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123 results for “thermal modelling”
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 from: Modeling of the larval response of green sea urchins to thermal stratification using a random walk approach
Larval transport in the ocean can be affected by their vertical position in the water column. In biophysical models that are often used to predict larval horizontal dispersal, generally larval vertical positions are either ignored or incorporated as static parameters. Here, we evaluate the ability of one dimensional random walk based model to predict larval vertical distribution of Strongylocentrotus droebachiensis in response to thermal stratification. Vertical swimming velocities were recorded at various temperatures and used to parameterize the model. Data from a previous laboratory study on the effects of thermal stratification on larval vertical distribution of S. droebachiensis were compared to the model results to evaluate the predictive ability of the model. The model predicts general trends in vertical distribution fairly well, but has a systematic bias which can be explained by un-quantified larval behaviors at the boundaries of the experimental water column. Overall, our behavioral model successfully reproduces the mechanism which regulates larval vertical distribution in response to thermal structure. Collectively, the findings suggest that simple behavioral models parameterized using simple lab experiments can prove useful in estimating the vertical distributions of invertebrate larvae in the laboratory and likely in the ocean. Such models can then be linked to bio-physical models to more accurately predict larval dispersal.
Multiscale modelling of flow, heat transfer and transformation during thermal treatment of starch suspensions
<p>Multiscale modelling of flow, heat transfer and transformation during thermal treatment of starch suspensions</p>
Pore-scale modeling and investigation on thermal-hydro-mechanical-chemical coupled rock dissolution and fracturing process
<p>Attached files include the executable file of the pore-scale multi-field coupled LBM-DEM program written by C language, post-processing programs to record the reactive surface area and reactive temperature written by MATLAB, and the simulation results of rock acid fracturing with 20MPa at the injection hole.</p>
Data for "A thermal-hydro-mechanical model for evaluating the stability of mountain glaciers"
<p>This dataset details the data produced in our study "A thermal-hydro-mechanical model for evaluating the stability of mountain glaciers".</p>
3D printed models are an accurate, cost-effective, and reproducible tool for quantifying terrestrial thermal environments
<p>Dataset used in Alujević et al. 2023. 3D printed models are an accurate, cost-effective, and reproducible tool for quantifying terrestrial thermal environments. </p>
Thermal state, slab metamorphism and interface seismicity in the Cascadia subduction zone based on 3-D modeling
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Data from: Modeling of the larval response of green sea urchins to thermal stratification using a random walk approach
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Data from: Predator-prey interactions shape thermal patch use in a newt larvae-dragonfly nymph model
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Dataset of Paper "Kinetic modeling of the synergistic thermal and spectral actions on the inactivation of Cryptosporidium parvum in water by sunlight"
<p>Dataset of Paper "Kinetic modeling of the synergistic thermal and spectral actions on the inactivation of Cryptosporidium parvum in water by sunlight"</p> <p>- Data of the experimental disinfection profiles for the viable <em>C. parvum</em> oocysts.</p>
Dataset of Paper: "Kinetic modeling of the synergistic thermal and spectral actions on the inactivation of viruses in water by sunlight" (DOI:
<p>Dataset of Paper: "Kinetic modeling of the synergistic thermal and spectral actions on the inactivation of viruses in water by sunlight"</p> <p>- Data of the experimental disinfection of the MS2 virus.</p> <p>- Data of the kinetic constant value of the MS2 virus inactivation for experimental data and predicted data using action spectra models with different quantum yield values.</p> <p>- Observed and predicted kinetic constants for the MS2 inactivation by SODIS for all the scenarios and conditions studied. The predicted kinetic constants were obtained from the complete kinetic model described in this work.</p>
MATLAB code related to the thermal model of hyperthermia treatment
<p>The code attached here is used to perform the analyses on hyperthermia treatment of brain tumours that are described in the article "The interplay of blood flow and temperature in regional hyperthermia: A mathematical approach" by Jesús J. Bosque, Gabriel F. Calvo, Víctor M. Pérez-García and María Cruz Navarro. For more information turn to the article or use the readme file provided with the code.</p>
Supplemental Information for: Late Paleozoic Gondanide deformation in the central Andes: Insights from RSCM thermometry and thermal modeling
<p>This supplemental information is data to accompany the article "<strong>Late Paleozoic Gondanide deformation in the central Andes: Insights from RSCM thermometry and thermal modeling"</strong>. The supplemental data includes information on Raman spectroscopy analytical methods (instrument parameters, settings, and procedures), Time temperature path model parameter setup and composite sample selection used in HeFTy, and an excel file of individual RSCM spot analysis for each sample that we analyzed. </p> <p>Data were collected in the field in the Eastern Cordillera of Bolivia in 2013 and 2014, and analyzed at Arizona State University in 2015 and 2016.</p>
Model dataset for Morrison et al. (2020) "Comparing growth rates of moist and dry convective thermals" submitted to JAS
<p>This model generated dataset includes simulation data and model files for work described in the paper "Comparing growth rates of moist and dry convective thermals" by Morrison et al., submitted to the Journal of the Atmospheric Sciences. Model data comes from the CM1 atmospheric model maintained by Dr. George Bryan at NCAR. The model data are idealized high resolution model runs. We are making this request through DASH because inclusion of these data in a public repository is now mandatory for AMS publications.</p>
Compositional and Thermal State of the Lower Mantle from the inversion of GLAD-M25 model
<p>The 3D chemical composition and temperature structure of the lower mantle from the inversion of GLAD-M25 model is included</p>
Spectrum data and thermal model data of comet/103P (v1.0)
<p class="15"><span><span>Hyperactive comets have attracted attention due to their high water production rate with an unclear mechanism, though some hypotheses are proposed to explain it. Based on the thermal theories of the comet nuclei, this paper studied a comet surface thermal model considering the sublimation of H</span></span><sub><span><span>2</span></span></sub><span><span>O. In this paper, a method for solving the sublimation rate of water ice by infrared spectra is proposed. The method adopts the assumption of comet nucleus surface temperature roughness and uses the numerical solution of the Fredholm equation. We use the HRI-IR spectr</span></span><span><span>um</span></span><span><span> </span><span>(1.05-4.8 μm) data by EPOXI to analyze the pixel water sublimation rate of hyperactive comet 103P/Hartley2. The results show that sublimation exists in most areas of the surface with or without surface roughness, and most of the water production rate (70% ~ 90%) may come from the comet nucleus. According to the sublimation law, it is estimated that the sublimation temperature of water ice on 103P is above 180K. If the dust-to-ice volume ratio is 3:1, the sublimation temperature is about 200-210K, which indicates that the water ice may sublimate underneath. This may explain why exposed water ice on the surface can</span><span><span> </span></span></span><span><span>hardly</span></span><span> <span>be observed while the active fraction of this comet is up to 100%.</span></span></p>
Models of low-mass helium white dwarfs including gravitational settling, thermal and chemical diffusion, and rotational mixing⋆
<p>MESA inlists, data (from a single run: rotation + diffusion, M1=1.4, M2=1.2, Porb=3.4 days, Z=0.02) and run_star_extras associated with <a href="http://adsabs.harvard.edu/abs/2016A%26A...595A..35I">Istrate et. al 2016</a>. MESA version 7624. The grid of models produced in this paper can be found <a href="http://adsabs.harvard.edu/abs/2016yCat..35950035I">here</a>.</p>
Data Tabels (One-dimensional N-layer thermal modelling as a basis for effective machine learning training data generation for nondestructive testing of composite parts.)
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A dataset to support dynamical modelling of the thermal dynamics of a super-insulated building
<p>This work consisted of controlled heating experiments for the purpose of control-oriented dynamical modelling. The building studied is the LivingLab at NTNU, which is a super-insulated single-family house. The dataset contains full details of the measurements, as well as preprocessed (reduced) data which can be readily used as input for dynamical modelling investigations.</p>
Air temperature and thermal comfort data measured and modelled for 121 workplaces in the Upper Rhine Valley
<p>The uploaded files contain the measured and modelled indoor data at different workplaces in the Upper Rhine Valley between August 1, 2021, and July 31, 2022 presented in the article "Predicting Indoor Air Temperature and Thermal Comfort in Occupational Settings Using Weather Forecasts, Indoor Sensors, and Artificial Neural Networks" by Sulzer et al. (2023), <a href="http://doi.org/10.1016/j.buildenv.2023.110077">doi.org/10.1016/j.buildenv.2023.110077</a>. Information about the characteristics of each workplace can be found in the appendix of the article. For every workplace two files are uploaded, one for the data of the indoor air temperature (Ta) and one for indoor physiological equivalent temperature (PET). The workplace ID and variable are stated in the filename. The columns in the files contain the following data:</p> <ul> <li>"Datetime (UTC)": This column contains the timestamp in UTC of the starting point of the interval used for the one-hour mean values .</li> <li>"MoBiMet data": This column contains the one-hour mean values of Ta or PET of the data derived every five minutes by the low-cost Mobile Biometeorology System (MoBiMet) at the corresponding workplace in °C. The MoBiMet are presented in detail in <a href="http://doi.org/10.3390/s22051828">doi.org/10.3390/s22051828</a>.</li> <li>"Used for": This column contains the information if the data point was used for training of the models (t), evaluation of the models (e), or not used for model training or evaluation due to missing indoor observation data (n).</li> <li>"Product 0 (ICON_outdoor)": This column, in the files for the indoor air temperature, contains the ICON-D2 data of the air temperature 2m a.g.l. in °C of the grid cell in which the associated work station is located.</li> <li>"Product 0 (ICON_outdoor) air temperatur 2m (C) input for PET calculation using RayMan","Product 1 (ICON_outdoor) vapor pressure 2m (hPa) input for PET calculation using RayMan","Product 1 (ICON_outdoor) wind speed 10m (m/s) input for PET calculation using RayMan", and "Product 1 (ICON_outdoor) global radiation surface (W/m²) input for PET calculation using RayMan": This columns contain the data of the outdoor air temperature 2m a.g.l. in °C, the vapor pressure 2m a.g.l., derived from the ICON-D2 weather forecast data of the grid cell in which the associated work station is located, which were used in RayMan Pro to calculate the PET for outdoors.</li> <li>"Product 2 (ANN_Generic)": This column contains the indoor data of PET or air temperature in °C modelled by an artificial neural network using generic data as input. The generic data contain hourly solar altitude and azimuth at each location, the weekday, and a sine and cosine function of the daily and yearly cycle.</li> <li>"Product 3 (ANN_AWS) without past data": This column contains the indoor data of PET or air temperature in °C modelled by an artificial neural network using generic data and the meteorological data of air temperature, vapor pressure, mean sea level pressure, global radiation, longwave downwelling radiation, and wind speed of an automated weather station in Freiburg (Station FRCHEM; 48°00’04’’ N; 7°50’55’’ E).</li> <li>"Product 3 (ANN_AWS) with past data": This column contains similar data than the column before but the artificial neural network models used "past data" of the automated weather station as additional input variables to model indoor air temperature and PET in °C. Additional to the hourly average of the meteorological data for each actual time (t), hourly averages for t-1 h, t-3 h, t-6 h, t-12 h, and t-24 h of air temperature, global radiation, and Longwave downwelling radiation are used as so called "past data".</li> <li>"Product 4 (ANN_ICON) without past data": This column contains the indoor data of PET or air temperature in °C modelled by an artificial neural network using generic data and the meteorological data of air temperature, vapor pressure, mean sea level pressure, global radiation, longwave downwelling radiation, and wind speed derived from the ICON-D2 weather forecast data of the grid cell in which the associated work station is located.</li> <li>"Product 4 (ANN_ICON) with past data": This column contains similar data than the column before but the artificial neural network models used "past data" of the ICON-D2 weather forecast data as additional input variables to model indoor air temperature and PET in °C. Additional to the hourly average of the meteorological data for each actual time (t), hourly averages for t-1 h, t-3 h, t-6 h, t-12 h, and t-24 h of air temperature, global radiation, and Longwave downwelling radiation are used as so called "past data".</li> <li>"Product 5 (ANN_Mixed) without past data": This column contains the indoor data of PET or air temperature in °C modelled by the same artificial neural network models as in Product 3 but applied for the same input data of Product 4.</li> <li>"Product 5 (ANN_Mixed) with past data": This column contains similar data than the column before but also takes into account the "past data".</li> </ul> <p>The data of Product 3 (ANN_AWS) and Product 5 (ANN_Mixed) are only available for locations in Freiburg, because the data of an automated weather station in Freiburg was used.<br> Data which is not available is stated as NA.</p>
ScienceDex guides
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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.