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1,028 results for “modelling & simulation”
All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching
<p>We present two different methods to estimate parameters within a partial differential equation (PDE) model of cancer invasion. The model describes the spatio-temporal evolution of three variables -- tumour cell density, extracellular matrix density and matrix degrading enzyme concentration -- in a one-dimensional tissue domain. The first method is a likelihood-free approach associated with Approximate Bayesian Computation (ABC); the second is a two-stage gradient matching method based on smoothing the data with a Generalized Additive Model (GAM) and matching gradients from the GAM to those from the model. Both methods performed well on simulated data. To increase realism, additionally we tested the gradient matching scheme with simulated measurement error and found that the ability to estimate some model parameters deteriorated rapidly as measurement error increased.</p>
Remcom Wireless InSite - Warehouse models and simulation configurations
<p>This dataset is provided in scope of the <a href="http://safelog-project.eu/">SafeLog</a> project. It comprises warehouse models and simulation configurations for Remcom Wireless InSite suite used in evaluating UWB signal propagation in warehouse environment.</p>
Convolutional Neural Network Formulation to Compare 4D Seismic and Reservoir Simulation Models
<p>This dataset contains the .npy (numpy) files of the simulation models and reference discussed in the paper "Convolutional Neural Network Formulation to Compare 4D Seismic and Reservoir Simulation Models".</p> <p>The folders contain all simulation models and reference maps already divided in subregions. Each .npy file is a numpy 2D array with delta IP or delta Sw values. The csv files contain the 3-tuples and the selected model in each.</p> <p>There are two csv files: the first is the dataset used for training the CNN, with 1280 labeled tuples evaluated by a single specialist. The second is the ground-truth, with 164 tuples evaluated by three specialists (in which 2 or more agreed on the selected model), used for validating the models and comparing different approaches.</p> <p>We also provide a Python code to read and visualize the .npy files.</p>
Important contribution of N2O5 hydrolysis to the daytime nitrate in Xi'an, China during haze periods: isotopic analysis and WRF-Chem model simulation
<p>To quantify the contributions of major formation pathways to nitrate, a field observation, coupling with a WRF-Chem model simulation, was conducted in Xi’an, an inland megacity of China. Here is the dataset of ions, stable isotope compositions of nitrogen and oxygen of nitrate, T, RH and the results simulated by WRF-Chem model.</p>
Impact of Different Nesting Methods on the Simulation of a Severe Convective Event Over South Korea Using the Weather Research and Forecasting Model
<p>The data from various platforms (NCEP FNL, TRMM, ERA5, AWS) and WRF Model output utilised to generate the figures in the current study (https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020JD033084) are available at this Zenodo data repository.</p>
Results of ISMIP6 CMIP6 forced simulations: a multi-model ensemble of the Greenland and Antarctic ice sheet evolution over the 21st century
<p>This archive provides the ice sheet model outputs produced as part of the publication "Payne et al. 2021 Future sea level change under CMIP5 and CMIP6 scenarios from the Greenland and Antarctic ice sheets", published in GRL</p> <p>Contact: Tony Payne a.j.payne@bristol.ac.uk, Sophie Nowicki sophien@buffalo.edu, ismip6@gmail.com </p> <p><br> Further information on ISMIP6 can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Greenland</p> <p>Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in the process producing this data set. Acknowledgements should have language similar to the below (if you only use CMIP5 forcing, remove CMIP6 and vice versa).</p> <p>“We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>For Greenland datasets </p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., Fettweis, X., and Nowicki, S.: Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution , The Cryosphere, 14, 985–1008, https://doi.org/10.5194/tc-14-985-2020, 2020.</p> <p>Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: <br> Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p>For Antarctica datasets</p> <p>Seroussi, H., Nowicki, S., Simon, E., Abe-Ouchi, A., Albrecht, T., Brondex, J., Cornford, S., Dumas, C., Gillet-Chaulet, F., Goelzer, H., Golledge, N. R., Gregory, J. M., Greve, R., Hoffman, M. J., Humbert, A., Huybrechts, P., Kleiner, T., Larour, E., Leguy, G., Lipscomb, W. H., Lowry, D., Mengel, M., Morlighem, M., Pattyn, F., Payne, A. J., Pollard, D., Price, S. F., Quiquet, A., Reerink, T. J., Reese, R., Rodehacke, C. B., Schlegel, N.-J., Shepherd, A., Sun, S., Sutter, J., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., and Zhang, T.: initMIP-Antarctica: an ice sheet model initialization experiment of ISMIP6, The Cryosphere, 13, 1441–1471, https://doi.org/10.5194/tc-13-1441-2019, 2019.</p> <p>Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H., Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134, https://doi.org/10.5194/tc-14-3111-2020, 2020.</p> <p><br> Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p> </p>
Data associated to the article "A semiclassical Thomas–Fermi model to tune the metallicity of electrodes in molecular simulations"
<p>Contains input files and data used to generate the figures of the article:</p> <p>A semiclassical Thomas–Fermi model to tune the metallicity of electrodes in molecular simulations</p> <p>Laura Scalfi, Thomas Dufils, Kyle G. Reeves, Benjamin Rotenberg and Mathieu Salanne, J. Chem. Phys. 153, 174704 (2020)</p> <p>https://doi.org/10.1063/5.0028232</p> <p>The folder typical_input_files contains typical MetalWalls input files used to perform the simulations.</p> <p>The folder DATA_FIGURES contains the processed data used to plot all the figures of the paper.</p>
Data from: Modelling and simulation of a thermally induced optical transparency in a dual micro-ring resonator
This paper introduces the simulation and modelling of a novel dual micro-ring resonator. The geometric configuration of the resonators, and the implementation of a simulated broadband excitation source, results in the realization of optical transparencies in the combined through port output spectrum. The 130 nm silicon on insulator rib fabrication process is adopted for the simulation of the dual-ring configuration. Two titanium nitride heaters are positioned over the coupling regions of the resonators, which can be operated independently, to control the spectral position of the optical transparency. A third heater, centrally located above the dual resonator rings, can be used to red shift the entire spectrum to a required reference resonant wavelength. The free spectral range with no heater currents applied is 4.29 nm. For a simulated heater current of 7 mA (55.7 mW heater power) applied to one of the through coupling heaters, the optical transparency exhibits a red shift of 1.79 nm from the reference resonant wavelength. The ring-to-ring separation of approximately 900 nm means that it can be assumed that there is a zero ring-to-ring coupling field in this model. This novel arrangement has potential applications as a gas mass airflow sensor or a gas species identification sensor.
Simulation models from: Can CRISPR-mediated gene drive work in pest and beneficial haplodiploid species?
<p>Gene drives based on CRISPR/Cas9 have the potential to reduce the enormous harm inflicted by crop pests and insect vectors of human disease, as well as to bolster valued species. In contrast with extensive empirical and theoretical studies in diploid organisms, little is known about CRISPR gene drive in haplodiploids, despite their immense global impacts as pollinators, pests, natural enemies of pests, and invasive species in native habitats. Here we analyze mathematical models demonstrating that, in principle, CRISPR homing gene drive can work in haplodiploids, as well as at sex-linked loci in diploids. However, relative to diploids, conditions favoring the spread of alleles deleterious to haplodiploid pests by CRISPR gene drive are narrower, the spread is slower, and resistance to the drive evolves faster. By contrast, the spread of alleles that impose little fitness cost or boost fitness was not greatly hindered in haplodiploids relative to diploids. Therefore, altering traits to minimize damage caused by harmful haplodiploids, such as interfering with transmission of plant pathogens, may be more likely to succeed than control efforts based on introducing traits that reduce pest fitness. Enhancing fitness of beneficial haplodiploids with CRISPR gene drive is also promising.</p>
Data from: Climate-mediated hybrid zone movement revealed with genomics, museum collection and simulation modeling
Climate-mediated changes in hybridization will dramatically alter the genetic diversity, adaptive capacity and evolutionary trajectory of interbreeding species. Our ability to predict the consequences of such changes will be key to future conservation and management decisions. Here we tested through simulations how recent warming (over a 32-year period) is affecting the geographic extent of a climate-mediated developmental threshold implicated in maintaining a butterfly hybrid zone (Papilio glaucus and Papilio canadensis; Lepidoptera: Papilionidae). These simulations predict a 68 km shift of this hybrid zone. To empirically test this prediction, we assessed genetic and phenotypic changes using contemporary and museum collections and document a 40 km northward shift of this hybrid zone. Interactions between the two species appear relatively unchanged during hybrid zone movement. We found no change in the frequency of hybridization and regions of the genome that experience little to no introgression moved largely in concert with the shifting hybrid zone. Model predictions based on climate scenarios predict this hybrid zone will continue to move northward, but with substantial spatial heterogeneity in the velocity (55-144 km/1°C), shape, and contiguity of movement. Our findings suggest that the presence of non-climatic barriers (e.g., genetic incompatibilities) and/or non-linear responses to climatic gradients may preserve species boundaries as the species shift. Further, we show that variation in the "geography" of hybrid zone movement could result in evolutionary responses that differ for geographically distinct populations spanning hybrid zones and thus have implications for the conservation and management of genetic diversity.
Grad-Shafranov equation: MHD simulation of the new solution obtained from the Fadeev and Naval models
<p>This article aims to obtain a new analytical solution of a specific form of the Grad-Shafranov (GS) equation using Walker's formula. The new solution has magnetic field lines with X-type neutral points, magnetic islands and singular points. The singular points are located on the x-axis. The X-points and the center of the magnetic islands do not appear on the x-axis an island appears at $z>0$ and the other two at $z<0$. The aforementioned property allows us to use this solution as an initial condition at $t=0$ s in an magnetohydrodynamic (MHD) numerical simulation by excluding the singular points of the solution, i.e., the x-axis, and maintaining the magnetic structure of the islands, as well as the X-type neutral points. For this, we numerically solve the equations of the classical ideal MHD in two dimensions using the Newtonian CAFE code. The code is based on high resolution shock capturing methods using the Harten-Lax-van Leer-Einfeldt (HLLE) flux formula combined with MINMOD reconstructor. The MHD simulation shows a very fast dissipation in less than one second of the magnetic islands present in the initial configuration. Almost all structures left the integration region at $13.2$ s, and the magnetic field vector reverses its polarity very quickly. In addition, our simulation allows us to observe the fast temporal evolution of the magnetic islands turning into elongated current sheets. As a limitation of the model, the difficulty in relating it to a physical system because of fast temporal evolution is considered.</p>
MD simulation trajectory and related files for POPC bilayer in low hydration (Berger model delivered by Tieleman, Gromacs 4.5)
<p>Equilibrated POPC lipid bilayer simulation in low hydration (7 water per lipid molecule) ran with Gromacs 4.5, Berger force field delivered by Peter Tieleman (http://wcm.ucalgary.ca/tieleman/downloads) with fixed double bond dihedrals, 60ns, T=298K, 128 POPC molecules, 896 water molecules. This data is used in the nmrlipids.blospot.fi project. More details from nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi. If data is used, please cite the nmrlipids.blogspot.fi project and the original publications related to the force field.</p>
Hydrated DPPC, MD simulation trajectory and related files for UA charmm36 model by Lee et al 2014
<p>MD simulation files</p> <p>72 hydrated DPPC + 2189 water TIP3P </p> <p>NPgT</p> <p>P=1atm, gamma=0, T=323K (liquid crystalline phase)</p> <p>20 ns equilibration (not here)</p> <p>50 ns trajectory (dcd file)</p> <p>Model : Lee S, Tran A, Allsopp M, Lim JB, Hénin J, Klauda JB. CHARMM36 United Atom Chain Model for Lipids and Surfactants. <em>J Phys Chem B</em>. 2014;118(2):547-556. doi:10.1021/jp410344g.</p> <p>----<br /> * bilayer-72DPPC-c36-AU.psf : NAMD2.10 structure file Obtained with psfgen utility, using</p> <p>1) topology from Lee et al. 2014</p> <p>2) positions from J. Klauda.</p> <p>http://terpconnect.umd.edu/~jbklauda/research/download.html</p> <p>----</p> <p>* dppc_c36_AU.equil.2.dcd : trajectory file of 2635 frames every 20 ps.</p> <p>---<br /> * measure_SCD_heads.tcl : file used to measure order parameters for the head hydrogens using vmd-1.9</p> <p>---</p> <p>*namd_input.tar files usefull to launch the simulations using NAMD(2.10).</p>
Hydrated DPPC, MD simulation trajectory and related files for UA charmm36 model by Lee et al 2014
<p>MD simulation files</p> <p>72 hydrated DPPC + 2189 water TIP3P </p> <p>NPgT</p> <p>P=1atm, gamma=0, T=323K (liquid crystalline phase)</p> <p>20 ns equilibration (not here)</p> <p>50 ns trajectory (dcd file)</p> <p>Model : Lee S, Tran A, Allsopp M, Lim JB, Hénin J, Klauda JB. CHARMM36 United Atom Chain Model for Lipids and Surfactants. <em>J Phys Chem B</em>. 2014;118(2):547-556. doi:10.1021/jp410344g.</p> <p>----<br /> * bilayer-72DPPC-c36-AU.psf : NAMD2.10 structure file Obtained with psfgen utility, using</p> <p>1) topology from Lee et al. 2014</p> <p>2) positions from J. Klauda.</p> <p>http://terpconnect.umd.edu/~jbklauda/research/download.html</p> <p>----</p> <p>* dppc_c36_AU.equil.2.dcd : trajectory file of 2635 frames every 20 ps.</p> <p>---<br /> * measure_SCD_heads.tcl : file used to measure order parameters for the head hydrogens using vmd-1.9</p> <p>---</p> <p>*namd_input.tar files usefull to launch the simulations using NAMD(2.10).<br /> </p>
MARLIM R3D - A REALISTIC MODEL FOR mCSEM SIMULATION
<p>The marine Controlled-Source Electromagnetic (mCSEM) method provides complementary information to seismic imaging in the exploration of sedimentary basins. The mCSEM can be used to help in subsalt structural imaging, but mainly for reservoir scanning and appraisal as EM methods are especially sensible to the fluid content within the rocks.</p> <p>The mCSEM interpretation workflow is heavily based on inversion and forward--modeling for hypothesis testing. Until the recent past, the effectiveness of a given interpretation workflow was achieved after the drilling results, as there wasn't any geological complex model available to serve as a benchmark.</p> <p>The Society of Exploration Geophysics (SEG) recognized that gap and launched the SEAM (SEG Advanced Modeling)-Phase I project aiming to advance the geophysical science through the construction of a multi-physics subsurface model and generation of an associated dataset.</p> <p>SEAM Phase-I, a representation of the deepwater Gulf of Mexico salt domain, was designed to take as much realism and geological complexity as possible. Following the success of that first model, SEG launched SEAM Phase-II focused on the solution of land seismic challenges like near-surface complexities and fractured reservoirs.</p> <p>In the present publication, we will also describe the workflow to build Marlim--R3D, a realistic and complex geoelectric model. Marlim-R3D aims to be a reference model for mcSEM modeling and inversion studies of turbidite reservoirs of the Brazilian continental margin. Our model is based on previous seismic interpretation and constrained by the input of available well-log information.</p> <p>The workflow used is composed of seven sequential steps: seismic and well-log dataset loading, well-tie, Vp (P-wave velocity) cube construction, Vp-resistivity calibration, time-depth conversion, resistivity cube construction, Quality-control check.</p> <p>As a result, we obtained an interpreted dataset composed by main stratigraphic horizons, pseudo-well logs, and the resistivity cubes. These elements will be freely available for research or commercial use, under the Creative Common License.</p> <p><strong>Files Description : </strong></p> <p><strong>Depth horizons: *xyz extension (Upper Oligocene, Upper Miocene, Sea Bottom, Top of Marlim Reservoir, Top and Base of the Salt)</strong></p> <p><strong>Pseudo Wells: *.LAS extension (extrated properties from modelling) ------- *.track extensions (well track)</strong></p> <p><strong>3D Cubes: *sgy </strong></p> <p><strong>Horizontal & Vertical Resistivity (Calculated resistivity from modelling - horizontal and vertical anisotropy) </strong></p> <p><strong>Log(RESH) & Log(RESV) files are the resistivity cubes but in common logarithmic scale </strong></p>
Simulated data for "Spot-On: robust model-based analysis of single-particle tracking experiments"
<p><strong>Generation of simulated data</strong></p> <p>To systematically evaluate the performance of Spot-On as well as other common analysis tools such as MSD<sub>i</sub> and vbSPT, we considered a comprehensive set of 3480 realistic SPT simulations spanning the range of plausible dynamics. The simulations were performed using simSPT, which is freely available at GitLab: https://gitlab.com/tjian-darzacq-lab/simSPT. The simulation methods are described in detail at GitLab. A full description of the parameters which allows exact reproduction of the simulations is available together with the data (see Data Availability section). Briefly, we parameterized simSPT to consider that particles diffuse inside a sphere (the nucleus) of 8 µm diameter illuminated using HiLo illumination (assuming a HiLo beam width of 4 µm), with an axial detection range of ~700 nm, centered at the middle of the HiLo beam. Molecules are assumed to have a half-life of 4 frames (when inside the HiLo beam) and of 40 frames when outside the HiLo beam. The localization error was set to 25 nm and the simulation was run until 100000 in-focus trajectories were recorded. More specifically, the effect of the exposure time (1 ms, 4 ms, 7 ms, 13 ms, 20 ms), the free diffusion constant (from 0.5 µm²/s to 14.5 µm²/s in 0.5 µm²/s increments) and the fraction bound (from 0 % to 95 % in 5 % increments) were investigated, yielding a dataset consisting of 3480 simulations. The advantage of simulations is that the ground truth is known. This allows a quantitative assessment of which method works the best.</p> <p><strong>Content of the archives:</strong></p> <ol> <li>170718_simSPT_simulations.zip the code and instructions to reproduce the simulations</li> <li>4um.tar.bz2 simulated data inside a 4 µm nucleus</li> <li>20um.tar.bz2 simulated data inside a 20 µm nucleus, in which virtually no confinement occurs.</li> <li>subsampled.tar.bz2 is a set of subsampled datasets, containing either 99999, 30000, 10000, 3000, 1000, 300, 100 or 30 trajectories. Each subsampling was done 50 times, yielding 50 files per subsmpling.</li> </ol> <p><strong>Formats:</strong></p> <p>The data is provided both in CSV and .mat formats. .mat files are provided in the following dataset: 10.5281/zenodo.835541</p>
Ecohydrological simulation using GEOSPACE-1D model
<p>This folder contains the setup and outputs for two simulations designed to demonstrate the capabilities and functionality of the GEOSPACE-1D model:</p> <ol> <li><strong>Baseline Simulation (BSL):</strong> Showcases the potential applications of the coupled GEOSPACE-1D system and verifies its correct functioning.</li> <li><strong>Infiltration-Only Simulation (INF):</strong> Replicates the baseline setup but excludes the evapotranspiration process, focusing solely on the infiltration dynamics within the soil column.</li> </ol> <p>The code in this repository is distributed under the GPLv3 License, as specified in the included <code>LICENSE.txt</code> file, ensuring clarity on usage and redistribution rights in line with open-source principles.</p> <p> </p> <p>This folder also includes a concise <strong>presentation </strong><code>(The_Sim_File.pdf)</code> that illustrates the structure and interpretation of a <code>.sim</code> file, providing a clear overview of how simulation parameters are defined and utilized within the GEOSPACE-1D framework. The recorded video of this presentation is avilable at https://vimeo.com/showcase/11593737/video/1087641833. </p>
Data Release for Retreat and Regrowth of the Greenland Ice Sheet During the Last Interglacial as Simulated by the CESM2-CISM2 Coupled Climate–Ice Sheet Model
<p>CESM2 and CISM2 data files for figures in "Retreat and Regrowth of the Greenland Ice Sheet During the Last Interglacial as Simulated by the CESM2-CISM2 Coupled Climate–Ice Sheet Model" (Sommers et al., 2021, Paleoceanography and Paleoclimatology)</p>
Model simulation data used in "How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe-AERCHEM, v1.0) sub-submodel of MESSy"
<p>This dataset contains the output of the ECHAM/MESSy Atmospheric Chemistry (EMAC) model simulation analyzed in the work "How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe-AERCHEM, v1.0) sub-submodel of MESSy" submitted to Geoscientific Model Development.</p>
Beaufort Gyre westward expansion simulation using a two-layer idelized model
<p>This data set supports the analysis presented in the manuscript: "Beta-drift of eddies drove Beaufort Gyre westward expansion during 2003-2014" (2023GL107142), which is under review for publication by Geophysical Research Letters. </p><p>The model code modified from<br> aronnax (https://github.com/edoddridge/aronnax)</p><p>please refer to:</p><p>Doddridge, E. W., & Radul, A. (2018). Aronnax: An idealised isopycnal ocean model. <i>Journal of Open Source Software</i>, 3(26), 592. <a href="http://doi.org/10.21105/joss.00592">http://doi.org/10.21105/joss.00592</a></p><p>for details of this code</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.