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62 results for “heat flow”
Geothermal heat source estimations through ice flow modelling at Mýrdalsjökull, Iceland - Datasets
<p>This repository contains data used in the study "<em>Geothermal heat source estimations through ice flow modelling at</em><br><em>Mýrdalsjökull, Iceland", </em>to be published in <strong>The Cryosphere. </strong>A detailed reference will be added after publication.</p> <p>Details on processing of the data and the creation of the simulated data can be found in the aforementioned publication.</p> <p><strong>Data Specifications:</strong></p> <ul> <li>Cartographic projection: ISN93 / Lambert 1993 (EPSG:3057, <a href="http://https/epsg.io/3057">https://epsg.io/3057</a>)</li> <li>Origin of Elevation: meters above GRS80 ellipsoid (WGS84)</li> <li>Raster data format: GeoTIFF</li> <li>Pléiades dataset includes only DEMs because the Pléiades ortho imagery is for licensed use only. Please contact the authors for further information on this.</li> </ul> <p><strong>File descriptions:</strong></p> <ul> <li><em><strong>bedrock_Magnusson_etal_2021.tif: </strong></em>contains bedrock data published by Magnússon et al. 2021 for the simulation domain used in the paper. See reference below.</li> <li><em><strong>surface_27092016_pleiades.tif: </strong></em>contains glacier surface data from September 27th, 2016 which is used as a starting geometry for the simulations described in the paper. This data is based on Pléiades satellite images.</li> <li><em><strong>surface_01092017_pleiades.tif: </strong></em>contains glacier surface data from September 1st, 2017 which is used as a reference target geometry for the simulations described in the paper. This data is based on Pléiades satellite images.</li> <li><em><strong>HM_run04.tif:</strong></em> contains the best fitting simulation based surface which was compared to <em><strong>surface_01092017_pleiades.tif </strong></em>in the paper.</li> <li><em><strong>HM_run04_hillshade.png: </strong></em>a simple hillshade image for preview purposes.</li> </ul> <p> </p>
WINTERC-G: a global upper mantle thermochemical model from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data
<p>WINTERC-G: A global, temperature and compositional model of the lithosphere<br> and upper mantle.<br> Version: v5.4, December 2020, J. Fullea, S. Lebedev, Z. Martinec, N. Celli<br> <br> Contact: Javier Fullea (jfullea@ucm.es)<br> Facultad de Fisica,<br> Universidad Complutense de Madrid (UCM),<br> Spain<br> ////////<br> Geophysics Section,<br> Dublin Institute for Advanced Studies<br> Dublin, Ireland<br> </p> <p>TYPE:<br> This contains files with:<br> i) the model directly on the triangular grid solved for in the surface wave inversion.</p> <p> ii) an interpolated grid at 0.5 deg lateral resolution for the density and density discontinuities used in the gravity field data inversion<br> </p> <p>If you have any questions regarding the methodology or the construction<br> of the model, please contact the authors. If you use the model, we would<br> request that you cite the reference indicated below, and appreciate<br> your feedback regarding the model and its application.</p> <p>Citation:</p> <p>Fullea, J., Lebedev, S., Martinec, Z., & Celli, N. L. (2021). WINTERC-G: mapping the upper mantle thermochemical heterogeneity from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data. Geophysical Journal International, 226(1), 146-191.</p> <p>*******************************<br> Summary: construction of the model.<br> WINTERC-G is a Waveform tomography and Gravity (geoid and gravity anomalies and gradiometric measurements<br> from ESA's GOCE mission) INversion model of the TEmpeRature and Composition of the lithosphere and upper mantle at<br> global scale. WINTERC-G is based on upon the integrated geophysical-petrological<br> approach LitMod (Afonso et al., 2008; Fullea et al. 2009) and, hence, all<br> relevant mantle rock physical properties modelled (seismic velocities and density) are<br> computed within a thermodynamically self-consistent framework allowing for a direct<br> parameterization in terms of the temperature and composition of the lithosphere-upper<br> mantle. The inversion is a two-step procedure. In a first step, we invert surface-wave, Rayleigh and Love<br> fundamental mode dispersion curves from a high resolution global dataset measured using waveform inversion,<br> along with surface heat flow and elevation (isostasy) for temperature and crustal structure<br> using a point-wise, non-linear, gradient-search inversion<br> over a triangular grid with an average 225 km lateral inter-knot spacing. In a second step we<br> use a fully parallelized spherical harmonic formalism to invert satellite gravity field data in<br> order to refine the initial crustal density and mantle composition distributions from the step 1<br> for a fixed temperature field.</p> <p>The parameter space in step 1 includes crust (densities and S-wave velocities for a three-layered crust)<br> and mantle variables (the depth of the thermal Lithosphere-Athenosphere-Boundary,<br> the thickness of the sublithospheric thermal buffer, the sublithospheric temperatures at 3 different<br> equispaced nodes down to 400 km, the lithospheric and sublithospheric mantle compositon, and<br> the the radial anisotropy at the 3 crustal layers and at 56, 80, 110, 150, 200, 260, 330,<br> and 400 km depths.</p> <p>The parameter space in step 2 is defined by the average crustal density, and the<br> mantle composition in the lithosphere and sublithosphere.<br> We use the output crustal density from step 1 as the<br> initial value in step 2 inversion. Mantle densities are derived based on the output temperature<br> field from step 1 (kept fixed) and the bulk mantle composition inversion variables.</p> <p> </p> <p> </p> <p>*******************************</p> <p>This archive contains the following files:<br> README (this file)<br> WINTERC-G_Vp-Vs.lis (triangular grid)<br> WINTERC-G_rad_anis_Vs.lis (triangular grid)<br> WINTERC-G_Temperature.lis (triangular grid)<br> WINTERC-G_Density.lis (triangular grid)<br> WINTERC-G_LAB.lis (triangular grid)<br> WINTERC_T_rho_1D.z (1D average model of temperature and density)<br> rho_*_out.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_continental.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Ice.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Bed.xyz (0.5 deg egular grid for gravity field)<br> Global_Moho_WINTERC-G.xyz (0.5 deg egular grid for gravity field)</p> <p><br> Files in the triangular grid with an average 225 km lateral inter-knot spacing (12232 grid points):</p> <p>* WINTERC-G_Vp-Vs.lis: Vp and Vs (in km/s) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) Vp (km/s) Vs(km/s)<br> 5640 93.72 4.135 -5.0 3.91 2.11</p> <p><br> * WINTERC-G_rad_anis_Vs.lis: radial anisotropy, (Vsh-Vsv)/Vs_iso (in %) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) anisotropy (%)</p> <p>* WINTERC-G_Temperature.lis: temperature (in ºC) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth (km, <0 downwards) T (ºC) dT (%) dT(K) <br> 6437 297.20 -2.524 -259.000 1431.9 -1.91 -27.9<br> The anomalies dT are in % and K with respect to the 1D model in WINTERC_T_rho_1D.z (column 2).</p> <p>* WINTERC-G_Density.lis: density (in kg/m3) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) rho (kg/m3) drho(%) drho(kg/m3)<br> The anomalies drho are in % and kg/m3 with respect to the 1D model in WINTERC_T_rho_1D.z (column 3).</p> <p>* WINTERC_T_rho_1D.z: 1D average model of temperature (column 2 in ºC) and density (column 3 in kg/m3) with a vertical grid step of 2 km <br> 5.00000000 0.0000000000000000 6.0259973839110526<br> 3.00000000 0.0000000000000000 38.960571309690394<br> 1.00000000 0.33634006819423840 174.42296045978722<br> -1.00000000 3.8888495253719624 1692.8437489147236<br> -3.00000000 23.974111923225379 1863.8834351235944<br> -5.00000000 47.727920701943034 2568.2414495590924<br> -7.00000000 89.633398074381162 2819.8386016341910<br> -9.00000000 137.01489361657013 2839.5325893195904<br> -11.0000000 182.35233447017222 2897.6600872935287<br> -13.0000000 224.46247069572485 2945.2036923862997<br> -15.0000000 260.63395547331390 3069.6809340323475<br> -17.0000000 292.28175449521456 3132.4574175461721<br> -19.0000000 322.29571965406632 3145.5747337463940<br> -21.0000000 351.58698283375054 3157.2401512748038<br> -23.0000000 380.30002225705056 3177.0000420059773<br> -25.0000000 408.50259805632055 3183.6651032398490<br> -27.0000000 436.22632217636487 3190.9586785996116<br> -29.0000000 463.48733903170023 3198.9369509456310<br> -31.0000000 490.29841705549831 3209.7229872383764<br> -33.0000000 516.71149258457456 3221.6329506091679<br> ...</p> <p>Files in the interpolated regular grid at 0.5 deg lateral resolution used for gravity field data inversion:</p> <p> * rho_c_out.xyz: average crustal density<br> * rho_submoho_out.xyz: mantle density below the Moho discontinuity<br> * rho_*_out.xyz: mantle density defined at different model depths: 20, 35, 56, 80, 110, 150, 200, 260, 330 and 400 km.</p> <p> Format for the density files:<br> # longitude latitude density (kg/m3)<br> <br> Files containing layer discontinuities:</p> <p> * ETOPO2_km_continental.xyz: surface elevation including ice sheet and 0 in marine areas (km, <0 upwards)</p> <p> * ETOPO2_km_depth_Ice.xyz: surface elevation including ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * ETOPO2_km_depth_Bed.xyz: bedrock surface elevation without ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * Global_Moho_WINTERC-G.xyz: crust-mantle discontinuity depth (km, >0 downwards)</p> <p> Format for the discontinuity files:<br> # longitude latitude depth (km)<br> <br> <br> The gravity field in WINTERC-G is computed using an spherical harmonic formalism and a model discretization<br> in 13 layers with laterally varying density. The first 7 layers are characterized by top and bottom boundaries with laterally varying radius whereas the last 6 layers are defined by top and bottom boundaries with constant radius:</p> <p>1/ Water: from ETOPO2_km_continental.xyz to ETOPO2_km_depth_Ice.xyz with rho=1030 kg/m3 (constant vertically)</p> <p>2/ Ice: from ETOPO2_km_depth_Ice.xyz to ETOPO2_km_depth_Bed.xyz with rho=910 kg/m3 (constant vertically)</p> <p>3/ Crust: from ETOPO2_km_depth_Bed to Global_Moho_WINTERC-G.xyz with rho=rho_c_out.xyz (constant vertically)</p> <p>4/ submoho-20km: from Global_Moho_WINTERC-G.xyz to z_20km (file with 20 km everywhere except where z_moho>20km) with rho=rho_submoho_out.xyz (top) and rho=rho_20km_out.xyz (bottom)</p> <p>5/ 20km-36km: from z_20km (file with 20 km everywhere except where z_moho>20km) to z_36km (file with 36 km everywhere except where z_moho>36km) with rho=rho_20km_out.xyz (top) and rho=rho_36km_out.xyz (bottom)</p> <p>6/ 36km-56km: from z_36km (file with 36 km everywhere except where z_moho>36km) to z_56km (file with 56 km everywhere except where z_moho>56km) with rho=rho_36km_out.xyz (top) and rho=rho_56km_out.xyz (bottom)</p> <p>7/ 56km-80km: from z_56km (file with 56 km everywhere except where z_moho>56km) to 80 km depth with rho=rho_56km_out.xyz (top) and rho=rho_80km_out.xyz (bottom)</p> <p>The next 6 layers are computed using the constant radius option:</p> <p>8/ 80km-110km: from z=80km to z=110 km with rho=rho_80km_out.xyz (top) and rho=rho_110km_out.xyz (bottom)</p> <p>9/ 110km-150km: from z=110km to z=150 km with rho=rho_110km_out.xyz (top) and rho=rho_150km_out.xyz (bottom)</p> <p>10/ 150km-200km: from z=150km to z=200 km with rho=rho_150km_out.xyz (top) and rho=rho_200km_out.xyz (bottom)</p> <p>11/ 200km-260km: from z=200km to z=260 km with rho=rho_200km_out.xyz (top) and rho=rho_260km_out.xyz (bottom)</p> <p>12/ 260km-330km: from z=260km to z=330 km with rho=rho_260km_out.xyz (top) and rho=rho_330km_out.xyz (bottom)</p> <p>13/ 330km-400km: from z=330km to z=400 km with rho=rho_330km_out.xyz (top) and rho=rho_400km_out.xyz (bottom)</p> <p> </p> <p> </p>
Development and analysis of entropy stable no-slip wall boundary conditions for the Eulerian model for viscous and heat conducting compressible flows
<p>The database used in the submission of "Development and analysis of entropy stable no-slip wall boundary conditions implementation of the Eulerian model for viscous and heat conducting compressible flows."</p> <p>Abstract: Nonlinear entropy stability analysis is used to derive entropy stable no-slip wall boundary conditions for the Eulerian model proposed by Svärd ( <em>Physica A: Statistical Mechanics and its Applications, 2018 </em>). and its spatial discretization based on entropy stable collocated discontinuous Galerkin operators with the summation-by-parts property for unstructured grids. A set of viscous test cases of increasing complexity are simulated using both the Eulerian and the classic compressible Navier–Stokes models. The numerical results obtained with the two models are compared, and differences and similarities are then highlighted.</p>
Magnetic field influence on heat transfer in inclined laminar ferronanofluid flow
Open the record for dataset details and reuse information.
Heat Flow Density in Europe - GeoDH project
<p>The dataset includes a shapefile showing areas in Europe where the Heat Flow Density is greater than 90 mW/m². <br><br>This dataset was developed for assessing the potential of Geothermal District Heating in Europe as part of the <strong>GeoDH project</strong> (<a href="http://geodh.eu/" target="_new" rel="noopener">http://geodh.eu/</a>). Please note that this represents the<strong> state of the art as of 2014</strong> and that geological, technological, and regulatory developments may have occurred since its creation, and users should verify if more recent data is available for their purposes.</p>
Estimation of groundwater flow rate by an actively heated fiber optics based thermal response test in a grouted borehole
<p>The dataset contains the numerical data and the <em>in-situ</em> measurements in the manuscript titled "Estimation of groundwater flow rate by an actively heated fiber-optics-based thermal response test in a grouted borehole". The data is stored in MAT files, which are Binary MATLAB files. There are a series of codes used in this manuscript to estimate groundwater flow rates. The codes were written in MATLAB Live Script, version 2021b.</p> <ul> <li>The numerical data contains temperatures of the heating stage in different thermal response tests in a numerical model, which considers the borehole effects. The model is set up by COMSOL Multiphysics, and a series of flow rates is set to the model respectively for different thermal response tests.</li> <li>The <em>in-situ</em> measurements include temperatures of the heating stage in an actively heated fiber-optics-based thermal response test, which was performed in July 2021 in the grouted borehole, which is located in the lower section of the Sima bend of the Yangtze River. The temperature for the flow rate estimation was thinned to 120 s records from 10s records for the limited computing resources.</li> <li>The <em>data_process.mlx</em> provides pre-processing for the observational data recorded by Silixa Ultima-M MK2 DTS. The <em>estimation_process.mlx </em>gives a groundwater flow estimation case in a grouted borehole.</li> </ul>
Understanding Sampling Bias in the Global Heat Flow Compilation
<p>Geothermal heat flow measurements, including calculated weights and geological, tectonic and topographic settings.</p> <p>Paper in review (July 14th, 2022)</p>
The thermal state of Volgo–Uralia from Bayesian inversion of surface heat flow and temperature [data set]
<p>This collection contains the dataset and the code which were used to find the thermal parameters’ lateral variations of the Volgo–Uralian subcraton through the Bayesian Markov Chain Monte Carlo (MCMC) statistical approach. The code originally was given in the analogous study of Antarctica's geothermal structure by Lösing et al. (2020) and it can be found in https://github.com/MareenLoesing/GHF-Antarctica-Bayesian. The main changes to the code of Lösing et al. (2020) are listed in the section 2 of the readme file.</p> <p>For an official use of the Bayesian inversion code please also cite: Lösing, M., Ebbing, J. & Szwillus, W. (2020) Geothermal Heat Flux in Antarctica: Assessing Models and Observations by Bayesian Inversion. Front. Earth Sci., 8, 105. doi:10.3389/feart.2020.00105</p> <p>The lateral variations of the thermal parameters for the single-layer and multi-layer crust are saved in “GHF_Volgo-Uralia_Single-layer.csv” and “GHF_Volgo-Uralia_Multi-layer.csv” respectively.</p>
District heating modelling data for the publication "Integration of feed flow temperatures in unit commitment models of future district heating systems"
<p>Modelling data for a district heating system model which has been used for the publication "Integration of feed flow temperatures in unit commitment models of future district heating systems" on the 4th Generation District Heating (4GDH) conference 2018.</p>
Visualization of adiabatic gas-liquid flow in a cross-corrugated plate heat exchanger channel: Part 1 - Original photographs, uniform two-phase distribution
<p>These measurement data are obtained and analyzed as part of a research project on adiabatic gas-liquid flow in a cross-corrugated plate heat exchanger channel. (See list of publications below). <br> The following Creative Commons license applies to the research data (images and measurement values) uploaded to the online repositories:<br> CC-BY 4.0<br> Author: Susanne Buscher</p> <p>The measurement data is published in 2 data sets: </p> <p>Data set I: Original image data (4 parts): <br> - uniform gas injection, part 1: https://doi.org/10.5281/zenodo.7985771; <br> - uniform gas injection, part 2: https://doi.org/10.5281/zenodo.7986374; <br> - uniform gas injection, part 3: https://doi.org/10.5281/zenodo.7986384; <br> - non-uniform gas injection (part 4): https://doi.org/10.5281/zenodo.8067163<br> This data set contains the original photographs of the two-phase flow in the cross-corrugated channel obtained with a high-resolution camera. In addition, the corresponding experimental parameters and flow patterns (for part 1-3 only) are included in the CSV files.<br> For uniform and non-uniform gas injection, respectively, the images were stored in sequentially numbered folders. The numbers of the folders correspond to the numbers of the measurement points listed in the attached CSV files with the associated experimental parameters.<br> The image folders are grouped in ZIP archives. Each ZIP archive contains the single-phase reference images which can be used for the two-phase images to conduct background subtraction, because the lighting conditions are equal for all images in one ZIP archive. </p> <p>Data set II: Measurement values and processed image data: <br> - https://doi.org/10.14279/depositonce-17868; <br> This data set contains all measurement values and calculated results of all measurement points in the Excel and CSV files (e.g. pressure drop, volumetric flow rates, void fraction, measurement uncertainties).<br> In addition, the results of the image processing algorithm are included in the Excel and CSV files (e.g. mean bubble diameter, maximum bubble diameter, local film flow ratio, extent of the two-phase distribution across the channel width, measurement uncertainties).<br> The image folders contain the pre-processed images which were the input to the digital image analysis (i.e. the aligned and cropped image section of the channel without inlet, outlet, and peripheral regions and after subtraction of the image background), and the post-processed images visualizing the output of the digital image analysis for this image (i.e. detected objects are inserted as colored regions in the image section; the meaning of colors was explained in the publications of 2022 and 2023). <br> In this dataset, the image folders are also subdivided into measurements with uniform and non-uniform gas injection and designated with the numbers of the measurement points, which are listed in the Excel and CSV files.</p> <p>The two datasets are the supplementary research data for the following publications: <br> - S. Buscher, 2023, Visualization, measurement, and modelling of adiabatic gas-liquid flow in a cross-corrugated plate heat exchanger channel, Doctoral thesis, Technische Universität Berlin, https://doi.org/10.14279/depositonce-17866. (supplemented by data sets I and II) <br> - S. Buscher, 2019, Visualization and modelling of flow pattern transitions in a cross-corrugated plate heat exchanger channel with uniform two-phase distribution, International Journal of Heat and Mass Transfer 144, 118643, https://doi.org/10.1016/j.ijheatmasstransfer.2019.118643. (supplemented by data set I, part 1-3)<br> - S. Buscher, 2021, Two-phase pressure drop and void fraction in a cross-corrugated plate heat exchanger channel: Impact of flow direction and gas-liquid distribution, Experimental Thermal and Fluid Science 126, 110380, https://doi.org/10.1016/j.expthermflusci.2021.110380. (supplemented by the measurement values in the Excel and CSV files of data set II)<br> - S. Buscher, 2022, Digital image analysis of gas-liquid flow in a cross-corrugated plate heat exchanger channel: A feature-based approach on various two-phase flow patterns, International Journal of Multiphase Flow 154, 104149, https://doi.org/10.1016/j.ijmultiphaseflow.2022.104149. (supplemented by data set II)</p>
Datasets associated with Uruti Basin gas hydrate heat flow feature investigated as part of Roger Revelle voyage RR1508
<p>Processed seismic reflection data and associated files from Roger Revelle voyage RR1508: 16 May - 18 June 2015. The research voyage aimed to characterise the thermal regime of the gas hydrate systems on the southern Hikurangi margin east of New Zealand. Data were processed using the Globe Claritas processing software.</p> <p>All SEG-Y files have the following binary header word definitions, required for loading data:</p> <p>Start time: 2-byte integer: Bytes 105-106</p> <p>Trace Sample Count: 2-byte integer: Bytes 115-116</p> <p>Sample Interval (microseconds): 2-byte integer: Byte 117-118</p> <p>CDP Number: 4-byte integer: Bytes 5-8</p> <p>CDP X location: 4-byte integer: Bytes 197-200 (Note: Coordinates are in metres of UTM Zone 60S, WGS84 Datum)</p> <p>CDP Y location: 4-byte integer: Bytes 201-204 (Note: Coordinates are in metres of UTM Zone 60S, WGS84 Datum)</p>
The asymmetric diurnal latent heat flux in Chi-Lan montane cloud-fog forest: CLM simulations and sap flow observations
<p>Chilan_30min_sap_flow_V_2020JJA.csv recorded the data of sap flow velocity during JJA 2020.</p> <p>CL_CTR.*.nc is the analyzed CTR simulations which consider fog interception as a source of canopy water.</p> <p>CL_EXP.*.nc is the analyzed EXP simulations that do not allow the canopy to hold the water.</p>
Experimental Investigation of the Heat Transfer between Finned Tubes and a Bubbling Fluidized Bed with Horizontal Sand Mass Flow
<p>Data repository for the paper:</p> <p>Thanheiser, S.; Haider, M.; Schwarzmayr, P. Experimental Investigation of the Heat Transfer between Finned Tubes and a Bubbling Fluidized Bed with Horizontal Sand Mass Flow. Energies 2022, 15, 1316. https://doi.org/10.3390/en15041316</p>
NUMERICAL ANALYSIS OF FLOW STRUCTURE AND HEAT TRANSFER IN BUBBLING FLUIDIZED BEDS
<p>The videos show the movements of selected particles in different geometries of fluidized bed heat exchangers. Two geometries without auxiliary measures and one with air cushion technology are shown.</p>
The data files for the article "Impact of the core deformation on the tidal heating and flow in Enceladus' subsurface ocean"
<p>The data are given for each figure and each file contains a header regarding information on the columns in the data files. </p>
Dataset for the Figure 2 in manuscript "Numerical study of coupled water and vapour flow, heat transfer, and solute transport in variably-saturated deformable soil during freeze-thaw cycles"
<p>This dataset includes the gathered experimental measurements of a freezing test by Wu (2017) for the model's verification shown in Figure 2 of the manuscript entitled 'Numerical study of coupled water and vapour flow, heat transfer, and solute transport in variably-saturated deformable soil during freeze-thaw cycles'' by Huang, X., and Rudolph, D.L.</p>
Premature Newborns Treated With Less Invasive Surfactant Administration Under Heated Humidified High-flow
ClinicalTrials.gov study NCT06398691. IPD Sharing: YES. Countries: 1. Publications: 3.
Dataset to accompany: Heat flux in low mass flux horizontal cryogenic flow
Open the record for dataset details and reuse information.
TIGAR tropical heat source experiments with realistic background flows
<p>A barotropic version of the TIGAR (Transient Inertia-Gravity And Rossby wave dynamics) model has been run at T170 resolution to simulate the effect of tropical heat sources mimicking convection in different realistic background flows. The heat source was treated as a forcing of the continuity equation. The background flows include seasonal mean zonally-averaged zonal wind from 1993, 1999, 2009, 2012 and 2016 derived from ERA5. TIGAR solves the rotating shallow water equations by applying Hough harmonics as spectral basis functions thereby enabling the analysis of Rossby and Inertia-gravity wave dynamics. More details of the model are available at: <a href="https://doi.org/10.1002/qj.4006">https://doi.org/10.1002/qj.4006</a> .</p>
Calibration of a movable heat pulse probe in borehole for measuring horizontal groundwater flow velocity in deep aquifers
<ol> <li>The first file is the flow profile data calculated by analytical solution in the steady-state flow. The calculation region is 30cm×30cm.</li> <li>The secend file is the temperature response data calculated by finite difference method. The simulated temperature response data corresponding to all heat pulse experiments are calculated and listed here.</li> </ol>
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.