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Northwest Europe NEMO-ERSEM ocean model hindcast and climate projection under RCP8.5
<p>Dataset of model hindcast and climate projection data from a NEMO-ERSEM simulation of the 7km-resolution Atlantic Margin Model (AMM7). Model description and data are presented in </p> <p>Wakelin, S. L., Y. Artioli, J. T. Holt, M. Butenschön, and J. Blackford (2020), Controls on near-bed oxygen concentration on the Northwest European Continental Shelf under a potential future climate scenario, Progress in Oceanography, 102400. doi: https://doi.org/10.1016/j.pocean.2020.102400.</p> <p>Coupled NEMO-ERSEM model simulations are used to study temperature, salinity and near-bed oxygen concentrations on the northwest European Continental Shelf (NWES). Data are from a hindcast (1980 to 2007) and a climate projection (1980 to 2099) under the RCP8.5 climate emissions scenario.</p> <p>The climate projection (1980 to 2099) under the RCP8.5 climate emissions scenario is described as experiment E1 in</p> <p>Holt, J., J. Polton, J. Huthnance, S. Wakelin, E. O'Dea, J. Harle, A. Yool, Y. Artioli, J. Blackford, J. Siddorn, and M. Inall (2018), Climate-Driven Change in the North Atlantic and Arctic Oceans Can Greatly Reduce the Circulation of the North Sea, Geophysical Research Letters, 45(21), 11,827-811,836. doi: 10.1029/2018gl078878.</p> <p>The dataset consists of </p> <ul> <li>Hindcast simulation data</li> </ul> <ol> <li>AMM7_hindcast_3D_S_1980_2007.nc - monthly mean salinity fields.</li> <li>AMM7_hindcast_3D_T_1980_2007.nc - monthly mean temperature fields.</li> <li>AMM7_hindcast_near_bed_O2o_1980_2007.nc - near-bed oxygen concentrations on the NWES.</li> </ol> <ul> <li>Climate projection data</li> </ul> <ol> <li>AMM7_RCP8_5_3D_S_1980_2099.nc - monthly mean salinity fields.</li> <li>AMM7_RCP8_5_3D_T_1980_2099.nc - monthly mean temperature fields.</li> <li>AMM7_RCP8_5_3D_U_1980_2099.nc - monthly mean eastwards currents.</li> <li>AMM7_RCP8_5_3D_V_1980_2099.nc - monthly mean northwards currents.</li> <li>AMM7_RCP8_5_near_bed_1980_2099.nc - monthly mean near-bed oxygen concentrations and near-bed bacterial respiration on the NWES.</li> <li>AMM7_RCP8_5_netPP_1980_2099.nc - monthly mean depth integrated net primary production.</li> </ol>
Continental Europe Digital Terrain Model geomorphometry derivatives at 30 m, 100 m and 250 m
<p>Digital Terrain Model geomorphometry derivatives based on the DTM for Continental Europe using the <a href="https://epsg.io/3035">EPSG:3035</a> projection system. Processed using <a href="http://www.saga-gis.org/">SAGA GIS</a>, <a href="https://grass.osgeo.org/grass78/">GRASS 7 GIS</a> and <a href="https://gdal.org/programs/gdaldem.html">GDAL</a> at 3 standard spatial resolutions: 30-m, 100-m and 250-m. Derivatives include:</p> <ul> <li>devmean = deviation from mean value derived using <a href="http://www.saga-gis.org/saga_tool_doc/7.4.0/statistics_grid_1.html">SAGA GIS</a>,</li> <li>downlocal / down = downslope local and general curvature derived using <a href="http://www.saga-gis.org/saga_tool_doc/7.1.1/ta_morphometry_26.html">SAGA GIS</a>,</li> <li>hillshade = hillshading derived using using GDAL <a href="https://gdal.org/programs/gdaldem.html">gdaldem</a> functions,</li> <li>mnr = Module Melton Ruggedness Number derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.2.4/ta_hydrology_23.html">SAGA GIS</a>,</li> <li>northerness/easterness = derived using <a href="https://grass.osgeo.org/grass78/manuals/addons/r.northerness.easterness.html">GRASS 7 GIS</a>,</li> <li>openp / openn = openness positive negative derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.2.5/ta_lighting_5.html">SAGA GIS</a>,</li> <li>slope = slope in percent derived using GDAL <a href="https://gdal.org/programs/gdaldem.html">gdaldem</a> functions,</li> <li>topidx = a topographic index (wetness index) derived using <a href="https://grass.osgeo.org/grass76/manuals/r.topidx.html">GRASS 7 GIS</a>,</li> <li>tpi = Topographic Wetness Index derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.1.3/ta_hydrology_20.html">SAGA GIS</a>,</li> <li>vbf = Multiresolution Index of Valley Bottom Flatness derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.2.6/ta_morphometry_8.html">SAGA GIS</a>,</li> </ul> <p>Detailed processing steps can be found <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers"><strong>here</strong></a>. Read more about the processing steps <a href="https://opendatascience.eu/building-continental-europe-digital-terrain-model-30-m-resolution-using-machine-learning"><strong>here</strong></a>.</p> <p>Derivatives were chosen aiming to support soil and vegetation mapping projects. The slope.percent map at 30-m has been converted from 0-100% scale to 0-200% (Byte format) to help decrease the file size.</p>
HD-SIM-RBV: a synthetic dataset with model-based simulations of blood volume changes during hemodialysis
<p>The HD-SIM-RBV dataset is a synthetic (model-based) dataset generated to enable the study of blood volume (BV) or relative blood volume (RBV) changes during hemodialysis (HD).</p> <p>The dataset includes the profiles of BV changes during a standard 4-hour HD session simulated using a lumped-parameter, physiologically-based model of the cardiovascular system and the whole-body water and solute kinetics in 5,000 virtual patients with randomly adjusted values of 90 physiological parameters.</p> <p>For each of the 90 selected parameters, a random value was drawn from a normal distribution with the mean equal to the baseline value used originally in the model (with a few exceptions) and the standard deviation (SD) assumed at the level of 10%, 20%, or 40% of the baseline value, depending on the nature of the given parameter and the likelihood of its variation in the population (for some parameters, SD was set below 10% - see Parameters.xlsx). Only values within ±2SD from the mean were accepted. </p> <p>Ultrafiltration was set randomly within ±1 L from the assigned fluid overload. All other parameters as well as dialysis settings were kept constant for all virtual patients (at the levels used in our previous work - see the references below).</p> <p> </p> <p>When using the dataset, please cite the associated conference paper:</p> <p>Pstras L, Waniewski J. A Model-Based Dataset for In-Silico Exploration of the Patterns of Relative Blood Volume Changes During Hemodialysis. 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, 149-150, 2023, doi: 10.1109/IEEECONF58974.2023.10404528.</p>
Raw data for "Development and characterization of a non-human primate model of disseminated synucleinopathy"
<p><span>In this study, the performance and biodistribution of the retrogradely-spreading AAV9-SynA53T vector was evaluated in the NHP brain. Conducted intraparenchymal deliveries of viral suspensions in the left putamen gave rise to a disseminated synucleinopathy in a circuit-specific basis.</span></p>
Dataset for Accuracy of Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and CNN-LSTM Model
<p>This research paper introduces a deep learning hybrid model employing Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting.The proposed method integrates the Variational Mode Decomposition (VMD) algo-rithm with the CNN-LSTM model to predict PV power generation from a solar farm in Boussada, Algeria, from January 1, 2019, to December 31, 2020. The performance of the developed model is benchmarked against other deep learning models (VMD-CNN, VMD-LSTM, CNN-LSTM) across various time horizons (15, 30, and 60 minutes) to provide a comprehensive evaluation. Our findings exhibit greater performance of the developed model compared to other architectures, showcasing promising results in solar power forecasting. This research contributes to the main goal of enhancing EMS by providing accurate solar energy forecasts.</p>
UAV-based colour-infrared orthomosaics and digital elevation models of basalts and rock glaciers on Disko Island, West Greenland
<p><span>This data set contains multispectral surveys conducted with an unoccupied aerial vehicle over rock glaciers and steep mafic outcrops (intrusive and flood volcanics) near the coastline of Disko Island.</span></p> <ul> <li><span>Acquisition date: 07.08.2019 – 10.08.2019</span></li> <li><span>Location: Illukunnguaq, Disko Island, Greenland</span></li> <li><span>UAV: SenseFly eBee Plus</span></li> <li><span>Flight altitude above ground level: >100m</span></li> <li><span>Image Overlap forward/side: various</span></li> <li><span>Camera: Parrot Sequoia multispectral</span></li> <li><span>EPSG: 32622</span></li> <li><span>Center coordinates: 69.885277°N, -52.577724°E</span></li> <li><span>Flight mode: automatic flight plan</span></li> </ul> <p><span>Data products: </span></p> <ul> <li><span>Orthomosaic colour-infrared, 10-16 cm pixel resolution</span></li> <li><span>Colour-infrared spectral bands: 790nm, 660nm, 550nm</span></li> <li><span>DEM, 20-30cm pixel resolution</span></li> <li><span>Data coverage: approx. 5500 x 2500 m</span></li> <li><span>Elevation profile: 20-680m </span></li> <li><span>Processing in Agisoft Metashape</span></li> </ul> <p><span>Additional data supplement for article:<br>Barnes, E. (2020). Assessment of Drone-Borne Multispectral Mapping in the Exploration of Magmatic Ni-Cu Sulphides–an Example from Disko Island, West Greenland. <br><em>URN: urn:nbn:se:uu:diva-418858</em></span></p> <p>MULSEDRO field campaign was conducted under scientific survey licence (VU-00158-2019) within mineral exploration licence MEL 2018-16 by Blue Jay Mining PLC. This research has been supported by the project MULSEDRO, funded by HZDR-HIF & EITRawMaterials (project ID 16193) and the European Union.</p>
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>
Dataset for training the Surrogate Model of microlaser neurons on the reduced MNIST classification task
<p>This dataset was used to train a surrogate multilayer perceptron surrogate model of microlaser neurons.</p> <p>It is in csv format. It was generated using the Yamada Model as found in </p> <p><span>Selmi F, Braive R, Beaudoin G, Sagnes I, Kuszelewicz R and Barbay S 2014 Relative Refractory Period in an Excitable Semiconductor Laser <em>Phys. Rev. Lett.</em> <strong>112</strong> 183902</span>.</p>
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>
Non-perturbative phase structure of the bosonic BMN matrix model --- data release
<p>This HDF5 file collects data and analysis results for non-perturbative lattice calculations investigating the phase structure of the bosonic part of the Berenstein--Maldacena--Nastase matrix model. See the README for further information.</p>
AMOC reconstruction between 1981 and 2016 from hydrographic data using an empirical linear regression model from Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, https://doi.org/10.5194/os-17-285-2021, 2021.
<p>Dataset used to create Figure 8 in Worthington et al., 2021 (https://doi.org/10.5194/os-17-285-2021). Details of the data and methods can be found in the journal article.<br> <br> Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, <a href="https://doi.org/10.5194/os-17-285-2021">https://doi.org/10.5194/os-17-285-2021</a>, 2021.</p>
QuaLiKiz-v2.6.2 turbulent transport model evaluations based on JET experimental plasma profiles
<p>This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: <a href="https://doi.org/10.1063/5.0038290">https://doi.org/10.1063/5.0038290</a>. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at <a href="https://www.qualikiz.com">www.qualikiz.com</a>.</p> <p>The data is saved under 3 keys in HDF5 format: "/input", "/output", and "/label". The inputs to the QuaLiKiz evaluations are provided under "/input", representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. Selected relevant outputs of the QuaLiKiz evaluations are provided under "/output", namely the local turbulent transport coefficients after applying a semi-empirical turbulent fluctuation saturation rule. Some useful metadata is provided under "/label", giving some degree of provenance tracking back to the JET experimental database, as well as describing the applied parameter variations and explaining why certain output rows were removed from the output structure.</p>
Dataset for "Modeling Dipolar Nonprotogenic Solvents with PC-SAFT-Type Equations of State: Pure Substance Properties"
<p>Dipolar nonprotogenic solvents (DNS) are important chemical substances used across a wide range of applications, including renewable green solvent media, sustainable energy sources, and as efficient solvents for fabricating and processing semiconductive materials used in organic photovoltaics. Therefore, for efficient solvent screening or process design, a description and prediction of the thermodynamic properties of DNS using thermodynamic models is essential. This dataset contains calculation results of four different modeling strategies within the PC-SAFT equation of state for pure-substance properties of six DNSs: gamma-valerolactone, propylene carbonate, acetonitrile, dihydrolevoglucosenone, 1-methyl-2-pyrrolidone, and sulfolane. The modeling strategies differ in the treatment of the strong dipolar interactions of DNSs. The pure-substance properties include liquid density, vapor pressure, enthalpy of vaporization, and residual isobaric liquid heat capacity. The PC-SAFT performance was analyzed and evaluated based on the calculated data. Additionally, the dataset includes input files for quantum mechanical calculations of optimal molecular geometries and dipole moments of the considered DNSs using Gaussian 16 software.</p>
Instructional Design Models Score Ranking
<p>Here we present selected 17 instructional design models (IDMs) from our previous study (Cajnko, M. M., Pavlin, M., Likozar, B., Vasilakis, C., Tsovou, S., Chaundry, S. R., Kapal, D., van Leeuwen, M., Miloshevski, V., Toman, Y., Deniz, B., Şensoy Mercan, N., Pavitola, L., Memmedova, S., Verdiyeva, L., Sarsar, F., & Andiç Çakır, Ö. (2024). Instructional Design Models [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10900874) that were quantitatively evaluated by 20 experts from different countries (Azerbaijan, Greece, Ireland, Italy, Slovenia, Spain, and Türkiye) according to their suitability for the use in the engineering education. Evaluation was performed based on 12 statements, where each statement was quantitatively evaluated with values from 0 to 3 (0: Absolutely not suitable; 1: Partially suitable structure; 2: Moderately suitable structure; 3: Suitable structure), resulting in the total score of 36 for each model. The 12 statements were: </p> <p>1. Suitability for engineering education;<br>2. Suitability for virtual laboratories studies;<br>3. Suitability for technology integration;<br>4. Suitability for project-based work;<br>5. Suitability for active learning strategies;<br>6. Suitability for students with any disability;<br>7. Suitability for interdisciplinary design;<br>8. Suitability for cross-cultural practices;<br>9. Suitability for student feedback;<br>10. Suitability for instructor feedback;<br>11. Suitability for assessment and evaluation;<br>12. Suitability for reusability.</p> <p>The preliminary score of each IDM was obtained by averaging the scores of 20 experts and the standard deviation was calculated. Next, the scores of each IDM that lied outside of the average score ± 2*standard deviation was eliminated and the new average value was calculated. This process was repeated until all the remaining scores lied withing average score ± 2*standard deviation. The final score of all IDMs is reported on the first page of this dataset, while scoring of each expert are reported on subsequent pages.</p> <p>The five best scored IDMs are Instructional design model for unified eLearning, Assure model, Agile instructional design, Kemp, Morrison, and Ross model – Effective instructional design model, and ADDIE.</p> <p>This created dataset has been added as a database to the VILLAGE project web page (<a href="https://www.thevillageproject.eu/idmodelsdatabase/">https://www.thevillageproject.eu/idmodelsdatabase/</a>)</p>
plioDA Model Simulation Prior
<p>Model simulation output used for the prior for the Pliocene data assimilation reconstruction (plioDA). The files contain 1˚x1˚ regridded fields for the climate variables surface air temperature (tas), sea-surface temperature (tos), precipitation (pr), evaporation (ev) and sea ice concentration (siconc).</p>
Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany
<p>The dataset consists of particulate matter pollution concentration, measured in three localities - Hermsdorf, Charlottenburg and Adlershof, in Berlin, Germany.</p> <p><a href="../api/records/10076056/draft/files/pm25_summer_rd_30s.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer_rd_30s.geojson</a> shows the observed PM2.5 concentration in a 30 second interval.</p> <p><a href="../api/records/10076056/draft/files/pm25_summer.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer.geojson</a> shows the concentrations shown is the local concentration (observed concentration - background concentration) in a 30 second interval. The background concentration is calculated as the lowest 5 percentile of the measured concentration for each measurement round. </p> <p><a href="../api/records/10076056/draft/files/PM2.5_lc_max.geojson/content" target="_blank" rel="noopener noreferrer">PM2.5_lc_max.geojson</a> contains the information from <a href="../api/records/10076056/draft/files/pm25_summer.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer.geojson</a> in a 25m resolution. Additionally, it contains the land use information for each coordinate.</p> <p>The original publication providing all necessary background information on study sites, methodology and data processing is the following: Venkatraman Jagatha, J., T. Sauter, C. Schneider (2024): Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany. MDPI Sensors, 24(13), 4193, DOI: 10.3390/s24134193. The paper is fully open access and can be downloaded at <a href="https://doi.org/10.3390/s24134193">https://doi.org/10.3390/s24134193</a>.</p> <p>Information on working with geojson file can be found under <a href="https://geojson.readthedocs.io/en/latest/">GeoJSON</a> .</p>
1600 years of modelled energy production and demand for European Countries (Norway, France, Italy, Spain, and Sweden)
<h3>Citation</h3> <p>When using this dataset, please cite the following paper: van der Most et al. Temporally compounding energy droughts in European electricity systems with hydropower, 10 January 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3796061/v1].</p> <h3>Description</h3> <p>This dataset contains daily renewable energy production and demand data used in the study "Temporally compounding energy droughts in European electricity systems with hydropower". The dataset includes production data for various renewable energy sources (offshore wind, onshore wind, solar photovoltaics, run-of-river, and hydropower reservoir inflow) and electricity demand. It was generated wit the use of 1600 years of climate model data and a daily renewable electricity production and demand modelling framework. The study focuses on five European countries with significant hydropower capacities: Norway, France, Italy, Spain, and Sweden.</p> <h3>Content</h3> <ul> <li> <p><strong>Energy Production Data</strong>:</p> <ul> <li>Offshore and Onshore Wind Power: Derived from 10 m wind speed data extrapolated to hub height, using power law equations and cubic power curves.</li> <li>Solar Photovoltaics (PV): Based on solar irradiance and temperature-dependent cell efficiency calculations.</li> <li>Hydropower: Includes inflow data for run-of-river and reservoir hydropower systems modelled with routed runoff data</li> <li>Hydropower dispatch is modelled at the national level using a linear optimization approach that aims to minimize the difference between demand and the sum of all renewable energy production over a year, directing the solution to following the load curves.</li> </ul> </li> <li> <p><strong>Energy Demand Data</strong>:</p> <ul> <li>Daily load data from ENTSO-E tranparancy fitted using a logistic smooth transmission regression approach to national mean, population-weighted daily near-surface temperatures from ERA5 reanalysis data.</li> <li>Demand curves account for weekdays and weekends but exclude cultural and socio-economic factors such as holidays.</li> </ul> </li> </ul> <h3>Methodology</h3> <p>The dataset is generated using the KNMI Large Ensemble Time Slice (KNMI-LENTIS) dataset, which includes 160 sets of 10-year physical climate model simulations of present-day climate (2000-2009). The simulations are conducted with the EC-Earth3 global climate model. The energy production and demand data are modeled to assess the impact of meteorological drivers on energy systems, with a focus on identifying periods of high residual loads (energy droughts). The model set-up has been validated with the use of ERA5 data in previous work. </p> <h3>Usage</h3> <p>This dataset is intended for researchers and policymakers interested in studying the impact of climate variability on renewable energy systems. It provides insights into how different meteorological conditions can lead to energy droughts and offers a basis for developing strategies to enhance the resilience of energy systems.</p> <p> </p>
Datasets for practical model selection for prospective virtual screening
<p>This repository contains datasets for the manuscript "Practical model selection for prospective virtual screening":</p> <ul> <li><strong>pria_rmi_cv.tar.gz</strong>: A compressed directory containing chemical screening data for the <strong>PriA-SSB AS</strong>, <strong>PriA-SSB FP</strong>, and <strong>RMI-FANCM FP</strong> binary datasets. The files also contain the associated continuous % inhibition values and chemical features represented as SMILES and Morgan fingerprints. The dataset has been split into five folds for cross validation.</li> <li><strong>pria_rmi_pcba_cv.tar.gz</strong>: A compressed directory containing chemical screening data for the <strong>PriA-SSB AS</strong>, <strong>PriA-SSB FP</strong>, and <strong>RMI-FANCM FP</strong> binary datasets as well as public PubChem BioAssay datasets. The files also contain the PriA-SSB and RMI-FANCM continuous % inhibition values and chemical features represented as SMILES and Morgan fingerprints. The dataset has been split into five folds for cross validation. Missing values are left blank.</li> <li><strong>pria_prospective.csv.gz</strong>: A compressed file containing chemical screening data for the binary dataset <strong>PriA-SSB prospective</strong>. The file also contains the continuous % inhibition values and chemical features represented as SMILES and Morgan fingerprints.</li> </ul> <p>If you use these data in a publication, please cite:</p> <p>Shengchao Liu<sup>+</sup>, Moayad Alnammi<sup>+</sup>, Spencer S. Ericksen, Andrew F. Voter, Gene E. Ananiev, James L. Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter. Practical Model Selection for Prospective Virtual Screening. Journal of Chemical Information and Modeling. 2018 <a href="https://doi.org/10.1021/acs.jcim.8b00363">doi:10.1021/acs.jcim.8b00363</a></p> <p>PubChem data were provided by the <a href="https://pubchem.ncbi.nlm.nih.gov/">PubChem database</a>. Follow the <a href="https://pubchemdocs.ncbi.nlm.nih.gov/citation-guidelines">PubChem citation guidelines</a> if you use the PubChem data. See <a href="https://doi.org/10.1177/2472555217712001">Voter et al. 2017</a> (PubChem AID <a href="https://pubchem.ncbi.nlm.nih.gov/bioassay/1272365">1272365</a>) for the PriA-SSB screening data and <a href="https://doi.org/10.1177/1087057116635503">Voter et al. 2016</a> (PubChem AID <a href="https://pubchem.ncbi.nlm.nih.gov/bioassay/1159607">1159607</a>) for RMI-FANCM.</p> <p>Version 1.1.0 updates all of the data files. We standardized the SMILES in all files by generating canonical SMILES with RDKit version 2016.03.4. In addition, we removed 2845 chemicals from pria_prospective.csv.gz that were duplicates of compounds in pria_rmi_cv.tar.gz.</p>
MOD-LSP: MODIS-Based Parameters for Variable Infiltration Capacity (VIC) Model over the Continental US, Mexico, and Southern Canada
<p>The MOD-LSP project contains MODIS-based land and surface (soil and vegetation) parameters for the Variable Infiltration Capacity (VIC) model (Liang et al., 1994), release 5.0 and later (Hamman et al., 2018). The MOD-LSP spatial domain covers the continental United States, Mexico, and southern Canada; the associated domain files can be found in the <a href="https://zenodo.org/record/2564019">PITRI archive</a> (Bohn et al. 2018). This spatial domain and 0.625° (6 km) grid resolution are compatible with the gridded daily meteorological forcings of Livneh et al. (2015) ("L2015" hereafter) (http://ciresgroups.colorado.edu/livneh/data/daily-observational-hydrometeorology-data-set-north-american-extent), which can be disaggregated to hourly time step via the MetSim tool (Bennett et al. 2018) using the <a href="https://zenodo.org/record/2564019">aforementioned PITRI domain files</a> (Bohn et al. 2018).</p> <p>These parameters have two main purposes: (1) to improve upon previous widely-used parameters over the region (e.g., L2015) with updated, higher-resolution land cover maps and spatially explicit observations of surface properties; and (2) to expand from a single parameter set corresponding to one point in time to a series of parameter sets that account for temporal variability at seasonal to decadal scales.</p> <p>A detailed description of methods, the data sources and purposes of different VIC parameter sets within MOD-LSP, and how to use them with VIC, can be found in the MOD-LSP User Guide.pdf, included here. The scripts that were used to create the MOD-LSP parameters are archived on <a href="https://zenodo.org/record/3364149">Zenodo and GitHub</a> (Bohn 2019).</p> <p>If you wish to present or publish results that use these parameter sets, please cite the following paper:</p> <p>Bohn, T. J., and E. R. Vivoni, 2019b: MOD-LSP, MODIS-based land surface properties for assessing land cover variability and change over North America. Sci. Data, 6, 144, doi: 10.1038/s41597-019-0150-2.</p> <p>In addition, if you use the domain files associated with the PITRI precipitation disaggregation to accompany the MOD-LSP parameter files in VIC simulations, please cite the following paper:</p> <p>Bohn, T. J., K. M. Whitney, G. Mascaro, and E. R. Vivoni, 2019: A deterministic approach for approximating the diurnal cycle of precipitation for use in large-scale hydrological modeling. J. Hydrometeorol., 20, 297–317, doi:10.1175/JHM-D-18-0203.1.</p> <p>Contents:</p> <ul> <li>MOD-LSP User Guide v1.0.pdf - Explains how parameters were generated and how to set up the files for input in VIC simulations.</li> <li>global_param.template - Template for global_parameter file, which lists the locations of the other input files and sets various simulation options. The template contains placeholders for some filenames and simulation options, which must be replaced with real values by the user.</li> <li>params.$DOMAIN.L2015.nc - VIC-5 compliant NetCDF parameter files with values taken from the L2015 project for domain $DOMAIN (which is one of "CONUS_MX" or "USMX").</li> <li>params.CONUS_MX.MOD_IGBP.mode.2000_2016.nc - VIC-5 compliant NetCDF parameter file over the CONUS_MX domain, with land cover fractions taken from the MODIS MCD12Q1.006 product and an annual cycle of land surface properties (LAI, Fcanopy, albedo) derived from the climatological mean of MODIS observations over the period 2000-2016.</li> <li>params.USMX.NLCD_INEGI.$LCID.2000_2016.nc - VIC-5 compliant NetCDF parameter file over the USMX domain, with land cover fractions taken from the NLCD_INEGI dataset, from year = $LCID, and an annual cycle of land surface properties (LAI, Fcanopy, albedo) derived from the climatological mean of MODIS observations over the period 2000-2016.</li> <li>params.USMX.NLCD_INEGI.$LCID.$YYYY_$YYYY.nc - VIC-5 compliant NetCDF parameter file over the USMX domain, with land cover fractions taken from the NLCD_INEGI dataset, from year = $LCID, and an annual cycle of land surface properties (LAI, Fcanopy, albedo) derived from the MODIS observations from a single year $YYYY.</li> <li>veg_hist.$DOMAIN.$LCTYPE.$LCID.2000_2016.nc - timeseries of monthly land surface properties (LAI, Fcanopy, albedo) from MODIS observations spanning years 2000-2016, over domain $DOMAIN, aggregated over land cover classification $LCTYPE from year $LCID.</li> </ul>
A blind test on wind turbine wake modelling based on wind tunnel experiments: Phase I – The benchmark case
<p>This data set ("Data files.zip") contains the wind tunnel measurement data from Phase I of the Blind test on wind turbine wake modelling based on wind tunnel experiments organised during the TWEET-IE project (www.tweet-ie.eu).</p> <p>This updated version <strong>replaces</strong> the older versions 1.0.0 (https://doi.org/10.5281/zenodo.10566401), 1.1.0 (https://doi.org/10.5281/zenodo.11370112), 2.0 (https://doi.org/10.5281/zenodo.12188194) and 2.1 (https://doi.org/ 10.5281/zenodo.13918935). In comparison to the previous version 2.1 the data documentation has been updated to follow the template of the TWEET-IE project documents, indicating the Grant Agreement Number with the European Union and the Call Topic of the project.</p> <p>All tests were conducted in the closed-loop, low-speed boundary layer wind tunnel of the Chair of Aerodynamics and Fluid Mechanics at Technische Universität München (TUM). The experiments concerned two wind turbines, aligned with the flow, one downstream of the other, at a distance of 5 diameters. For Phase I, no control was applied to the wind turbine models, which were operating at constant RPM. The turbine models, designed and manufactured by TUM, were instrumented with multiple sensors and actuators and had a diameter of 1.1M. Measurements include velocity, power and loads on the turbines. A detailed description of the experimental set up can be found in the accompanying document ("Data documentation.pdf"). </p> <p>File "Submission procedure.zip" includes the format description and the templates of the output data that should be submitted by the participants in the blind test comparison.</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.