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82 results for “Coupled Systems”
Quantifying Both Socioeconomic and Climate Uncertainty in Coupled Human-Earth Systems Analysis
<p>This data repository is associated with the paper:</p> <p>Morris,J., A. Sokolov, J. Reilly, A. Libardoni, C. Forest, S. Paltsev, A Schlosser, R. Prinn and H. Jacoby (2025). Quantifying Both Socioeconomic and Climate Uncertainty in Coupled Human-Earth Systems Analysis. <em>Nature Communications </em><strong>16</strong>, 2703. https://doi.org/10.1038/s41467-025-57897-1</p> <p>This paper quantifies key socio-economic and climate uncertainties using the MIT Integrated Global System Model. </p>
Readout of an antiferromagnetic spintronics system by strong exchange coupling of Mn2Au and Permalloy
<p>Data corresponding to figures 2 and 3 of <a href="https://arxiv.org/abs/2106.02333">arXiv:2106.02333</a>.</p> <p>Manuscript accepted by Nature Communications.</p>
MCR LTER: Coral Reef: Coupled Natural-Human Systems: Survey of fish being sold on the roadside 2020-2021
This dataset includes the results of a survey on fish sold by the roadside in Moorea, French Polynesia. During 2020-2022, more than 7000 fish were identified and sized from photographs taken during the market surveys. These data were collected as part of CNH-L: Multiscale Dynamics of Coral Reef Fisheries: Feedbacks Between Fishing Practices, Livelihood Strategies, and Shifting Dominance of Coral and Algae (BCS-1714704) with additional support from the Moorea Coral Reef LTER (OCE- 1637396). This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 22-24354 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2023). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Moisture-Precipitation Couplings for Mesoscale Convective Systems in Tracking Data and Idealized Simulations
<p>Morphological properties, collocated synoptic conditions, and collocated rainfall for mesoscale convective systems in 1) the ISCCP Convective Tracking (CT) dataset with coincident data from the ERA-Interim (ERA-I) reanalysis and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) product and 2) long-channel radiative-convective equilibrium (RCE) simulations in the System for Atmospheric Modeling (SAM).</p> <p><strong>ISCCP_tracking_colloc.tar.gz </strong>- NetCDF files by year from 2000 to 2004 inclusive including ISCCP-CT morphological properties of MCSs, a series of collocated synoptic variables from ERA-5 (including specific humidity, temperature, vertical velocity, and cloud condensate profiles), and collocated precipitation intensity and accumulation from MSWEP.</p> <p><strong>RCE_colloc_execution1.tar.gz</strong> - NetCDF files including RCE-SAM extent of MCSs (RCE_COL_cluster-sizes_*.nc), as well as collocated synoptic variables. Collocation of synoptic variables is performed for these files by extracting and averaging the variables over grid cells where the precipitation is greater than either its mean (RCE_COL_MEAN_*.nc) or its 99th percentile (RCE_COL_99_*.nc).</p> <p><strong>RCE_colloc_execution2.tar.gz</strong> - NetCDF files including RCE-SAM extent of MCSs (RCE_COL_cluster-sizes_*.nc), as well as collocated synoptic variables. Collocation of synoptic variables is performed for these files by taking either the mean (RCE_COL_MEAN_*.nc) or the 99th percentile (RCE_COL_99_*.nc) value over all grid cells within the MCS.<br><br>For the NetCDF files from RCE output, the numeric value in the file name is the corresponding sea surface temperature from 280 to 310 K.</p>
Advanced open source data formats for geometrically and physically coupled systems - examples
<p>Some model files to describe a geometrically and physically coupled PDE and a ODE system, arising from discretization in space. The files correspond to:</p> <ul> <li>a simplified two component problem with coupling and an FMU in <strong>withFMU.json</strong></li> <li>the corresponding ODE in <strong>io_withFMU_FECoupled.json</strong></li> <li>a PDE model of a complete machine in <strong>ictimt_coupledModel.zip</strong>. This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> <li>the corresponding discrete model in <strong>ictimt_feCoupled.zip </strong>This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> </ul> <p> </p> <p> </p>
The coupled ice sheet-Earth system model Bern3D v3.0: Model output
<p>This dataset contains model output of climate and ice sheet variables for the simulations performed in the study:</p> <p>Pöppelmeier, F., Joos, F., Stocker, T. F. (2023). The coupled ice sheet-Earth system model Bern3D v3.0. Journal of Climate.</p> <p>2D and 3D output variables are available for the preindustrial (PI) and Last Glacial Maximum (LGM) control simulations. Timeseries output is provided for CO<sub>2</sub> experiments for which CO<sub>2</sub> concentrations were increased to 2 and 4 times PI concentrations with rates of 0.5, 1, and 2% per year. Timeseries output is also provided for the simulation of the entire last glacial cycle in the standard setup and with logarithmically scaled dust for the aerosol radiative forcing. More details are provided in the above mentioned manuscript.</p>
MCR LTER: Coral Reef: Coupled Natural-Human Systems: GPS-enabled survey of fish in Moorea’s lagoons
This dataset includes counts and sizes of fish within Moorea’s lagoons, with more than 130,000 fish recorded between 2018 and 2021. In summer of each year observers swim through Moorea’s lagoons, observing a 5-m wide swath and recording species and sizes of fish encountered in every minute of swim (about 10 linear meters of sampling). The fish counts are limited to a fixed species list (57 taxa) which was chosen to capture most of the reef associated species targeted in Moorea’s fishery, as well as important herbivores. Only fish larger than 10 cm were recorded, a size chosen to match the small end of fish observed in the catch. The resulting counts were georeferenced based on simultaneously collecting GPS data. Data are presented here on the scale of a minute of sampling. This scale coincides with benthic data which was simultaneously collected but is presented in a different data set (EDI data package: knb-lter-mcr.4014.1) doi:10.6073/pasta/f1e4cbcd79ebbbb0b0cbb79ab2ff8901 These data were collected as part of CNH-L: Multiscale Dynamics of Coral Reef Fisheries: Feedbacks Between Fishing Practices, Livelihood Strategies, and Shifting Dominance of Coral and Algae (BCS-1714704) with additional support from the Moorea Coral Reef LTER (OCE- 1637396).
MCR LTER: Coral Reef: Coupled Natural-Human Systems: GPS Benthic Data
This dataset includes cover of benthic organisms and substrates in more than 600,000 photographic samples of Moorea’s lagoon habitats, collected between 2018 and 2021. In summer of each year swimmers towed downward facing cameras, taking 1.5 photos per second of habitats they passed over. The resulting pictures were processed through a computer vision algorithm (CoralNet), resulting in percent cover estimates in each photo of organisms such as coral and algae and substrates such as sand and rubble. These data were georeferenced based on simultaneously collecting GPS data. Data are presented here on the scale of the photograph, and are also aggregated to the scale of a minute of sampling and a transect of swimming. The latter scales are chosen to coincide with fish counting data which was simultaneously collected but is presented in a different data set (EDI data package ID: knb-lter-mcr.4013.1) doi:10.6073/pasta/ff3de88334edda65fef26de45ff26c1d These data were collected as part of CNH-L: Multiscale Dynamics of Coral Reef Fisheries: Feedbacks Between Fishing Practices, Livelihood Strategies, and Shifting Dominance of Coral and Algae (BCS-1714704) with additional support from the Moorea Coral Reef LTER (OCE- 1637396).
Code: The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems
<p>This is the code archive for the publication "The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems" in Earth's Future.</p> <p>Abstract:</p> <p>Studies have recently focused on using coupled natural–human systems (CNHS) to inform policymaking. However, model uncertainty can increase with model complexity and affect the variance of the model outcomes. Therefore, this study explores an uncertainty analysis of coupled hydrological and human decision models to better evaluate CNHS modeling properties. Five coupled models are proposed with different model complexities for human behavior settings (i.e., model structure and the number of calibrated parameters): one static, two adaptive, and two learning adaptive. Learning adaptive models (the most complex) have both a learning component (capturing long-term trends) and an adaptive component (capturing short-term variations), while adaptive models omit the learning component. The static model is the simplest, without learning or adaptive components. Applying the law of total variance, the model output uncertainty is decomposed into three sources: (1) climate change scenario uncertainty, (2) climate internal variability, and (3) different model configurations with parameter sets or model structures that are equally capable of producing similar outcomes. Our exploratory analysis demonstrated that model uncertainty would likely increase with model complexity given uncertain input data (e.g., climate forcing) and different model configurations; the inclusion of a learning mechanism in the human system can potentially offset the impact of the natural system on uncertainty through coupling natural and human systems. We also discuss other uncertainty sources, such as assumptions about model structure due to incomplete knowledge and metrics for calibration target selection for future studies.</p>
Dual spring force couples yield multifunctionality and ultrafast, precision rotation in tiny biomechanical systems
<p>Small organisms use propulsive springs rather than muscles to repeatedly actuate high acceleration movements, even when constrained to tiny displacements and limited by inertial forces. Through integration of a large kinematic dataset, measurements of elastic recoil, energetic math modeling, and dynamic math modeling, we tested how trap-jaw ants (Odontomachus brunneus) utilize multiple elastic structures to develop ultrafast and precise mandible rotations at small scales. We found that O. brunneus develops torque on each mandible using an intriguing configuration of two springs: their elastic head capsule recoils to push and the recoiling muscle-apodeme unit tugs on each mandible. Mandibles achieved precise, planar, circular trajectories up to 49,100 radians/sec (470,000 rpm) when powered by spring propulsion. Once spring propulsion ended, the mandibles moved with unconstrained and oscillatory rotation. We term this mechanism "dual spring force couple" meaning that two springs deliver energy at two locations to develop torque. Dynamic modeling revealed that dual spring force couples reduce the need for joint constraints and thereby reduce dissipative joint losses, which is essential to the repeated use of ultrafast, small systems. Dual spring force couples enable multifunctionality: trap-jaw ants use the same mechanical system to produce ultrafast, planar strikes driven by propulsive springs and for generating slow, multi-degree of freedom mandible manipulations using muscles, rather than springs, to directly actuate the movement. Dual spring force couples are found in other systems and are likely widespread in biology. These principles can be incorporated into microrobotics to improve multifunctionality, precision, and longevity of ultrafast systems.</p>
Supplementary Data: Full Results: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the full output data from each of the scenarios considered in the above publication. They also include the post-processed input data, which might be useful if you want to rerun the scenarios with only small changes to the input data.</p> <p>The scripts to build the model, input data and result summaries can be found in a <a href="https://zenodo.org/record/1146665">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p>For each scenario, there is a <a href="https://github.com/PyPSA/PyPSA">PyPSA</a> network file in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5 format</a> and a CSV of shadow prices.</p> <p>To read in a network file do:</p> <pre><code class="language-python">import pypsa network = pypsa.Network("network_file_name.h5")</code></pre> <p>All data is released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0).</p>
FIGURE 1 in Coupling finite element analysis and multibody system dynamics for biological research
FIGURE 1. Simplification of the center of head movement as a joint in extinct Temnospondyli amphibian when biting. Elaborated from the original image (en.wikipedia.org/wiki/File:Jammerbergia_formops.jpg). Under license: CC BY-SA 3.0 (creativecommons.org/licenses/by-sa/3.0/).
FIGURE 4 in Coupling finite element analysis and multibody system dynamics for biological research
FIGURE 4. Von Mises stress distribution in the skull for the Static Analysis in FEA in cases 1A, 2A, 3A, 1B, 2B and 3B.
FIGURE 2 in Coupling finite element analysis and multibody system dynamics for biological research
FIGURE 2. Studied test cases of different feeding movements when applying a force F=800 N in the direction of the red arrow (when the force is perpendicular at the view the red arrow is a red dot). Case 1A, 2A and 3A with a fixed boundary condition in the condyle without the web of beams. Case 1B, 2B and 3B with the web of beams in the condyle and a fixed boundary condition.
Data input for the RegMex model experiment on the power system and flexible sector coupling
<p>This file provides the input data used in the power system flexibility model experiment performed within the RegMex project. Comprehensive information about the project can be found in the project report [Lechtenböhmer2018] (in German, see link in the file). In the experiment performed with the data documented here, three scenarios were considered, labelled "Import", "Decentralized" and "Offshore". This file contains the input for all scenarios. All further information on the model and scenario configuration is available from the project report. Many technology parameter have been derived as own assumptions within previous projects, relying on different sources. Details can be found in the cited PhD and masters theses. In the experiment, Germany was modelled with 18 regions reflecting the transmission grid operator zones (see map in the file).</p>
Deformation simulation results of Capriccio method coupled systems for conducting comparative one- and multidimensional studies on the coupling of the finite element method with particle-based techniques
<p>readme_3Dresults.txt</p> <p><br> <strong>Description</strong>:</p> <p>This readme explains the content and path structure of the results obtained from a<br> deformation test conducted on slightly different MD-FE coupled systems performing the<br> Capriccio method in a three-dimensional space within the associated project thesis [1],<br> published on the following dataset: <a href="https://doi.org/10.5281/zenodo.7924367">https://doi.org/10.5281/zenodo.7924367</a></p> <p>Furthermore, input files and parameters as well as potential tables required to reproduce<br> the obtained data are provided as well.</p> <p>The molecular dynamics (MD) part is executed in LAMMPS and the finite element (FE) method<br> part by a MATLAB script as described in Section 4.1 of [1]. The whole setup of the 3D<br> models is elaborated in Section 4.2 of [1]. A discussion of some results is given in<br> Chapter 6 of [1] in the context of assessing their comparability with the corresponding 1D<br> model.</p> <p><br> <strong>Context</strong>:</p> <p>[1] L. Laubert, "Establishing a framework for conducting comparative one- and<br> multidimensional studies on the coupling of the finite element method with<br> particle-based techniques", Project Thesis, Friedrich-Alexander-Universität<br> Erlangen-Nürnberg (FAU), 2023.</p> <p><br> <strong>Contact</strong>:</p> <p>Lukas Laubert<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universiät Erlangen-Nürnberg<br> Egerlandstraße 5<br> 91058 Erlangen</p> <p><br> <strong>License</strong>:</p> <p>Creative Commons Attribution Non Commercial 4.0 International</p> <p><br> <strong>Path structure and files</strong>:</p> <p>- The ZIP compressed files each contain a folder containing all simulation files as well as<br> postprocessing variables:<br> * /FE_data/ contains all output files after each FE simulation in each iteration step<br> * /MD_data/ contains all output files after each MD simulation in each iteration step<br> * /input_files/ contains the input FE model "cgps_dpd_c_1_2000.inp", the MD particle<br> configurations "cgps_dpd_c_1_2000.data", the AP particle coordinates <br> "cgps_dpd_c_1_2000.ac" as well as further Abaqus CAE FE files that<br> can be used to adapt the present FE model<br> * /input_parameters/ contains the parameter dataset; "Capriccio.prm" is the main parameter<br> dataset, whose adaptations lead to similar adjustments in the other parameter files<br> * "Capriccio_FEMD_main_meggie_WZ.sh" is a shell script for executing simulations<br> * "job.out" is an output protocol that documents the progress of the simulations<br> * "Job.err" is an error protocol that documents detected errors during the simulations<br> * "log.lammps" logs MD parameter sets<br> * "meta.info" provides version information of used softwares among few other information<br> * "next_job.info" documents the next load step and iteration step that is to be executed<br> when simulation jobs are restarted on the used computation cluser<br> * **_workspace_vars.mat comprises a set of postprocessing variables obtained by executing a<br> postprocessing script provided by Capriccio group</p> <p>- "md_dpd_main-CBpot-writeobs-sandw.in" is an input script that further defines and loads<br> MD simulation parameter</p> <p>- ***_table are potential tables applied during the MD simulations<br> * "Angle_table" lists the angle bending potential<br> * "Bond_table" lists the bond potentials<br> * "Nonbond_table" lists the non-bonded interaction potential</p>
PALM Model System v 6.0 input and configuration files for coupled large eddy simulations of land surface heterogeneity effects and diurnal evolution of late summer and early autumn atmospheric boundary layers during the CHEESEHEAD19 field campaign
<p>Namelist, configuration and forcing files for the PALM Model System 6.0 revision number 21.10-rc.2 used for the numerical simulations Coupled Large Eddy Simulations of land surface heterogeneity induced atmospheric boundary layer response during the CHEESEHEAD19 field campaign.</p>
Dual spring force couples yield multifunctionality and ultrafast, precision rotation in tiny biomechanical systems
Open the record for dataset details and reuse information.
Input data for performing chemistry coupled PALM model system 6.0 simulations with different chemical mechanisms
<p>The data presented here comprised of input files that have been used to run chemistry coupled PALM model system 6.0 simulations for the article entitled "Development of an atmospheric chemistry model coupled to the PALM model system 6.0: Implementation and first applications". In this article we describe the implementation of an online-coupled gas-phase chemistry model in the turbulence resolving PALM model system 6.0.</p> <p>List of the input data required for performing chemistry model simulations with different chemical mechanisms is given below. A text file comprised of measured concentrations of NO, NO<sub>2</sub> and O<sub>3</sub> is also added.</p> <ol> <li>Fortran parameter (PARIN) files for four mechanisms and one meteorology-only simulation.</li> <li>Static file</li> <li>Dynamic file</li> <li>Two files (shortwave and longwave input data) for rrtmg radiation model</li> <li>Observation from two air quality stations in Berlin, Germany .</li> <li>PALM model source code revision 4450 (palm_trunk_rev-4450.tar.gz)</li> <li>PALM model source code revision 4601 (palm_trunk_rev-4601.tar.gz)</li> </ol> <p>The PALM model system 6.0 revision 4451 and 4601 (for chemistry flux profiles only) have been used for these simulations. </p>
Monsoon Mission Coupled Forecast System Version 2.0: Model Description and Indian Monsoon Simulations Figures
<p>Monsoon Mission Coupled Forecast System Version 2.0: Model Description and Indian Monsoon Simulations Figures</p>
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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.