Find research datasets worth reusing

opencc-zeroFeb 2023View details →

Arctic nekton uncovered by eDNA metabarcoding: diversity, potential range expansions and benthopelagic coupling

The Arctic Ocean is home to a unique fauna that is disproportionately affected by global warming but that remains under-studied. Due to their high mobility and responsiveness to global warming, cephalopods and fishes are good indicators of the reshuffling of Arctic communities. Here, we established a nekton biodiversity baseline for the Fram Strait, the only deep connection between the North Atlantic and Arctic Ocean. Using universal primers for fishes (12S) and cephalopods (18S), we amplified environmental DNA (eDNA) from seawater (50–2700 m) and deep-sea sediment samples collected at the LTER HAUSGARTEN observatory. We detected twelve cephalopod and 31 fish taxa in the seawater and seven cephalopod and 28 fish taxa in the sediment, including the elusive Greenland shark (Somniosus microcephalus). Our data suggest three fish (Mallotus villosus, Thunnus sp. and Micromesistius poutassou) and one squid (Histioteuthis sp.) range expansions. The detection of eDNA of pelagic origin in the sediment also suggests that M. villosus, Arctozenus risso and M. poutassou as well as gonatid squids are potential contributors to the carbon flux. Continuous nekton monitoring is needed to understand the ecosystem impacts of rapid warming in the Arctic and eDNA proves to be a suitable tool for this endeavor.

Influence of Adaptive Coupling Points on Coalition Formation in Multi-Energy Systems: Simulation Result Tables

The dataset contains the result tables used for the paper "Influence of Adaptive Coupling Points on Coalition Formation in Multi-Energy Systems".  Short description of the tables: <ul> <li>all_in_one_tab.csv: contains all calculated metrics and attributes of the graph over time and adaptation rate</li> <li>event_tab.csv: contains all toggle events of the coupling points</li> <li>impact_dict.csv: contains the calculated impact values for every coupling point</li> <li>node_region.csv: contains region attributes for every node over adaptation rate</li> <li>static_component_properties.csv: contains static attributes </li> <li>static_node_component_properties.csv: contains static node attributes</li> </ul>

Sensitivity of Arctic Surface Temperature to Including a Comprehensive Ocean Interior Reflectance to the Ocean Surface Albedo within the Fully Coupled CESM2

CESM2 simulations were performed to study the light attenuation effects at the ocean surface layer on Arctic surface temperature. This dataset provides some simulated variables analyzed in our study.

Data used to create figures and tables in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"

This dataset contains all simulation output and observational data of ground-based/satellite-retrieved meteorological and air quality for computing statistical metrics in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows: 1. Simulation and observational results of meteorological and air quality including four folders:      Day_PBLH: Daily PBLH data      Hour_air: Hourly air quality data regarding PM2.5, O3, SO2, NO2 and CO      Hour_met: Hourly meteorological data regarding T2, Q2, RH2, WS10 and precipitation      Hour_radiation: Hourly surface radiation data 2. Simulation and satellite-retrieved results of meteorological and air quality including nine folders:     AOD: Yearly and seasonal AOD data     CF: Yearly and seasonal CF data     CO: Yearly and seasonal CO data     LWP: Yearly and seasonal LWP data     NO2: Yearly and seasonal NO2 data     O3: Yearly and seasonal O3 data     Precipitation: Yearly and seasonal precipitation data     Radiation: Yearly and seasonal radiation data     SO2: Yearly and seasonal SO2 data

Computational results and python files for the work "Divergence-conforming velocity and vorticity approximations for incompressible fluids obtained with minimal facet coupling"

This repository contains data accompanying the paper "Divergence-conforming velocity and vorticity approximations for incompressible fluids obtained with minimal facet coupling". The implementation is based on the python-interface of the NGSolve open source Finite Element library (ngsolve.org). The file solve_problem_allione.py represents a minimum working example where the proposed MCS/HDG (set the use_MCS flag) method is used to solve the problem from the numerics section of the paper. The files FlowTemplates.py and krylovspace_extension.py contain a somewhat larger and more modular implementation of the proposed method that also features preconditioned iterative solvers, including support for the NgsAMG NGSolve extension library as well as the NGSolve-PETSc interface. The files errors_hdg.pickle, errors_mcs.pickle and kappas.pickle contain the raw data the tables and pictures in the paper were generated from. This data was generated with the scripts conv3d_hdg.py, conv3d_mcs.py and calc_kappas.py which use the FlowTemplates.py infrastructure.

opencc-by-4.0Jan 2023View details →

Flow-field correction method to constrain AMOC in coupled model (IPSL-CM6A-LR)

We use the standard version of IPSL-CM6A-LR (Boucher et al., 2020). The ocean component of IPSL-CM6A-LR is the NEMO oceanic model Version 3.6. The dynhpg.F90 file is a modified version of the routine that implements the flow-field correction method, and the namelist_ORCA1_cfg is the modified namelist used to activate the flow field correction, set the parameters, and read the input temperature, salinity, and mask. The mask specifies the region where this method is applied. To implement this method using the IPSL coupled model, consider following these steps: 1. Install the standard configuration in a path 2. Copy the provided dynhpg.F90 in modipsl/modeles/NEMOGCM/CONFIG/ORCA1_LIM3_PISCES/MY_SRC 3. Compile 3. Copy and modify the namelist_ORCA1_cfg in modipsl/config/IPSLCM6/testffc/PARAM The input conservative temperature (in degC), absolute salinity (in g/kg), and mask are provided to the model for the flow field correction are available in the following files: data_sal_sa_1.5LFC.nc (salinity)  data_sal_sa_weak1.5LFC.nc (salinity) data_tem_bigthetao_1.5LFC.nc (temperature) data_tem_bigthetao_weak1.5LFC.nc (temperature) rhdmsk_data_bothBC2.v2.nc (mask) Note that data_sal_sa_1.5LFC.nc and data_tem_bigthetao_1.5LFC.nc are used to constrain the AMOC to the strong state in Jiang et al., (2023).  data_sal_sa_weak1.5LFC.nc and data_tem_bigthetao_weak1.5LFC.nc are used to constrain it to the weak state in Jiang et al., (2023). Finally, some of the main simulated outputs are given. All the outputs are the annual mean ensemble mean (3 members) data lasting for 100 years. The initial states of the 3 members are sampled in the years of 1850, 2000 and 2080 of the CMIP6 piControl simulation available on ESGF, corresponding to neutral, strong, and weak AMOC states respectively. heatc_strong_3runs_1Y.nc (ocean heat content in J/m2) sos_strong_3runs_1Y.nc (sea surface salinity in psu) tos_strong_3runs_1Y.nc (sea surface temperature in degC) precip_strong_3runs_1Y.nc (precipitation in kg/(s*m2)) diaptrW_strong_3runs_1Y.nc (meridional streamfunction in Sv) slp_strong_3runs_1Y.nc (sea level pressure in Pa) geop500_strong_3runs_1Y.nc (geopotential height at 500 hPa in m) nettop0_strong_3runs_1Y.nc (clear-sky solar radiation at the top of atmosphere in W/m2) nettop_strong_3runs_1Y.nc (net downward flux at the top of atmosphere in W/m2) t2m_strong_3runs_1Y.nc (air temperature at 2-m in K) vitu850_strong_3runs_1Y.nc (zonal wind at 850 hPa in m/s) cld_strong_3runs_1Y.nc (low-level and high-level cloudiness, unitless) tauuo_strong_3runs_1Y.nc (surface downward stress in the x-direction in N/m2) tauvo_strong_3runs_1Y.nc (surface downward stress in the y-direction in N/m2) The files listed above are the results from simulations where the AMOC is constrained to the strong state (i.e., using the input data_tem_bigthetao_1.5LFC.nc and data_sal_sa_1.5LFC.nc). Similarly, the same fields from simulations where the AMOC is constrained to the weak state (i.e., using the input data_tem_bigthetao_weak1.5LFC.nc and data_sal_sa_weak1.5LFC.nc) are: heatc_weak_3runs_1Y.nc (ocean heat content in J/m2) sos_weak_3runs_1Y.nc (sea surface salinity in psu) tos_weak_3runs_1Y.nc (sea surface temperature in degC) precip_weak_3runs_1Y.nc (precipitation in kg/(s*m2)) diaptrW_weak_3runs_1Y.nc (meridional streamfunction in Sv) slp_weak_3runs_1Y.nc (sea level pressure in Pa) geop500_weak_3runs_1Y.nc (geopotential height at 500 hPa in m) nettop0_weak_3runs_1Y.nc (clear-sky solar radiation at the top of atmosphere in W/m2) nettop_weak_3runs_1Y.nc (net downward flux at the top of atmosphere in W/m2) t2m_weak_3runs_1Y.nc (air temperature at 2-m in K) vitu850_weak_3runs_1Y.nc (zonal wind at 850 hPa in m/s) cld_weak_3runs_1Y.nc (low-level and high-level cloudiness, unitless) tauuo_weak_3runs_1Y.nc (surface downward stress in the x-direction in N/m2) tauvo_weak_3runs_1Y.nc (surface downward stress in the y-direction in N/m2)

opencc-by-4.0Dec 2022View details →

Code and data for "Two-way coupled long-wave isentropic ocean-atmosphere dynamics"

This repository contains the code and data needed to replicate the figures and simulations in the JFM Paper: "Two-way coupled long-wave isentropic ocean-atmosphere dynamics". Please see the readme.txt file for details. Edit: -v3 added a jfm_pysonly.zip which contains only the Python scripts to create the figures to allow for a faster separate download -v2 updated with JFM paper DOI https://doi.org/10.1017/jfm.2023.131 in file headers

opencc-zeroApr 2023View details →

Graft‐host coupling changes can lead to engraftment arrhythmia: A computational study

This dataset contains examples and raw data related to the cited publication (doi: 10.1113/jp284244). Computational models derived from histological images are provided. Raw values in the data spreadsheet are given as fraction of simulations for a particular configureation that resulted in graft-initiated host excitation.

Analysis of the Scalar and Vector Random Coupling Models For a Four Coupled-Core Fiber

The files with simulation results for ECOC 20223 submission "Analysis of the Scalar and Vector Random Coupling Models For a Four Coupled-Core Fiber"."4CCF_eigenvectorsPol" file is the Mathematica code which enables to calculate supermodes (eigenvectors of M(w)) and their propagation constants of 4-coupled-core fiber (4CCF). These results are uploaded to the python notebook "4CCF_modelingECOC" in order to plot them to get Fig. 2 in the paper. "TransferMatrix" is the python file with functions used for modeling, simulation and plotting. It is also uploaded in the python notebook "4CCF_modelingECOC", where all the calculations for figures in the paper are presented. ! UPD 25.09.2023: There is an error in the formula of birefringence calculation. It is in the function "CouplingCoefficients" in  "TransferMatrix" file. There the variable "birefringence" has to be calculated according to the formula (19) [A. Ankiewicz, A. Snyder, and X.-H. Zheng, "Coupling between parallel optical fiber cores–critical examination", Journal of Lightwave Technology, vol. 4, no. 9,pp. 1317–1323, 1986]:(4*U**2*W*spec.k0(W)*spec.kn(2, W_)/(spec.k1(W)*V**4))*((spec.iv(1, W)/spec.k1(W))-(spec.iv(2, W)/spec.k0(W)))The correct formula gives almost the same result (the difference is 10^-5), but one has to use a correct formula anyway.! UPD 9.12.2023: I have noticed that in the published version of the code I forgot to change the wavelength range for impulse response calculation. So instead of seeing the nice shape as in the paper you will see resolution limited shape. To solve that just change the range of wavelengths, you can add "wl = [1545e-9, 1548e-9]" in the first cell after "Total power impulse response".P.s. In case of any questions or suggestions you are welcome to write me an email ekader@chalmers.se

Data used to simulations in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"

This dataset contains input data of simulations by WRF-CMAQ, WRF-Chem and WRF-CHIMERE in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows: 1. WRF-CMAQ input data including emission, ICs and lateral BCs of meteorology and air quality: YYYYMM.zip represents the input data for each month for simulations. Due to the large size of the compressed file containing input data each month, there may be interruptions when uploading it to Zenodo. Therefore, we will split each compressed file into 50MB. If users want to browse the file, they can download the segmented files, and then merge them into the YYYYMM.zip file using the Linux command line "unzip 'YYYYMM.zip.*' -d combined"

opencc-zeroMay 2023View details →

Laminar phase coupling data for: Spike-phase coupling patterns reveal laminar identity in primate cortex

The cortical column is one of the fundamental computational circuits in the brain. In order to understand the role neurons in different layers of this circuit play in cortical function it is necessary to identify the boundaries that separate the laminar compartments. While histological approaches can reveal ground truth they are not a practical means of identifying cortical layers in vivo. The gold standard for identifying laminar compartments in electrophysiological recordings is current-source density (CSD) analysis. However, laminar CSD analysis requires averaging across reliably evoked responses that target the input layer in cortex, which may be difficult to generate in less well-studied cortical regions. Further, the analysis can be susceptible to noise on individual channels resulting in errors in assigning laminar boundaries. Here, we have analyzed linear array recordings in multiple cortical areas in both the common marmoset and the rhesus macaque. We describe a pattern of laminar spike-field phase relationships that reliably identifies the transition between input and deep layers in cortical recordings from multiple cortical areas in two different non-human primate species. This measure corresponds well to estimates of the location of the input layer using CSDs but does not require averaging or specific evoked activity. Laminar identity can be estimated rapidly with as little as a minute of ongoing data and is invariant to many experimental parameters. This method may serve to validate CSD measurements that might otherwise be unreliable or to estimate laminar boundaries when other methods are not practical.

opencc-by-4.0Sep 2022View details →

Coupled Situational Awareness System to Improve Transportation Infrastructure Performance during Extreme Events

The dense road networks and numerous low water crossings throughout Texas may be contributing to the higher recurrence rates of floods that pose a danger to vehicles. A timely issue that should be addressed by researchers is the compounding of disaster. Flooding can be combined with other life-threatening occurrences such as power loss and interruptions of health and emergency services. During these events, rescue requests from the stranded communities overwhelm the emergency response facilities; impassable roadways and the paucity of reliable information on the affected areas and their accessibility hamper emergency response operations, causing several detours and delays that put both the responders and evacuees at risk. This research presents a framework for improved situational awareness during extreme flooding events by combing a flood inundation model with transportation infrastructure performance assessment. The flood inundation model can be driven by real-time radar rainfall data in an efficient manner. The road network work model can use land use, census data, and locations of critical facilities in combination with spatial analysis. The proposed framework is demonstrated on a small catchment in San Antonio, Texas. The study includes the following tasks: literature review, vehicle-related flood fatality analysis, review of flood warning systems in Texas, and the proposed framework of a road flooding forecasting system that can predict land surface flooding in detail and the impacts on the road network and provide information on the spatial and temporal evolution of road network access during flooding events. It is recommended that the framework be used for identifying the transportation network-wide impacts of flood ‘hot-spots’ and assessment of transportation-related flood mitigation alternatives. The framework can also support disaster planning and emergency preparedness measures in preparation for major events. Examples may include contingency planning for deployment of barricades, mitigation of critical facilities, and large-scale evacuation planning. The methodology can provide useful outputs on system-wide costs of flooded roads that can be used to inform regional mitigation efforts.

opencc-zeroMay 2023View details →

Data for: Coupled anaerobic methane oxidation and metal reduction in soil under elevated CO2

Continued current emissions of carbon dioxide (CO2) and methane (CH4) by human activities will increase global atmospheric CO2 and CH4 concentrations and surface temperature significantly. Fields of paddy rice, the most important form of anthropogenic wetlands, account for about 9% of anthropogenic sources of CH4. Elevated atmospheric CO2 may enhance CH4 production in rice paddies, potentially reinforcing the increase in atmospheric CH4. However, what is not known is whether and how elevated CO2 influences CH4 consumption under anoxic soil conditions in rice paddies, as the net emission of CH4 is a balance of methanogenesis and methanotrophy. In this study, we used a long-term free-air CO2 enrichment experiment to examine the impact of elevated CO2 on the transformation of CH4 in a paddy rice agroecosystem. We demonstrate that elevated CO2 substantially increased anaerobic oxidation of methane (AOM) coupled to manganese and/or iron oxides reduction in the calcareous paddy soil. We further show that elevated CO2 may stimulate the growth and metabolism of Candidatus Methanoperedens nitroreducens, which is actively involved in catalyzing AOM when coupled to metal reduction, mainly through enhancing the availability of soil CH4. These findings suggest that a thorough evaluation of climate-carbon cycle feedbacks may need to consider the coupling of methane and metal cycles in natural and agricultural wetlands under future climate change scenarios.

The simulated outputs analyzed in the article: "Understanding the influences of ocean waves on Arctic sea ice simulation: a modeling study with an atmosphere-ocean-wave-sea ice coupled model"

In Ice-mass_[experiment] files, they include daily-averaged sea ice concentration and sea ice mass/area budgets. In Flux_[experiment] files, they include daily-averaged net ice surface flux, net shortwave/longwave radiation at the ice surface, latent/sensible heat flux at the ice surface, conductive heat flux at the top ice layer, and ice-ocean heat flux.

Channel flow with hydrodynamically controlled benthic-pelagic coupled oxygen fluxes

This dataset was used for the manuscript "Hydrodynamic control of sediment-water fluxes: Consistent parameterization and impact in coupled benthic-pelagic models" by Umlauf et al. (2023, JGR Oceans, <a href="https://doi.org/10.1029/2023JC019651" target="_blank" rel="noopener">10.1029/2023JC019651</a>).

Notebooks and calculation files for: Modeling of the 3-Coupled-Core Fiber: Comparison Between Scalar and Vector Random Coupling Models

The files with simulation results for JLT submission "Modeling of the 3-Coupled-Core Fiber: Comparison Between Scalar and Vector Random Coupling Modelsr". "3CCF_supermodes" file is the Mathematica code which enables to calculate supermodes (eigenvectors of M(w)) and their propagation constants of 3-coupled-core fiber (4CCF). These results are uploaded to the python notebook "3CCF_modelingJLTPaper" in order to plot them to get Fig. 3 in the paper. "TransferMatrix" is the python file with functions used for modeling, simulation and plotting. It is also uploaded in the python notebook "3CCF_modelingJLTPaper", where all the calculations for figures in the paper are presented.     ! UPD 25.09.2023: There is an error in the formula of birefringence calculation. It is in the function "CouplingCoefficients" in  "TransferMatrix" file. There the variable "birefringence" has to be calculated according to the formula (19) [A. Ankiewicz, A. Snyder, and X.-H. Zheng, “Coupling between parallel optical fiber cores–critical examination”, Journal of Lightwave Technology, vol. 4, no. 9,pp. 1317–1323, 1986]: (4*U**2*W*spec.k0(W)*spec.kn(2, W_)/(spec.k1(W)*V**4))*((spec.iv(1, W)/spec.k1(W))-(spec.iv(2, W)/spec.k0(W))) The correct formula gives almost the same result (the difference is 10^-5), but one has to use a correct formula anyway.   P.s. In case of any questions or suggestions or if you need more explanations, you are welcome to write me an email ekader@chalmers.se. If it seems like the code does not work or mistakes in simulations are found, I also appreciate letting me know.

opencc-by-4.0Dec 2021View details →

Pre-built Sector-coupled Euro-Calliope Model

Sector-coupled Euro-Calliope subnational-scale pre-built models Built using <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a> commit hash: 6fd0bf3dce2a0799ac9821b50e9b1513fa783018 This model is pre-packaged and ready to be loaded into Calliope, based on 2010 - 2018 input data. To run the model you will need to do the following: a. Install a specific conda environment to be working with the correct version of Calliope (<code>conda env create -f requirements.yml</code>) b. Include specific scenarios to pick up the relevant sectors. For the study accompanying this release, The following scenarios were included <code>"industry_fuel_shared,transport,heat,config_overrides,res_2h,gas_storage,link_cap_dynamic,freeze-hydro-capacities,add-biofuel"</code>, where: <ul> <li> <code>industry_fuel_shared</code>: Includes all non-electrical industry demands and the necessary technologies to generate those fuels synthetically. This includes e.g. annual methanol requirements for the chemical industry. <code>shared</code> refers to the fact that all regions' annual demand is pooled and can be met across all regions. The other option is to set this to <code>industry_fuel_isolated</code>, where a region must meet its own annual demand by generation of fuel within the region. </li> <li> <code>transport</code>: This ensures ICE and EV light and heavy vehicle technologies and annual demands are in the model. It also includes reference to constraints required to make smart-charging of EVs work (e.g. weekly demand requirements). </li> <li> <code>heat</code>: This ensures that all heat provision technologies and hourly demands are in the model. Carriers added are <code>heat</code> (space heating and hot water) and <code>cooking</code>. Technologies added can be found in <code>heat-techs.yaml</code>. </li> <li> <code>config_overrides</code>: This includes high-level simplifications, such as removal of technologies that are considered redundant (e.g. less interesting combined heat and power technologies). </li> <li> <code>res_2h</code>: Sets the model with a 2h resolution. Can be omitted or can be one of <code>res_2h</code>, <code>res_3h</code>, <code>res_6h</code>, <code>res_12h</code>. The full hourly resolution model takes ~2 days to complete. </li> <li> <code>gas_storage</code>: Includes underground methane storage facilities, based on latest data on a national level. Can be omitted to remove the option of this technology. </li> <li> <code>link_cap_dynamic</code>: Sets a limit on transmission line capacities. The limits are chosen subjectively based on current capacity, such that lines with smaller current capacities can proportionally increase much more (e.g. 100x) than larger lines (e.g. 2x). See <code>national/links.yaml</code> for other override options to apply here. </li> <li> <code>freeze-hydro-capacities</code>: Sets hydro capacities to equal "today's" capacities. This seems more reasonable than setting current capacities as upper limits, as this causes the model to install no hydro. </li> <li> <code>add-biofuel</code>: Enables a biofuel supply stream with a distinct <code>biofuel</code> carrier, with annual limits on biofuel that can be provided (based on JRC residuals). This differs from Euro-Calliope v1.0 which is a black box technology converting biofuel to electricity directly. </li> </ul> c. decide on a SPORES run to undertake, e.g. the scenario <code>spores_supply</code> will run SPORES for primary energy supply technologies. SPORES scenarios can be found in the file <code>spores.yaml</code>. d. pick your run year, by pointing to the relevant model config file (e.g. <code>model-2018.yaml</code> for the 2018 weather year). e. run the model via the dedicated scripts found in this directory. These scripts include the addition of custom constraints and have been copied directly from the workflow, where they would normally be initiated as part of the internal process. However, you can load and run them in an interactive session / with your own python script to call them: <code class="language-python">[1] import create_input </code> <code class="language-python">[2] create_input.build_model(path_to_model_yaml, scenarios_string, path_to_netcdf_of_model_inputs) </code> <code class="language-python">[3] import run </code> <code class="language-python">[4] run.run_model(path_to_netcdf_of_model_inputs, path_to_netcdf_of_results)</code>   <code class="language-python">Note: The only difference between this version and v0.1 is that spurious hidden files specific to MacOS have been removed from the dataset.</code>

opencc-zeroJun 2023View details →

Supporting materials and data from: Hypotheses concerning global magnetospheric convection, magnetosphere-Ionosphere coupling, and auroral activity at Uranus

Summary: The data and figures contained in this dataset are supporting materials for the paper referenced in the title and abstract below. This study involved an analytical and numerical assessment of the Uranian magnetosphere and its interaction with the solar wind and interplanetary magnetic field (IMF). The data and figures contained in this dataset include supplementary results for a greater number of IMF orientations and Uranian seasons than the examples presented in the corresponding paper. Abstract from corresponding paper: doi:10.1029/2023JA031791 with Journal of Geophysical Research Space Physics: We investigate the unique magnetosphere of Uranus and its interaction with the solar wind. Following previous work, we developed and validated a simple yet valuable and illustrative model of Uranus' offset, tilted, and rapidly-spinning magnetic field and magnetopause (nominal and fit to the Voyager-2 inbound crossing point) in three-dimensional space. With this model, we investigated details of the seasonal and interplanetary magnetic field (IMF) orientation dependencies of dayside and flank reconnection along the Uranian magnetopause. We found that anti-parallel (magnetic field shear angle greater than 170-degrees) reconnection occurs nearly continuously along the Uranian dayside and/or flank magnetopause under all seasons of the 84 (Earth) year Uranian orbit and the most likely IMF orientations. Such active and continuous driving of the Uranian magnetosphere should result in constant loading and unloading of the Uranian magnetotail, which may be further complicated and destabilized by sudden changes in the IMF orientation and solar wind conditions plus the reconfigurations from the rotation of Uranus itself. We demonstrate that unlike the other magnetospheric systems that are Dungey-cycle driven (i.e., Mercury and Earth) or rotationally driven (Jupiter and Saturn), global magnetospheric convection of plasma, magnetic flux, and energy flow may occur via three distinct cycles, two of which are unique to Uranus (and possibly also Neptune). Our simple model is also used to map signatures of dayside and flank reconnection down to the Uranian ionosphere, as a function of planetary latitude and longitude. Such mapping demonstrates that "spot"-like auroral features should be very common on the Uranian dayside, consistent with observations from Hubble Space Telescope. We further detail how the combination of Uranus' rapid rotation and unique and very active global magnetospheric convection should be consistent with fueling of the surprisingly intense trapped radiation environment observed by Voyager-2 during its single flyby. Summarizing, Uranus is a very special magnetosphere that offers new insights on the nature, complexity, and diversity of planetary magnetospheric systems and the acceleration of particles in space plasmas, which might have important analogs to exoplanetary magnetospheric systems. Our hypotheses can be tested with further work involving more advanced models, new auroral observations, and unprecedented missions to explore the in situ environment from orbit around Uranus, which should include a complement of magnetospheric instruments in the payload.

opencc-by-4.0Jul 2023View details →

Lake-Watershed coupling model in Qinghai Lake Basin, with SHUD model

Files structure     <ul> <li> Calibration: The souce code and the part of results of the calibration. <ul> <li> CalibFiles: The configuration of the CMAES methods. </li> <li> cames_out: Part of the output of CMAES method, as the entire file is huge. </li> <li> input: the SHUD model input for the calibration method. </li> <li> qhh.nse.dai.R: the kernel R code to run the CMAES method. </li> <li> sub.sh: the script to submit job to LSF managment on High Performance Computer. </li> </ul> </li> <li> Code_Analysis: The R code for analysis the result and plot figures. </li> <li> Code_AutoSHUD: The R code to build the QHH model. To run the script, please call Step1_RawDataProcessng.R, Step2_DataSubset.R, Step3_BuidModel.R sequentially. <ul> <li> autoSHUD_qhh.txt: The configration of the autoshud project for the QHH project. </li> <li> GerReady.R: The initial files to build the environment, which is called by other script automatically. </li> <li> Step1_RawDataProcessng.R: Raw data processing. </li> <li> Step2_DataSubset.R: Data processing for soil, landuse and forcing data . </li> <li> Step3_BuidModel.R : build the SHUD model. </li> </ul> </li> <li> Code_SHUD: the source code of SHUD used in this project. </li> <li> Data: The rawdata download from data centers. </li> <li> Modeling: The output folder for AutoSHUD code. These is the foundation of the simulation.   </li> </ul>

Simulations for: The Energetics and Ion Coupling of Cholesterol Transport Through Patched1

Free energy profiles: Coarse-grained (CG) potential of mean force (PMF) free energy and histogram outputs, obtained via the gromacs weighted-histogram analysis method (WHAM). PMF profile numbers match those described in the accompanying manuscript for cholesterol movement between the: - PTCH1-molA ECD base and the sterol binding domain (SBD) (PMF_1a_PTCH1_molA-SHH_cholesterol) - PTCH1-molB ECD base and the SBD (PMF_1a_PTCH1_molB-free_cholesterol)   - PTCH1-molA ECD base and the sterol sensing domain (SSD) (PMF_1b_PTCH1_molA-SHH_cholesterol) - PTCH1-molA SSD and the membrane (PMF_2_PTCH1_molA-SSD_cholesterol_OHup) - PTCH1-molB SSD and the membrane (PMF_2_PTCH1_molB-SSD_cholesterol_OHup) - PTCH1-molB SSD and the membrane in a flipped conformation (PMF_2_PTCH1_molB-SSD_cholesterol_OHdown) - Membrane and solvent (PMF_3_Membrane_cholesterol) - PTCH1-molA SBD and the solvent (PMF_4_PTCH1_molA-SSH_cholesterol) - PTCH1-molB SBD and the solvent (PMF_4_PTCH1_molB-free_cholesterol) 2000 rounds of Bayesian Bootstrapping were performed. File description for each system: - histo.xvg: umbrella window histograms - bsres.xvg: Average free energy profile and computed bootstrapping error.     Atomistic simulations: Subset of atomistic molecular dynamics simulations of wild-type (WT) Patched1 (PTCH1) and Dispatched1 (DISP1) in distinct ion-bound states: - PTCH1 with Na+ bound at Site 1 initially (PTCH1_WT_Na_Site1) - PTCH1 apo in 0.15 M NaCl (PTCH1_WT_apo_inNaCl) - PTCH1 apo in 0.15 M KCl (PTCH1_WT_apo_inKCl) - PTCH1 with 3 x Na+ ions bound to anionic triad residues (PTCH1_WT_3xNa) - PTCH1 with 3 x K+ ions bound to anionic triad residues (PTCH1_WT_3xK) - DISP1 apo in 0.15 M NaCl (DISP1_WT_apo) - DISP1 with 3 x Na+ ions bound to anionic triad residues (DISP1_WT_3xNa) - DISP1 with 2 x Na+ ions bound, generated by sequential ion removal from the end of DISP1_WT_3xNa simulations (DISP1_WT_2xNa) - DISP1 with 1 x Na+ ion bound, generated by sequential ion removal from the end of DISP1_WT_2xNa simulations (DISP1_WT_1xNa) - DISP1 without Na+ bound, generated by sequential ion removal from the end of DISP1_WT_1xNa simulations (DISP1_WT_0xNa) All simulations were run for 3 x 100 ns except for PTCH1_WT_apo_inNaCl which was run for 3 x 50 ns. The PTCH1 and DISP1 conformations were obtained from the Protein Data Bank (PDB IDs: 6DMY, 7RPH).  File description for each system: - md_fit_firstframe.pdb : Initial frame used for atomistic simulations (replicate 1, each replicate equilibrated independently).  - md_fit_X.xtc : Gromacs trajectory file for each replicate (X=replicate number).