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1,670 results for “forcing”
UKESM1-forced BORIS-1 seafloor biomass under CMIP6 SSPs
<p>Annual mean seafloor biomass for periods 1980 to 2014 (Historical) and 2015 to 2100 (Future) for Shared Socioeconomic Pathways SSP126 to SSP585. Simulations use the benthic BORIS model (Kelly-Gerreyn et al., Biogeosciences, 2014) forced using output from the UKESM1 model (Sellar et al., JAMES, 2019; Yool et al., GMD, 2021) in the same experimental design used in Yool et al. (GCB, 2017). Simulations use Matlab v2020a. Each file contains seafloor detritus, total seafloor biomass and seafloor biomass for each of BORIS-1's 16 size classes.</p> <p>Kelly-Gerreyn, B. A., Martin, A. P., Bett, B. J., Anderson, T. R., Kaariainen, J. I., Main, C. E., Marcinko, C. J., and Yool, A.: Benthic biomass size spectra in shelf and deep-sea sediments, Biogeosciences, 11, 6401–6416, https://doi.org/10.5194/bg-11-6401-2014, 2014.</p> <p>Sellar, A. A., Jones, C. G., Mulcahy, J., Tang, Y., Yool, A., Wiltshire, A. O’Connor, F. M., Stringer, M., Hill, R., Palmiéri, J.,<br> Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N.<br> L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards,<br> J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C.<br> D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R.,Woodhouse, M., Zeng, G., and Zerroukat, M.: UKESM1: Description and evaluation of the UK Earth System Model, J. Adv. Model. Earth Sy., U J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019.</p> <p>Yool, A., Palmiéri, J., Jones, C. G., de Mora, L., Kuhlbrodt, T., Popova, E. E., Nurser, A. J. G., Hirschi, J., Blaker, A. T., Coward, A. C., Blockley, E. W., and Sellar, A. A.: Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 historical simulations, Geosci. Model Dev., 14, 3437–3472, https://doi.org/10.5194/gmd-14-3437-2021, 2021.</p> <p>Yool, A., Martin, A.P., Anderson, T.R., Bett, B.J., Jones, D.O.B., Ruhl, H.A.: Big in the benthos: Future change of seafloor community biomass in a global, body size-resolved model. Glob Change Biol., 23: 3554– 3566, https://doi.org/10.1111/gcb.13680, 2017.</p> <p> </p>
Data and code for figures: Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing
<p>This directory contains the datasets and code (if applicable) for generating the figures in the research article "Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing", Physical Review Applied 20, 024022 (2023).</p>
Example of Force Digital Calibration Certificate used in ComTraForce 18SIB08 project to demonstrate Digital Twin concept
<p>Force Digital Calibration Certificate (DCC) was developed in the frameworks of 18SIB08 ComTraForce project. It was used to demonstrate the way of data connection between the physical object (force transducer) and virtual object (Finite Element model) within the developed Digital Twin concept. The developed at PTB v3.1.2 xsd schema was used to convert analog calibration certificate to machine readable XML format. The DCC covers static and continuous calibration processes. Note that the current Force DCC is not a Good Practice example. Please follow further developments of force DCC Good Practice example at https://gitlab.com/ptb/dcc.</p>
Simulation dataset to benchmark 3D force inference methods
<p>Dataset of 47 artificial images (.tif) and corresponding segmentation masks (.tif), generated from simulations of foam-like cell structures (early embryos) of various cell numbers (2 to 11), cell sizes and interfacial tensions.<br> The ground truth simulation tensions and pressures to be inferred are provided as Numpy arrays (.npy).</p> <p>This dataset was used to benchmark a method to infer cellular forces in 3D from microscopy images of multicellular contours, that is available on <a href="https://github.com/VirtualEmbryo/foambryo">https://github.com/VirtualEmbryo/foambryo</a>.<br> Non-manifold multimaterial meshes corresponding to artificial microscopy images are also provided as binary files (.rec) and may be opened with our delaunay-watershed Python code, available on <a href="https://github.com/VirtualEmbryo/delaunay-watershed">https://github.com/VirtualEmbryo/delaunay-watershed</a>.</p> <p><strong>Credits, contact, citations</strong><br> If you use this dataset, please cite the published version of the following preprint: <br> <em>Ichbiah, S., Delbary, F., McDougall, A., Dumollard, R., & Turlier, H. (2023). Embryo mechanics cartography: inference of 3D force atlases from fluorescence microscopy. bioRxiv, 2023-04. </em><a href="https://doi.org/10.1101/2023.04.12.536641">https://doi.org/10.1101/2023.04.12.536641</a><br> <br> We hope that this dataset may be useful to benchmark future 3D force inference methods.<br> If you have any question on this dataset, please contact <a href="mailto:herve.turlier@college-de-france.fr?subject=%5BZenodo%5D%203D%20tension%20inference%20benchmark%20dataset">Hervé Turlier</a>.</p> <p><strong>License</strong><br> Copyright (c) 2023 Turlier Lab - <a href="https://www.turlierlab.com/">https://www.turlierlab.com/</a><br> This dataset is licensed under the <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
A bite force database of 654 insect species
<p>The insect bite force database as described in Rühr et al. (<strong>accepted</strong>): A bite force database for 654 insect species. doi: <a href="https://doi.org/10.1038/s41597-023-02731-w">1038/s41597-023-02731-w</a>.</p><p>The code used to convert the raw measurements to the final database and to create all tables and figures of the original publication can be found on its <a href="https://github.com/Peter-T-Ruehr/InsectBiteForceDatabase">GitHub Page</a> (under release <a href="https://github.com/Peter-T-Ruehr/InsectBiteForceDatabase/releases/tag/v1.0.0">v1.0.0</a>).</p>
Adsorption free energies and potentials of mean-force for interactions between amino acids, lipid fragments, and nanoparticles
<p>This dataset contains tabulated potentials of mean force (PMFs) and associated adsorption (binding) free energies for interactions of amino acids side chain analogues and lipid fragments (LF) with a range of materials: titanium dioxide, iron oxide, amorphous silica, quartz, and a range of carbon-based materials including amorphous carbon, graphene and carbon nanotubes both in a pristine form and functionalized by certain chemical groups. All data were computed from atomistic molecular dynamics simulations as a part of the SmartNanoTox project 2016-2020. Version 2 of the dataset includes additional materials: zink oxide, zink sulfate in pristine and PMMA-coated forms computed within NanoSolveIt project (2019-2023). The data are intended to be used in coarse-grained models describing interactions of nanomaterials with nanoparticles, for the prediction of the binding affinity of proteins and lipids to nanoparticles, and as biological "fingerprints" of nanomaterials characterizing behavior of the nanomaterials in biological environments. </p>
Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (<a href="https://doi.org/10.1034/j.1600-0870.2001.00241.x" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the article "<em>The evolution of a non-autonomouys chaotic system under non-periodic forcing: a climate change example</em>", published by Chaos (<a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a>.</p> <p>All files uploaded were generated from simulations run by the authors.</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p> <p><strong>Note:</strong> This version (v1.1) is the same version as v1.0 but with the correct README file.</p>
Snail Shell Strength and Total Crush Force of a Northern Michigan Snail as a Function of Predation Risk at the University of Michigan Biological Station Stream Research Facility (5/31/23-8/1/23)
Many prey organisms respond to the non-consumptive effects of predators by altering their physiology, morphology, and behavior. These inducible defenses can create refuges for prey by decreasing the likelihood of consumption by predators. Some prey, as in marine mollusks, have been shown to alter their morphology in response to the presence of size-limited predation. To extend this work into the freshwater realm, we presented pointed campeloma snails (Campeloma decisum) to chemical cues from a natural predator, the rusty crayfish (Faxonius rusticus), to better understand how snail morphology changes under the threat of predation. The total force needed to crush shells, total shell length, aperture width, and total weight, along with changes to these three body measurements were recorded for each individual and used to quantify morphological changes as a function of risk. Snails exposed to crayfish chemical cues needed significantly more force to crush their shells than controls (p = 0.002). Total shell length was greater in crayfish exposed snails than control snails (p = 0.002), and snails in the crayfish treatment also showed significantly more change in shell length than control snails (p = 0.003). Similarly, aperture width was significantly greater in exposed snails (p = 0.002). However, exposed snails exhibited significantly less change in aperture width than controls (p = 0.017). Finally, we found that snails exposed to crayfish weighed significantly more than snails in the control (p = 0.0009). Thus, the results of this study show that morphology of gastropods is altered in the presence of predators, and this may be an antipredator tactic directly related to risk.
Central Baltic EwE scenario forcing
<p>Dataset describes forcing functions for climate and nutrient load scenarios simulated by Central Baltic EwE</p>
Estimate of the atmospherically-forced contribution to sea surface height variability based on altimetric observations
<p>This repository contains the estimate of the atmospherically-forced contribution to sea level variability described in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>, and derived from the Ssalto/Duacs altimeter products produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (<a href="http://www.marine.copernicus.eu">http://www.marine.copernicus.eu</a>).</p> <p>The files contain successive 5-day averages of sea level anomaly, with the same global coverage and 0.25° grid as the Ssalto/Duacs altimeter products. The estimate is created using a spatial bandpass filter, with cutoff scales of ~1.5° and 10.5°. Zeros in the mask file indicate regions in which it has not been possible to evaluate the quality of the estimate.</p> <p>The cutoff scales applied to the altimetry data were determined through analysis of output from the OceaniC Chaos – ImPacts, strUcture, predicTability (Penduff et al, 2014) experiment, comprising a 50-member ensemble of ocean-sea ice model hindcasts with 0.25° horizontal resolution (<a href="http://doi.org/10.5194/gmd-10-1091-2017">Bessières et al., 2017</a>). The spatiotemporal coherence between the model-based estimates of the atmospherically-forced (ensemble mean) and total simulated sea surface height signals was analysed, and found to exhibit distinct partitioning between the atmospherically-forced and intrinsic contributions in a spatial (but not temporal) sense, thus suggesting that meaningful estimation of the two components can be achieved based on simple spatial filtering. Verification of the method using the model data indicates good accuracy, with a global mean correlation of 0.9 between the estimate based on spatial filtering and the ensemble mean sea surface height. Full details of the methodology and verification may be found in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>.</p> <p>----</p> <p><strong>References</strong>:</p> <p>Bessières, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and Sérazin, G., 2017. Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091–1106, <a href="https://doi.org/10.5194/gmd-10-1091-2017">doi: 10.5194/gmd-10-1091-2017</a>.</p> <p>Close, S., Penduff, T., Speich, S. and Molines J.-M., 2020. A means of estimating the intrinsic and atmospherically-forced contributions to sea surface height variability applied to altimetric observations. Progr. Oceanogr. <a href="https://doi.org/10.1016/j.pocean.2020.102314">doi: 10.1016/j.pocean.2020.102314</a></p> <p>Penduff, T., Barnier, B. , Terray, L., Bessières, L., Sérazin, G., Grégorio, S., Brankart, J., Moine, M., Molines, J., Brasseur, P., 2014. Ensembles of eddying ocean simulations for climate, CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling, 19.</p>
Matlab code for a multipolar decomposition of optical forces
<p>This dataset supplements Figure 5 from the publication "Multipolar Origin of the Unexpected Transverse<br> Force Resulting from Two-Wave Interference*" by Karim Achouri, Andrei Kiselev, and Olivier J. F. Martin. Here, we provide the code for the multipolar analysis of the optical force based on the vector spherical harmonic decomposition. Based on the Mie solution, electric fields can be obtained numerically in the far-field by using the software developed by Dr. Karim Achouri https://github.com/kachourim/MieScatteringPEC. This code analyzes the far-field scattered by a perfect electric conductor sphere placed in vacuum for different sphere radii. In the framework of the Maxwell stress tensor, we find the optical force acting on a sphere along the z-direction in the illumination configuration presented in Figure 1*. The use of the vector spherical decomposition allows to observe contributions from different multipolar interactions. In this code,we analyze the force appearing as a result of the interaction between electric dipole aligned along x, px, and electric quadrupole with components xz, Qexz and compare it with the total force along the z-axis appearing as a result of interaction between all multipoles supported by the sphere with given radius.</p>
Data used to create figures in the ACP Letters manuscipt "The value of remote marine aerosol measurements for constraining radiative forcing uncertainty" by Regayre et al. (2020)
<p>This dataset was created from perturbed parameter ensembles (PPEs) using the HadGEM-UKCA atmospheric composition climate model. All data needed to reproduce figures in the Regayre et al. (2020) ACP Letters article "The value of remote marine aerosol measurements for constraining radiative forcing uncertainty" are included. Other output from the PPEs can be obtained by contacting the lead author.</p> <p>The following data are included here:</p> <ul> <li>CCN measurement data degraded to match the model-measurement comparison resolution.</li> <li>Unconstrained and constrained CCN<sub>0.2</sub> output from the PPE used to make Figure 1. These compressed files contain 48 .dat files. Each .dat file contains the PPE mean, variance and 95% creidble interval data. Files are named consecutively, containing data from 90<sup>o</sup>S to 90<sup>o</sup>N at 0<sup>o</sup>E, then continuing Eastward. When combined, these files provide data for each latitude/longitude pair at the N48 spatial resolution.</li> <li>A zip file of an netcdf file containing 26-dimensional data for parameter values, used to create the sample of 1 million model variants from our statistical emulators of model output.</li> <li>A zip file containing a folder of files made of one million ones and zeros that indicate the retention/rejection criteria from applying our constraint methodology for various constraint combination scenarios, for each model variant. A value of 1 indicates the model variant was retained. Data in these files is in the same order as the unconstrained sample file of parameter values.</li> <li>Compressed files containing global, annual mean RF<sub>aci</sub> and ERF<sub>aci</sub> values for the unconstrained set of one million model variants. The compressed netcdf files contain RF (ERF), RF<sub>aci</sub> (ERF<sub>aci</sub>) and RF<sub>ari</sub> (ERF<sub>ari</sub>) values.</li> </ul>
GAP-20 machine learning force field for phosphorus
<p>This dataset contains the force-field parameter files and reference database described in the manuscript "A general-purpose machine-learning force field for bulk and nanostructured phosphorus" (to be published).</p>
Adele 3D seismic survey segy format used in the FORCE 2020 machine learning competition for fault identification
<p>Adele seismic 3D survey segy format used in the FORCE 2020 machine learning competition for fault identification.</p> <p>Dataset is courtesy of GEOSCIENCE Australia who need to be acknowledged in each publication</p> <p> </p>
Amundsen Sea MAR simulations forced by ERAinterim
<p><strong>MAR simulations produced by Marion Donat-Magnin at IGE, Grenoble, France.</strong></p> <p><br> This simulation is evaluated in the following article:</p> <p>Donat-Magnin, M., Jourdain, N. C., Gallée, H., Amory, C., Kittel, C., Fettweis, X., Wille, J. D., Favier, V., Drira, A., and Agosta, C. (2020). Interannual variability of summer surface mass balance and surface melting in the Amundsen sector, West Antarctica, The Cryosphere, 14, 229–249, <a href="https://doi.org/10.5194/tc-14-229-2020">https://doi.org/10.5194/tc-14-229-2020</a> </p> <p><br> Here are provided the monthly means over 1979-2017. Daily outputs available on demand.<br> <br> See netcdf metadata for more information. Note that what is called runoff in the outputs is not actually a runoff (into the ocean) but more the net production of liquid water at the surface (which can either form ponds or flow into the ocean).</p> <p> </p> <p>Monthly files provided on MAR grid (see MAR_grid10km.nc). We also provide climatological (1979-2017 average) surface mass balance (SMB), surface melt rates and net liquid water production ("runoff") on a standard 8km WGS84 stereographic grid (see files ending as mean_polar_stereo.nc).</p> <p> </p> <p>The following variables are provided:</p> <ul> <li>CC Cloud Cover</li> <li>LHF Latent Heat Flux</li> <li>LWD Long Wave Downward</li> <li>LWU Long Wave Upward</li> <li>QQp Specific Humidity (pressure levels)</li> <li>QQz Specific Humidity (height levels)</li> <li>RH Relative Humidity</li> <li>SHF Sensible Heat Flux</li> <li>SIC Sea ice cover</li> <li>SP Surface Pressure</li> <li>ST Surface Temperature</li> <li>SWD Short Wave Downward</li> <li>SWU Short Wave Upward</li> <li>TI1 Ice/Snow Temperature (snow-layer levels)</li> <li>TTz Temperature (height levels)</li> <li>UUp x-Wind Speed component (pressure levels)</li> <li>UUz x-Wind Speed component (height levels)</li> <li>VVp y-Wind Speed component (pressure levels)</li> <li>VVz y-Wind Speed component (height levels)</li> <li>UVp Horizontal Wind Speed (pressure levels)</li> <li>UVz Horizontal Wind Speed (height levels)</li> <li>ZZp Geopotential Height (pressure levels)</li> <li>mlt Surface melt rate</li> <li>rfz Refreezing rate</li> <li>rnf Rainfall</li> <li>rof Runoff (i.e. net production of surface liquid water)</li> <li>sbl Sublimation</li> <li>smb Surface Mass Balance</li> <li>snf Snowfall</li> </ul>
FESOM2 simulations with increasing sea-ice model complexity under different atmospheric forcings
<p><strong>Introduction</strong></p> <p>This dataset has been compiled in support of the paper "Impact of sea-ice model complexity on the performance of an unstructured sea-ice/ocean model under different atmospheric forcings" by Zampieri et al., submitted to the Journal of Advances in Modeling Earth Systems (JAMES) published by the American Geophysical Union (AGU).</p> <p><strong>Scientific description of the dataset</strong></p> <p>The dataset contains the results of sea-ice simulations performed with the Finite-volumE Sea ice-Ocean Model version 2 (FESOM2), based on six model configurations: C1-E, C1-N, C2-E, C2-N, C3-E, and C3-N. As described in the paper, the complexity of the sea-ice model increases from the setup C1 to C3. The suffix -E and -N indicate respectively the ERA5 and NCEP atmospheric forcings used as boundary conditions for the FESOM2 model. As two iterations of the Green's function approach for the optimization of the parameter space have been performed, each configuration features three separate simulations: a control run (cnt), a first-round of optimization (opt_1), and a second and final round of optimization (opt_2). The parameter optimization is based on various sea-ice observations retrieved over the period 2002–2015. In total, 18 simulations compose the dataset (6 configurations x 3 realizations). The following 2D monthly-averaged variables are provided: the sea-ice concentration, the sea-ice thickness, the meridional and zonal components of the sea-ice velocity, and the snow thickness on top of the sea ice. The fields are defined on a global unstructured mesh denominated "CORE2", which is also included in the database.</p> <p><strong>Technical description of the dataset</strong></p> <p>As an unstructured model output is not widely diffused in the sea-ice community, we include here some suggestions for handling and analyzing the simulation results.</p> <p>The files can be interpolated to a regular grid using the following <strong><a href="https://code.mpimet.mpg.de/projects/cdo">CDO</a></strong> commands:</p> <ol> <li>Add grid description to model file: <strong><em>cdo setgrid,CORE2_mesh.nc var.fesom.yyyy.nc temp.nc</em></strong></li> <li>Interpolate to regular grid: <strong>cdo remapycon,r360x180 temp.nc var.fesom.interpolated.yyyy.nc</strong></li> </ol> <p>Furthermore, the python package<strong> <a href="https://code.mpimet.mpg.de/projects/cdo">pyfesom2</a></strong> can be used for plotting the unstructured model data and for interpolating it to a regular grid. The R package <strong><a href="https://github.com/FESOM/spheRlab">spheRlab</a></strong> can be used for plotting the model data directly on its unstructured grid and for performing further analysis. More information can be found on the <strong><a href="https://fesom.de/cmip6/work-with-awi-cm-unstructured-data/">FESOM website</a></strong>.</p> <p>The following naming convention is adopted for the model variables:</p> <ul> <li><strong><em>a_ice</em></strong> → sea-ice concentration</li> <li><strong><em>m_ice</em></strong> → sea-ice volume per unit area of ice</li> <li><strong><em>m_snow </em></strong>→ snow-volume per unit area of ice</li> <li><strong><em>vice</em></strong> → meridional component of the sea-ice velocity</li> <li><strong><em>uice</em></strong> → zonal component of the sea-ice velocity</li> </ul> <p>Three types of simulation are included:</p> <ul> <li><strong>cnt </strong>→ control run before the parameters optimization (2000–2019)</li> <li><strong>opt_1 </strong>→ after the first iteration of the parameter optimization method (2000–2015)</li> <li><strong>opt_2</strong> → after the second iteration of the parameter optimization method (2000–2019)</li> </ul> <p>Do not hesitate to contact the corresponding author (lorenzo.zampieri@awi.de) for additional information about the data processing and for any other issue with this dataset.</p> <p> </p> <p> </p>
Dataset for: Wood et al Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article Wood et al (2020) 'Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing' published in Environmental Research Letters (<a href="https://doi.org/10.1088/1748-9326/abce27">https://doi.org/10.1088/1748-9326/abce27</a>).</p> <p>To isolate the role of sea surface temperature (SST) patterns for the Southern Hemisphere circulation response in the abrupt-4xCO2 experiments in CMIP5 and CMIP6, we perform experiments using IGCM4.</p> <p>Five 120-year long simulations were performed following a 5-year spin-up period. In the control simulation (CTRL) we prescribe an annually repeating cycle of climatological monthly mean SSTs using the multi-model mean (MMM) of the ‘ts’ field for the first 200 years of the CMIP5 piControl simulations. Following the CMIP6 protocol (Eyring et al., 2016), greenhouse gas (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) concentrations are set at preindustrial (year 1850) values and ozone is prescribed as a zonally averaged monthly mean preindustrial climatology.</p> <p>In two perturbation simulations (4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub>) the same boundary conditions are used as in CTRL, but with an annually repeating cycle of climatological monthly mean SST anomalies added using the MMM ‘ts’ field for either the CMIP5 or CMIP6 FAST (years 4-10) responses. In both the 4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub> simulations CO<sub>2</sub> is quadrupled from its preindustrial concentration. This enables a like-for-like comparison with the CMIP5 and CMIP6 abrupt-4xCO2 simulations. Two further perturbation simulations (SHET-only<sub>CMIP5</sub> and SHET-only<sub>CMIP6</sub>) are used to isolate the effect of differences in SH extratropical SST patterns alone. In both simulations CO<sub>2</sub> is kept at preindustrial values, and CTRL SSTs are used with the SST anomalies from either 4xCO2-FULL<sub>CMIP5</sub> or 4xCO2-FULL<sub>CMIP6</sub> added poleward of 18°S. Similarly to McCrystall et al. (2020), the SST anomalies are smoothed between 18°S and 29°S using a cosine squared weighting function with weights of 0 at 18°S and 1 at 29°S. This minimizes sharp gradients in SST across the tropical-extratropical boundary.</p> <p>To enable a clean determination of the effects of SST patterns alone, in all perturbation simulations we keep sea ice fixed at preindustrial values by only adding SST anomalies where the MMM sea ice concentration in the CMIP5 piControl simulations is less than 15% (i.e., equatorward of the sea ice edge). Furthermore, to remove the effect of differences in the change in global mean SST, the SST anomalies in each CMIP model are normalised by the respective global mean SST anomaly and then scaled to a global mean value of 2.2 K (the pooled MMM of CMIP5 and CMIP6). The CMIP6 FAST SST anomalies are added to the CMIP5 preindustrial control SSTs, so as to isolate the effect of differences in the fast SST responses between CMIP5 and CMIP6, and not the effect of differences in the base state.</p>
Atomic Force Microscopy Images of Various Specimens
<p>This data set consists of ten atomic force microscopy images in MI format as well as corresponding previews in PNG format.</p> <p>The microscopy images are of various materials and have been scanned with AFM equipment from Keysight Technologies. Details on the individual images:</p> <ul> <li>image_7.mi - calibration grid with 5 µm pitch size</li> <li>image_8.mi - Celgard (a polymer membrane used in batteries)</li> <li>image_9.mi - Titanium-Tungsten film</li> <li>image_10.mi - AFM image</li> <li>image_11.mi - self-assembled monolayer of lipids on gold</li> <li>image_12.mi - capacity calibration sample for Scanning Microwave Microscopy imaging</li> <li>image_13.mi - capacity calibration sample for Scanning Microwave Microscopy imaging</li> <li>image_14.mi - PS-LDPE-12M (a polymer blend of Polystyrene and Polyolefin Elastomere)</li> <li>image_15.mi - AFM Calibration grid</li> <li>image_16.mi - AFM Calibration grid</li> </ul> <p>The images can be opened using, e.g. Gwyddion: http://gwyddion.net/<br /> The Python package Magni can be used to load the images into Python (using the magni.afm.io module): https://github.com/SIP-AAU/Magni</p> <p>The images are provided as-is without warranty of any kind.</p>
Simulated Local Electrical Impedance in Atrial Tissue With Varying Contact Force
<p>In this dataset we can find geometrical setups that served as an input to carry forward electrical impedance simulations with EIDORS. <br>A 3D geometrical models of one ablation catheters combining measurements of local impedance (LI) and contact force (CF) commercially available is included. The objective of these in silico experiments laid on understanding how CF and tissue deformation affect LI measurements.<br>To achieve it, using the catheter against the tissue, several grams of force are applying.<br>The dataset consists of the original geometrical models before deformation and a couple of examples of the deformed one.</p> <h2>Data structure</h2> <ul> <li>geos: original geometries of the catheter and the tissue in stl <ul> <li>catheter.stl</li> <li>tissue.stl</li> </ul> </li> <li>geos_deformed: deformed geometries at 5 and 10 grams, respectively. Includes the catheter, the mesh, and the tissue <ul> <li>5 g <ul> <li>catheter.stl</li> <li>tissue_5g.stl</li> <li>mesh_5g.stl</li> </ul> </li> <li>10 g <ul> <li>catheter.stl</li> <li>tissue_10g.stl</li> <li>mesh_10g.stl</li> </ul> </li> </ul> </li> </ul>
Supplementary data for "Effect of Uncertainty in Water Vapor Continuum Absorption on CO2 Forcing, Longwave Feedback, and Climate Sensitivity"
<h3>This dataset is supplementary to the article "Effect of Uncertainty in Water Vapor Continuum Absorption on CO2 Forcing, Longwave Feedback, and Climate Sensitivity".</h3> <h3>spectral_olr.nc</h3> <p>This file contains the spectral outgoing longwave radiation (OLR) calculated using the line-by-line radiative transfer model ARTS and the radiative-convective equilibrium model konrad. It contains spectral OLR for surface temperatures from 270K to 330K for different strengths of the water vapor continuum absorption.</p> <h3>opacity_emission_level.py</h3> <p>This file also contains the spectrally resolved optical depth and the emission level of outgoing longwave radiation for the considered absorption species (H2O lines, H2O continuum, H2O self continuum, H2O foreign continuum, CO2, N2, and O2).</p> <h3>continuum_reference_conditions.nc</h3> <p>This file contains the reference continuum absorption coefficients that were used to calculate the adjustment to the foreign continuum for the single-constraint experiment.</p> <h3>continuum_all_profiles.nc</h3> <p>This file contains the reference continuum absorption coefficients that were used to calculate the adjustment to the foreign continuum for the general-constraint experiment.</p> <h3>modified_continuum_input_files_single_constraint.zip and modified_continuum_input_files_general_constraint.zip</h3> <p>These files contain the modified continuum data files used for the implementation of the MT_CKD continuum model in the line-by-line model ARTS for the single-constraint and general-constraint experiments, respectively.</p> <h3>tau_column.nc and tau_profile.nc</h3> <p>These files contain separately for each absorption species the vertically integrated opacity spectra, and the opacity profiles at two selected wavenumbers.</p> <p> </p>
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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.