Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
87
datasets available to search
ShareScore release 0.7.1
Dataset results
87 results for “Heat flux”
Data and software: Stress and heat flux via automatic differentiation
<h4><strong>glp-archive</strong></h4><h2><strong>Code and Data for "Stress and heat flux with automatic differentiation"</strong></h2><p>This repository contains data, code, and related artefacts supporting the following publication (<a href="https://arxiv.org/abs/2305.01401">preprint</a>):</p><p>Stress and heat flux via automatic differentiation</p><p>by Marcel F. Langer, J. Thorben Frank, and Florian Knoop</p><p><i>J. Chem. Phys.</i> 159, 174105 (2023) <a href="https://doi.org/10.1063/5.0155760">doi:10.1063/5.0155760</a></p><p>This repository is available at <a href="https://github.com/sirmarcel/glp-archive">https://github.com/sirmarcel/glp-archive</a>. Selected versions are archived on Zenodo, under <a href="https://doi.org/10.5281/zenodo.7852529">doi:10.5281/zenodo.7852529</a>.</p><h2><strong>Overview</strong></h2><p>Each subfolder in this repository contains a README.md with additional information. The subfolders are:</p><ul><li>results/: Data and code that produced the figures in the manuscript</li><li>work/: Computational workflows, models, etc.</li><li>infra/: Project-specific infrastructure code</li><li>meta/: Scripts for assembling this archive; can be ignored but is retained for transparency.</li></ul><h2><strong>Related external code</strong></h2><p>The work in this repository relies on a few tools that the authors maintain separately:</p><ul><li><a href="https://github.com/sirmarcel/glp">glp</a> implements the quantities discussed in the manuscript</li><li><a href="http://github.com/thorben-frank/mlff">mlff</a> implements the so3krates model</li><li><a href="https://github.com/flokno/tools.mlff">tools.mlff</a> provides tools for the equation of state experiments</li></ul><p>These tools were developed during the work in the manuscript. The following versions/tags reflect what was used to obtain results:</p><ul><li>glp @ v0.1.0 (tag)</li><li>mlff @ v1.0 (branch)</li><li>mlff.tools @ v0.0.1</li></ul><p>We additionally note that the GK-MD functionality has been factored out into <a href="https://github.com/sirmarcel/gkx">gkx</a>.</p><h2><strong>Versions</strong></h2><ul><li>v1.1: published version, archived at <a href="https://doi.org/10.5281/zenodo.8406532">doi:10.5281/zenodo.8406532</a></li><li>v1.0: arXiv submission v1, archived at <a href="https://doi.org/10.5281/zenodo.7852530">doi:10.5281/zenodo.7852530</a></li></ul>
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Half-hourly growing season, chamber-based, CO2 flux data, 2009-2021
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data contains CO2 fluxes measured using an automated chamber system that measures net ecosystem CO2 exchange (NEE). Measurements are made every ~1.5 hours and modeled half-hourly. Half hour ecosystem respiration is modeled using an exponential Q10 relationship when light conditions are low (PAR<5umol/m2/s) and using a hyperbolic light relationship when PAR>5umol/m2/s. GPP is calculated as the difference between NEE and Reco.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Growing season, chamber-based, CO2 flux data, 2009-2021
This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warming affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieved using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011. This data set includes measured values of CO2 fluxes during the growing season.
RADIT: A Machine Learning-Reconstructed Dataset of River Discharge, Temperature, and Heat Flux into the Arctic Ocean
<p>The Reconstructed Arctic-draining river DIscharge and Temperature (RADIT) dataset provides daily records of river discharge, temperature, and heat flux for 25 major Arctic-draining rivers from 1950 to 2023. Using machine learning methods and ERA5-Land reanalysis data, we reconstructed these key hydrological variables with high accuracy (most NSEs > 0.8).</p> <p>Due to licensing restrictions and to encourage adherence to the stated licenses of the original input data, this dataset only provides the reconstructed (filled) values. Users can obtain the complete historical observational data from their original publicly available sources as detailed in our documentation. By combining these original observations with our reconstructed data, a comprehensive and continuous daily dataset from 1950 to 2023 can be assembled. Clear instructions and links for downloading the original observational data used in this study can be found at: <a href="https://github.com/zhwang24/RADIT-Reconstructed-Arctic-River-Data" target="_blank" rel="noopener">https://github.com/zhwang24/RADIT-Reconstructed-Arctic-River-Data</a>. Should you encounter any issues or have questions, please feel free to contact the first author, Zihan Wang (zhwang2018@163.com).</p>
Sensible heat fluxes control cloud trail strength
<p>This dataset contains a minimal set of data used to create figures in Johnston et al. (2023) Sensible Heat Fluxes Control Cloud Trail Strength, Quarterly Journal of the Royal Meteorological Society. Additional liquid water path data from the control experiment (H250E250) is also provided to better visualise the cloud field in this central experiment.</p>
Data used in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)"
<p>Data files used in the analysis in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)".</p> <p>Data were collected during the RV NB Palmer NBP2202 cruise, during the 2022 TARSAN campagine in the Amundsen Sea.</p> <p>Underway data provides daily files from the underway and meteorology sensors in JGOFS format. CTD data collected from the cruise. Information about sensors and data formats is included in the data report.</p> <p>Glider data was processed through the UEA Seaglider Toolbox (https://bitbucket.org/bastienqueste/uea-seaglider-toolbox/src/toolbox/) and is provided in Matlab format.</p> <p> </p> <p>Manuscript abstract:</p> <p>In coastal polynyas, where sea–ice formation occurs, it is crucial to have accurate estimates of heat fluxes in order to predict future rates of sea–ice formation. The Amundsen Sea Polynya is the fourth largest coastal polynya around Antarctica, yet remains poorly observed because of its remoteness. Consequently, we rely on models and reanalysis that are unvalidated to study the effect of atmospheric forcing on polynya dynamics. We use summer ship-board data from the NBP22/02 cruise to understand the turbulent heat flux dynamics in the Amundsen Sea Polynya and evaluate our ability to represent these dynamics in ERA5. We show that cold and dry air outbreaks from Antarctica enhance air–sea temperature and humidity gradients, triggering episodic heat loss events. The heat loss is larger along the ice shelves, and it is also where the ERA5 turbulent heat flux exhibits the largest biases, underestimating the flux by up to 141~W~m$^{-2}$ due to its coarse resolution and misrepresentation of ice-shelf location. By reconstructing a turbulent heat flux product from ERA5 variables using a nearest neighbour approach to obtain sea surface temperature, we decrease the bias to 107 W m$^{-2}$. Using a 1D-model, we show that the mean co-located ERA5 heat loss underestimation of -28~W~m$^{-2}$ led to an overestimation of the summer evolution of sea surface temperature (heat content) by +0.76~°C (+8.2e+07~J) over 35-days. By obtaining the reconstructed flux, the reduced heat loss bias (12 W~m$^{-2}$) reduced the seasonal bias in sea surface temperature (heat content) to -0.17~°C (-3.30e+07~J) over the 35-days. This study shows that caution should be applied when retrieving ERA5 turbulent flux along the ice shelves, and that a reconstructed flux using ERA5 variables shows better accuracy.</p> <p> </p> <p> </p>
Data and software: Heat flux for semi-local machine-learning potentials
<p><br> This repository contains data, code, and related artefacts supporting the following publication:</p> <p>"Heat flux for semi-local machine-learning potentials"<br> by Marcel F. Langer, Florian Knoop, Christian Carbogno, Matthias Scheffler, and Matthias Rupp<br> arXiv: TBD<br> doi: TBD<br> </p> <p>More details can be found in the main README.md file, and the README.md files in the subfolders.</p> <p><br> For any further questions, feel free to contact mail@marcel.science, @marceldotsci on Twitter, or @marcel@sigmoid.social.</p> <p> </p>
Infilled climate and heat flux data for Tvan towers data loggers (CR3000), 2008 - ongoing.
Two identical 3-meter towers were installed near T-Van in 2007, and continuous meteorological and eddy covariance data are presented beginning in 2008. The sampling interval was 5 seconds for the meteorological data and 10 Hz for the eddy covariance data, and 30-minute means of all variables were calculated using a Campbell Scientific CR3000 datalogger. The 30-minute mean data were subsequently averaged to create this 24-hour mean dataset. Information about specific sensors, instrumental orientation, units, and data post-processing and infilling procedures are contained in the metadata for this file.
NACLIM - Fluxes: Hornbanki Section Atlantic inflow volume and heat fluxes
<p><strong>Last Update: 31 October 2014</strong></p> <p><strong>Data set:</strong> Atlantic inflow volume and heat fluxes at Hornbanki section </p> <p><strong>Description:</strong> Monthly mean of Atlantic inflow volume and heat transports </p> <p><strong>Period: </strong>January 1994 – July 2014</p> <p><strong>Location:</strong> 66°50′ N 21°30′ W (map) </p> <p><strong>Instruments: </strong>CTD, moored current meters </p> <p><strong>Variables:</strong> AW_transp, Heat_transp - Atlantic Water volume transport [Sv] and respective heat transport [TW]</p> <p><strong>Source:</strong> Steingrímur Jónsson and Hedinn Valdimarsson (MRI)</p>
Changes in core-mantle boundary heat flux patterns throughout the supercontinent cycle: Data
<p>This repository accompanies the paper</p> <p> </p> <p>```</p> <p>Dannberg, J., Gassmoeller, R., Thallner, D., LaCombe, F., Sprain, C.: Changes in core-mantle boundary heat flux patterns throughout the supercontinent cycle.</p> <p>```</p> <p> </p> <p>This repository contains instructions for how to obtain the boundary conditions from GPlates, ASPECT code, data and model setups, and scripts for converting the ASPECT model output to spherical harmonics so it can be used in geodynamic simulations. To reproduce the workflow follow the steps:</p> <p> </p> <p>- To create the velocity boundary conditions for the ASPECT models, download the plate reconstruction from 'Merdith, A.S., Williams, S.E., Collins, A.S., Tetley, M.G., Mulder, J.A., Blades, M.L., Young, A., Armistead, S.E., Cannon, J., Zahirovic, S. and Müller, R.D., 2021. Extending full-plate tectonic models into deep time: Linking the Neoproterozoic and the Phanerozoic. Earth-Science Reviews, 214, p.103477', which can be found here:</p> <p> </p> <p>https://doi.org/10.5281/zenodo.4485738</p> <p> </p> <p>To create the 'lat_lon_velocity' files, take the following steps in GPlates:</p> <p> </p> <p>1. Load all of the files from the Merdith et al, 2021 plate reconstruction into a Feature Collection which can be saved as a project (the project for our visualization is 'project.gproj').</p> <p>2. Establish the output grid (ours is lat_lon_velocity_domain_91_181): Features -> Generate Velocity Domain Points -> Latitude Longitude -> Number of latitudinal grid intervals=91, Number of longitudinal grid intervals=181, number of nodes=16652.</p> <p>3. To output the point velocities we used for the models: Reconstruction -> Export -> Add Export -> Velocities, GPML(*.gpml), velocity_%nMa</p> <p>- The data files we created following this workflow are part of this data publication and can be found in the `lat_lon_velocity` folder.</p> <p> </p> <p>- The global spherical convection models of the publication were created using two different ASPECT configurations:</p> <p> </p> <p>Models `thermal`, `thermochemical`, and `p-T-dependent` were run using:</p> <p> </p> <p>```</p> <p>-----------------------------------------------------------------------------</p> <p>-- This is ASPECT, the Advanced Solver for Problems in Earth's ConvecTion.</p> <p>-- . version 2.4.0-pre (limit_shear_heating, 4f45a72fe)</p> <p>-- . using deal.II 9.4.0-pre (3d869ba6cd1fd462624e09dc232e34ed17880701)</p> <p>-- . with 64 bit indices and vectorization level 2 (256 bits)</p> <p>-- . using Trilinos 12.18.1</p> <p>-- . using p4est 2.3.2</p> <p>-----------------------------------------------------------------------------</p> <p>```</p> <p> </p> <p>Models `strong basalt` and `weak ppv` were run using:</p> <p> </p> <p>```</p> <p>-----------------------------------------------------------------------------</p> <p>-- This is ASPECT, the Advanced Solver for Problems in Earth's ConvecTion.</p> <p>-- . version 2.5.0-pre (limit_shear_heating_and_ppv, a4812c95a)</p> <p>-- . using deal.II 9.4.2</p> <p>-- . with 64 bit indices and vectorization level 3 (512 bits)</p> <p>-- . using Trilinos 13.2.0</p> <p>-- . using p4est 2.3.2</p> <p>-----------------------------------------------------------------------------</p> <p>```</p> <p> </p> <p>- The two modified ASPECT versions are included in this data package. The repository including full</p> <p>version history is until further notice available as branch `limit_shear_heating` and branch `limit_shear_heating_and_ppv`</p> <p>in the repository `https://github.com/jdannberg/aspect.git`.</p> <p> </p> <p>- Running these models also requires plugins that are located in the `shared_libs` folder in this repository and that need to be compiled. Navigate into this directory and follow the steps:</p> <p> </p> <p>1. `cmake -D Aspect_DIR=PATH_TO_ASPECT` (replace `PATH_TO_ASPECT` with the directory where you compiled ASPECT).</p> <p>2. `make`</p> <p>- Now the models in this repository can be started. You should start them from the `input_files` directory so that all paths are set correctly and you can start them with the ASPECT executable in your build folder.</p> <p> </p> <p>- The files in `aspect_input_files` correspond to the models presented in the paper following the same naming scheme.</p> <p> </p> <p>- To convert the ASPECT heat flux output to spherical harmonics we used the script `analyze_heatflux_mpi_gmt.py` in `SPH_scripts`,</p> <p>which requires modification to point to the correct ASPECT statistics file and the correct output directory.</p> <p> </p> <p>- The final heat flux output is included in this data package in the `heat_flux` folder, which includes archives of the processed heat flux output in 1 Myr time intervals with 0 being the start of the model run and the highest timestep number representing the present day state.</p>
Datasets for "A 2D Kaleidoscope of Electron Heat Fluxes Driven by Auroral Electron Precipitation"
<p>These three datasets are supplemental material for the Geophysical Research Letters article, A 2D Kaleidoscope of Electron Heat Fluxes Driven by Auroral Electron Precipitation.</p> <p> </p> <p>Data Set DS1. Te data plotted in Figure S1. This is the 3-beam averaged Te data described in Text S1. The first row is the heading that, after Time, lists the altitudes in meters of the data in each column. The first column is time in the format: Year-Month-Day/Hour-Minute-Second.</p> <p> </p> <p>Data Set DS2. Te data errors plotted in Figure S1. This is the 3-beam averaged Te data propagated errors described in Text S1. The first row is the heading that, after Time, lists the altitudes in meters of the data in each column. The first column is time in the format: Year-Month-Day/Hour-Minute-Second.</p> <p> </p> <p>Data Set DS3. THEMIS ASI data at the PFISR location used to calculate the heat flux plotted in Figure S1. The first row is the header. Time, the first column, is in the format: Year-Month-Day/Hour-Minute-Second. The following columns are: geographic longitude, geographic latitude, energy flux for a Gaussian distribution, mean energy for a Gaussian distribution, energy flux for a Maxwellian distribution, mean energy for a Maxwellian distribution. Units are included in the header.</p>
CMB heat flux PCA results
<p>Results of the CMB heat flux PCA from the Coltice et al. (2019) mantle convection model in the numpy (.npy) format. The PCA is computed on the snapshots of the simulations between 300 Myr and 1131 Myr in the simulation time.</p> <p>-avg_pattern.npy: Spherical harmonic decomposition of the CMB heat flux average pattern</p> <p>-patterns.npy: Spherical harmonic decomposition of the PCA components patterns</p> <p>-sing_val.npy: Singular value of the PCA components</p> <p>-weights.npy: Time dependent weights of the PCA components</p>
Assets (code, scripts and datasets) for the manuscript "Correction of the Air-Sea Heat Fluxes in Ocean General Circulation Models Using Neural Networks"
<p>This dataset contains all relevant software and data related to the manuscript "Correction of the Air-Sea Heat Fluxes in Ocean General Circulation Models Using Neural Networks", submitted to AGU journals.</p>
Supplementary materials for the manuscript "Revisiting the Contributions of Surface Sensible and Latent Heat Fluxes to Tropical Cyclones"
<p>A subset of outputs of the STD, CTL, SH+LH-_OUT and SH-LH+_OUT experiments in "Revisiting the Contributions of Surface Sensible and Latent Heat Fluxes to Tropical Cyclones".</p>
Image-based deformation measurements for validation of fusion divertor armour under high heat flux loading
<p>This data set contains all the images, DIC data and python scripts and FE input files that allows the reproduction of all results in the paper "Image-based deformation measurements for validation of fusion divertor armour under high heat flux loading" that this data is linked to.</p>
Soil temperature, moisture, and ground heat flux measurements at LPTEG-TREES-1 site, 2019/07/01-2019/09/09
<p>This dataset includes the original measurements of soil temperature, moisture, and surface ground heat flux reconstructed from heat flux plate measurements at the LPTEG-TREES-1 site (N66°53’55’’, E66°45’27’’). Soil temperature (T_soil, °C) was measured at 2 cm below the peat layer surface. Soil liquid water content (theta_liq, m<sup>3</sup>/m<sup>3</sup>) was measured 2 cm below the mineral soil layer surface. Observation for ground heat flux at the soil surface (G_obs, W/m<sup>2</sup>) was reconstructed from the heat flux plate (buried 6 cm below the mineral soil surface) measurement plus the energy storage above the heat flux plate calculated based on soil temperature and soil heat capacity.</p>
Datasets for simulating heat and CO2 fluxes in Beijing using SUEWS V2020b
<p>Data, model runs and codes used in the manuscript "<em>Simulating heat and CO<sub>2</sub> fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance</em>". The source code for Surface Urban Energy and Water balance Scheme (SUEWS) V2020b is also included.</p> <p> </p> <p><strong>[Latest Version]</strong></p> <p>June 9, 2023: Version 2 was created. (1) Modifications in <strong>LAI</strong>: the input data of LAI (<em>LAI/source_data/Beijing_LAI.csv</em>) was previously derived from a MODIS product MOD15A2H; it was replaced with the LAI time series at a higher spatial resolution derived from data provided by Landsat 7 ETM+; the code for data retrieval (<em>LAI/LAI_from_GEE.txt</em>) was added. (2) Modifications in <strong>ModelRuns</strong>: some of the external parameters given to SUEWS were adjusted, including the optimized LAI parameters (for case LAI and case gs_LAI), and Fr<sub>nonheat</sub> (from 0.2 to 0.5); a <em>read_me.txt</em> was added. (3) Modifications in <strong>Fig&Statistics</strong>: the shaded area for the line charts were changed from standard deviation to the interquartile range.</p> <p> </p> <p><strong>[Data&Code Description]</strong></p> <p>The folders are:</p> <p><strong>1. Observations</strong><br> The units for CO2 flux (Fc), latent heat flux (QE), and sensible heat flux (QH) are μmol m<sup>-2</sup> s<sup>-1</sup>, W m<sup>-2</sup>, and W m<sup>-2</sup>, respectively, unless otherwise stated.<br> <strong>1.1. Observation datasets</strong><br> 1.1.1. <em>radiation_flux_2010-2011.csv</em> is radiation flux observations from May 2010 to July 2011 at 140 m on the IAP tower, Beijing.<br> 1.1.2. <em>turbulent_flux_before_QC_2016.csv</em> is turbulent flux observations before quality control (QC) obtained with the eddy covariance (EC) technique for the year 2016 at 47 m on the IAP tower.<br> 1.1.3. <em>turbulent_flux_after_QC_2016.csv</em> is turbulent flux observations after quality control (QC).<br> 1.1.4. <em>Fc_mean_diurnal_by_season.csv</em> is the mean diurnal seasonal cycle of Fc observations.<br> 1.1.5. <em>Fc_gapfilled_with_MDC.csv</em> is the Fc time series for the year 2016 gap-filled with Mean Diurnal Cycle (MDC) method.<br> 1.1.6. <em>meteorological_observations </em>is a folder including meteorological observations at different heights measured at the IAP tower for the years 2010-2012.<br> <strong>1.2 Codes for observations processing</strong><br> 1.2.1. <em>turbulent_flux_QC.py</em> is for turbulent flux QC covering wind direction filtering, stationarity test, friction velocity filtering, and nighttime filtering.<br> 1.2.2. <em>mean_diurnal_cycle_of_flux_observations.py</em> is for obtaining the mean diurnal cycle of flux observations.<br> 1.2.3. <em>Fc_gap_filling.py</em> is for filling the gaps in the Fc time series with the MDC method.</p> <p><strong>2. LAI</strong><br> This folder includes a project aiming to obtain the parameters for the leaf area index (LAI) model adopted by SUEWS. In this LAI model, the LAI is related to air temperature only. Incorporating hourly air temperature observations and the 'real' LAI (e.g. observed LAI, remotely sensed LAI), the optimization method Covariance Matrix Adaptation Evolutionary Strategies (CMA-ES) will derive the optimized parameters for the LAI model. For more details, please read the description text file (<em>read_me.txt</em>) under this folder.<br> 2.1. <em>read_me.txt.</em><br> 2.2. <em>source_data</em> is a folder including the input for CMA-ES optimization. The input files of Beijing are included, allowing a quick run to reproduce the results demonstrated in the manuscript.<br> 2.3. <em>result_data</em> is a folder including the output for CMA-ES optimization.<br> 2.4. The code for data retrieval from the Google Earth Engine platform (<em>LAI/LAI_from_GEE.txt</em>) was added. The others are Python scripts regarding CMA-ES, including the LAI model, data de-spiking, interpolation, CMA-ES training, and result visualization.</p> <p><strong>3. ModelRuns</strong><br> This folder includes SUEWS source code, four model runs conducted to test the sensitivity to vegetation-related parameters (i.e., maximum conductance, LAI model parameters), and four model runs to test sensitivity to the radius of the model domain.<br> 3.1. <em>SUEWS_SourceCode</em> is a folder including SUEWS V2020b source Fortran codes. For detailed descriptions, readers are referred to SUEWS webpage (https://suews.readthedocs.io/en/latest/).<br> 3.2. <em>Input</em> is a folder including the necessary input files shared by each of the model runs. Please copy all the files to a model run folder (e.g. <em>VegetationSensitivity\case-base\Input\</em>) before starting a SUEWS run.<br> 3.3. <em>VegetationSensitivity</em> is a folder including four SUEWS runs to test the model sensitivity to vegetation-related parameters: <em>case-base</em> is the control run, <em>case-gs</em> is a model run where maximum conductance of vegetation has been modified to more site-specific values, <em>case-LAI</em> is a model run where LAI parameters have been optimized with CMA-ES, and <em>case-gs_LAI</em> is a model run where both maximum conductance and LAI parameters have been modified. Note that all the model runs have selected 1000 m as the radius of the model domain. To conduct a quick model run to reproduce the output, enter the command "./SUEWS_V2020b" in the model run folder (e.g. <em>VegetationSensitivity\case-base\</em>) under a Linux environment (e.g., Ubuntu).<br> 3.4. <em>RadiusSensitivity</em> is a folder including four SUEWS runs to test the model sensitivity to the size of the model domain, covering circles with a 500 m, 750 m, 1000 m, 1500 m radius, respectively. Note that the vegetation-related parameters are the same as the model run <em>VegetationSensitivity\case-gs_LAI</em>.</p> <p> 3.5. read_me.txt is a note on how to reproduce the SUEWS cases shown in the manuscript.</p> <p><strong>4. Fig&Statistics</strong><br> This folder includes Python scripts and data to reproduce a portion of the figures and statistics in the manuscript quickly. They are sorted into the following sub-folders:<br> 4.1 <em>validation_WFDE5</em>,<br> 4.2 <em>evaluation_radiation</em>,<br> 4.3 <em>evaluation_turbulent</em>,<br> 4.4 <em>accumulated_Fc</em>,<br> 4.5 <em>Fc_component_MDC</em>.</p>
Methods (psychrometric and regression) to calculate wet-bulb temperature, and monthly oceanic rainfall sensible heat flux
<p>This upload includes: 1) a lookup table for computing wet-bulb temperature using the psychrometric method, 2) difference between wet-bulb temperature computed using the psychrometric method and the regression method, and 3) monthly mean oceanic rainfall sensible heat flux for the period 2005–14.</p>
Data for: Hit2flux: A machine learning framework for boiling heat flux prediction using hit-based acoustic emission sensing
Open the record for dataset details and reuse information.
Data from: Marine heatwaves amplify benthic community metabolism and solute flux in a seafloor heating experiment
Open the record for dataset details and reuse information.
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.