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5,805 results for “Data model”
Data Sets ''Omeprazole and Proteinoids in Neuron Models''
<p>Data Sets ''Omeprazole and Proteinoids in Neuron Models''</p>
Data and Code for "Mesoscale modeling of deformations and defects in thin crystalline sheets"
<p>Research data and code supporting the paper ""Mesoscale modeling of deformations and defects in thin crystalline sheets"".</p> <p> </p> <p><strong>Code (apfc-python-surf.zip)</strong></p> <p>The implementation of the APFC model is performed in python by exploiting the pseudo-spectral Fourier method. Library pyfftw is adopted. However, standard fft libraries can be used as well by changing the corresponding module/functions. The code supports equations of the APFC model both coupling with the evolution of the surface considered in this work and on a simple flat domain. Updates can be found in the GitLab repository linked below.</p> <p> </p> <p><strong>GitLab repository for the code</strong></p> <p><a href="https://gitlab.com/3ms-group/apfc_python/">https://gitlab.com/3ms-group/apfc_python/</a></p> <p> </p> <p><strong>Data</strong></p> <p>The data.zip files contain the simulation results and auxiliary scripts used to produce the results illustrated in the paper's figures. The folder numbering refers to the one used for the figures in the final version.</p> <p> </p> <p>For further information please contact the authors.</p>
Contrasting Views of the Electric Double Layer in Electrochemical CO2 Reduction: Continuum Models vs Molecular Dynamics (data for figures)
<p>This is the data used to create the figures in the article:</p> <h4>Contrasting Views of the Electric Double Layer in Electrochemical CO<sub>2</sub> Reduction: Continuum Models vs Molecular Dynamics</h4> <div>Evan Johnson and Sophia Haussener</div> <div>The Journal of Physical Chemistry C <strong>2024</strong> <em>128</em> (25), 10450-10464</div> <p>DOI: 10.1021/acs.jpcc.4c03469</p> <p>See the file "Naming conventions" for the file names and column/row meanings. </p>
Data from: Fundamental properties of adjoint model and adjoint sensitivity under fully-nonlinear hydrostatic internal gravity waves
<p>This folder contains data from</p> <p>Shimizu, K. (2024), Fundamental properties of adjoint model and adjoint sensitivity under fully-nonlinear hydrostatic internal gravity waves, Journal of Geophysical Research: Oceans, 129, e2023JC020577. https://doi.org/10.1029/2023JC020577</p> <p>Its contents are briefly described in ReadMe.txt.</p>
Datasets supporting the paper 'City-scale high-resolution flood models and the role of topographic data: a case study of Kathmandu, Nepal.'
<p>###########################################################<br>Datasets supporting the publication:<br><strong>Watson, C.S., Gyawali, J., Creed, M., and Elliott, J.R. City-scale high-resolution flood models and the role of topographic data: a case study of Kathmandu, Nepal. Geocarto International. DOI: <a href="https://doi.org/10.1080/10106049.2024.2387073">https://doi.org/10.1080/10106049.2024.2387073</a><br></strong></p> <p><strong>-Please refer to the publication for details on the production of each dataset.</strong><br>-<strong>Please cite the publication and this dataset repository when using the data.</strong><br>###########################################################</p> <p><strong>Contents:</strong></p> <p><strong>Stream centrelines:</strong></p> <p>streams_fabdem.gpkg</p> <p>streams_GLO30.gpkg</p> <p>streams_kh9_1974.gpkg</p> <p>streams_merit.gpkg</p> <p>streams_merit_hydro.gpkg</p> <p>streams_pleiades.gpkg</p> <p>streams_reference.gpkg</p> <p><strong>Flood maps:<br></strong></p> <p>fastflood_1in100year_flood_depth_metres.tif</p> <p>flood_depth_difference__fastflood_Shrestha_et_al_2023_metres.gpkg</p> <p>Height_above_channel_metres.tif</p> <p><strong>1974 Orthoimage:</strong></p> <p>KH9_1974_orthoimage_DZB1209_500101L007001_DZB1209_500101L008001.tif</p>
Replication data for: "Revisiting the 'East African Paradox': CMIP6 models also struggle to reproduce strong observed Long Rain drying trends."
<h3><strong>Intro</strong></h3> <p>This repository contains replication data for Schwarzwald, Kevin and Richard Seager (2024), "'Revisiting the “East African Paradox': CMIP6 models also struggle to reproduce strong observed MAM long rain drying trends." (under revision at Journal of Climate). This includes the data necessary to replicate all main text figures and most figures in Supplementary Materials. Additional figures in Supplementary Materials require raw precipitation time series, detailed below. This repository also includes a copy of the code necessary to replicate the entire study; the latest version of the code, in addition to instructions on how to use it, is kept at <a href="https://github.com/ks905383/gha_trends">this GitHub archive</a>. </p> <h3><strong>Structure</strong></h3> <p>The repository is structured as follows: </p> <ul> <li><code>code</code>: Static / stable version of reproduction code for Schwarzwald and Seager (2024) that uses and creates these data (see <a href="https://github.com/ks905383/gha_trends">here</a> for more detailed instructions)</li> <li><code>figures</code>: Static / stable versions of main and supplemental figures for Schwarzwald and Seager (2024).</li> <li><code>climate_raw</code>: "raw" (often pre-processed) climate data files; only some files are included, see below for more details</li> <li><code>climate_proc</code>: Processed climate data files upon which the analysis is based, used by main and supplemental figure code</li> <li><code>aux_data</code>: Certain auxiliary data files (fonts, critical values) and intermediate files for long code processes. Created and used by the replication code. </li> </ul> <h3><strong>Notes on raw climate data</strong></h3> <p>Due to space limitations (and a desire to not create yet another cloud copy of CMIP6 data), this repository only contains processed CMIP6 data: the calculated linear trends of rainfall, sea surface temperatures, and 500 hPa geopotential height used in the analysis of Schwarzwald and Seager (2024). The raw data used to create these files can be downloaded from the <a href="https://aims2.llnl.gov/search/cmip6/">ESGF</a>, and consists of every available CMIP6 monthly rainfall, sea surface temperature (SST), and geopotential height file on the archive at the time of processing for the experiments detailed in the manuscript. For precipitation, only a bounding box (-3 S to 12.5 N, 32 E to 55 E) around East Africa was downloaded, and saved with the file suffix "_HoAfrica" (see replication code README for more details). One example precipitation file (one ensemble member of ACCESS-CM2 historical precipitation) is included in this repository for reference. </p> <p>Similarly, this repository only contains processed NMME data: calculated linear trends of rainfall used in the analysis of Schwarzwald and Seager (2024). The raw data used to create these files can be downloaded from the <a href="https://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/">IRI Data Library</a> and consists of every available monthly rainfall hindcast / forecast file from the NMME archive for the models used (see manuscript Table S1). As above, only a bounding box around East Africa was downloaded, and files were standardized to a partial CMIP* file format (one file per variable, with CMIP file and variable name conventions, but with forecast lead time as an additional dimension). Preprocessing is a bit more extensive than for CMIP6 models: first, hindcasts and forecasts were concatenated into a single file (since hindcasts are saved in the archive only up to the time when the model was operationalized), and then reindexed such that the "time" variable refers to the time <em>for which the forecast is made</em>, and not the time <em>at</em> which the forecast is made. One example precipitation file (CanCM4i rainfall hindcasts/forecasts) is included in this repository for reference. </p> <p>This repository does, however, contain preprocessed copies of the "raw" gridded observational rainfall data products used (in addition to the processed trends), since harmonizing and standardizing the data into a format easily compatible with the CMIP6 data was a nontrivial amount of work that would be tedious to replicate. For the ten gridded observational data products used, monthly rainfall was brought into the CMIP* file format (one file per variable, with CMIP file and variable name conventions), with one notable exception: all observational rainfall is saved in units of mm/day. </p> <p>Reanalysis and gridded ocean observations can be downloaded from each product's respective repositories. As before, the code assumes the data have been preprocessed into something akin to the CMIP* file format. </p> <h3><strong>Notes on processed climate data</strong></h3> <p>Note that the repository includes a file (<code>aux_data/pr_doyavg_CHIRPS_historical_seasstats_dunning_19810101-20141231_HoAfrica.nc</code>) containing CHIRPS seasonal characteristics in East Africa, created as part of Schwarzwald et al., 2023, <em>Climate Dynamics</em>. This file is primarily used to set the boundaries of the study region, see the manuscript for details of how it was calculated. </p> <h3><strong>Licensing, citing, questions</strong></h3> <p>These data are offered under a CC 4.0 license, which allows redistribution and reuse as long as they are correctly cited; note that for much of these data (especially for "raw" data), this requires citations to the original creators. </p> <p>For questions, please feel free to reach out to corresponding author Kevin Schwarzwald. </p>
Model output data for Xueyi Jing et al., "A Scheme of Sea Surface Upward Longwave Radiation with Sea Spray Layer Effect"
<p>CESM Model output data used in "A Scheme of Sea Surface Upward Longwave Radiation with Sea Spray Layer Effect" .</p> <p> </p> <p>Seasonal files were derived from monthly outputs by CDO. "F2000_f09_CTL*******" are results for control run, "F2000_f09_LW_Test1*******" are results for sensitivity experiment. </p>
Lithospheric structure and strength variations in Antarctica from joint modeling of elevation, geoid and seismic data
<p>These models include Moho depth, LAB depth and integrated lithospheric strength based on a 1D approach involving thermal analysis under local isostasy and a rheological method.</p>
SIP ice data, presented in "Relationship between Cole-Cole model parameters in permittivity and conductivity formulation"
<p>2-point SIP measurement on ice sample of deionized water. Data are shown in publication: Relationship between Cole-Cole model parameters in permittivity and conductivity formulation.</p> <p>Details of the measurements and the data format are given in the readme.md file.</p>
The netCDF output data of Parallel Princeton Ocean Model based on OpenACC
<p>This dataset represents the output results from the simulated seamount case, where the outputs vary depending on whether parallel (p) or serial (s) execution is used, as well as the different simulation durations and resolutions applied.</p>
Model and Data for the T&C-CROP Validation Paper: T&C-CROP: Representing mechanistic crop growth with a terrestrial biosphere model (T&C,v1.5): Model formulation and validation.
<p>Here included is the code used to run T&C-CROP as used for the GMD paper submission alongside with the necessary weather data and raw field data used as part of the validation exercise. </p> <p> </p>
Langmark: annotations for scenes with semantic inconsistencies connecting distributional semantic models to vision science – data and code
<p>Data (including object annotations) and code from the following manuscript:</p> <p><em>Langmark: annotations for scenes with semantic inconsistencies connecting distributional semantic models to vision science</em>.</p>
Supplementary data for: Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C
<p>Supplementary example data for the work presented in "<em>Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C</em>". </p> <p><strong>Abstract: </strong></p> <p>Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing our understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavour. Here, we use specially designed graphene microelectrode arrays (G-MEAs), which are compatible with high spatial resolution imaging across various scales as well as permit high temporal resolution electrophysiological recordings to address these challenges. Furthermore, alongside G-MEAs, we have developed an easy-to-implement machine learning algorithm to efficiently process the large datasets collected from MEA recordings. We demonstrate that the combined use of G-MEAs, machine learning (ML) spike analysis, and four-dimensional (4D) structured illumination microscopy (SIM) enables monitoring the impact of disease progression on hippocampal neurons which have been treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC), and show that synaptic boutons, compared to untreated controls, significantly increase in size, leading to a loss in neuronal signalling capacity.</p> <p> </p>
Supporting data ocean model GMD submission: From Weather Data to River Runoff: Leveraging Spatiotemporal Convolutional Networks for Comprehensive Discharge Forecasting
<p>Ocean model salinity data used for the comparison of the ConvLSTM river runoff model and the original E-HYPE based model simulations.</p>
Code and measurement data - Capacity and internal resistance diagnosis of batteries with voltage-controlled models
<p><strong>This dataset contains the research data (Matlab code, measurement data, figure files) of the journal article: <a href="https://doi.org/10.1149/1945-7111/ad6938">Wolfgang G. Bessler, “Capacity and resistance diagnosis of batteries with voltage-controlled models,” J. Electrochem. Soc. 171, 080510 (2024), https://doi.org/10.1149/1945-7111/ad6938</a>.</strong><br><br></p> <p><strong>Abstract:</strong></p> <p>Capacity and internal resistance are key properties of batteries determining energy content and power capability. We present a novel algorithm for estimating the absolute values of capacity and internal resistance from voltage and current data. The algorithm is based on voltage-controlled models (VCM). Experimentally-measured voltage is used as input variable to an equivalent circuit model. The simulation gives current as output, which is compared to the experimentally-measured current. We show that capacity loss and resistance increase lead to characteristic fingerprints in the current output of the simulation. In order to exploit these fingerprints, a theory is developed for calculating capacity and resistance from the difference between simulated and measured current. The findings are cast into an algorithm for operando diagnosis of batteries operated with arbitrary load profiles. The algorithm is demonstrated using cycling data from lithium-ion pouch cells operated on full cycles, shallow cycles, and dynamic cycles typical for electric vehicles. Capacity and internal resistance of a “fresh” cell was estimated with high accuracy (mean absolute errors of 0.9 % and 1.8 %, respectively). For an “aged” cell, the algorithm required adaptation of the model’s open-circuit voltage curve in order to obtain high accuracies.</p> <p> </p> <p><strong>Copyright and IP information:</strong></p> <p>Copyright 2024 by Wolfgang G. Bessler. The Matlab codes and the research data provided here are under <strong><a href="https://creativecommons.org/licenses/by-nc/4.0/legalcode">CC-BY-NC-4.0</a></strong> license (Creative Commons Attribution Non Commercial 4.0 International). Please note that the algorithms themselves are subject to intellectual property rights, including, but not necessarily limited to, German patent DE102022129314 and international patent WO2024/099513A1. Any use of the codes and algorithms presented here is subject to these property rights.</p> <p><br><strong>Quick start:</strong></p> <p>Copy all files into one folder. Open and run capacityAndResistanceDiagnosisFigures8and10.m with Matlab. Observe the reproduction of Figure 8 of the paper.</p> <p><br><strong>Description of the files:</strong></p> <p>Matlab code (tested using versions R2019a and R2022b):</p> <ul> <li>capacityAndResistanceDiagnosisFigures8and10.m: this is the main Matlab script. It performs the capacity and resistance diagnosis on experimental data V(t) and I(t). The script reproduces Figures 8 ("fresh" cell) or 10 ("aged" cell) of the paper, depending on which lines you uncomment in upper part of the script.</li> <li>calculateDeltaR.m, calculatefC.m: functions that evalue deltaR and fC, which are two key outputs of the diagnosis algorithm. </li> <li>simulateVCMSimple.m, simulateVCMDynamic.m: functions that simulate the voltage-controlled equivalent circuit models (either "simple" of "dynamic"). Input is V(t), output are I(t) and SOC(t).</li> <li>interpolateCurve.m: performs linear interpolation of the OCV(SOC) curve. We use this because Matlab's interp1() function is awfully slow.</li> </ul> <p>Experimental data:</p> <ul> <li>Experimental_data_fresh_cell.csv: Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (99 h total with 1 s resolution) of a "fresh" lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</li> <li>Experimental_data_aged_cell.csv: Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (85 h total with 1 s resolution) of a "pre-aged" lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</li> <li>OCV_vs_SOC_curve_fresh_cell.csv: Tabulated experimentally-derived open-circuit voltage (OCV) as function of state of charge (SOC) of the "fresh" lithium-ion cell. 1001 data points between SOC = 0 and SOC = 1 in increments of 0.001.</li> <li>OCV_vs_SOC_curve_aged_cell.csv: Tabulated experimentally-derived open-circuit voltage (OCV) as function of state of charge (SOC) of the "aged" lithium-ion cell. 1001 data points between SOC = 0 and SOC = 1 in increments of 0.001.</li> </ul> <p>Figure files:</p> <ul> <li>Figures.zip: contains .emf (Windows format) and .fig (Matlab format) versions of Figures 2-10 of the paper.</li> </ul>
Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome
<p>Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being Data-Dependent Acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-Independent Acquisition Mass Spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS metaproteomic measurements using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data highlighting specific needs to further improve DIA tools to enable analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising data collection strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation.</p>
Model Data for Dynamic Controls on the Asymmetry of Mouth Bars: Role of Alongshore Currents
<p>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 <strong>"Dynamic Controls on the Asymmetry of Mouth Bars: Role of Alongshore Currents"</strong>. </p> <p>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.</p> <p>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:</p> <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> <p>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.</p> <p>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. </p>
Data package for paper "Transformer models for astrophysical time series and the GRB prompt-afterglow relation"
<p>This is a data package accompanying the paper "Transformer models for astrophysical time series and the GRB<br>prompt-afterglow relation". The code used to acquire the data is in the "data" folder. The code used to analyse the data is in the "analysis" folder.</p> <p>DOI paper: <a href="https://doi.org/10.1093/rasti/rzae026">10.1093/rasti/rzae026</a></p>
Bayesian hierarchical model gridded solar-induced fluorescence (BHM gridded SIF) data product
<p>This archive provides the solar-induced fluorescence (SIF) data product documented in "Estimation of solar-induced chlorophyll fluorescence using Bayesian hierarchical regression". The archive includes daily NetCDF files with the global gridded SIF estimates and associated uncertainties.</p>
Data and Models for Salt Transport in Desert, Urban, and Irrigated Landscapes
<p>Folders contain data and models (SWAT, SWAT-MODFLOW) for analysis and modeling of salt fate and transport in urban, desert, and irrigated landscapes. Study regions include the Arkansas River Basin and the South Platte River Basin (Colorado, USA). The Education folder contains documentation of a STEM kit created at Colorado State University for guiding middle school and high school students through a 2-hour lab on salt pollution, transport, and mitigation in a mixed irrigation-desert watershed landscape.</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.