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285 results for “evolution models”
Replication Package for 'Analyzing the Evolution and Maintenance of ML Models on Hugging Face'
<p>Replication Package attached to the 'Analyzing the Evolution and Maintenance of ML Models on Hugging Face' article. Within the README and accompanying scripts, you will find detailed instructions to guide you through the analysis conducted in the article.</p>
Imbalanced regressive neural network model for whistler-mode hiss waves: spatial and temporal evolution
<p>This dataset contains the whistler-mode hiss waves obtained from the Van Allen Probes. It is accompanied by the manuscript "<span>Imbalanced regressive neural network model for whistler-mode hiss waves: spatial and temporal evolution". </span></p>
Simulation outputs associated with Maffre et al. "GEOCLIM7, an Earth System Model for multi-million years evolution of the geochemical cycles and climate." (submitted to GMD)
Open the record for dataset details and reuse information.
A Meta-Model to Support the Migration and Evolution of CI/CD Pipelines
<p> </p> <h1><strong>Reproducibility Package for “A Meta-Model for Reengineering CI/CD Pipelines”</strong></h1> <h2><strong>Abstract</strong></h2> <p>In modern industrial software development, DevOps has become the leading approach for managing highly iterative software production processes. DevOps integrates development and operations activities, with Continuous Integration, Continuous Delivery, and Continuous Deployment (CI/CD) playing a crucial role in ensuring the iterative delivery of high-quality software.</p> <p>CI/CD relies on pipelines composed of various automated activities, often implemented using commercial tools. However, due to the rapid evolution of these tools, CI/CD pipelines frequently require migration to newer versions or entirely different platforms. Since this migration process is predominantly manual, it is both time-consuming and error-prone.</p> <p>To assist software engineers in this challenge, we propose a novel Model-Driven Engineering (MDE) approach to automate the migration of CI/CD pipelines. Inspired by the traditional reengineering horseshoe model, our method abstracts existing CI/CD pipeline artifacts into an intermediate meta-model representation. Using this meta-model, we can generate semantically equivalent pipelines for different CI/CD tools.</p> <p>Our main contribution is a meta-model designed to represent the structure of existing CI/CD pipelines, building the foundation for MDE-based migration.</p> <h2><strong>Contents of the Reproducibility Package</strong></h2> <p>This package is provided inside the <code>reproducibility.zip</code> file, organized into the following folders:</p> <h3><strong>1. <code>devops2</code> – CI/CD Meta-Model</strong></h3> <ul> <li>Our CI/CD meta-model, built using the Eclipse Modeling Framework (EMF).</li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF).</li> <li><strong>Usage:</strong> Import into EMF or any Ecore-compatible library..</li> </ul> <h3><strong>2. <code>org.xtext.example.mydsl11</code> – Xtext-Based GitHub Actions Parser</strong></h3> <ul> <li>An Xtext-based DSL for parsing GitHub Actions configuration files.</li> <li>Used in an initial version of our research; the PyEcore parser (included below) provides better results.</li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF) and Xtext.</li> <li><strong>Usage:</strong> Import into EMF with Xtext installed.</li> </ul> <h3><strong>3. <code>GitHubActionsDataset</code> – Dataset of GitHub Actions Configurations</strong></h3> <ul> <li>A collection of 200 randomly selected GitHub Actions configuration files used to validate our initial GitHub Actions parser.</li> </ul> <h3><strong>4. <code>org.xtext.example.mydsl13</code> – Xtext-Based CircleCI Parser</strong></h3> <ul> <li>An Xtext-based DSL for parsing CircleCI configuration files.</li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF) and Xtext.</li> <li><strong>Usage:</strong> Import into EMF with Xtext installed.</li> </ul> <h3><strong>5. <code>org.eclipse.acceleo.module.sample7</code> – Acceleo-Based Code Generator</strong></h3> <ul> <li>An Acceleo-based generator that translates our meta-model into GitHub Actions configurations.</li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF) and Acceleo.</li> <li><strong>Usage:</strong> Import into EMF with Acceleo installed.</li> </ul> <h3><strong>6. <code>codegeneration</code> – Example Configuration Files</strong></h3> <ul> <li>Example configurations used to validate the syntactical correctness of our models.</li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF) and Acceleo.</li> <li><strong>Usage:</strong> Import into EMF with Acceleo installed.</li> </ul> <h3><strong>7. <code>modelequivalence</code> – Validation of Model Equivalence</strong></h3> <ul> <li>Contains CircleCI scripts used for validating model equivalence, including: <ul> <li>Their representations in our meta-model.</li> <li>The corresponding GitHub Actions configurations generated from these models.</li> </ul> </li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF) or any other Ecore-compatible library.</li> <li><strong>Usage:</strong> Import into EMF or any Ecore-compatible library.</li> </ul> <h3><strong>8. <code>casestudy</code> – Migration Case Study (CircleCI → GitHub Actions)</strong></h3> <ul> <li>A case study demonstrating how our meta-model supports migration from CircleCI to GitHub Actions.</li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF) and Acceleo.</li> <li><strong>Usage:</strong> Import into EMF with Acceleo installed.</li> </ul> <h3><strong>9. <code>pyecoreparser</code> – PyEcore-Based Parser (GitHub Actions & Travis CI)</strong></h3> <ul> <li>A PyEcore-based parser for extracting CI/CD configurations from GitHub Actions and Travis CI, converting them into our meta-model.</li> <li><strong>Includes:</strong> <ul> <li>The 200 GitHub Actions and Travis CI configuration files used for validation.</li> <li>The 10 randomly selected GitHub Actions and Travis CI configuration files used for comparison with manual modeling.</li> <li>A README file with installation instructions and usage guidelines.</li> </ul> </li> <li><strong>Requirements:</strong> Python, PyEcore library, PyYAML library.</li> <li><strong>Usage:</strong> Follow the README file for setup and usage instructions.</li> </ul> <h3><strong>10. <code>Mapping</code> – Platform-to-Meta-Model Mapping</strong></h3> <ul> <li>A mapping that aligns different CI/CD platforms with our meta-model.</li> </ul> <h3><strong>11. <code>automaticmanualvalidation</code> – Automatic vs. Manual Model Validation</strong></h3> <ul> <li>A set of 20 pipelines (10 from GitHub Actions, 10 from Travis CI) modeled in two ways: <ul> <li>Automatically using the PyEcore parser.</li> <li>Manually by the authors for comparison.</li> </ul> </li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF) or any other Ecore-compatible library.</li> <li><strong>Usage:</strong> Import into EMF or any Ecore-compatible library.</li> </ul>
Evolution of Castanea in North America: RADseq and ecological modeling reveal a history of radiation, range shifts, and disease
<p><b>Premise of the Study: </b>Chestnuts and chinquapins are some of the best known and most widely loved of any plants in North America. Despite the fame of this clade, relatively little genomic sequencing has been done, and much is still unknown about their evolution. </p> <p><b>Methods: </b>Here we use ddRAD data to infer the species-level phylogeny for <i>Castanea </i>and assess the phylogeography of the North American species using samples collected from populations that span the full extent of the species' ranges. We also construct species distribution models using digitized herbarium specimens and observational data from field surveys. </p> <p><b>Key Results: </b>We identified strong population structure within <i>Castanea dentata</i> (American Chestnut) that reflects a stepwise northern migration since the last glacial maximum. Our species distribution models further confirm this scenario and match closely with the <i>Castanea</i> fossil pollen record. We also found significant structure within the <i>Castanea pumila</i> lineage, most notably a genetic cluster that corresponds to the frequently recognized "<i>Castanea pumila var. ozarkensis</i>."</p> <p><b>Conclusions: </b>The two North American <i>Castanea</i> species have contrasting patterns of population structure, but each is typical of plant phylogeography in North America. Within the <i>C. pumila</i> complex we find novel genetic structure that provides new insights to <i>C. pumila</i> taxonomy. Our results also identify a series of distinctive populations that will be valuable in on going efforts to conserve and restore the Chestnuts and Chinquapins in North America.</p>
Supporting data - The Met Office operational wave forecasting system: the evolution of the Regional and Global models
<p>Supporting data for the GMD draft paper "The Met Office operational wave forecasting system: the evolution of the Regional and Global models" (© Crown copyright Met Office):</p> <p>1) idealised_sensitivity_resolution.tar.gz: idealised experiment folders with grid and model definition files needed to run the experiments; and output .nc files used in analysis. </p> <p>2) analysis_MO_waves_system.tar.gz: folders for GS512L4EUK-AN and AMM15SL2-AN runs with 2-year model-observations matchup .nc files used in figures and analysis.</p> <p>3) forecast_MO_waves_system.tar.gz: folders for GS512L4EUK-FCST and AMM15SL2-FCST runs during summer (#-s) and winter (#-w) with model-observations matchup .nc files used in figures and analysis.</p>
Model dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"
<div> <p>This is the Noah-MP model simulation dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"</p> <p> </p> </div>
ICSME 2024 Research Track: "What Happened to my Models?" History-Aware Co-Existence and Co-Evolution of Metamodels and Models
<p> </p> <h1>ICSME 2024 Research Track: “What Happened to my Models?” History-Aware Co-Existence and Co-Evolution of Metamodels and Models</h1> <p> </p> <p>This repository provides the dataset and results for the evaluation of the paper “What Happened to my Models?” of the ICSME 2024 Research track.<br>The dataset consists of the following files:</p> <ul> <li><strong>RQ1-Type-Refactors.zip </strong>contains the operations and refactoring performed on each of the given metamodels used by our approach in RQ1.</li> <li><strong>RQ1-Type-Results.zip</strong>: contains the group results of RQ1 as shown in our paper with additional metrics of other operations not highlighted in our paper due to space limitations.</li> <li><strong>RQ2-3-PlantUML-Models.zip</strong>: contains the 250 PlantUML models and the metamodel. This folder contains the co-evolved PlantUML models, the operations performed during the co-evolution, their metrics and the state of the models bore and after the co-evolution. </li> <li><strong>RQ2-3-PlantUML-Results.zip:</strong> contains the group results of RQ2 and RQ3 as shown in our paper with additional metrics of other operations not highlighted in our paper due to space limitations</li> <li><strong>RQ2-3-FHIR-Models.zip</strong>: contains the 1180 FHIR models and the metamodel. This folder contains the co-evolved FHIR models, the operations performed during the co-evolution, their metrics and the state of the models bore and after the co-evolution.</li> <li><strong>RQ2-3-Results.zip:</strong> contains the grouped results of RQ2 and RQ3 as presented in our paper.</li> <li><strong>Additionalnformation.pdf:</strong> contains additional information on how to read the files extracted by our approach, i.e., how to read the models and operations and how our executable refactoring catlaog works since we only focused on Property Refactorings in the paper.</li> <li><strong>Results.pdf</strong>: Contains an overview of the results (the results from our paper + additional results)</li> <li><strong>Tools.zip: </strong>Contains the tools used for the evalution. For an explaination how to use it read the <strong>Additionalnformation.pdf, </strong>see below<strong> </strong>or contact the authors</li> </ul> <p><strong>Running the tools:</strong></p> <p><em>Windows 10/11<br></em><em>JDK 20 or above</em></p> <p>The tools consist of two programs: </p> <ul> <li><strong>importer.jar<br></strong>This file is used to import a FHIR or PlantUML file into the server and co-evolve it.</li> <li><strong>server_FHIR_.jar<br></strong>The server stores the models and co-evolves them. The files provided already have the metamodels preloaded, that are used in RQ2 and RQ3, i.e., FHIR_STU3 contains the FHIR metamodel version DSTU2 and STU3 and our hybrid PlantUML, while FHIR_ synthetic contains the FHIR metamodel DSTU2 and our synthetically created one.</li> </ul> <p><strong>How to use the Tools</strong></p> <p>First, start the server by starting the jar. The server also has an experimental GUI mode that allows engineers to check the types and instances that were created. <em><strong>Note: </strong>This mode is currently under development and is still unstable. The mode <em>is accessible</em> by adding -gui as a parameter.</em></p> <p><em>java -jar server_FHIR_STU3.jar -gui</em></p> <p>Otherwise, just run the server normally:</p> <p><em>java -jar server_FHIR_STU3.jar</em></p> <p>After the server has booted up, it exports InstanceTypes and operations created for the FHIR and PlantUML metamodels. Next, you start the importer. The importer has two modes: the PlantUML mode, where it imports a PlantUML state machine and co-evolves it and the FHIR mode, where it imports an FHIR file and co-evolves it into either STU3 or our synthetic version (depending on which of the preloaded servers is running). Just run the tool by providing either FHIR or PlantUML, the imported file and the path where the output should be stored.</p> <p><strong><em>FHIR-Mode:</em></strong></p> <p><em>java -jar importer.jar FHIR C:\Users\Admin\Desktop\Aaron697_Brekke496_2fa15bc7-8866-461a-9000-f739e425860a.json C:\Users\Admin\Desktop\results</em></p> <p><strong>PlantUML Mode:</strong></p> <p><em>java -jar importer.jar PlantUML C:\Users\Admin\Desktop\branch.puml C:\Users\Admin\Desktop\results</em></p> <p><em><strong>Note: </strong></em><em>Please run both the tool and the server in a command line to receive additional information about the importing and co-evolution since the tool is otherwise without a user interface.</em></p> <p> </p>
Scherrenberg et al. (2024) supplement (Climate of the past): Ice-sheet model code, and output of Northern Hemisphere ice-sheet evolution of the past 800 kyr
<p>Supplement to Scherrenberg et al. (2024), article in Climate of the Past.</p> <p>This data-set contains ice-sheet model (IMAU-ICE) code (see IMAU_ICE_Code.zip; see https://github.com/IMAU-paleo/IMAU-ICE/tree/main for the most recent version of the model), the model output and configuration files (see Data_output.zip), and scripts to create figures (see Scripts_and_Figures.zip).</p> <p>Please note that additional input fields are required to run IMAU-ICE and to produce the figures. See Scherrenberg et al., (2024) for more information or contact the corresponding author.</p> <p>Citation: M.D.W. Scherrenberg, C.J. Berends, R.S.W. van de Wal: Late Pleistocene glacial terminations accelerated by proglacial lakes, climate of the past, special issue "icy landscapes of the past", 2024</p>
Exploring Stellar Evolution Models of sdB Stars using MESA
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2015ApJ...806..178S">Exploring Stellar Evolution Models of sdB Stars using MESA</a></p>
Dataset: Risk Transfer Model for Flood Risk Evolution in a Multi-reservoir System
<p>The files in this record contain data for real-time optimal flood control decision making and risk propagation under multiple uncertainties considered for publication in Water Resources Research.</p> <p> </p> <p>The files consist of:</p> <p> </p> <p>Data:</p> <ul> <li>Figure 11;</li> <li>Figure 12;</li> <li>Figure S1;</li> <li>Figure S4</li> <li>Relative prediction error</li> <li>Reservoir information</li> <li>Streamflow</li> </ul> <p>Model code:</p> <ul> <li>Calculation of entropy</li> <li>Forecasting error simulation model</li> <li>LHS</li> <li>Analytic code of transfer model</li> </ul>
Associated modeling data & materials for manuscript: Observing the evolution of the Sun's global coronal magnetic field over eight months
<p>This archive contains the magnetohydrodynamic (MHD) modeling materials associated with the manuscript:</p> <p>"<em>Observing the evolution of the Sun’s global coronal magnetic field over eight months</em>"</p> <p>by Zihao Yang, Hui Tian, Steven Tomczyk, Xianyu Liu, Sarah Gibson,<br>Richard Morton, and Cooper Downs</p> <p>Science, 386(6717), 76-82, <strong>2024</strong>, DOI: <a title="Observing the evolution of the Sun&rsquo;s global coronal magnetic field over eight months" href="http://doi.org/10.1126/science.ado2993" target="_blank" rel="noopener">10.1126/science.ado2993</a></p> <p>This archive is intended for transparency and reproduceability purposes. It contains the MHD model source code, run inputs, run outputs, and example python scripts for working with the model data.</p> <p># Contents<br>The subfolders are organized as follows:</p> <p>### source<br>This folder contains the the high-performance MHD code "Magnetohydrodynamic Algorithm outside a Sphere" (MAS) and associated files. MAS is written in Fortran. The dependencies are very straightforward. See `README_MAS.txt` and the associated Makefile for compilation instructions.</p> <p>### runs<br>This folder contains the three MHD model runs that are described in the manuscript. See `README_Runs.txt` for more information on the inputs and outputs. Each folder contains all files required for recreating the run.</p> <p>### scripts<br>This folder contains some example python scripts that illustrate how to read the model data files. This includes an example that will convert the raw 3D data to physical units and place all variables on a common, non-staggered mesh. See `README_Scripts.txt` for more information.</p> <p># Additional Notes<br>MAS is developed and maintained by Predictive Science Inc. (PSI) in San Diego California.</p> <p>The version of MAS in the source folder is not the most recent version. It is the exact version of MAS from the main branch that was used for MHDweb CORHEL runs circa 2022 when similar runs were first posted to PSI's website. For this reason, we used this exact version of MAS for the runs described in the manuscript. As such, MAS is licensed here using the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license. Please see the LICENSE file or visit https://creativecommons.org/licenses/by-nc-nd/4.0/ for details. </p> <p>We are currently working on a project that includes a public, open source release of MAS on GitHub, which will be licensed appropriately. This archive is not intended for that purpose.</p> <p>If you have questions, concerns, or issues installing or running this code for reproduceability purposes, please contact Cooper Downs <cdowns@predsci.com>.</p>
Supplementary data: model results for "Labor market evolution is a key determinant of global agroeconomic and environmental futures"
<p>This compressed dataset includes the queried CVS files from 16 GCAM data bases generated for the study titled "<strong>Labor market evolution is a key determinant of global agroeconomic and environmental futures</strong>".</p> <p>The data sets provided here came from the GCAM model output. Please find the model and code information at the GitHub repo: <a href="https://github.com/realxinzhao/paper-nc2024-LandBasedCDR-GCAM" target="_blank" rel="noopener">realxinzhao/paper-nc2024-LandBasedCDR-GCAM</a>.</p> <p>In addition, the data were used for generating results used in the paper. See more information at <a href="https://github.com/realxinzhao/paper-nfood2024-AgLaborEvolution-DisplayItems" target="_blank" rel="noopener">realxinzhao/paper-nfood2024-AgLaborEvolution-DisplayItems</a>.</p>
Research Data for "Evaluating the electronic structure and stability of epitaxially grown Sr-doped LaFeO3 perovskite alkaline O2 evolution model electrocatalysts"
<p>This is the research data supporting figures and tables for the paper "Evaluating the electronic structure and stability of epitaxially grown Sr-doped LaFeO3 perovskite alkaline O2 evolution model electrocatalysts" appearing in <em>RSC Applied Interfaces </em>under DOI <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4LF00260A">https://doi.org/10.1039/D4LF00260A</a>.</p>
Research Data for "Electronic structure and stability of the active surface phase of NixCo3-xO4 spinel alkaline O2 evolution electrocatalysts: from an epitaxial model catalyst perspective"
<p>These datasets support main text and supplementary figures and tables of the paper "Electronic structure and stability of the active surface phase of NixCo3-xO4 spinel alkaline O2 evolution electrocatalysts: from an epitaxial model catalyst perspective" appearing in <em>ACS Applied Energy Materials </em>under DOI: <a href="https://doi.org/10.1021/acsaem.4c01688">https://doi.org/10.1021/acsaem.4c01688 </a></p>
Datasets concerning "Variations of Heat Flux and Elastic Thickness of Mercury from Thermal Evolution Modeling"
<p><strong>Datasets concerning timeseries from average temperature profiles:</strong></p> <p>Average_profiles.rar</p> <p>Tables containing the time evolution of the elastic lithospheric thickness calculated with the average mantle temperature profile.<br>Calculations have been done with dry and wet rheologies (crust and mantle), using the conversion package from :<br>"Adrien Broquet. AB-Ares/Te_HF_Conversion: 0.2.3 (Version 0.2.3). Zenodo. <a href="http://doi.org/10.5281/zenodo.4973893" rel="nofollow">http://doi.org/10.5281/zenodo.4973893</a>"<br>In total 32 tables, 16 for each rheology.</p> <p> </p> <p><strong>Datasets concerning timeseries from localized temperature profiles:</strong></p> <div> <div>Localized_profiles.rar</div> </div> <p>Tables containing the time evolution of the elastic lithospheric thickness calculated with the respective localized mantle temperature profile of each investigated point of interest (Caloris Basin, Discovery Rupes, Goossens et al., 2022 points 1-4).<br>Calculations have been done with dry and wet rheologies (crust and mantle), using the conversion package from :<br>"Adrien Broquet. AB-Ares/Te_HF_Conversion: 0.2.3 (Version 0.2.3). Zenodo. <a href="http://doi.org/10.5281/zenodo.4973893" rel="nofollow">http://doi.org/10.5281/zenodo.4973893</a>"<br>In total 32 tables, 16 for each rheology.</p> <p> </p> <p><strong>Datasets concerning maps of CMB heat flux at present day:</strong></p> <p>LatLon_Maps.rar</p> <p>Tables containing present day output of the CMB heat flux for each case investigated<br>Format in each file is : <br>Longitude | Latitude | CMB heat flux <br>1 degree of resolution<br>A python code is provided to visualize easily the data (Map_visualization.py)</p> <div> </div> <div> </div> <div>Sh_Maps.rar</div> <div> </div> <div>Tables containing present day output of the CMB heat flux for each case investigated under the form of spherical harmonics coeffcients, up to the spherical harmonic degree 59.</div> <div>A python code is provided in order to plot easily the spherical harmonics data (PlottingSH_maps.py).</div> <div> </div> <div> </div> <p> </p>
D3.10 - Users document for methods and models of the long-term coastline evolution.
<p>The D3.10, related to Task 3.5 and entitled "User Document for Methods and Models of the Long-term Coastline Evolution," is a WP3 deliverable, specifically a report describing the methods and models used to study long-term morpho-dynamic processes in a climate-change scenario. </p>
Thermal evolution of dark matter in the early universe from a symplectic glueball model---Data release
<p>This is the data release to reproduce the plots shown in "Thermal evolution of dark matter in the early universe from a symplectic glueball model".</p>
Data of 3D PPMLR-MHD model Simulation for manuscript "Formation and Evolution of Nightside Transpolar arc and Its Relationship with Energetic Plasma in the Magnetotail Lobe"
<p><span>Data of 3D PPMLR-MHD model Simulation for manuscript "Formation and Evolution of Nightside Transpolar arc and Its Relationship with Energetic Plasma in the Magnetotail Lobe"</span></p> <p><span>These data come from a fully run of a 3D MHD Simulation model that is named PPMLR-MHD model (detailed descriptions below).</span></p> <p><span>There are 2 types of data files:</span></p> <p><span>1) X15dXXXX.mat is saved simulation parameters. </span></p> <p><span>2) Xing15XXXX_heatflux.mat is saved heat flux from simulation parameters. </span></p> <p><span>XXXX is the number of files, and files with the same serial number correspond to the same time.</span></p> <p><span> </span></p> <p><span>The first type files of data include the following parameters:</span></p> <p><span>time, x, y, z, logrho, Vx, Vy, Vz, Bx, By, Bz, Pr, Jx, Jy, Jz</span></p> <p><span>Where, time is simulation time, which need to plus the start time to transfer them to universal time: time+16:00.</span></p> <p><span> (x,y,z) are the three components of the position of simulation point in GSM coordinates;</span></p> <p><span> logrho is the plasma density at the simulation point;</span></p> <p><span> (Vx, Vy,Vz) are the three components of plasma velocity at the simulation point in GSM coordinates;</span></p> <p><span> (Bx, By,Bz) are the three components of magnetic field at the simulation point in GSM coordinates;</span></p> <p><span> Pr is the plasma dynamic presure at the simulation point;</span></p> <p><span> (Jx, Jy,Jz) are the three components of plasma electric current at the simulation point in GSM coordinates;</span></p> <p><span> </span></p> <p><span>The second type file of data includes the simulated heat flux along the magnetic field lines at the simulation point in GSM coordinates. </span></p> <p><span>PPMLR-MHD model</span></p> <p><span>The PPMLR-MHD model is on the basis of an extension of the piecewise parabolic method (1) with a Lagrangian remap to magnetohydrodynamics (MHD) (2, 3). It is a three-dimensional MHD model, designed specially for the solar wind–magnetosphere–ionosphere system (4-6). The model possesses a high resolution in capturing MHD shocks and discontinuities and a low numerical dissipation in examining possible instabilities inherent in the system (4).</span></p> <p><span>The model uses a Cartesian coordinate system with the Earth’s center at the origin and X, Y, and Z axes pointing towards the Sun, the dawn-dusk direction, and the north, respectively. The size of the numerical box extends from 25 RE to –100 RE along the Sun-Earth line and from –50 RE to 50 RE in Y and Z directions, with 240×240×240 grid points and a minimum grid spacing of 0.2 RE. An inner boundary of radius 3 RE is set for the magnetosphere to avoid the complexities associated with the plasmasphere and large MHD characteristic velocity from the strong magnetic field (6). An electrostatic ionosphere shell with height-integrated conductance is imbedded, allowing an electrostatic coupling process introduced between the ionosphere and the magnetospheric inner boundary. The Earth’s magnetic field is approximated by a dipole field with a dipole moment of 8.06×1022 A/m in magnitude. The model is run to solve the whole system by inputting the real interplanetary conditions for the current event.</span></p>
Using a multi-layer snow model for transient paleo studies: surface mass balance evolution during the Last Interglacial
<p>This archive provides source data of figures in the main text of the manuscript "Using a multi-layer snow model for transient paleo studies: surface mass balance evolution during the Last Interglacial".</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.