Find research datasets worth reusing

opencc-by-4.0Jul 2019View details →

Research data supporting "Investigating the accumulation and translocation of titanium dioxide nanoparticles with different surface modifications in static and dynamic human placental transfer models"

Research data supporting the publication: Aengenheister, L. et al., 2019, "Investigating the accumulation and translocation of titanium dioxide nanoparticles with different surface modifications in static and dynamic human placental transfer models",  Eur J Pharm Biopharm. https://doi.org/10.1016/j.ejpb.2019.07.018

opencc-by-4.0Jul 2019View details →

Model simulated potential natural vegetation state in the western US under preindustrial, historic, and future (RCP8.5) atmospheric conditions using multiple parameterizations of the dynamic vegetation model TRIFFID.

DATA DESCRIPTION Author contact information: Linnia R. Hawkins Oregon State University lhawkins@oregonstate.edu; linnia.hawkins@gmail.com Data supporting 2019 Journal of Advances in Modeling Earth Systems publication Simulations of the equilibrium vegetation distribution in the western US performed with the climate model HadAM3p-HadRM3p-MOSES2-TRIFFID step1: Identify Influential Parameters All files labeled step1. EXPERIMENT DESCRIPTION: Data used in step 1: identify influential parameters     sensitivity experiment adjusting one parameter at a time 38 individual parameters were adjusted to 7 values, equally spaced over a defined plausible range. For reference nine simulations with the default model parameterization are included, initiated with unique initial potential temperature perturbations.  The data contains the vegetation state variables at the end of four-year simulations (January 2004 to December 2007) during which two equilibrium time steps with the dynamic vegetation model TRIFFID (Cox et al., 2001). The results are averaged over three simulations initiated with unique atmospheric potential temperature perturbations. FILE DESCRIPTION: NETCDF: Each netcdf file contains the fractional coverage (field1391), leaf area index (field1392), and the canopy height (field1393) for 5 plant functional types (PFTs: broadleaf, needleleaf, c3 grass, c4 grass, shrub) simulated for November 29, 2007.      File labeling scheme:            step1_parameter_settingindex_3ICave.nc         parameter: the name of the only model parameter adjusted         setting index: the index of the parameter setting (1-7)             index of 1 references the lowest plausible parameter setting             index of 7 references the highest plausible parameter setting             index of 4 references the parameter setting half way between the lowest and highest plausible parameter settings.          3ICave: references that the results have been averaged over 3 initial conditions.     Variables:         field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub]          field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]         field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]   step2: ParameterSensitivity all files labeled step2 EXPERIMENT DESCRIPTION: Data used in step 2: parameter sensitivity  Perturbed Parameter Experiment (PPE) simultaneously adjusting 18 parameters. Latin hypercube sampling was employed to generate 250 unique parameterizations references with a SETID (ranging from 1-359) The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds.  Data is averaged over 5 initial atmospheric conditions. FILE DESCRIPTION: NETCDF: files contain either the fractional coverage (field1391) or the above ground biomass (field1512) for 5 plant functional types (PFTs) simulated for November 1907.     File labeling scheme:            step2_variable_parametersetindex_5ICave.nc         variable: the name of the variable contained in the file         setting index: the index of the parameter setting corresponding to the parameter set text files         5ICave: references that the results have been averaged over 5 initial atmospheric conditions.     Variables:         field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub]          field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub] TXT: files contain a list of the model parameterizations (for each PFT and variable) and the corresponding to the parameter set index.      Parameters are labeled in row 1     Parameter set indices are shown in column 1 RESTART: restart_region_TPPE_c374_1903-12-01.nc     The restart file contains the model state variables after spinup. This file was used to initiate all model simulations in step2. step3: ParameterSetSelection All files labeled step3 EXPERIMENT DESCRIPTION: Data used in step 3: parameter set selection PPE simultaneously adjusting 10 parameters.  Latin hypercube sampling was employed to generate 140 unique model parameterizations, referenced with a SETID (ranging from 3-276). The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds.  Data is averaged over 5 initial atmospheric conditions. FILE DESCRIPTION: NETCDF: files contain either the biomass (field1512) fractional coverage (field1391) canopy height (field1393) for 5 plant functional types (PFTs) simulated for November 1907 or the net primary productivity (NPP; item3262_monthly_mean) for December 1903 through November 1907.      File labeling scheme:            step3_variable_parametersetindex_5ICave.nc         variable: the name of the variable(s) contained in the file         setting index: the index of the parameter setting corresponding to the parameter set text files         5ICave: references that the results have been averaged over 5 initial atmospheric conditions.     Variables:         field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub]          field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]         field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]         item3262_monthly_mean – Net primary productivity – units: (kgC/m2/sec) – all PFTs TXT: files contain a list of the model parameterizations (for each PFT) and the corresponding to the parameter set index.      Parameters are labeled in row 1     Parameter set indices are shown in column 1 production_runs files labeled PI, historical, and future EXPERIMENT DESCRIPTION: Data simulated in the production runs. Model spinup was performed under preindustrial conditions with 10 unique model parameterizations (pset0-pset9). The resulting vegetation distribution for each parameterization after spinup are included and labeled PIrestarts. These restarts were used to initiate (or restart) the simulations under historic and future (RCP8.5) climate conditions. Files labeled historic contains the simulated vegetation state after 5 year simulations (2004-09-01 to 2009-08-30) with one TRIFFID equilibrium round occurring at the end. Files labeled future contain the simulated vegetation state after 5 year simulations (2054-09-01 to 2059-08-30) with one TRIFFID equilibrium round occurring at the end.  FILE DESCRIPTION: NETCDF: files contain the fractional coverage (field1391), leaf area index (field1392), canopy height (field1393), and biomass(field1512) for 5 plant functional types (in the order broadleaf, needleleaf, C3 grass, C4 grass, shrub).     File labeling scheme:         pset* where * refers to the model parameterization 0-9         files were simulated with the model parameterization *, initiated with a unique initial condition (perturbation to the potential temperature field).      Variables (PIrestart)         field1391 – fractional coverage of PFT – units: fraction – [broadleaf]          field1391_1 – fractional coverage of PFT – units: fraction – [needleleaf]         field1391_2 – fractional coverage of PFT – units: fraction – [c3grass]         field1391_3 – fractional coverage of PFT – units: fraction – [c4grass]         field1391_4 – fractional coverage of PFT – units: fraction – [shrub]         field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf]         field1392_1 – leaf area index of PFT – units: m2/m2 – [needleleaf]         field1392_2 – leaf area index of PFT – units: m2/m2 – [c3grass]         field1392_3 – leaf area index of PFT – units: m2/m2 – [c4grass]         field1392_4 – leaf area index of PFT – units: m2/m2 – [shrub]         field1393 – canopy height of PFT – units: meters – [broadleaf]         field1393_1 – canopy height of PFT – units: meters – [needleleaf]         field1393_2 – canopy height of PFT – units: meters – [c3grass]         field1393_3 – canopy height of PFT – units: meters – [c4grass]         field1393_4 – canopy height of PFT – units: meters – [shrub]              Variables (historic/future)         field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub]          field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]         field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]         field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]

opencc-by-4.0Jul 2019View details →

Water Vapor Sorption Properties of Illinois Shales under Dynamic Water Vapor Conditions: Experimentation and Modeling

Data sets for the Publication 'Water Vapor Sorption Properties of Illinois Shales under Dynamic Water Vapor Conditions: Experimentation and Modeling' in Water Resources Research.

opencc-by-4.0Sep 2019View details →

Dataset from: Transport and water age dynamics in soils: a comparative study of spatially-integrated and spatially-explicit models

This dataset contains high-resolution vegetated lysimeter experimental dataset carried out in EPFL, Lausanne, Switzerland in March-August 2016. The lysimeter is 100 cm long with a diameter of 120 cm. During this experiment, a simultaneous spike injection of five different solutes (2,5-DFBA,  2-TFMBA,  3,4-DFBA, 2,6-DFBA, 3-TFMBA) took place on the 3rd of March 2016 at 14:00 in an hour. The solutes' mass recovery at the bottom of lysimeter was observed for these solutes. This dataset contains three files which are described in the following: <ul> <li>"hydrologic_data.dat" contains the hourly fluxes (precipitation, irrigation, evapotranspiration measured from load cells, and the water draining at the bottom of lysimeter ) in mm/hr between the 19th of Feb-the 1st of Sep 2016.</li> <li>"tracer_data.dat" contains the tracer concentration observed at the bottom of lysimeter in mg/L. These samples are collected at variable frequencies with an approximate average of 1.5 samples per day.</li> <li>"additional_data.dat" informs you on dry mass and soil volume in the lysimeter, vegetation type, and volume of injection per solute.</li> </ul>

Data for "Neural space-time model for dynamic multi-shot imaging"

Data for "Neural space-time model for dynamic multi-shot imaging" Link to preprint: <a href="https://www.biorxiv.org/content/10.1101/2024.01.16.575950">https://www.biorxiv.org/content/10.1101/2024.01.16.575950</a> Code: <a href="https://github.com/rmcao/nstm">https://github.com/rmcao/nstm</a>   Raw images from structured illumination microscopy (SIM): <ul> <li>beads.tif: a dense microbead sample with vibrating motion (Fig.2)</li> <li>mito.tif: a live RPE-1 cell expressing StayGold-tagged mitochondrial matrix protein (Fig.3)</li> <li>er.tif: a RPE-1 cell expressing StayGold-tagged endoplasmic reticulum (Fig.4)</li> <li>f-actin.tif: a live RPE-1 cell tagged with F-Actin Halo-JF585 (Extended Data Fig.6)</li> <li>f-actin_extradelay.tif: a live RPE-1 cell tagged with F-Actin Halo-JF585 with extra time delay between rotations (Extended Data Fig.6)</li> </ul>   Baseline reconstruction: <ul> <li>beads_fairSIM.tif: conventional reconstruction of the dense microbead sample using fairSIM software (https://www.fairsim.org)</li> <li>mito_cudasirecon.tif: conventional reconstruction for a mitochondria-labeled cell (mito.tif) using cuda-accelerated SIM reconstruction software (https://github.com/scopetools/cudasirecon)</li> <li>er_cudasirecon.tif: conventional reconstruction for an endoplasmic reticulum-labeled cell (er.tif) using cuda-accelerated SIM reconstruction software</li> <li>f-actin_cudasirecon.tif: conventional reconstruction for a F-Actin Halo-JF585 labeled cell (f-actin.tif) using cuda-accelerated SIM reconstruction software</li> <li>f-actin_extradelay_cudasirecon.tif: conventional reconstruction for a F-Actin Halo-JF585 labeled cell with extra delay (f-actin_extradelay.tif) using cuda-accelerated SIM reconstruction software</li> </ul>

opencc-by-4.0Aug 2023View details →

Contrasting Views of the Electric Double Layer in Electrochemical CO2 Reduction: Continuum Models vs Molecular Dynamics (data for figures)

This is the data used to create the figures in the article: <h4>Contrasting Views of the Electric Double Layer in Electrochemical CO2 Reduction: Continuum Models vs Molecular Dynamics</h4> <div>Evan Johnson and Sophia Haussener</div> <div>The Journal of Physical Chemistry C 2024 128 (25), 10450-10464</div> DOI: 10.1021/acs.jpcc.4c03469 See the file "Naming conventions" for the file names and column/row meanings.

Developing and Benchmarking Sulfate and Sulfamate Force Field Parameters via Ab Initio Molecular Dynamics Simulations to Accurately Model Glycosaminoglycan Electrostatic Interactions

To cite and for more details: Riopedre-Fernandez et al. J. Chem. Inf. Model. 2024, 64 (18), 7122–7134. DOI: <a href="https://doi.org/10.1021/acs.jcim.4c00981">https://doi.org/10.1021/acs.jcim.4c00981</a> The dataset includes molecular dynamics simulations of sulfated saccharides and their sulfated analogs in the presence of calcium cations in aqueous solution. Several force field parameter sets were compared (CHARMM36, GLYCAM06, AMOEBA, Drude) and new have been developed (prosECCo75 and GLYCAM-ECC75). The uploaded files contain the following simulation input files or/and simulation trajectories: 1) Sulfated_Molecules_Umbrella_Sampling_AIMD: Umbrella sampling ab initio molecular dynamics simulations of calcium-methylsufate and calcium N-methylsulfamate ion pairs in water. 2) Sulfated_Molecules_Umbrella_Sampling_FFMD: Umbrella sampling force field molecular dynamics simulations of calcium-methylsufate and calcium N-methylsulfamate ion pairs in water. 3) Sulfated_Molecules_AWH_FFMD: Accelerated weight histogram force field molecular dynamics simulations of calcium interacting with both methylsufate and N-methylsulfamate in water. 4) Disaccharides_FFMD: Unbiased force field molecular dynamics simulations of calcium-sulfated disaccharide aqueous solutions. UPD. Version 2.0 has updated one of the disaccharide-containing simulations (GLYCAM06, N-sulfation) due to incorrect calcium LJ parameters in the original upload.

Forecasting model of seasonal dynamics of boll weevil Anthonomus grandis grandis (Coleoptera: Curculionidae) in cotton crops using artificial neural networks

Open the record for dataset details and reuse information.

Data/Code for: Sediment dynamics in the energetic nearshore zone: Acoustic remote sensing and model validation

This archive contains data and postprocessed results used in the article "Sediment dynamics in the energetic nearshore zone: Acoustic remote sensing and model validation" by G. Wilson, P. Dickhudt & J. Aldrich.  All data and code in this archive is copyright of the authors.  Please contact the authors prior to publishing new results or derivative works based on data/code from this archive.

opencc-by-4.0Jun 2024View details →

Model Data for Dynamic Controls on the Asymmetry of Mouth Bars: Role of Alongshore Currents

The Model dataset is exported and integrated from Delft3D numerical setup files and results by using the Quickplot module, which is used in the manuscript entitled "Dynamic Controls on the Asymmetry of Mouth Bars: Role of Alongshore Currents".  In terms of the Delft3D setup files, please see the zipped file "Delft3D_setup_files.zip", including the run 2-A0.75 and run 4-N0.075. In terms of model result dataset, each folder is organized based on the unique RunID assigned to the Delft3D simulations, as detailed in the Table 1 in the main text of the manuscript.  Given the employment of multiple decomposition domains within the model, the numerical results are exported individually for each domain. To facilitate the analysis of "bulk" data, encompassing three dimensions (two in horizontal space and one in time), all single domains are intergrated by Matlab script into three comprehensive data files. The data files with the ".mat" extension can be directly opened and loaded in MATLAB. The prefixes of the MAT-files are as follows: <ul> <li>The "bedlevel_daily_*.mat" files contain daily map data of the bed level (ZZ) spanning 74 simulation days (73 time steps in total). This data is used to calculate metrics for assessing mouth bar asymmetry.</li> <li>The "map_u_c_h_day5_*.mat" files represent map data (recorded every 15 minutes, 97 time steps in total) including depth-averaged velocity (u), suspended sediment concentration (c), water depth (h), bed level (bl), and sediment flux (qs) after t=5 hydrodynamic days. This data is used to analyze sediment plume and currents during the period of the initial formation of mouth bars, providing predictive insights. <ul> <li>The "map_u_c_h_day5_include_channel_*.mat" files serve a unique purpose in visualizing the flow vectors (u) and the suspended sediment concentration (c) at the river mouth, incorporating the extended channel. We present two specific scenarios: "2-A0.75" and "4-N0.075," which directly correlate with the cases illustrated in Figure 8 of the manuscript, facilitating a more comprehensive understanding and analysis.</li> </ul> </li> <li>The "map_h_u_qs_afterday74_*.mat" files contain hourly map data comprising bed level (h), water depth (d), depth-averaged velocity (U for instantaneous and v for tidally-averaged), and suspended sediment flux (QS for instantaneous and q for tidally-averaged) within a tidal cycle of 12-hr semidiurnal tide (13 time steps). This data is utilized to visualize and extract distributary channels after 10 morphological years of evolution. The MATLAB code for extracting channels follows the GRL paper <a href="https://doi.org/10.1029/2018GL080447">(Gao et al., 2018)</a> and JGR-ES paper <a href="https://doi.org/10.1029/2017JF004584">(Gao et al., 2019)</a>, also seen in <a href="https://github.com/weilungao/ShorelineExtraction">https://github.com/weilungao/ShorelineExtraction</a>. For added convenience, the comprehensive integration packages, titled 'ShorelineExtraction-master' and 'DeltaicDistributaryNetwork-main', containing essential Matlab codes, have been pre-uploaded to this dataset.</li> </ul> The MATLAB-compatible '.m' code files enable direct opening and data processing. Simply execute the files in sequential order, prefixed "step0-4", for seamless data manipulation. The default list for 32 simulation cases is given by "file_case_index_group.txt". Individual cases can be processed separately by modifying the loading script within the code files as needed. Tip: To ensure the smooth execution of the script, kindly place all downloaded single files or packaged folders (once unzipped but keep the subdirectories) within the same directory.

Data for "Entanglement Dynamics in Monitored Kitaev Circuits: Loop Models, Symmetry Classification, and Quantum Lifshitz Scaling"

We provide the data and scripts used to produce the figures shown in our publication "Entanglement Dynamics in Monitored Kitaev Circuits: Loop Models, Symmetry Classification, and Quantum Lifshitz Scaling".

Displacement time series from Foamquake and Gelquake in single- and double asperity configurations: Supplementary material to "Scaled seismotectonic models of megathrust seismic cycles through the lens of dynamical system theory"

This dataset includes displacement data from 4 experiments performed with Foamquake and Gelquake (Mastella et al. 2022, Corbi et al., 2013), two scaled seismotectonic models reproducing the megathrust seismic cycle running at the Laboratory of Experimental Tectonics LET (Univ. Roma Tre). These models enable the generation of hundreds of quasi-periodic cycles of stress accumulation and sudden release through the spontaneous nucleation of frictional instabilities within one or many analog seismic asperities. Models are monitored by the means of a high-resolution top-view monitoring camera acquiring images at 7.5 and 50 frames per second for Gelquake and Foamquake, respectively. This dataset has been created with particle image velocimetry (PIV, using MatPIV (Sveen 2004)) through the cross-correlation between consecutive images. The PIV provides us with velocity field time series. These are integrated to obtain displacement time series. From the whole model surface, in each experiment we selected data from a cross-section striking parallel to the trench and located at the downdip center of the asperities. Cross sections are discretized in 28 and 29 target points in Gelquake and Foamquake, respectively.  Displacement time series have been normalized to zero mean and unit variance to ensure the same level of magnitude for comparison between different experiments. Linear and second order polynomial trends have been removed to make the stick-slip confined in a given range and avoid non-stationary behavior. Time series data are not passed through filters (e.g., smoothing or moving average). Filename informs about the nature of the analog upper plate (i.e., foam and gel) and geometrical configuration of asperities (i.e., mono and twin). Together with individual files for each experiment, this dataset includes a Matlab script (i.e., all_timeseries.m) that allows visualization of displacement time series from individual target points.  This dataset is supplementary to the paper in SEISMICA "Scaled seismotectonic models of megathrust seismic cycles through the lens of dynamical system theory” by Corbi et al. (2024), where detailed descriptions of models and experimental results can be found.

opencc-by-sa-4.0Sep 2024View details →

Illustrations for 'Disturbances in the evergreen boreal forest and their impact on 21st century vegetation and climate dynamics - A stochastic modeling approach' (Doctoral thesis)

This repository contains all the original illustrations I created for my doctoral thesis at the Technical University of Munich. This work is published under a Creative Commons CC-BY-SA license, which means that you are free to use and adapt this work under the same license for commercial and non-commercial applications as long as you credit the original work. To credit, please cite this repository as well as my doctoral thesis.

Molecular dynamics of free HMO1 model and HMO1 bound to the DNA

<div>Files:</div> <div>1. Initial models of free HMO1 and HMO1 bound to DNA.</div> <div>2. 1 µs MD trajectories of free HMO1 and HMO1 bound to DNA.</div> <div>3. MD protocol for GROMACS</div>

opencc-zeroSep 2024View details →

Impact of water models on structure and dynamics of enzyme tunnels

<ul> <li>1-initial_topologies_coordinates.tar.gz <ul> <li>primary input coordinates and parameter-topology files of all initial systems (LinBwt, LinB32, and Linb86 variants of haloalkane dehalogenase) in OPC and TIP3P water models</li> <li>prepared with the tleap module of AMBER18 package</li> <li>parm7 and crd formatted</li> </ul> </li> <li>2-cap_domain_gate_distances.tar.gz <ul> <li>datasets with minimum distance calculation between Asp146 and Leu176</li> <li>calculated by CPPTRAJ module of AMBER 18 for each performed simulation</li> <li>plain text formatted</li> </ul> </li> <li>3-protein_trajectories-linbwt.tar.gz <ul> <li>three replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of LinBwt in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb32-closed.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of closed state LinB32 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb32-open.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of open state LinB32 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb86-closed.tar.gz <ul> <li> two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of closed state LinB86 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb86-open.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of open state LinB86 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>4-basic_analyses.tar.gz <ul> <li>datasets on RMSF, RMSD, RoG, and RDF from the CPPTRAJ module of AMBER 18</li> <li>for selected replicas 3x LinBwt, 2x LinB32-closed, 2x LinB32-open, 2x LinB86-closed and 2x LinB86-open</li> <li>plain text and PDB formatted</li> </ul> </li> <li>5-caver_analyses.tar.gz <ul> <li>results of tunnel analyses for two replicas of open & closed state each for LinB32 & LinB86 in OPC and TIP3P, and three replicas of LinBWT in OPC and TIP3P</li> <li>generated by CAVER 3.0 using "Divide-and-conquer approach" (MethodsX, 10, 2023, 101968)</li> <li>comprising csv and pdb formatted: tunnel_profiles.csv and bottlenecks.csv, stripped_system.10001.pdb, v_origins.pdb</li> <li>For this and following analyses, the names of the trajectories were modified as follows: <ul> <li>linbwt_opc1_2 = md1_opc_linbwt; linbwt_opc2_2 = md2_opc_linbwt; linbwt_opc3_2 = md3_opc_linbwt;</li> <li>linbwt_tip3p1_2 = md1_tip3p_linbwt; linbwt_tip3p2_2 = md2_tip3p_linbwt; linbwt_tip3p3_2 = md3_tip3p_linbwt;</li> <li>linb32-closed_opc1_2 = md1_closed_opc_linb32; linb32-closed_opc2_2 = md2_closed_opc_linb32;</li> <li>linb32-open_opc1_2 = md1_open_opc_linb32; linb32-open_opc2_2 = md2_open_opc_linb32;</li> <li>linb32-closed_tip3p1_2 = md1_closed_tip3p_linb32; linb32-closed_tip3p2_2 = md2_closed_tip3p_linb32;</li> <li>linb32-open_tip3p1_2 = md1_open_tip3p_linb32; linb32-open_tip3p2_2 = md2_open_tip3p_linb32;</li> <li>linb86-closed_opc1_2 = md1_closed_opc_linb86; linb86-closed_opc2_2 = md2_closed_opc_linb86;</li> <li>linb86-open_opc1_2 = md1_open_opc_linb86; linb86-open_opc2_2 = md2_open_opc_linb86;</li> <li>linb86-closed_tip3p1_2 = md1_closed_tip3p_linb86; linb86-closed_tip3p2_2 = md2_closed_tip3p_linb86;</li> <li>linb86-open_tip3p1_2 = md1_open_tip3p_linb86; linb86-open_tip3p2_2 = md2_open_tip3p_linb86.</li> </ul> </li> </ul> </li> <li>6-transport_tools_analyses.tar.gz <ul> <li>results of comparative analyses for all simulations generated in 5-caver_analyses.tar.gz</li> <li>generated by TransportTools 0.9.3</li> <li>comprising csv, pdb, plain text and py formatted: configuration file (config_TT.ini), tunnel_profiles (data folder) for all filtered tunnels and bottlenecks (data folder) for all filtered tunnels, statistics (statistics folder) and visualization (visualization folder)         </li> </ul> </li> </ul>

Fisher zeroes and dynamical quantum phase transitions for two- and three-dimensional models

Data for the publication Fisher zeroes and dynamical quantum phase transitions for two- and three-dimensional models. Included are data for return rates and Fisher zeroes. The names of the data files contain all metadata necessary for specifying the type of data, with a list of the paramters used. For more details on the models and paramters see the article. <ul> <li>Firstly the three models are considered labelled by Kit for the 2D spinless px+ipy topological superconductor, Sq for the 2D spinfull topological superconductor, and 3D for the 3D topological insulator.</li> <li>After _N_ the system size is written, referring the number of sites along one direction (for finite size calcualtions only).</li> <li>RR refers to the return rate. For Kit the columns are $t$, $l(t), $\dot{l}(t)$, $\ddot{l}(t)$. If only two columns exit then there is just $t$, $\dot{l}(t)$. For Sq the columns are $t$, $l(t), $\dot{l}(t)$, $|\lambda_0(t)|$. For 3D the columns are $t$, $\dot{l}(t)$, $\ddot{l}(t)$.</li> <li>Density refers to the density of Fisher zeroes along the real time axis, with the columns being simply time and density.</li> <li>Fisher to Fisher zeroes in the complex plane with kx, etc meaning it is resolved along this momentum direction. The columns are the real and imaginary parts of the complex time argument.</li> <li>Critical_k are the critical momenta.</li> <li>Critical_k_En lists the energy for each critical momenta in the matching Critical_k file.</li> <li>For Kit the list of quench parameters are 100 times {$\mu_0$, $\Delta_0$, $\mu_1$, $\Delta_1$}.</li> <li>For Sq the list of quench parameters are 100 times {$\nu_0$, \nu_1$, $\mu_0$, $\Delta_0$, $\alpha_0$, $B_0$, $\mu_1$, $\Delta_1$, $\alpha_1$, $B_1$}.</li> <li>For 3D the list of quench parameters are 100 times {$v_0$,$w_0$,$v_1$,$w_1$}.</li> <li>For Fisher data branch labels naturally the branch p.</li> <li>Zoom, etc, label different zooms in differnet regions, as made explicit by the times inside the files.</li> </ul> This work was supported by the National Science Centre (NCN, Poland) under the grant 2019/35/B/ST3/03625, by the German Research Council (DFG) via the Re- search Unit FOR 2316 and by the National Science and Engineering Resource Council (NSERC) of Canada via the Discovery Grant program.

opencc-by-4.0Oct 2024View details →

Data analysis & code: Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS

This file contains code to optimize the allometric parameters, to plot the figures, and details of the underlying data analysis in "Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS" (Bergkvist et al.).

opencc-by-4.0Oct 2024View details →

Model results and data associated with "Antecedent effect models as an exploratory tool to link climate drivers to herbaceous perennial population dynamics data"

Model results and data (including Bayesian posteriors) associated with "Antecedent effect models as an exploratory tool to link climate drivers to 3 herbaceous perennial population dynamics data". This is a repository created to store the posteriors of the models fit within this project. Because these occupy so much space, it makes sense to store them in a separate repository. There are two directories: <ul> <li>model_results/ contains all of the posteriors (files with character pattern main_posterior_#.csv). The three types of files contained in this directory are described in metadata_model_results.xlsx. The number # corresponds to column "index" in file raw_data/design_insample.csv.</li> <li>raw_data/ is mostly not essential: it contains the raw data to fit models, and it replicates folder data/ in repository https://dx.doi.org/10.5281/zenodo.13909628.</li> </ul>

opencc-by-4.0Oct 2024View details →

Experimental data for Dynamic cover effects in lateral bedrock channel bank abrasion: Experiment and model comparison

Experimental data for bank erosion.xlsx contains the data used for the figures in the paper, and the distribution of bedrock bank erosion in the longitudinal direction in Run 1 - Run 18.