<br> enddo</p> <p>enddo</p> <p>close (1)<br> close (2)</p>
The simulated outputs analyzed in the article: "Understanding the influences of ocean waves on Arctic sea ice simulation: a modeling study with an atmosphere-ocean-wave-sea ice coupled model"
<p>In Ice-mass_[experiment] files, they include daily-averaged sea ice concentration and sea ice mass/area budgets.</p> <p>In Flux_[experiment] files, they include daily-averaged net ice surface flux, net shortwave/longwave radiation at the ice surface, latent/sensible heat flux at the ice surface, conductive heat flux at the top ice layer, and ice-ocean heat flux. </p>
Lattice kinetic Monte Carlo model to simulate RNA polymerase II clusters during stem cell differentiation
<p>This data set includes Python scripts (numerical simulation and analysis) and already generated simulation data for RNA polymerase II clusters during stem cell differentiation. It includes the whole data to recreate panels.</p>
Simulated population time series used to build and test a model of accuracy for population-based global biodiversity indicators
<p class="MsoNormal">Global biodiversity is facing a crisis, which must be solved through effective policies and on-the-ground conservation. But governments, NGOs, and scientists need reliable indicators to guide research, conservation actions, and policy decisions. Developing reliable indicators is challenging because the data underlying those tools is incomplete and biased. For example, the Living Planet Index tracks the changing status of global vertebrate biodiversity, but taxonomic, geographic and temporal gaps and biases are present in the aggregated data used to calculate trends. But without a basis for real-world comparison, there is no way to directly assess an indicator's accuracy or reliability. Instead, a modelling approach can be used.</p> <p class="MsoNormal">We developed a model of trend reliability, using simulated datasets as stand-ins for the "real world", degraded samples as stand-ins for indicator datasets (e.g. the Living Planet Database), and a distance measure to quantify reliability by comparing sampled to unsampled trends. The model revealed that the proportion of species represented in the database is not always indicative of trend reliability. Important factors are the number and length of time series, as well as their mean growth rates and variance in their growth rates, both within and between time series. We found that many trends in the Living Planet Index need more data to be considered reliable, particularly trends across the global south. In general, bird trends are the most reliable, while reptile and amphibian trends are most in need of additional data. We simulated three different solutions for reducing data deficiency, and found that collating existing data (where available) is the most efficient way to improve trend reliability, and that revisiting previously-studied populations is a quick and efficient way to improve trend reliability until new long-term studies can be completed and made available.</p>
Simulation data for the office cell building energy model with the attached overhang
<p>Simulation data for 729,000 variants of the office cell building model with the overhang attached over the window. The variants are determined by the overhang depth and height, location, presence of obstacles, orientation and cooling and heating set points. The office cell model is described in the manuscript "Predicting the shape of loads for an office cell with an overhang from a small number of building energy simulations".</p>
Global Datasets of Hourly Carbon and Water Fluxes Simulated Using a Satellite-based Process Model with Dynamic Parameterizations
<p>This new global hourly dataset serves as a 'handshake' among process-based models, remote sensing, and the eddy covariance flux network, providing a reliable long-term estimate of global gross primary productivity (GPP) and evapotranspiration (ET) with diurnal patterns and facilitating studies related to ecosystem functional properties, global carbon, and water cycles.</p> <p>The dataset include the GPP and ET of sunlit and shaded leaf components at an hourly timescale and a spatial resolution of 0.25-degree from 2001 to 2020.</p>
[Northwest Borneo Simulations] Discrete-Continuous Model for Submarine Mass Failure-induced Tsunamigenesis
<p>Recent studies on submarine landslides, also known as submarine mass failures (SMF), show that they can be sources of hazardous tsunamis. In this study, a novel modelling technique which describes submarine mass failure-induced tsunamigenesis was developed. This was achieved through developing a cellular automata model, using ultradiscretization with the rules derived from the two-dimensional diffusion equation, to describe the discrete elements of the SMF such as rockfalls. Then, it will be coupled, using its displacement vector output, to the depth-averaged Navier-Stokes equations in generating tsunami waves.<br> <br> When the coupled model was applied and validated to an actual event, simulation results on the Anak Krakatau Flank Collapse show that the model was able to achieve 90.10% accuracy. On the other hand, when the model was applied to a potentially catastrophic scenario on the Northwest Borneo Trough using the calibrated parameters of the validation data, the simulation results suggest that there could be a wave height of 8.76 meters with 23.99 m/s propagating near shorelines of Balabac Island, Palawan; while the tsunami wave run-up height on Brunei and Sabah are likely to be 1.14 meters and 2.18 meters, respectively. Additionally, the model experiments suggest that the coupled model communicates well and is able to provide high percentages of total released energy transferred, 90.56% on Anak Krakatau Flank Collapse simulation and 92.91% on Northwest Borneo Trough simulation.</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.