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310 results for “State Model”
Model-based analysis of tuberculosis genotype clusters in the United States reveals high degree of heterogeneity in transmission, and state-level differences across California, Florida, New York, and Texas.
<p>Data and codes for the publication</p>
A dataset of modeled hydrologic alteration and ecological consequences in stream reaches of the conterminous United States
<p>A dataset of modeled anthropogenically induced hydrologic alteration values for 43 hydrologic indices and modeled losses in native fish biodiversity as a result of streamflow modification, both mapped at the NHDPlus V1 and V2 stream-reach resolution.</p>
Preprocessed Data and Pretrained Models for Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings
<p>This is preprocessed data and pretrained models from two of our papers:</p> <p>"Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings," by Erica Cooper, Cheng-I Lai, Yusuke Yasuda, Fuming Fang, Xin Wang, Nanxin Chen, and Junichi Yamagishi. (ICASSP 2020)<br> <a href="https://arxiv.org/abs/1910.10838">https://arxiv.org/abs/1910.10838</a></p> <p> "Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis," by Erica Cooper, Xin Wang, Yi Zhao, Yusuke Yasuda, and Junichi Yamagishi. (arXiv) <a href="https://arxiv.org/abs/2011.04839">https://arxiv.org/abs/2011.04839</a></p> <p>This data is meant to be used with our open-source implementation, which can be found here: https://github.com/nii-yamagishilab/multi-speaker-tacotron</p> <p>More information about the directory structure and how to use the data can be found in the READMEs on GitHub.</p>
Multi-objective optimization of equation of state molecular parameters: SAFT-VR Mie models for water
<p>Supporting document containing information on the Pareto points and thermodynamic calculations.</p>
Do Go Chasing Waterfalls: Enoyl Reductase (FabI) in Complex with Inhibitors Stabilizes the Tetrameric Structure and Opens Water Channels - trajectories employed in Markov State Models and water analyses
<p>The following trajectories were employed in the generation of MSM models and water analyses:</p> <p>SaFabI_60us_align.zip</p> <p>EcFabI_60us_align.zip</p> <p>waters_SaFabI.tar.gz</p> <p>waters_EcFabI.tar.gz</p>
Unsupervised detection of Large-scale Weather Patterns in the Northern Hemisphere via Markov State Modelling: from Blockings to Teleconnections
<p><em><span>Data Source</span></em></p> <p>This dataset is derived from the <em><span>NCEP-NCAR Reanalysis 1</span> data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at <a href="https://psl.noaa.gov/">https://psl.noaa.gov</a></em>., a robust atmospheric dataset that includes a wide range of climatic measurements essential for comprehensive climate analysis. The original data can be accessed at the NOAA Physical Sciences Laboratory website: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html.</p> <p> </p>
Coupled PPE model output - land parameter impacts on the mean climate state
<p>Terrestrial processes influence the atmosphere by controlling land-to-atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model (CESM2) under preindustrial conditions. We find that an observationally-informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (standard deviation = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagates to the entire Earth system, and provides insights into where and how land process uncertainty influences climate.</p>
Thermodynamic characterization of the (H2 + C3H8) system significant for the hydrogen economy: Experimental (p, rho, T) determination and equation of-state modelling
<p>File: 2023_IJHE_Manuscript_repository.docx</p> <p>This is an author-created, un-copyedited version of an article accepted for publication in the International Journal of Hydrogen Energy (2023, 48 (23), 8645-8667). The editor of the Journal is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher-authenticated, Open-Access version is available online at: https://doi.org/10.1016/j.ijhydene.2022.11.170<br><br>File: 2023_IJHE_Results_Repository.xlsx</p> <p>This is the MS Excel data file of the paper. </p> <p> </p> <p> </p>
G³M 1.0 steady-state model output
<p>Steady-state model output of the global gradient-based groundwater model G³M 1.0. Related to the publication in GMD "Beyond the bucket - Developing a global gradient-based groundwater model (G³M v1.0) for a global hydrological model from scratch"</p> <p>Contains the following global outputs:</p> <p>* hydraulic head</p> <p>* surface water body to/from groundwater flows</p> <p>* conductance values for surface water bodies</p>
Diagnostic Evaluation of Large-domain Hydrologic Models calibrated across the Contiguous United States
<p>Data repository for: Rakovec, O., Mizukami, N., Kumar, R., Newman, A., Thober, S., Wood, A. W., et al. ( 2019). Diagnostic evaluation of large‐domain hydrologic models calibrated across the contiguous United States. <em>Journal of Geophysical Research: Atmospheres</em>, 2019; 124: 13991–14007. <a href="https://doi.org/10.1029/2019JD030767">https://doi.org/10.1029/2019JD030767</a></p> <p>If you use this dataset in scientific publication, the aforementioned publication needs to be acknowledged.</p> <p><strong>rakovec_JGRA_2019.tar.gz </strong>refers to the mHM model simulations</p> <p><strong>Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</strong> refers to a dataset published earlier in Mizukami et al. (2017, doi: 10.1002/2017WR020401)</p> <p>#########################################################################</p> <p>## BASIN-WISE DISCHARGE SIMULATIONS:</p> <p>#########################################################################</p> <p><strong>(1) VIC model: CONUS-wide runs based on the Mizukami et al. 2017 WRR paper</strong></p> <p>stored under: Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</p> <p>meaning, 1 parameter set applied across all basins.</p> <ul> <li>Calibration period: ./$BASIN_ID/output/hcdn_calib_case04_rgn0.txt</li> <li>Validation period: ./$BASIN_ID/output/hcdn_vali_case04_rgn0.txt</li> </ul> <p>Note the time stamp is missing in the VIC files, and should be following for</p> <ul> <li>calibration period: init_date="1999-10-01"</li> <li>validation periods: init_date="1989-10-01"</li> </ul> <p>Finally, headers are missing for the VIC files, should be:</p> <ul> <li>qsim is first column</li> <li>qobs is second column</li> </ul> <p><strong>(2) VIC model: onsite calibrations </strong></p> <p>stored under: Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</p> <p>meaning, each basin has different parameter set </p> <ul> <li>Calibration period: ./$BASIN_ID/output/hcdn_calib_case04.txt</li> <li>Validation period: ./$BASIN_ID/output/hcdn_vali_case04.txt</li> </ul> <p><strong>(3) mHM model: CONUS-wide runs based on Rakovec et al. 2019 JGR-A</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>meaning, 1 parameter set applied across all basins.</p> <ul> <li>Calibration period: ./mHM_basins/$BASIN_ID/calib_001/output_calibMB_eval/daily_discharge.out</li> <li>Validation period: ./mHM_basins/$BASIN_ID/calib_001/output_validMB_eval/daily_discharge.out</li> </ul> <p><strong>(4) mHM model: onsite calibrations </strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>meaning, each basin has different parameter set </p> <ul> <li>Calibration period: ./mHM_basins/$BASIN_ID/calib_001/output/daily_discharge.out</li> <li>Validation period: ./mHM_basins/$BASIN_ID/calib_001/output_valid/daily_discharge.out</li> </ul> <p><strong>(5) mHM model: default parameter set from EU/Germany</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>the prior parameter set taken from the develop git branch of mhm</p> <p>Note that the calibration and validation periods are in one file:</p> <ul> <li>./mHM_basins/$BASIN_ID/def_000/output/daily_discharge.out </li> </ul> <p>#########################################################################</p> <p>## CONUS-WISE FLUXES,STATES,PARAMETERS</p> <p>#########################################################################</p> <p><strong>(1) mHM model: CONUS-wide runs based on Rakovec et al. 2019 JGR-A</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <ul> <li>Fluxes/states: mHM_entire_domain/calib_001/output/mHM_Fluxes_States.nc (monthly time step: 1950-2010)</li> <li>Model parameters (from the restart file): mHM_entire_domain/calib_001/output/mHM_restart_001.nc</li> </ul> <p> </p>
Scripts developed to modelling the nocturnal ecological continuum of the State of Geneva, Switzerland, based on high-resolution nighttime imagery.
<p>The zipfile contains the scripts developed in the paper on the modelling of the nocturnal ecological continuum of the State of Geneva, Switzerland, based on high-resolution nighttime imagery, as published in Remote Sensing Applications: Society and Environment journal (RSASE - Elsevier). </p> <ul> <li>Scripts developed for the extraction of light sources from night orthophotography ; [SAFE Software Inc. (2017). FME Desktop Esri Edition.Version 2017.1.]</li> <li>ModelBuilder developed for the visibility modelling of light sources ; [ESRI (2017). ArcGIS Desktop Pro.Version 2.1.]</li> </ul>
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.
<p>DATA DESCRIPTION<br> Author contact information:<br> Linnia R. Hawkins<br> Oregon State University<br> lhawkins@oregonstate.edu; linnia.hawkins@gmail.com<br> Data supporting 2019 Journal of Advances in Modeling Earth Systems publication</p> <p>Simulations of the equilibrium vegetation distribution in the western US performed with the climate model HadAM3p-HadRM3p-MOSES2-TRIFFID</p> <p>step1: Identify Influential Parameters<br> All files labeled step1.<br> EXPERIMENT DESCRIPTION: Data used in step 1: identify influential parameters <br> sensitivity experiment adjusting one parameter at a time 38 individual parameters were adjusted to 7 values, equally spaced<br> over a defined plausible range. For reference nine simulations with the default model parameterization are included, initiated with unique initial potential temperature perturbations. </p> <p>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.</p> <p>FILE DESCRIPTION:<br> 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. </p> <p> File labeling scheme: <br> step1_parameter_settingindex_3ICave.nc</p> <p> parameter: the name of the only model parameter adjusted<br> setting index: the index of the parameter setting (1-7)<br> index of 1 references the lowest plausible parameter setting<br> index of 7 references the highest plausible parameter setting<br> index of 4 references the parameter setting half way between the lowest and highest plausible parameter settings. <br> 3ICave: references that the results have been averaged over 3 initial conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> </p> <p>step2: ParameterSensitivity<br> all files labeled step2<br> EXPERIMENT DESCRIPTION:<br> Data used in step 2: parameter sensitivity <br> Perturbed Parameter Experiment (PPE) simultaneously adjusting 18 parameters.<br> Latin hypercube sampling was employed to generate 250 unique parameterizations references with a SETID (ranging from 1-359)<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p>FILE DESCRIPTION:<br> NETCDF: files contain either the fractional coverage (field1391) or the above ground biomass (field1512) for 5 plant functional types (PFTs) simulated for November 1907.</p> <p> File labeling scheme: <br> step2_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]</p> <p>TXT: files contain a list of the model parameterizations (for each PFT and variable) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p>RESTART: restart_region_TPPE_c374_1903-12-01.nc<br> The restart file contains the model state variables after spinup. This file was used to initiate all model simulations in step2.</p> <p>step3: ParameterSetSelection<br> All files labeled step3<br> EXPERIMENT DESCRIPTION:<br> Data used in step 3: parameter set selection<br> PPE simultaneously adjusting 10 parameters. <br> Latin hypercube sampling was employed to generate 140 unique model parameterizations, referenced with a SETID (ranging from 3-276).<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p><br> FILE DESCRIPTION:<br> 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. </p> <p> File labeling scheme: <br> step3_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable(s) contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> item3262_monthly_mean – Net primary productivity – units: (kgC/m2/sec) – all PFTs</p> <p>TXT: files contain a list of the model parameterizations (for each PFT) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p><br> production_runs<br> files labeled PI, historical, and future<br> EXPERIMENT DESCRIPTION:<br> 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. </p> <p>FILE DESCRIPTION:<br> 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).</p> <p><br> File labeling scheme:<br> pset* where * refers to the model parameterization 0-9<br> files were simulated with the model parameterization *, initiated with a unique initial condition (perturbation to the potential temperature field). </p> <p> Variables (PIrestart)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf] <br> field1391_1 – fractional coverage of PFT – units: fraction – [needleleaf]<br> field1391_2 – fractional coverage of PFT – units: fraction – [c3grass]<br> field1391_3 – fractional coverage of PFT – units: fraction – [c4grass]<br> field1391_4 – fractional coverage of PFT – units: fraction – [shrub]<br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf]<br> field1392_1 – leaf area index of PFT – units: m2/m2 – [needleleaf]<br> field1392_2 – leaf area index of PFT – units: m2/m2 – [c3grass]<br> field1392_3 – leaf area index of PFT – units: m2/m2 – [c4grass]<br> field1392_4 – leaf area index of PFT – units: m2/m2 – [shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf]<br> field1393_1 – canopy height of PFT – units: meters – [needleleaf]<br> field1393_2 – canopy height of PFT – units: meters – [c3grass]<br> field1393_3 – canopy height of PFT – units: meters – [c4grass]<br> field1393_4 – canopy height of PFT – units: meters – [shrub]<br> </p> <p> Variables (historic/future)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> <br> </p>
A Novel Hybrid Finite Element-Spectral Boundary Integral Scheme for Modeling Earthquake Cycles: Application to Rate and State Faults with Low-Velocity Zones
<p>We present a novel hybrid finite element (FE) - spectral boundary integral (SBI) scheme that enables efficient simulation of earthquake cycles. This combined FE-SBI approach captures the benefits of finite elements in modelling problems with nonlinearities, as well as the computational superiority of SBI. The domain truncation enabled by this scheme allows us to utilize high-resolution finite elements discretization to capture inhomogeneities or complexities that may exist in a narrow region surrounding the fault. Combined with an adaptive time stepping algorithm, this framework opens new opportunities for modeling earthquake cycles with high-resolution fault zone physics. In this initial study, we consider a two dimensional (2-D) anti-plane model with a vertical strike-slip fault governed by rate and state friction in the quasi-dynamic limit under the radiation damping approximation. The proposed approach is first verified using the benchmark problem BP-1 from the Southern California Earthquake Center (SCEC) sequence of earthquake and aseismic slip (SEAS) community verification effort. The computational framework is then utilized to model the earthquake sequence and aseismic slip of a fault embedded within a low-velocity fault zone (LVFZ) with different widths and compliance levels. Our results indicate that sufficiently compliant LVFZs contribute to the emergence of sub-surface events that fail to penetrate to the free surface and may experience earthquake clusters with nonuniform inter-seismic time. Furthermore, the LVFZ leads to slip rate amplification relative to the homogeneous elastic case. We discuss the implications of our results for understanding earthquake complexity as an interplay of fault friction and bulk heterogeneities. The complete work consists of all files listed below. </p>
Loading-Dependent Structural Model of Polymeric Micelles Encapsulating Curcumin by Solid-State NMR Spectroscopy
<p>(Raw) experimental and calculation data, which was the basis for this publication.</p> <ul> <li>DOSY</li> <li>solid-state NMR</li> <li>PXRD</li> <li>Dissolution Rates</li> <li>GIPAW (CASTEP) calculations</li> </ul>
Post-processed SAM (System for Atmospheric Modeling) simulation output for "Tipping to an Aggregated State by Mesoscale Convective Systems"
<p>Statistics output files for all variables, for a select number of SAM (System for Atmospheric Modeling v. 6.11) simulation runs used in the study "Tipping to an Aggregated State by Mesoscale Convective Systems". The following simulations are included: DIU, OCEAN, DIU2OCEAN branch A1, DIU2OCEAN branch A2.</p>
Data and codes for "The impact, costs, and cost-effectiveness of tuberculosis outbreak investigations in the United States: a model-based analysis"
<p>Data and codes for the manuscript entitled:</p> <p>"<strong>The impact, costs, and cost-effectiveness of tuberculosis outbreak investigations in the United States: a model-based analysis"</strong></p> <p> </p> <p><strong>Please see Readme.txt for details</strong></p>
Simulation data of Schmidt et al., An Electro-Chemo-Mechanic Model Resolving Delamination between Components in Complex Microstructures of Solid-State Batteries, 2024, DOI: https://doi.org/10.1149/1945-7111/ad76dc
<p>This data set includes the simulation results of the relevant simulations published in the paper: "Schmidt et al., An Electro-Chemo-Mechanic Model Resolving Delamination between Components in Complex Microstructures of Solid-State Batteries, 2024, DOI: https://doi.org/10.1149/1945-7111/ad76dc".</p> <p>Please refer to the paper for the details of the model as well as the parameterization of the model for the respective simulations.</p> <p>The data is provided in a zip archive. After extracting you find a short README.txt with further hints on the structure and available data.</p>
State-of-the-art evolution models for single, binary and magnetic massive stars
<p>The Universe is threaded by magnetic fields on all scales, from planetary systems to galaxy clusters. In massive stars, they play a pivotal and multifaceted role. They are thought to be crucial in transporting angular momentum throughout the stellar interior and maintaining the overall angular-momentum budget. They may also contribute to chemical mixing, can interact with convective fluid motions and may give rise to distinct seismic signatures. For example, the spin rates of white dwarfs, neutron stars and black holes are strongly determined by the coupling of a star’s core to its envelope. In this talk, I will discuss recent progress in our understanding of how magnetic fields affect single and binary stars, and what are possible origins of the different types of magnetic fields in massive stars. In particular, I will highlight connections to observations and how these can be used to further our understanding of how magnetic fields influence the evolution and final fates of stars.</p>
Mechanical behaviour of inorganic solid-state batteries: Can we model the ionic mobility in the electrolyte with Nernst-Einstein's relation?
<p>(Top left) Governing forces given by Nernst-Einstein’s relationship, where the forward electric force is in a dynamic equilibrium with the backward viscous force. (Bottom left) The dynamic viscosity is a measure of the fluid resistance to flow. (Right) For brittle inorganic solid electrolytes characterised by cracks formation and electrolyte fracture, the stress-strain relationship before the electrolyte fracture can be approximated as linear.</p>
Supplementary Material to "An improved grand-potential phase-field model of solid-state sintering for many particles"
<p>Supplementary Material to the publication "An improved grand-potential phase-field model of solid-state sintering for many particles" by Seiz, Hierl and Nestler. This contains the pre-study for determining the effective stiffness for the rigid-body velocity calculation and video files showing the 3D evolution of the green body in more detail than possible in the paper itself.</p> <p> </p> <p>slicethru_{start,end}.webm: Moving slices through the 400^3 nm green body at t=0.045ms and t=1.8ms representing the start and end of the simulation respectively. White/transparency indicates the surrounding vapor, with the colourmap showing different grains. Any interfaces are shown as black lines.</p> <p> </p> <p>greenbody_400.webm : Time evolution of the 400^3 nm green body based on the solid-vapor interface. White/transparent indicates the surrounding vapor, with the brownish material indicating the grains.</p> <p> </p> <p>prestudy.zip: Contains the notebook and data used for the pre-study for determining the effective stiffness. A binder is available at</p> <pre>https://mybinder.org/v2/git/https%3A%2F%2Fgit.scc.kit.edu%2Fxt5201%2Fsupplementary-material-for-improved-pf-sintering-model/master?labpath=eval-kvar.ipynb</pre> <p> </p> <p> </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.