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.
447
datasets available to search
ShareScore release 0.9.0
Dataset results
447 results for “Model validation”
WILLOW - Norther: data set for the full-scale validation of model-based virtual sensing methods for an operational offshore wind turbine
<h1><em><strong>1. General description </strong></em></h1> <p>This data set contains as-build design information, as well as full-scale vibration response measurements from an operational offshore wind-turbine. The turbine is part of the Norther wind farm which is located in the Belgian North Sea<em> </em>and includes a total of 44 Vestas V164 (8.4MW) wind turbines on monopile foundations, see <a href="../api/records/11093262/draft/files/Fig1_Norther_locaction.png/content" target="_blank" rel="noopener noreferrer">Fig1_Norther_locaction.png</a>. This data set is intended to verify and validate model-based virtual sensing algorithms, using data as well as modeling information from a real turbine. </p> <h2><em><strong>1.1 Summary of the shared structural information</strong></em></h2> <p>The included information entails a detailed description of the geometric properties of the monopile and transition piece, distributed and lumped structural masses . All information shared in this record is conform the as-designed documentation. An example of the lumped masses considered in the model input files is presented in "<a href="../api/records/11093262/draft/files/Fig2_Sensor_Network.png/content" target="_blank" rel="noopener">Fig2_Sensor_Network.png"</a></p> <h2><em><strong>1.2 Summary of the shared geotechnical information</strong></em></h2> <p>Monopiles are distinguished by the significant role of soil-structure interaction. Ground reaction is most typically included in the structural model as non-linear p-y curves. Different p-y curves are available for a certain number of soils in the standards applicable to offshore structures (API RP 2GEO, 2011, and ISO 19901-4:2016(E), 2016).</p> <p>The required soil properties to define p-y curves according to the API framework are given in the soil profile provided in a separate Excel. Rather than symbols, the name of the soil properties is generally used as column header (e.g., <em>Undrained shear strength</em>). Therefore, it is straightforward to identify each soil parameter. The only soil parameter that might lead to confusion is:</p> <ul> <li><em>"epsilon50 [-]" </em>represents the vertical strain at half the maximum principal stress difference in a static undrained triaxial compression test on an undisturbed soil sample.</li> </ul> <p>It's worthy to note that estimates for the small shear strain stiffness, referred to as Gmax, are also included. Despite not being required as an input to define the API p-y curves, this parameter remains a key input for other soil reaction frameworks than the API (e.g., PISA). </p> <h2><em><strong>1.3 Summary of the shared measurement data</strong></em></h2> <p>Two sets of measurement data have been curated for validation purposes; the first interval has been collected during parked conditions, whereas the second interval has been collected during rated operational conditions. Both records have a length of 2 hours, and are subdivided into 10-minute data sets. Furthermore 1Hz SCADA data has been made available for the selected intervals. All different data sources are time synchronized and have been subjected to several internal quality checks. </p> <p>The sensor network on NRT-WTG is illustrated in in <strong>Fig. 2, </strong>whereas a description of the sensor types is presented in <strong>Tab.1.</strong> The acceleration sensors are installed in the horizontal plane, and measure tangential (Y) and orthogonal (X) to the wall, where the positive Y direction is pointing clockwise and the positive X direction is pointing inwards. All strain sensors are installed vertically and are located on the inside of the wall.</p> <table> <tbody> <tr> <td><strong>Data type </strong></td> <td><strong>Sensor type</strong></td> <td><strong>Fs (Hz)</strong></td> <td> <p><strong>Level mLAT (m)</strong></p> </td> <td><strong>Description </strong></td> </tr> <tr> <td>Acceleration (g) </td> <td>Piezo-electric acc. sensor (<strong>ACC</strong>)</td> <td>30</td> <td>15, 69, 97 </td> <td>3 Bi-directional accelerometers at different levels. LAT 15 installed at 240 degree heading; LAT 69 and 97 at 60 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Resistive strain gauge (<strong>SG</strong>)</td> <td>30</td> <td>14</td> <td>6 SGs: equally spaced around the inner circumference of the can. Headings: 50, 110, 170, 230, 290, 350 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Fiber-Bragg Grating strain gauge (<strong>FBG</strong>)</td> <td>100</td> <td>-17, -19</td> <td>2 FBGs per level at 165 and 255 degree respectively.</td> </tr> </tbody> </table> <p><strong>Table 1. Description of sensor types.</strong></p> <p>The FBG strain time series have been synchronized with the SG time series using using a cross-correlation based approach. Therefore the SG data has been used to genereate refrence strain time series at the headings of the FBG sensors; the FBG data is subsequently synchronized with regard to this reference time series. No synchronization of the acceleration data was needed, since these are collected using the same data aquisition system as the SG data. </p> <p>The SG strain time series have been calibrated and temperature compensated, whereas this is not the case for the FBG strain time series. The latter have a yet to be determined calibration offset. </p> <p>In conjunction to the sensor channels presented in <strong>Tab. 1</strong>, 1 Hz SCADA data is provided. A summary of the provided SCADA parameters, all sampled at 1Hz, is presented in <strong>Tab 2.</strong></p> <table> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Wind speed</td> <td>m/s</td> <td>Wind speed as recorded in the turbine SCADA</td> </tr> <tr> <td>Wind direction</td> <td>°</td> <td>Wind direction relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Yaw angle</td> <td>°</td> <td>Yaw orientation of the nacelle relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Pitch angle</td> <td>°</td> <td>Rotor blade pitch as recorded in the turbine SCADA</td> </tr> <tr> <td>Rotor speed</td> <td>rpm</td> <td>Rotor speed in rotations per minute as recorded in the turbine SCADA</td> </tr> <tr> <td>Power</td> <td>kW</td> <td>Active power of the turbine as recorded in the turbine SCADA</td> </tr> </tbody> </table> <p><strong>Table 2. </strong>List of provided SCADA parameters</p> <p> </p> <p>A summary of the selected intervals and relevant corresponding scada parameters is given in <strong>Tab 3</strong>.</p> <table> <tbody> <tr> <td><strong>Scenario </strong></td> <td><strong>T1 (UTC)</strong></td> <td><strong>T2 (UTC) </strong></td> <td><strong>Windspeed</strong></td> <td><strong>RPM </strong></td> <td><strong>Pitch </strong></td> </tr> <tr> <td>Parked</td> <td> <p>03/07 01:30</p> </td> <td> <p>03/07 03:30</p> </td> <td>< 4.5 m/s</td> <td>~1</td> <td>~18 °</td> </tr> <tr> <td>Rated</td> <td> <p>05/07 22:30</p> </td> <td> <p>06/07 00:30 </p> </td> <td>~15 m/s</td> <td>10.5</td> <td>8.1°</td> </tr> </tbody> </table> <p><strong>Table 3. </strong>Selected data intervals and relevant scada parameters</p> <p> </p> <h1><em><strong>2. Included in this version </strong></em></h1> <h2><em><strong>2.1 Version - 0.1.0</strong></em></h2> <ul> <li>Relevant Design information can be found in: <ul> <li>Geometry data for NRT-WTG: "WILLOW-Geometry_v4.xlsx"</li> <li>Best estimate soil profile: "WILLOW-BE_soil_profile.xlsx"</li> </ul> </li> <li>Acceleration, strain and scada data can be found in the following parquet files: <ul> <li>Measurement data for the parked case: "NRT-WTG_Parked.parquet.gz"</li> <li>Measurement data for the rated case: "NRT-WTG_Rated.parquet.gz"</li> </ul> </li> </ul> <p> </p> <h1><em><strong>3. Importing parquet files </strong></em></h1> <p>To import the measurement data into Python it is recommended to use pandas:</p> <pre>import pandas as pd<br># Read Parquet file with Pandas: relative_file_path = '<a href="../api/records/11093262/draft/files/NRT-WTG_Parked.parquet.gz/content" target="_blank" rel="noopener noreferrer">NRT-WTG_Parked.parquet.gz</a>' data = pd.read_parquet(relative_file_path ) <br><br>Once the dataframe has been imported, the users can process/re-arrange the raw data according the their needs; it should be noted that the imported dataframe contains NAN values - these are caused by the different sampling rates of the provided signals. </pre>
OpenFOAM cases of the paper "Development and validation of an open-source CFD model for the efficiency assessment of data centers"
<p>This dataset contains the<em> underling data</em> for the paper "Development and validation of an open-source CFD model for the efficiency assessment of data centers”, submitted for the consideration and open review in Open Research Europe (ORE).</p> <p><strong>Validation1.tar.xz:</strong> OpenFOAM files and scripts for the simulation of flow and thermal structures in an enclosed environment (Wang and Chen, 2009).</p> <p><em>Wang, Miao; Chen, Qingyan (2009). Assessment of Various Turbulence Models for Transitional Flows in an Enclosed Environment (RP-1271). HVAC&R Research, 15(6), 1099–1119. doi:10.1080/10789669.2009.10390881</em></p> <p><strong>Validation2-kOmegaSSTModel.tar.xz:</strong> OpenFOAM files and scripts for the simulation of forced convection in a room (Zhang et al. 2007) using k-omega SST turbulence model. </p> <p><em>Zhao Zhang, Wei Zhang, Zhiqiang John Zhai & Qingyan Yan Chen (2007) Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature, HVAC&R Research, 13:6, 871-886, DOI: 10.1080/10789669.2007.10391460</em></p> <p><strong>Validation2-RNGkEpsilonModel.tar.xz:</strong> OpenFOAM files and scripts for the simulation of forced convection in a room (Zhang et al. 2007) using RNG k-epsilon turbulence model. </p> <p><em>Zhao Zhang, Wei Zhang, Zhiqiang John Zhai & Qingyan Yan Chen (2007) Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature, HVAC&R Research, 13:6, 871-886, DOI: 10.1080/10789669.2007.10391460</em></p> <p><strong>Validation3.tar.xz:</strong> OpenFOAM files and scripts for the simulation of strong natural convection in a model fire room (Murakami et al. 1995).</p> <p><em>Murakami, S., S. Kato, and R. Yoshie. 1995. Measurement of turbulence statistics in a model fire room by LDV. ASHRAE Transactions 101(2):287–301.</em></p> <p><strong>Validation4.tar.xz:</strong> OpenFOAM files and scripts for the simulation of thermal distribution in an open-aisle data center (Abdelmaksoud et al. 2013).</p> <p><em>W.A. Abdelmaksoud, T.Q. Dang, H. Ezzat Khalifa, R.R. Schmidt Improved computational fluid dynamics model for open-aisle air-cooled data center simulations J. Electron. Packag., 135 (2013), pp. 030901-30913</em></p> <p><strong>Results_Validation1.tar.xz:</strong> Simulation results of the Validation case 1.</p> <p><strong>Results_Validation2.tar.xz:</strong> Simulation results of the Validation case 2.</p> <p><strong>Results_Validation3.tar.xz:</strong> Simulation results of the Validation case 3.</p> <p><strong>Results_Validation4.tar.xz:</strong> Simulation results of the Validation case 4.</p> <p><strong>layout.csv:</strong> Input file for the Validation case 4.</p>
Wind measurement data from the publication: "Development of a load model validation framework applied to synthetic turbulent wind field evaluation"
<h3>Dataset description:</h3> <p>This datasat represents supplementary material used in the contribution "Development of a load model validation framework applied to<br>synthetic turbulent wind field evaluation" by Meyer, Huhn and Gottschall.</p> <p>Wind measurements from the Testfeld BHV are made available. For installation details, see the mentioned reference.</p> <p> </p> <h3>File description:</h3> <ul> <li>Lidar_HWS.nc - Horizontal wind speed measurements (10 min averages) from a WindCube V2 vertical profiler for one day with a low-level jet occurrence ( <div> <div>2021-04-20)</div> </div> </li> <li>Cups_HWS.nc - Horizontal wind speed measurements (10 min averages) from cup anemometer installed on a met mast for the same day</li> <li>Ensemble_averaged_Spectra.nc - Ensemble averaged spectra for neutral and near neutral situations from a Gill Windmaster at 110m above ground level, used to fit the Mann and KSEC model parameters</li> </ul> <h3> </h3> <h3>Referencing:</h3> <p>When used, please cite like the following:</p> <p>Meyer, Paul J., Matthias L. Huhn, and Julia Gottschall. 2024. "Development of a Load Model Validation Framework Applied to Synthetic Turbulent Wind Field Evaluation" <em>Energies</em> 17, no. 4: 797. https://doi.org/10.3390/en17040797</p> <p> </p> <p> </p>
Deflections of the s-FKLP model for validation purposes
<p>The test data included are for plate deflections obtained according to the s-FKLP model produced for model validation. The methodology and analysis of the obtained data is included in the Open Access article:</p> <p>Stempin, P.; Pawlak, T. P. & Sumelka, W.<br>Formulation of non-local space-fractional plate model and validation for composite micro-plates <br><em>International Journal of Engineering Science, </em><em>Elsevier BV, </em><strong>2023</strong><em>, 192</em>, 103932.</p> <p>DOI: https://doi.org/10.1016/j.ijengsci.2023.103932</p> <p> </p>
LigPCDS: Labeled Dataset of X-ray Protein Ligand Images in 3D Point Cloud and Validated Deep Learning Models
<p>The difference electron density from X-ray protein crystallography was used to create the first dataset of labeled ligand images in 3D point clouds, named <strong>LigPCDS</strong>. The dataset contain 244,226 entries of free organic ligands containing 3D representations labeled with two major labeling approaches: SP-based and AtomSymbol-based.</p> <p> </p> <p>The data from free organic molecules (non-covalent ligands) was retrieved from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) in december 2019 with resolutions ranging from 1.5 to 2.2 Å. The ligand images (blobs) were interpolated from their calculated difference electron density map in a 3D grid-like bounding box, around their atomic positions, and stored in point clouds. These ligand grid representations were further processed to retrive the final ligands representation in 3D point clouds using a mask of the shape of the ligand. A grid spacing of 0.5 Å gave the best results. The density value of the grid points was used as feature. The labeling approach used the structure of the ligands to propose vocabularies of chemical classes based on the chemical atoms themselves and their cyclic substructures. These structure annotations were applied pointwise to the ligand 3D representations using an atomic sphere model. Four proposed vocabularies were validated by successfully training good performance deep learning models for the semantic segmentation of a stratified dataset from LigPCDS, using 78902 entries.</p> <p>The four validated deep learning models are: (i) the LigandRegion, composed by generic atoms of any type; (ii) the AtomCycle, composed by generic atoms outside cycles and generic cycles; (iii) the AtomC347CA56, composed by generic atoms outside cycles, not aromatic cycles of size 3 to 7 and aromatic cycles of size 5 and 6; and (iv) the AtomSymbolGroups, composed by the atoms symbols with groupings. The mean accuracy of these models in their cross-validation was between 49.7% <span lang="EN-GB">[-19.4,20.</span><span lang="EN-GB">2]</span> and 77.4% <span lang="EN-GB">[-11.7,12.1]</span> in terms of Intersection over Union (mIoU) metric and between 62.4% <span lang="EN-GB">[-18.8,19.</span><span lang="EN-GB">7]</span> and 87.0% <span lang="EN-GB">[-8.4,8.8]</span> in F1-score (mF1), confidence interval between squared brackets. The models i, ii and iii and the used labeled representations in 3D point cloud are contained in the SP-based record; and model iv and its used labeled representations are contained in the AtomSymbol-based record.</p> <p>The dataset and validated models may be used to tackle problems regarding known and unknown ligand building to drug discovery and fragment screening pipelines. </p> <p>The code used to create and validated the LigPCDS is available at the following repository: https://github.com/danielatrivella/np3_ligand</p> <p>This repository also contains the NP³ Blob Label application for ligand building using the validated deep learning models from LigPCDS.</p>
Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer
<p>Dataset of the paper "Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer" published in Remote Sensing [1].</p> <p>[1] Brugger P, Fuertes FC, Vahidzadeh M, Markfort CD, Porté-Agel F. Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer. <em>Remote Sensing</em>. 2019; 11(19):2247. https://doi.org/10.3390/rs11192247.</p>
Dataset of publication "Derivation and validation of a reference data-based real gas model for hydrogen"
<p>In this repository, a new real gas model for hydrogen based on the Reference Fluid Thermodynamic and Transport Properties Database (REFPROP) v10.0 is provided for the use in the simulation software OpenFOAM v2012. The model is valid in a temperature and pressure range of 150-400 K and 0.1-1000 bar, respectively. Usage beyond this range is not recommended as it may lead to unrealistic results.</p>
Auxiliary files and data to generate eddy flux and validate 2D model for MALTA
<p>This repository contains the following directories to accompany the manuscript 'A Zonally-Averaged Global Atmospheric Transport Model for Long-lived Trace Gases', submitted to JAMES:</p><p>1) <strong>GEOSChem </strong>This directory contains the run directory template and (slurm) runscript to generate the tracer fields used to generate the eddy fluxes. The GEOSChem model will have to be installed locally to run this, and the run directory built to your local area. It may be easiest to just copy the relevant bits in /Tracer_2D_template/ (i.e., the .rc files, /RestartFiles/, input.geos, reset_restart.py and species_database.yml) into a GEOSChem Transport run directory and change the directories in the copied files. If using slurm on an HPC, just change the directories in the runtracers_inputs.sh script to match that of your own HPC. Else, a different script will have to be written copying the slurm functionality.</p><p>2) <strong>GEOSChem_SF6 </strong>This directory contains the monthly mean SF6 mole fractions generated using GEOSChem used to validate the 2D model MALTA. Emissions come from the EDGAR v4.2 emissions inventory. Emissions after 2008 continue to use 2008 as the emissions value.</p><p>3) <strong>CFC11_inversion</strong> This directory contains the relevant script and files to quantify emissions of CFC-11 using an output mole fraction from the TOMCAT 3D model using MALTA, and compare these to the TOMCAT emissions used to generate the mole fractions. The directory paths at the beginning of the main script in CFC11_inversion.py must be changed to point to the remaining files in the /CFC11_inversion/ directory, and a save directory must be specified, before running locally. MALTA must be installed to run this.</p><p>4) <strong>singapore.dat </strong>This file contains the QBO winds above Singapore, taken from https://www.geo.fu-berlin.de/en/met/ag/strat/produkte/qbo/index.html</p><p> </p>
Control model validation dataset
<p>The dataset is associated with the LiftWEC H2020 research project deliverables "D3.3 Tool validation and extension report" and "D4.3 Open-access experimental data from 2D LiftWEC tests". The mathematical model is based on the hypothesis and equations presented in the D3.3, "Section 4. Validation of fundamental hypothesis for global model". The model has been programmed in Python, and the lift and drag coefficients were derived from the experimental data using the method of the least squares.</p> <p>The presented data shows a very good validation agreement between the developed model and experimental data in terms of tangential and radial forces generated on hydrofoils. The estimated values of lift and drag coefficients show great potential for wave energy extraction using rotating foils.</p>
Consensus models to predict oral rat acute toxicity and validation on a dataset coming from the industrial context
<p>We report predictive models of acute oral systemic toxicity representing a follow-up of our previous work in the framework of the NICEATM project. It includes the update of original models through the addition of new data and an external validation of the models using a dataset relevant for the chemical industry context. A regression model for LD50 and classification model for toxicity classes according to the Global Harmonized System categories were prepared. ISIDA descriptors were used to encode molecular structures. Machine learning algorithms included Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayesian. Selected individual models were combined in consensus.</p> <p>The different datasets were compared using the Generative Topographic Mapping approach. It appeared that the NICEATM datasets were lacking some relevant chemotypes for chemical industry. The new models trained on enlarged data sets have applicability domain (AD) sufficiently large to accommodate industrial compounds. The fraction of compounds inside the models’ AD increased from 58 % (NICEATM model) to 94 % (new model). Yet, the increase of training sets only slightly improved of the models’ prediction performance: RMSE values decreased from 0.56 to 0.47 and balanced accuracies increased from 0.69 to 0.71 for NICEATM and new models, respectively.</p>
Visual and inertial data for validation of gliding models of ornithopters
<p>This dataset contains data from different gliding flights with an ornithopter in low wind conditions. For each experiment, the inertial information is provided.</p> <p>Additionally, the flights have been recorded from three different points of view to track and triangulate its position. The videos are provided and the position of the camera has been determined using a Leica Total Station system with submillimeter accuracy. A sample of the 2D track of the ornithopter is provided for each video and experiment. The tracking along the three cameras are synchronized.</p> <p> </p> <p>------Camera Pose structure------</p> <p> </p> <p>Three cameras with four points: three to measure orientation and the last one the lens position. The last two points are the measured fall.<br> Camera 1 -> top left, bottom left and top right.<br> Camera 2 -> top left, top rigth and bottom right.<br> Camera 3 -> top left, bottom left and top right.<br> Then there are 14 rows. The pattern is: Point1, Point2, Point3 and Lens Position.</p> <p>------IMU structure------</p> <p>time, quaternion w, quaternion x, quaternion y, quaternion z, accelerometer x, accelerometer y, accelerometer z, Gyroscope x, Gyroscope y, Gyroscope z, magnetometer x ,magnetometer y ,magnetometer z<br> units: time->ms, accelerometer->g, gyroscope->ยบ/s</p> <p> </p>
Development and validation of statistical shape models of the primary functional bone segments of the foot.
<p>This dataset comprises manually segmented three-dimensional point clouds (.STL) of magnetic resonance images of the primary functional segments of the foot - first metatarsal, midfoot (second-to-fifth metatarsals, cuneiforms, cuboid, and navicular), calcaneus, and talus. These data were used to create statistical shape models of the foot bones, utilising the GIAS2 toolbox (https://pypi.org/project/gias2/).</p>
HydroGeoSphere Model Input Files and Results for Validation of Pesticide Leaching to Groundwater for Nine EU FOCUS Scenarios
<p>This dataset includes HydroGeoSphere (HGS) model (Aquanty, 2024) input and output files for nine Forum for the Co-ordination of Pesticide Models and their Use (FOCUS) scenarios (EC, 2014) for simulation of leaching of four test contaminants to groundwater. It is recommended that users are familiar with HGS software in order to best make use of the available files. Scenarios are included in separate subfolders named using the first four letters of the FOCUS scenario location name, e.g. folder "chat" contains the model run for the "Chateaudun" scenario. It is recommended that users familiarize themselves with the (EC, 2014) groundwater scenarios. HGS model inputs are specified in the *.grok ASCII text file for each scenario in each subfolder. Soil material properties and evapotranspiration properties are included in HGS input files in ASCII text format in the "material_properties" subfolder. Solute application timing for each scenario are include in the "solute_app" subfolder. And climate times series inputs are included in the "weather" subfolder.</p>
Experimental data for validation of a variational RANS level III flow model: water waves over an array of obstacles and Ogee weir flows
<p>Experimental dataset for the validation of a variational RANS level III flow model. The experimental data correspond to experiments on unsteady of water waves over an array of obstacles and steady curved flows over an Ogee weir. The experiments were conducted at the Hydraulics Laboratory at the Univeristy of Córdoba. </p>
A Synthetic Hyperspectral Dataset for Development and Validation of Phytoplankton Size Class Retrieval Models
<p><strong>A Synthetic Hyperspectral Dataset for Development and Validation of Phytoplankton Size Class Retrieval Models.</strong></p> <p>Please refer to the following scientific paper for a description of the dataset.</p> <blockquote> <p>Holtrop, T.; Van Der Woerd, H.J. (accepted) HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters. <em>Remote Sens. </em><strong>2021</strong>, 13, 0.</p> </blockquote>
OpenFOAM cases for the Validation of the CHT Model
<p>This dataset contains the<em> underlying data</em> for the paper " <em>Conjugate Heat Transfer Modeling of a Cold Plate Design for Hybrid-Cooled Data Centers</em>” in Energies Journal.</p> <p>https://www.mdpi.com/1996-1073/16/7/3088 </p> <p>Numerical simulations are performed using the open-source CFD code OpenFoam. Features of the numerical model described in the paper can be summarized as:</p> <ul> <li>This data set contains <em>OpenFoam</em> cases for the validation of the thermal model with the experimental data in the literature (Saitoh et al. 1993) using both <em>buoyantPimpleFoam</em> and <em>chtMultiRegionFoam</em> solvers.</li> </ul> <p><em>Saitoh, T.; Sajiki, T.; Maruhara, K. Benchmark solutions to natural convection heat transfer problem around a horizontal circular cylinder. Int. J. Heat Mass Trans. 1993, 36, 1251–1259.</em></p> <ul> <li>A multi-region unstructured mesh was created using Salome software and exported as unv files. The generated unv files can be found in the corresponding directories.</li> <li><em>Allmesh</em> script imports regions from the unv files to the <em>OpenFoam</em> and runs <em>createPatch</em> file for the application of boundary conditions .</li> <li><em>Allrun</em> script runs transient simulation using parallel computing.</li> <li><em>postProcess</em> script compares case results with experimental results and generates a plot in the directory results.</li> <li>Flow inside air and water regions are considered as laminar due to the low Reynolds numbers.</li> <li>Numerical schemes used in the solutions of constitutive equations are carefully selected to obtain results consistent with the experimental data.</li> </ul> <p>A new function object is developed for the calculation of the Nusselt number on the cylinder. This library can be downloaded via the following link and sould be compiled before cases are run: </p> <p><a href="https://github.com/DSTECHNO/NusseltNumber">https://github.com/DSTECHNO/NusseltNumber</a></p> <p><strong>FreeConvectionBPF.tar.gz:</strong> <em>OpenFoam</em> files and scripts for the transient simulation of free convection case using <em>buoyantPimpleFoam</em> solver.</p> <p><strong>FreeConvectionCHT.tar.gz:</strong> <em>OpenFoam</em> files and scripts for the transient simulation of free convection case using <em>chtMultiRegionFoam</em> solver.</p> <p><strong>ForcedConvectionBPF.tar.gz:</strong> OpenFoam files and scripts for the transient simulation of forced convection case using <em>buoyantPimpleFoam</em> solver.</p> <p><strong>ForcedConvectionCHT.tar.gz: </strong><em>OpenFoam</em> files and scripts for the transient simulation of forced convection case using <em>chtMultiRegionFoam</em> solver.</p>
Building measured data for model validation
<p>Measured indoor/outdoor temperatures, solar radiation and heating load of a 103-m2 building in Athens, Greece. Data include measurements of two weeks, one without heating delivery to the building and another with heating delivery (with fan coils).</p>
Data related to the manuscript "Bayesian Calibration and Validation of a Large-scale and Time-demanding Sediment Transport Model"
<p>1) Riverbed_Elevation_Measurements.txt<br> Description: Measured riverbed geometry of available years<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2002 [m asl], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation <br> 2013 [m asl]<br> ----------------------------------------------------------------------------------------------------------------------------<br> 2) Hydro_FT_2D_manual.txt<br> Description: Simulation results of the manually calibrated full model<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]</p> <p>3.1) Hydro_FT_2D_CollocationPointBase.txt<br> Description: Parameter combinations of the collocation point base for each of the 20 simulations conducted with the full model to <br> construct the surrogate<br> Rows: Critical Shields parameter, Grain Roughness, Grain Size distribution</p> <p>3.2) Hydro_FT_2D_CollocationResults.txt<br> Description: Simulation results of the 20 simulations conducted with the full model at the collocation points<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig<br> [m asl], Elevations 2010 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig [m asl], Elevations 2013 [m asl] of<br> simulation 1 through 20<br> ----------------------------------------------------------------------------------------------------------------------------<br> 4.1) aPC_MC_N_Combinations_Weights_prior.txt<br> Description: ID of prior MC runs with tested parameter combinations and corresponding importance weights<br> Rows: ID of MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights<br> 4.2) aPC_MC_2005_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2005<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of MC run 1 through 100,000<br> 4.3) aPC_MC_2010_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2010<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of MC run 1 through 100,000<br> 4.4) aPC_MC_2013_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2013<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2013 [m asl] of MC run 1 through 100,000<br> <br> 4.5) aPC_MC_N_Combinations_Weights_posterior.txt<br> Description: ID of accepted (posterior) MC runs with tested parameter combinations and corresponding importance weights<br> Rows: ID of accepted MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights<br> 4.6) aPC_MC_2005_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2005<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of accepted MC run 1 through 857<br> 4.7) aPC_MC_2010_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2010<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of accepted MC run 1 through 857<br> 4.8) aPC_MC_2013_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2013<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2013 [m asl] of accepted MC run 1 through 857<br> ----------------------------------------------------------------------------------------------------------------------------<br> 5) aPC_MAP.txt<br> Description: Simulation results conducted with the stochastically calibrated aPC surrogate model using the MAP parameter <br> combination<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]</p> <p>6) Hydro_FT_2D_MAP.txt<br> Description: Simulation results conducted with the stochastically calibrated full model using the MAP parameter combination<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]<br> ----------------------------------------------------------------------------------------------------------------------------<br> 7) dz.txt<br> Description: Riverbed Evolution for all nodes in the section of interest (n=1138) obtained with differently calibrated models for all <br> considered time periods<br> Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m], <br> Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],<br> Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],<br> Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m]</p> <p>8) dz_CalibrationNodes.txt<br> Description: Riverbed Evolution for calibration nodes (n=204) obtained with differently calibrated models for all considered time<br> periods<br> Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m], <br> Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],<br> Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],<br> Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m]</p> <p> </p>
Model outputs for validation and inference of high‐resolution information (downscaling) of ENETwild abundance model for wild boar, January 2020 update
<p>These maps are models obtained in intermediate phases of the ENETWILD project based on available information. There are frequent updates in order to improve the results.</p> <p>Objectives:</p> <p>- Validation of previously produced hunting yield maps and new ones<br> - Downscaling to 10x10 km grid >>> file "January_2020_HY_nut01_10x10.tif"<br> - Downscaling to 2x2 km grid >>> file "January_2020_HY_nut00_2x2.tif"</p> <p><br> Model settings and predictors: <br> - Assuming cells as municipality in 10x10 km grid downscaling<br> - Assuming cells as hunting grounds in 2x2 km grid downscaling </p> <p>Conclusions guiding future methodological steps:<br> - To update wild boar hunting yield data for some specific regions<br> - To increase hunting yield data resolution<br> - To explore model independent parametrization for each bioregion</p> <p>For further details and methodological approach see the paper:</p> <p>ENETWILD-consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, G. Body, A. Cohen, R. Soriguer, J. Vicente (2020) Validation and inference of high-resolution information (downscaling) of ENETwild abundance model for wild boar. EFSA supporting publication 2020:EN-1787. 23pp. doi:10.2903/sp.efsa.2020.EN-1787.</p> <p>Permission for reuse hunting yield outputs is granted under the terms indicated by EFSA.<br> </p>
Original dataset for "A validation of co-authorship credit models with empirical data from the contributions of PhD candidates"
<p><strong>Publication reference:</strong><br> Donner, P. (2020). A validation of co-authorship credit models with empirical data from the contributions of PhD candidates. Quantitative Science Studies, v. 1, i. 2, p. 551-564. <a href="https://doi.org/10.1162/qss_a_00048">https://doi.org/10.1162/qss_a_00048</a>.</p> <p> </p> <p>The file contains one row per authorship contribution statement. Rows of publications and theses are grouped.</p> <p><strong>Description of columns:</strong></p> <p>dissertation_id - an integer identifying each dissertation thesis</p> <p>university - university at which the dissertation thesis was written and PhD degree conferred</p> <p>year - publication year of the dissertation thesis</p> <p>author - dissertation thesis author name</p> <p>title - dissertation thesis title</p> <p>subject - the field of research</p> <p>publication_id - an integer identifying each publication; publication associated with more than one thesis have the same id across theses</p> <p>reference - bibliographic reference for the publication associated with the thesis</p> <p>author_count - number of authors of the publication</p> <p>author_position - position in the author byline of the credited author</p> <p>credit - claimed credit of the author in percent</p> <p>corresponding_author - flag for whether the publication author of this row is a orresponding author</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.