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1,670 results for “FORCE”
MD simulation of DMPC bilayer with CHARMM36 force field. 5 w/l.
<p>MD simulation of DMPC bilayer with CHARMM36 force field. 5 w/l.</p> <p>Dataset contains simulation files including trajectories (.xtc) (original production.xtc, and equilibrated and centered centered_pbcmol-20-500ns.xtc).</p> <p>System: DMPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 1000</p> <p>Simulation time: 1000 ns</p> <p>Simulation engine: GROMACS 2019.5</p> <p>Temperature: 314 K</p>
MD simulation of POPC bilayer with OPLS4 force field. 7.5 w/l.
<p>MD simulation of POPC (1-palmitoyl-2-oleoyl-phosphatidylcholine) bilayer with OPLS4 force field. 7.5 w/l</p> <p>Dataset contains original desmond trajectories (_trj), topologies (-out.cms), input files (.cfg, .msj, .cms), and other files</p> <p>For the ease of the upload, trajectory files (_trj) are divided to 250ns pieces and tarred (named desmond_md_popc7.5wl_opls4_x-xns_trj.tar.gz)</p> <p>Dataset also contains Gromacs converted files (.xtc, .gro and .top) </p> <p>System: POPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 1500</p> <p>Simulation time: 500 ns</p> <p>Simulation engine: Desmond 2022-2</p> <p>Temperature: 300 K</p>
MD simulation of POPC bilayer with OPLS4 force field. 10 w/l.
<p>MD simulation of POPC (1-palmitoyl-2-oleoyl-phosphatidylcholine) bilayer with OPLS4 force field. 10 w/l</p> <p>Dataset contains original desmond trajectories (_trj), topologies (-out.cms), input files (.cfg, .msj, .cms), and other files</p> <p>For the ease of the upload, trajectory files (_trj) are divided to 250ns pieces and tarred (named desmond_md_popc10_opls4_x-xns_trj.tar.gz)</p> <p>Dataset also contains Gromacs converted files (.xtc, .gro and .top) </p> <p>System: POPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 2000</p> <p>Simulation time: 500 ns</p> <p>Simulation engine: Desmond 2022-2</p> <p>Temperature: 300 K</p>
MD simulation of POPC bilayer with OPLS4 force field. 20 w/l
<p>MD simulation of POPC (1-palmitoyl-2-oleoyl-phosphatidylcholine) bilayer with OPLS4 force field. 20 w/l</p> <p>Dataset contains original desmond trajectories (_trj), topologies (-out.cms), input files (.cfg, .msj, .cms), and other files</p> <p>For the ease of the upload, trajectory files (_trj) are divided to 250ns pieces and tarred (named desmond_md_popc20_opls4_x-xns_.tar.gz)</p> <p>Dataset also contains Gromacs converted files (.xtc, .gro and .top) </p> <p>System: POPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 4000</p> <p>Simulation time: 500 ns</p> <p>Simulation engine: Desmond 2022-2</p> <p>Temperature: 300 K</p> <p> </p>
Upstream and downstream wind-stress forcing of seasonal variability of Luzon Strait Deep Overflow transport
<p>This is the dataset used for figures in paper "Upst<span>ream and downstream wind-stress forcing of seasonal variability of Luzon Strait Deep Overflow transport".</span></p>
GROMACS Coordinates and force field files for the SC lipid barrier
Open the record for dataset details and reuse information.
Supporting data for: "Weakening of the AMOC and Strengthening of Labrador Sea Deep Convection in Response to External Freshwater Forcing"
<p>This repository contains the key supporting data (in the netcdf format) for the following paper:<br>Wei, X., Zhang, R. Weakening of the AMOC and strengthening of Labrador Sea deep convection in response to external freshwater forcing. <em>Nat Commun</em> <strong>15</strong>, 10357 (2024). https://doi.org/10.1038/s41467-024-54756-3</p> <p>In this study, control and water hosing ensembles are conducted using a coupled climate model (GFDL CM4) with an eddy-permitting ocean component. The anomaly is defined as the difference between the water hosing and control ensembles (anomaly = water hosing - control). This paper investigates mechanisms of the AMOC weakening and its subsequent impact on the Labrador Sea open-ocean deep convection in response to external freshwater forcing.</p> <p><strong>Descriptions of data files in this repository:</strong></p> <p><strong>Main figures:</strong></p> <p>1. The anomalies of the OSNAP AMOC and the Labrador Sea March mixed layer depth (MLD), as shown in Fig. 1 in the paper. </p> <p>The anomalies of the maximum AMOC and the AMOC at a relatively dense level around sigma0=27.84 kg/m3 across the entire OSNAP section in density space:</p> <p>Anomaly_AMOC_max_OSNAP.nc</p> <p>Anomaly_AMOC_denser_OSNAP.nc</p> <p>The anomalies of the maximum AMOC across OSNAP West and OSANP East in density space:</p> <p>Anomaly_AMOC_max_OSNAP_WEST.nc</p> <p>Anomaly_AMOC_max_OSNAP_EAST.nc</p> <p>The March mixed layer depth (MLD) in the Labrador Sea:</p> <p>Anomaly_MLD_003_March_Labrador.nc</p> <p>The spatial map of March MLD climatology and anomaly:</p> <p>Control_MLD_003_March_spatial.nc</p> <p>Anomaly_MLD_003_March_spatial.nc</p> <p>2. The climatological mean (from the control and water hosing ensembles) and anomalies of the AMOC streamfunction across the OSNAP section, in density-space and depth-space, as shown in Fig. 2 in the paper.</p> <p>OSNAP West:</p> <p>Control_moc_sigma0_OSNAP_WEST.nc</p> <p>Control_moc_z_OSNAP_WEST.nc</p> <p>WaterHosing_moc_sigma0_OSNAP_WEST.nc</p> <p>WaterHosing_moc_z_OSNAP_WEST.nc</p> <p>Anomaly_moc_sigma0_OSNAP_WEST.nc</p> <p>Anomaly_moc_z_OSNAP_WEST.nc</p> <p>OSNAP East:</p> <p>Control_moc_sigma0_OSNAP_EAST.nc</p> <p>Control_moc_z_OSNAP_EAST.nc</p> <p>WaterHosing_moc_sigma0_OSNAP_EAST.nc</p> <p>WaterHosing_moc_z_OSNAP_EAST.nc</p> <p>Anomaly_moc_sigma0_OSNAP_EAST.nc</p> <p>Anomaly_moc_z_OSNAP_EAST.nc</p> <p>Entire OSNAP section:</p> <p>Control_moc_sigma0_OSNAP.nc</p> <p>Control_moc_z_OSNAP.nc</p> <p>WaterHosing_moc_sigma0_OSNAP.nc</p> <p>WaterHosing_moc_z_OSNAP.nc</p> <p>Anomaly_moc_sigma0_OSNAP.nc</p> <p>Anomaly_moc_z_OSNAP.nc</p> <p>3. The climatological mean (from the control ensemble) and anomalies of salinity, potential temperature, and potential density across the OSNAP section, as shown in Fig. 3 in the paper.</p> <p>Control_salinity_OSNAP.nc</p> <p>Control_temperature_OSNAP.nc</p> <p>Control_sigma0_OSNAP.nc</p> <p>Anomaly_salinity_OSNAP.nc</p> <p>Anomaly_temperature_OSNAP.nc</p> <p>Anomaly_sigma0_OSNAP.nc</p> <p>4. The sigma-z diagram of climatological mean (from the control and water hosing ensembles) and anomalies of the AMOC transport across OSNAP East, i.e. integrated volume transport across OSNAP East over each potential density bin and depth bin, as shown in Fig. 4 in the paper.</p> <p>Control_SigmaZ_OSNAP_EAST.nc</p> <p>WaterHosing_SigmaZ_OSNAP_EAST.nc</p> <p>Anomaly_SigmaZ_OSNAP_EAST.nc</p> <p>5.The deep ocean potential density anomalies along with its thermal and haline components over the west boundary and eastern regions of the OSNAP East subsection, and the AMOC anomalies at a relatively dense level around sigma0=27.84 kg/m3 across OSNAP East in density space, as shown in Fig. 5 in the paper.</p> <p>Anomaly_sigma_west.nc</p> <p>Anomaly_sigmaS_west.nc</p> <p>Anomaly_sigmaT_west.nc</p> <p>Anomaly_sigma_east.nc</p> <p>Anomaly_sigmaS_east.nc</p> <p>Anomaly_sigmaT_east.nc</p> <p>Anomaly_sigma_diff.nc</p> <p>Anomaly_sigmaS_diff.nc</p> <p>Anomaly_sigmaT_diff.nc</p> <p>Anomaly_AMOC_denser_OSANP_EAST.nc</p> <p>6.The transient salt-based FWF anomalies, dye-based FWF anomalies and their difference at the upper ocean (413m), as shown in Fig. 6 in the paper, and at the deep ocean (2250m), as shown in Fig. 7 in the paper.</p> <p>Anomaly_FWF_salt_413m_10yr.nc</p> <p>Anomaly_FWF_dye_413m_10yr.nc</p> <p>Anomaly_FWF_diff_413m_10yr.nc</p> <p>Anomaly_FWF_salt_413m_20yr.nc</p> <p>Anomaly_FWF_dye_413m_20yr.nc</p> <p>Anomaly_FWF_diff_413m_20yr.nc</p> <p>Anomaly_FWF_salt_413m_30yr.nc</p> <p>Anomaly_FWF_dye_413m_30yr.nc</p> <p>Anomaly_FWF_diff_413m_30yr.nc</p> <p>Anomaly_FWF_salt_413m_40yr.nc</p> <p>Anomaly_FWF_dye_413m_40yr.nc</p> <p>Anomaly_FWF_diff_413m_40yr.nc</p> <p>Anomaly_FWF_salt_413m_50yr.nc</p> <p>Anomaly_FWF_dye_413m_50yr.nc</p> <p>Anomaly_FWF_diff_413m_50yr.nc</p> <p>Anomaly_FWF_salt_2250m_10yr.nc</p> <p>Anomaly_FWF_dye_2250m_10yr.nc</p> <p>Anomaly_FWF_diff_2250m_10yr.nc</p> <p>Anomaly_FWF_salt_2250m_20yr.nc</p> <p>Anomaly_FWF_dye_2250m_20yr.nc</p> <p>Anomaly_FWF_diff_2250m_20yr.nc</p> <p>Anomaly_FWF_salt_2250m_30yr.nc</p> <p>Anomaly_FWF_dye_2250m_30yr.nc</p> <p>Anomaly_FWF_diff_2250m_30yr.nc</p> <p>Anomaly_FWF_salt_2250m_40yr.nc</p> <p>Anomaly_FWF_dye_2250m_40yr.nc</p> <p>Anomaly_FWF_diff_2250m_40yr.nc</p> <p>Anomaly_FWF_salt_2250m_50yr.nc</p> <p>Anomaly_FWF_dye_2250m_50yr.nc</p> <p>Anomaly_FWF_diff_2250m_50yr.nc</p> <p>7.Climatological mean (from the control ensemble) and anomalies of salinity, potential temperature, potential density and zonal velocity along the Iceland-Scotland Overflow pathway, as shown in Fig. 8 in the paper.</p> <p>Control_salinity_ISOW.nc</p> <p>Control_temperature_ISOW.nc</p> <p>Control_sigma_ISOW.nc</p> <p>Control_u_ISOW.nc</p> <p>Anomaly_salinity_ISOW.nc</p> <p>Anomaly_temperature_ISOW.nc</p> <p>Anomaly_sigma_ISOW.nc</p> <p>Anomaly_u_ISOW.nc</p> <p>8.The salt-based FWF anomalies, dye-based FWF anomalies and their difference across the Iceland-Scotland Overflow section, as shown in Fig. 9 in the paper.</p> <p>Anomaly_FWF_salt_ISOW.nc</p> <p>Anomaly_FWF_dye_ISOW.nc</p> <p>Anomaly_FWF_diff_ISOW.nc</p> <p><strong>Supplementary figures:</strong></p> <p>S1. The anomalies of the density-space AMOC streamfunction as shown in Supplementary Fig. 1 in the paper.</p> <p>SuppFig1.nc</p> <p>S2. The extra-tropical North Atlantic subsurface (413m) temperature anomalies as shown in Supplementary Fig. 2 in the paper.</p> <p>SuppFig2.nc</p> <p>S3. The climatological mean (from the control and water hosing ensembles) and anomalies of the AMOC streamfunction, surface forced water mass transformation (WMTS) and interior mixing forced water mass transformation (WMTM), as shown in Supplementary Fig. 3 in the paper.</p> <p>SuppFig3_streamfunction.nc</p> <p>SuppFig3_surfaceWMT.nc</p> <p>SuppFig3_interiorWMT.nc</p> <p>SuppFig3_mask_section.nc</p> <p>S4. The climatological mean of the velocity across the OSNAP section in the control ensemble, as shown in Supplementary Fig. 4 in the paper.</p> <p>SuppFig4.nc</p> <p>S5. The transient salinity anomalies, potential temperature anomalies and potential density anomalies across the OSNAP section, as shown in Supplementary Fig. 5 in the paper.</p> <p>SuppFig5_11_20yr.nc</p> <p>SuppFig5_21_30yr.nc</p> <p>SuppFig5_31_40yr.nc</p> <p>SuppFig5_41_50yr.nc</p> <p>S6. The transient salt-based FWF anomalies, dye-based FWF anomalies and their difference at 625m, as shown in Supplementary Fig. 6 in the paper.</p> <p>SuppFig6_10yr.nc</p> <p>SuppFig6_20yr.nc</p> <p>SuppFig6_30yr.nc</p> <p>SuppFig6_40yr.nc</p> <p>SuppFig6_50yr.nc</p> <p>S7. The salt-based FWF anomalies, dye-based FWF anomalies and their difference across the OSNAP section, as shown in Supplementary Fig. 7 in the paper.</p> <p>SuppFig7.nc</p> <p>S8. The anomalies of March Labrador Sea mixed layer depth (MLD), vertical potential density difference, surface and deep ocean potential density, as shown in Supplementary Fig. 8 in the paper.</p> <p>SuppFig8.nc</p> <p>S9. The climatological mean salinity difference between the ISOW-associated NEADW layer and the core Labrador Sea Water layer, and the inverse horizontal grid resolution of GFDL CM4 (this study) and CMIP6 models as shown in Supplementary Fig. 9 in the paper. The CMIP6 model data were downloaded from https://aims2.llnl.gov/search/cmip6/.</p> <p>The climatological mean salinity difference between the ISOW-associated NEADW layer and the core Labrador Sea Water layer in WOA18 as shown in Supplementary Fig. 9 in the paper. The WOA18 data were downloaded from the NOAA National Centers for Environmental Information (formerly the National Oceanographic Data) https://www.ncei.noaa.gov/products/world-ocean-atlas/.</p> <p>SuppFig9.nc</p> <p>S10. The supplementary Fig. 10 shares the same data of climatological mean of the AMOC streamfunction across the OSNAP section in density-space in the control ensemble with Figure 2 in the paper. The OSNAP observation data were downloaded from www.o-snap.org.</p> <p>Control_moc_sigma0_OSNAP_WEST.nc</p> <p>Control_moc_sigma0_OSNAP_EAST.nc</p> <p>Control_moc_sigma0_OSNAP.nc</p> <p><strong>Acknowledgments</strong></p> <p>We acknowledge the use of the following datasets and model code in this study: The World Ocean Atlas 2018 (WOA18) data were downloaded from the NOAA National Centers for Environmental Information (formerly the National Oceanographic Data) https://www.ncei.noaa.gov/products/world-ocean-atlas/. The Data from the OSNAP (Overturning in the Subpolar North Atlantic Program) array were downloaded from https://www.o-snap.org/. OSNAP data were collected and made freely available by the OSNAP project and all the national programs that contribute to it (www.o-snap.org). The CMIP6 (Coupled Model Intercomparison Project Phase 6) model data were downloaded from https://aims2.llnl.gov/search/cmip6/. The source code of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model version 4 (CM4) is publicly available at https://doi.org/10.5281/zenodo.3339397. The surface forced water mass transformation (WMTS) is calculated using the source code developed by Drake et al. 2024 at https://github.com/hdrake/xwmt. The relevant citations for the above datasets and model code are listed in Wei and Zhang 2024.</p>
Supporting Data for "Radiation-circulation destabilization of ITCZ position in an idealized GCM: Response to hemispherically asymmetric forcing"
<p>Code and netcdf files of processed Isca simulations to reproduce the figures of the submitted manuscript of Timothy M. Merlis, Chiung-Yin Chang, Pablo Zurita-Gotor, and Isaac M. Held (2024): "Radiation-circulation destabilization of ITCZ position in an idealized GCM: Response to hemispherically asymmetric forcing".</p>
Data for Paper: Wind-wave momentum flux in steep, strongly forced, surface gravity wave conditions
<p>Laboratory measurements of wind, waves, and airside static pressure under low to moderate wind forcing (U10 ~ 6 -16 m/s) collected in Oct 2022 in the SUSTAIN wind-wave facility at the University of Miami.</p> <p>This dataset includes 11 runs, all of which contain monochromatic waves generated by the wave paddles with various wind forcing exerted above. All data is in ".mat" formate readable via MATLAB.</p> <p>Experiment set up and positions of instruments are documented in more details in the manuscript Tan et al (2024): Wind-wave momentum flux in steep, strongly forced, surface gravity wave conditions.</p> <p> Fig_3: time series static pressure p sampled at 100 Hz and horizontal/vertical wind speed (u/w) sampled at 1000 Hz</p> <p>Fig_4: Frictional velocity u_star_Rn* obtained at differenet heights (h) using frictional velocity</p> <p>Fig_5: a folder that containes the phase-averaged, spline-interpolated static pressure (p2_total), X-coordinate (long-wave phase), Y coordinate (heights above the stationary water) and the u/w at respective heights to generate airflow streamlines</p> <p>Fig_6 and 7: NSS-based phase-averaged, spline-interpolated pressure (delta_P_new).</p> <p>Fig_8: phase-averaged form stress based on measurements and NSS for all 11 runs</p> <p>Fig_9: NSS-based form stress deviation from measured form stress (NSS miscal) against wind-steepness and wave age;</p> <p>Fig_10 and 11: wave growth rate (gamma) against wave age (Cp/ustar) and two other parameterization from Fig.10</p> <p>(The revised version contains the projection of Donelan (1999) and Yang et al. (2013)'s data to the U10/Cp parameterization in panel (b) per reviewer's suggestion);</p> <p>Fig_12: form stress values (tau_form) and form stress to total stress (tau_tot) ratio.</p> <p>(The revised version contains U10 per reviewer's suggesion).</p> <p>This project was Funded in part by Office of Naval Research/Naval Research Laboratory base program unit 73-1Y91.</p> <p>Please cite our JGR: Oceans paper "Wind-wave momentum flux in steep, strongly forced,1 surface gravity wave conditions" if you were to use our dataset.</p> <p>Contact: Peisen Tan <pxt254@miami.edu> for different levels of raw data collected in this experiment.</p> <p>We kindly ask the readers who use our dataset to cite our paper:</p> <p><span>Tan, P.</span><span>, </span><span>Savelyev, I.</span><span>, </span><span>Laxague, N. J. M.</span><span>, </span><span>Haus, B. K.</span><span>, </span><span>Curcic, M.</span><span>, </span><span>Matt, S.</span><span>, et al. (</span><span>2025</span><span>). </span><span>Wind-wave momentum flux in steep, strongly forced, surface gravity wave conditions</span><span>. </span><em>Journal of Geophysical Research: Oceans</em><span>, </span><span>130</span><span>, e2024JC021616. </span><a href="https://doi.org/10.1029/2024JC021616">https://doi.org/10.1029/2024JC021616</a></p> <p>We would also appreciate if you can send us a copy of your manuscript if you have used our data. Thank you!</p>
Model data for "A model study on investigating the sensitivity of aerosol forcing on the volatilities of semi-volatile organic compounds" by Irfan et al
<p><span>Abstract: </span>This dataset contains simulation results from global aerosol-climate model ECHAM-SALSA. These simulations were performed to study the sensitivity of simulated SOA mass, CCN and radiative forcings to the assumed volatility distributions of biogenic SOA precursor species. The study employed volatility basis set (VBS) approach to represent and simulate SOA in the atmosphere. The study involved a comparative analysis between finely resolved 9-bin VBS setup with a simplified 3-bin VBS setup. It also included how the SOA mass, CCN and radiative forcing are sensitive to the volatitility of individual VBS bins.</p> <p><span>Methods: </span>Global scale aerosol-climate model simulations were performed using ECHAM-SALSA. We performed three diferent simulations each using 9-bin and 3-bin VBS setups with the volatilities increased (VBSx10) and decreased (VBSx0.1) by one-order of magnitude with respect to the original volatility (VBSx1). Another set of six different simuations were performed by increasing and decreasing the volatilities of one VBS bin at a time while keeping the original volatilities of other bins.</p> <p><span>TechnicalInfo: </span>In this study, all the ECHAM-SALSA simulations used T63 spectral truncation and 47 hybrid sigma pressure levels in horizontal and vertical resolution respectively. Simulations were performed for the year 2010 with half a year spin-up. The data was simulated with 3-hourly output for the simulation period. We then calculated monthly means of the summer months from 3-hourly data for all the model values except CDNC. We used the the 3-hourly data to analyse CDNC from grids with cloud fraction ≥ 0.95. A detailed description of model simulations is given in the setup file of each simulation.</p> <p><span>TechnicalInfo: </span>The external URL leads to a bucket containing setup files, the complete dataset (post-processed NETCDF files) presented in the manuscript, and the python scripts used for data analysis for each of the simulations.</p>
PARASO ERA5 forcings
<p>External forcings from ERA5, used for forcing the PARASO configuration. Due to their size, only three months are provided here (from 01-JAN-2000 to 31-MAR-2000), for testing purposes. The data should be uncompressed with the uncpr_paraso.sh script.</p> <p><strong>Model description: </strong>Pelletier, C., Fichefet, T., Goosse, H., Haubner, K., Helsen, S., Huot, P.-V., Kittel, C., Klein, F., Le clec'h, S., van Lipzig, N. P. M., Marchi, S., Massonnet, F., Mathiot, P., Moravveji, E., Moreno-Chamarro, E., Ortega, P., Pattyn, F., Souverijns, N., Van Achter, G., Vanden Broucke, S., Vanhulle, A., Verfaillie, D., and Zipf, L.: PARASO, a circum-Antarctic fully coupled ice-sheet–ocean–sea-ice–atmosphere–land model involving f.ETISh1.7, NEMO3.6, LIM3.6, COSMO5.0 and CLM4.5, Geosci. Model Dev., 15, 553–594, <a href="https://doi.org/10.5194/gmd-15-553-2022">10.5194/gmd-15-553-2022</a>, 2022.</p> <p><strong>Source code (no COSMO)</strong>: Pelletier, Charles, Klein, François, Zipf, Lars, Haubner, Konstanze, Mathiot, Pierre, Pattyn, Frank, Moravveji, Ehsan, & Vanden Broucke, Sam. (2021). PARASO source code (no COSMO) (v1.4.3). Zenodo. <a href="https://doi.org/10.5281/zenodo.5576201">10.5281/zenodo.5576201</a></p> <p><strong>Other input data: </strong>Pelletier, Charles, Klein, François, Zipf, Lars, Vanden Broucke, Sam, Haubner, Konstanze, & Helsen, Samuel. (2021). Input data for PARASO, a circum-Antarctic fully-coupled 5-component model (v1.4.3) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5588468">10.5281/zenodo.5588468</a></p> <p> </p> <p>The ERA5 data (Hersbach, 2018) was downloaded on 01-SEP-2019 from the Copernicus Climate Change Service (C3S) Climate Data Store. The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p><strong>Reference</strong>: Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1979 to present, <a href="https://doi.org/10.24381/cds.adbb2d47">https://doi.org/10.24381/cds.adbb2d47</a>, downloaded from the Copernicus Climate Change Service (C3S); Climate Data Store (CDS) on 01-SEP-2019, 2018.</p>
dave-heslop74/Roberts-Hematite-FORCs: Roberts-Hematite-FORCs raw data release
<p>No description provided.</p>
Top-down effects override climate forcing on reproductive success in a declining sea duck
Population performance is predicted to be more strongly influenced by detrimental species interactions such as predation under benign climatic conditions, and by climate forcing under harsh conditions, reflected in geographical gradients in biotic interaction strength. Less appreciated is the potential for site-specific changes in drivers with the advent of anthropogenic alteration of predator-prey relationships, including apex predator restoration and spread of invasive predators. Particularly interesting is the relative impact of climate and biotic interactions on population performance when these conflict. In this 31-year study (1990-2020), we revisit a common eider (Somateria mollissima) population from SW Finland, Baltic Sea, fifteen years on from an earlier study showing that climate warming positively affected reproductive parameters and performance. However, the population is simultaneously exposed to increasing predation by the rapidly recovering native apex predator and invasive mammals. Based on the current population trend, we predicted (i) a weakening of the previously documented positive effects of a warming climate on vital rates, (ii) intensified predation, and (iii) increasing top-down control of vital rates and accompanying population decline. Five out of seven breeding parameters (annual spread in female body condition, breeding phenology and synchrony, interval between arrival and breeding, fledgling production) were best explained by predation indices, whereas climate signals (winter NAO, Baltic Sea maximum ice cover) on breeding parameters have weakened. Particularly intriguing is that the previous positive association between mild ice winters and subsequent reproductive output has disappeared during the past 15 years, highlighting the non-linear nature of climate change responses. Indirect predation effects (selective disappearance, changed reproductive strategies, nest-site selection and population age distribution) can potentially explain also the remaining breeding parameters (annual mean body condition and clutch size). The observed regime shift in predation risk appears to prevent this now endangered population from reaping the potential benefits of a warming climate.
Response of East Asian Precipitation to Precessional Orbital Forcing during Glacials –Sensitivity to Model Spatial Resolution
<p>Output data (precipitation, wind vector, etc) from three resolution experiments (F19,F09,F05) . LGM means Last glacial maximum. cntl means control run(21ka), HighSolin means the orbital year modified to 174ka. Please see more in the publication. </p>
Data for "Snow albedo feedbacks enhance snow impurity-induced radiative forcing in the Sierra Nevada"
<p>This is the data prepared for submission of "<strong>Snow albedo feedbacks enhance snow impurity-induced radiative forcing in the Sierra Nevada</strong>". Three folders are included: 1) The model output with aerosol deposition in snow (aero); 2) The model output without aerosol deposition in snow (noaero) 3) processed observations used to validate the model results (SPIReS)</p>
Amyloid-beta 16-22 peptide monomer simulation with the CHARMM-Drude force field and OpenMM (Run 2)
<p>Amyloid-beta 16-22 peptide (monomer) simulations with the CHARMM-Drude force field and OpenMM. This is the second independent simulation runs out of 3.</p> <p>Part 1-2 are 200 ns long, 3-8 are 100 ns each. Total trajectory length is 1 microseconds. Frame saving frequency is 10 ps.</p> <p>The system contains ~ 150 mM NaCl.</p>
Amyloid-beta 16-22 peptide monomer simulation with the CHARMM-Drude force field and OpenMM (Run 3)
<p>Amyloid-beta 16-22 peptide (monomer) simulations with the CHARMM-Drude force field and OpenMM. This is the last independent simulation runs out of 3.</p> <p>Part 1-2 are 200 ns long, 3-8 are 100 ns each. Total trajectory length is 1 microseconds. Frame saving frequency is 10 ps.</p> <p>The system contains ~ 150 mM NaCl.</p>
Direct and indirect effects of anthropogenic forcing on lake surface water temperature
<p>Here are the data from this paper (Direct and indirect effects of anthropogenic forcing on lake surface water temperature)</p>
Amyloid-beta 16-22 peptide monomer simulation (without salt) with the CHARMM-Drude force field and OpenMM (Run 1)
<p>Amyloid-beta 16-22 peptide (monomer) simulations with the CHARMM-Drude force field and OpenMM. Initial structures are obtained from CHARMM-GUI. This is the first independent simulation runs out of three. The system does not contain any ions.</p> <p>Total trajectory length is 1 microseconds. Frame saving frequency is 10 ps.</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with OpenMM v. 7.5.1.</p> <p> </p>
Amyloid-beta 16-22 peptide dimer simulation (150 mM NaCl) with the CHARMM-Drude force field and OpenMM (Run 2)
<p>MD simulations of the Amyloid-beta 16-22 dimer at 150 mM NaCl concentration with CHARMM-Drude force field and OpenMM. Initial structure is obtained from CHARMM-GUI. In the initial configuration, two amyloid-beta 16-22 monomers are not interacting. This repository contains the second out of three independent runs.</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with OpenMM v. 7.5.1.</p> <p>The trajectory is divided into 7 parts: part_1-2 are each 200 ns long; part_3-6 are each 100 ns long, and part_7 is 640 ns long. Frames are saved in every 10 ps.</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.