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77 results for “internal model”
Supplementary material for "Song et al., Modelling Simul. Mater. Sci. Eng., 2021: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies"
<p>This zip archive contains supplementary material in the form of datasets and jupyter notebooks that are used in the following publication:</p> <ul> <li>authors: Hengxu Song, Nina Gunkelmann, Giacomo Po, and Stefan Sandfeld</li> <li>journal: Modelling Simul. Mater. Sci. Eng.</li> <li>year: 2021</li> <li>title: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies</li> </ul>
Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models
<p>This data set contains the simulations and data analysis files used in the publication: "<em>Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models</em>", by D. Cortés-Ortuño, K. Fabian and L. V. de Groot.</p> <p>The data set includes:</p> <ul> <li>Scripts and output files from MERRILL simulations</li> <li>Jupyter notebooks with data analysis</li> <li>Figures</li> </ul> <p>A preprint of this work can be found in:</p> <p>David Cortés-Ortuño, Karl Fabian and Lennart V. de Groot. <em>Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models.</em> DOI: 10.1002/essoar.10510574.1. Earth and Space Science Open Archive. <a href="https://doi.org/10.1002/essoar.10510574.1">https://doi.org/10.1002/essoar.10510574.1</a></p> <p>The README file in this dataset (in markdown format) contains full details about the simulations. The dataset also contains pre-computed data files to calculate the inversions and produce the figures and analyze the inversion data without processing the vbox files.</p> <p>To cite this dataset you can use the following bibtex entry:</p> <pre><code>@Misc{Cortes2022, author = {Cortés-Ortuño, David and Fabian, Karl and de Groot, Lennart V.}, title = {{Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models}}, publisher = {Zenodo}, year = {2022}, doi = {10.5281/zenodo.6501818}, url = {https://doi.org/10.5281/zenodo.6501818}, } </code></pre> <p> </p>
How do native and non-native speakers recognize emotions in the instructor's voice in educational videos? Exploring the first step of the cognitive-affective model of e-learning for international learners [dataset]
<p>Dataset for the journal article <em>How do native and non-native speakers recognize emotions in the instructor’s voice in educational videos? Exploring the first step of the cognitive-affective model of e-learning for international learners.</em></p>
Science ready spectra and their best-fitting models described in the research paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al.
<p>Science ready spectra of nine ultra-diffuse galaxies in the Coma cluster collected with the Binospec multi-object spectrograph and their best-fitting PEGASE.HR templates obtained using the NBursts full spectrum fitting code. These spectra were presented in the paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al. accepted for publication in the Astrophysical Journal on Sep/3/2019 (arXiv:1901.05489).</p> <p>Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For six galaxies there are two files provided: (i) one-dimensional optimally extracted integrated spectrum and (ii) two dimensional spectrum for spatially resolved radial velocity information. For the remaining three galaxies, only spatially resolved spectra are provided.</p>
The International Transport Energy Modeling (iTEM) Open Data & Harmonized Transport Database
<p>This dataset and documentation contains detailed information of the iTEM Open Database, a harmonized transport data set of historical values, 1970 - present. It aims to create transparency through two key features:</p> <ul> <li>Open-Data: Assembling a comprehensive collection of publicly-available transportation data</li> <li>Open-Code: All code and documentation will be publicly accessible and open for modification and extension. <a href="https://github.com/transportenergy">https://github.com/transportenergy</a></li> </ul> <p>The iTEM Open Database is comprised of individual datasets collected from public sources. Each dataset is downloaded, cleaned, and harmonised to the common region and technology definitions defined by the iTEM consortium https://transportenergy.org. For each dataset, we describe the name of the dataset, the web link to the original source, the web link to the cleaning script (in python), variables, and explain the data cleaning steps (which explains the data cleaning script in plain English).</p> <p>Shall you find any problems with the dataset, please report the issues here <a href="https://github.com/transportenergy/database/issues">https://github.com/transportenergy/database/issues</a>. </p> <p> </p>
Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming
<p>This dataset contains simulated and observed maps of surface temperature change over the satellite-era and domain averaged trends of tropospheric warming. The data accompanies software that was used to disentangle the forced and unforced components of tropical tropospheric temperature change. This work was documented in:</p> <blockquote> <p>Po-Chedley, S., J.T. Fasullo, N. Siler, Z.M. Labe, E.A. Barnes, C.J.W. Bonfils, B.D. Santer (2022): "Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming," Proceedings of the National Academy of Sciences, doi: 10.1073/pnas.2209431.</p> </blockquote> <p>The software is available at: https://github.com/LLNL/MDAS</p>
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.
Converting between the International Prostate Symptom Score (IPSS) and the Expanded Prostate Cancer Index Composite (EPIC) urinary subscales: modeling and external validation
<p><strong>Background</strong>: Prostate-related quality of life can be assessed with a variety of different questionnaires. The 50-item Expanded Prostate Cancer Index Composite (EPIC) and the International Prostate Symptom Score (IPSS) are two widely used options. The goal of this study was, therefore, to develop and validate a model that is able to convert between the EPIC and the IPSS to enable comparisons across different studies. </p> <p><strong>Methods</strong>: Three hundred forty-seven consecutive patients who had previously received radiotherapy and surgery for prostate cancer at two institutions in Switzerland and Germany were contacted via mail and instructed to complete both questionnaires. The Swiss cohort was used to train and internally validate different machine learning models using fourfold cross-validation. The German cohort was used for external validation.</p> <p><strong>Results</strong>: Converting between the EPIC Urinary Irritative/Obstructive subscale and the IPSS using linear regressions resulted in mean absolute errors (MAEs) of 3.88 and 6.12, which is below the respective previously published minimal important differences (MIDs) of 5.2 and 10 points. Converting between the EPIC Urinary Summary and the IPSS was less accurate with MAEs of 5.13 and 10.45, similar to the MIDs. More complex model architectures did not result in improved performance in this study. The study was limited to the German versions of the respective questionnaires.</p> <p><strong>Conclusions</strong>: Linear regressions can be used to convert between the IPSS and the EPIC Urinary subscales. While the equations obtained in this study can be used to compare results across clinical trials, they should not be used to inform clinical decision-making in individual patients. Trial registration This study was retrospectively registered on clinicaltrials.gov on January 14th, 2022, under the registration number NCT05192876.</p>
Compilation of Moon internal structure models and seismic event locations presented in Garcia et al., Space Science Review, 2019 study
<p>Compilation of of previously published internal structure model of the Moon in "named discontinuities" seismological format + 3 new models associated to the above mentioned study + previously published Moon quake location estimates by various authors.</p> <p>The study, the internal structure models and quake locations compilation were performed by the ISSI international research team on Moon Seismology and internal structure described here: http://www.issibern.ch/teams/internstructmoon/</p>
Model setup code and input for internal tide-eddy simulation
<div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <p>The dataset includes the setup code and input files for simulations of internal tide-eddy interactions using MITgcm. Due to the large size of the model output data, it is not included but can be made available upon request at <a target="_new">yangwangow@gmail.com</a>. If you have any questions about using or testing these files, please feel free to reach out. Cheers!</p> </div> </div> </div> </div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Converting between the International Prostate Symptom Score (IPSS) and the Expanded Prostate Cancer Index Composite (EPIC) urinary subscales: modeling and external validation
Open the record for dataset details and reuse information.
Spin-up time and internal variability analysis for overlapping time slices in a regional climate model
<p>In order to increase computational efficiency, several long-term regional climate simulations were split into overlappings time slices. These overlappings slices were used to explore the relative role of spin-up time and internal variability in the discontinuities that are produced once the slices are joined.</p> <p>This dataset was generated using the Weather Research and Forecasting (WRF) model and includes two sets of time slices for the periods 2002-2006 and 2006-2010 over the CORDEX South American domain at 0.44º horizontal resolution (SAM-44), regular on a rotated latitude-longitude projection. The data was forced by the scenario RCP 8.5 and driven by the Canadian Earth System model (CanESM2). The model configuration files are also attached.</p>
Connectivity matrix of the internal connectivity of a SSCX model
<p>This is a simplified view of the <i>internal</i> synaptic connectivity of the model deposited under <a href="https://zenodo.org/records/8026353">https://zenodo.org/records/8026353.</a></p><p>While in this view a lot of data is lost compared to the detailed representation linked above (such as the pre- and postsynaptic locations of synaptic contacts and their physiology), it can still be used for structural connectomics analyses.</p><p>The data is in a format that can be loaded using our <a href="https://github.com/BlueBrain/ConnectomeUtilities"><i>Connectome_Utilities</i></a> package. We have converted the internal connectivity of the <a href="https://www.microns-explorer.org/">MICrONS</a> dataset into the same format (<a href="https://zenodo.org/records/8364070">found here</a>), allowing researchers to easily run the same analysis scripts on both datasets.</p>
Movements during sleep reveal the developmental emergence of a cerebellar-dependent internal model in motor thalamus
<p>With our eyes closed, we can track a limb's moment-to-moment location in space. If this capacity relied on sensory feedback from the limb, we would always be a step behind because sensory feedback takes time: For the execution of rapid and precise movements, such lags are not tolerable. Nervous systems solve this problem by computing representations—or internal models—that mimic movements as they are happening, with the associated neural activity occurring after the motor command but before the sensory feedback. Research in adults indicates that the cerebellum is necessary to compute internal models. What is not known, however, is when—and under what conditions—this computational capacity develops. Here, taking advantage of the unique kinematic features of the discrete, spontaneous limb twitches that characterize active sleep, we captured the developmental emergence of a cerebellar-dependent internal model. Using rats at postnatal days (P) 12, P16, and P20, we compared neural activity in the ventral posterior (VP) and ventral lateral (VL) thalamic nuclei, both of which receive somatosensory input but only the latter of which receives cerebellar input. At all ages, twitch-related activity in VP lagged behind the movement, consistent with sensory processing; similar activity was observed in VL through P16. At P20, however, VL activity no longer lagged behind movement, but instead precisely mimicked the movement itself; this activity depended on cerebellar input. In addition to demonstrating the emergence of internal models of movement, these findings implicate twitches in their development and calibration through, at least, the preweanling period.</p>
Evidence of an internal model of friction when controlling kinetic energy at impact to slide an object along a surface toward a target
<p>This repository contains zip files for data (Data.zip) and Matlab functions library (Library.zip), and the Matlab script (Matlab_Script_for_Cumputed_data.m) used in the Famié et al PLOSONE study. In addition, the <em>README FIRST MATLAB program.pdf</em> file explains the Matlab code and data organization. </p>
Data for "International Transport costs: New Findings from modeling additive costs"
<p>Downloading the data will provide you with a .zip file.</p> <p>These data are US imports from 1974 to 2020, by partner, transport mode and product at the 5 digit level in the "Hummels" data and by partner, transport mode, district of entry, district of unlading and product at the 10 digit level for the rest of the data.</p> <p>All the data originally come from the Census Bureau "Foreign Trade" (see https://www.census.gov/foreign-trade/index.html) and belong to the public domain.</p> <p>"Hummels_JEP _data" was downloaded from David Hummels’s website (https://www.krannert.purdue.edu/faculty/hummelsd/research/jep/data.html -- Hummels, David, "Transportation Costs and International Trade in the Second Era of Globalization", <em>Journal of Economic Perspectives</em>, Vol 21, No 3, pp 131-154. It covers 1974 to 2004.</p> <p>The other main files were bought directly from the Census Bureau ("Annual Merchandise Trade files"). They cover 1997-1999 and 2002-2020.</p> <p>Various files are included for the conversion of country codes (dist_cepii.dta, countrycodes_use.txt), product codes (HS2002_SITC2.txt) and to associate quantity units with hts codes (hts... and ..._hts_...).</p>
Parameters for the modelization of operator, worker, and bystander exposure to pesticides in market gardening areas in Burkina Faso using three international exposure assessment models.
<p>Parameters used for the modelization of operator, worker, and bystander exposure to pesticides in market gardening areas in Burkina Faso using the following exposure assessment models:</p> <p>EFSA 2014: EFSA, 2014. Guidance on the assessment of exposure of operators, workers, residents and bystanders in risk assessment for plant protection products. EFSA J. 12, 3874. doi:10.2903/j.efsa.2014.3874</p> <p>HAIR2010: Kruijne, R., Deneer, J.W., Lahr, J., Vlaming, J., 2011. HAIR2010 Documentation: calculating risk indicators related to agricultural use of pesticides within the EU 205.</p> <p>WHO 2011: WHO, 2011. Generic risk assessment model for indoor and outdoor space spraying - WHO Pesticide Evaluation Scheme 1–72.</p> <p><br> This work was realized in the framework of the research component "Pesticides" (1.2.4) of the 3E program funded by the Swiss Agency for Development and Cooperation (SDC).</p> <p><br> Details on algorithm adaptations, scenario definitions and model outputs are available in the PhD thesis of Dr. Edouard Lehmann available from 2018 at https://infoscience.epfl.ch/</p>
The role of external inputs and internal cycling in shaping the global ocean cobalt distribution: insights from the first cobalt biogeochemical model
<p>Model output for cobalt biogeochemistry model on ORCA2 grid.</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>
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>
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.