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6,410 results for “results”
Ecotoxicity test results for the paper entitled "Evaluation of a bioelectrochemical reductive/oxidative sequential process for chlorinated aliphatic hydrocarbons (CAHs) removal from a real contaminated groundwater"
<p>Within the frame of the ELECTRA No. 826244 H2020 project, a sequential reductive/oxidative bioelectrochemical process at a laboratory scale was tested with real groundwater from a contaminated site in Northern Italy for chlorinated aliphatic hydrocarbons (CAHs) removal. The changes in groundwater ecotoxicity were followed by a complex ecotoxicity test battery including testorganisms from various trophic levels. The datasets (supplementary to Chapter 3.5) for this publication are presented here. Referred publication: Edoardo Dell'Armi, Marco Zeppilli, Maria Letizia Di Franca, Bruna Matturro, Viktória Feigl, Mónika Molnár, Zsófia Berkl, Imre Németh, Hafsa Yaqoubi, Simona Rossetti, Marco Petrangeli Papini, Mauro Majone, Evaluation of a bioelectrochemical reductive/oxidative sequential process for chlorinated aliphatic hydrocarbons (CAHs) removal from a real contaminated groundwater, Journal of Water Process Engineering, 49, 2022, 103101, https://doi.org/10.1016/j.jwpe.2022.103101.</p>
Ecotoxicity test results for the paper entitled "Passive electrobioremediation approaches for enhancing hydrocarbons biodegradation in contaminated soils"
<p>Within the frame of the ELECTRA No. 826244 H2020 project, a microbial electrochemical snorkel (MES) was applied for the treatment of soil contaminated by hydrocarbons at a laboratory scale. The changes in soil ecotoxicity were followed by plant tests applying <em>Sinapis alba</em> and <em>Triticum aestivum</em>. The datasets (supplementary to Figure 7) for this publication are presented here. Referred publication: Carolina Cruz Viggi, Matteo Tucci, Marco Resitano, Bruna Matturro, Simona Crognale, Viktória Feigl, Mónika Molnár, Simona Rossetti, Federico Aulenta, Passive electrobioremediation approaches for enhancing hydrocarbons biodegradation in contaminated soils, Science of The Total Environment, 845, 157325, 2022, https://doi.org/10.1016/j.scitotenv.2022.157325.</p>
Computed surface and chemical potentials, expansion coefficients, structures, models and results for the PMFPredictor Toolkit
<p>The PMFPredictor toolkit enables the prediction of the potentials of mean force describing the interaction between a surface and a small molecule in aqueous solution, which would otherwise be obtained from lengthy metadynamics simulations. This repository contains files to enable the operation of the toolkit, with source code available at https://github.com/ijrouse/PMFPredictor-Toolkit and corresponding to release v0.5-alpha.</p> <p>In PMFPredictor-Repository.zip we provide supplementary data necessary for the operation of the PMFPredictor Toolkit including:</p> <ul> <li>Structures of surfaces ("Structures/Surfaces") and chemicals ("Structures/Chemicals") in a united tabulated (.csv) format, listing x/y/z co-ordinates, atom IDs, mass (in amu), charge (in elementary units), Lennard Jones 6-12 parameters: sigma (in nm) and epsilon (in kJ/mol).</li> <li>Interaction potentials of surfaces ("SurfacePotentials") and chemicals ("ChemicalPotentials") with probe atoms and molecules in tabulated format with distances relative to reference points in nm and energies in kJ/mol. Also included in these folders are the potentials with the molecular probes in individual files.</li> <li>Hypergeometric expansion coefficients of the interaction potentials ("Datasets/SurfacePotentialCoefficientsNoise-1-oct12.csv" and "Datasets/ChemicalPotentialCoefficients-oct10.csv") in tabulated form, corresponding to potentials with units of nm for distance and kJ/mol for energy. Descriptions of the headers are provided in DatasetHeaderDescription.txt, included in the archive.</li> <li>Trained TensorFlow models for the prediction of potentials of mean force from HG interaction coefficients, suitable for loading via the Keras backend.</li> <li>PMFs generated for a range of surfaces and chemicals as output from the trained model, in both text format and figures showing comparisons to training PMFs where available. PMFs are supplied as tabulated data with comma separated values of distance in nm and interaction energies in kJ/mol.</li> <li>Adsorption energies in kJ/mol evaluated at T=300K extracted from all PMFs and compared to the values obtained from known PMFs where available.</li> </ul> <p>The surface_pmfpredictor.zip archive contains PMFs selected for the operation of the UnitedAtom software package for the calculation of protein-nanoparticle interactions. This data is included in the main repository file and provided separately to avoid the download of unnecessary data if only the final PMFs are required. As with the main set, these are provided in tabulated form with distance [nm], energy [kJ/mol] pairs. This repository also contains the sets of figures illustrating these PMFs for each surface. Both archives contain further information on the contents, including descriptions of the surfaces and chemicals for which PMFs are computed. We also supply the training data used to build the model in a separate archive, PMFPredictor-TrainingData.zip, along with a text file containing descriptions of all headers in this file. This training data is quite large when uncompressed, c.a. 7 Gb, hence its exclusion from the main archive.</p> <p>If you use results from this repository please cite the following paper in addition to the repository itself:</p> <p>I. Rouse, V. Lobaskin, Machine-learning based prediction of small molecule -- surface interaction potentials, arXiv:2211.07999<br> https://arxiv.org/abs/2211.07999</p>
Source code and simulation results for computing resonance expansions of quadratic quantities with regularized quasinormal modes
<p>This data publication supplements the article "Resonance expansion of quadratic quantities with regularized quasinormal modes" [1]. Tabulated data related to the figures in the manuscript is provided along with the Matlab scripts used to generate the results. The Riesz projection software package RPExpand [2] has been extended to support quasi normal modes (QNMs) and, in particular, the proposed method for quadratic quantities. A current version is contained in the directory <code>Code</code>. Furthermore, the input files required for scattering and resonance simulations with the finite element method (FEM) solver JCMsuite [3] are contained.</p> <p><strong>Requirements</strong></p> <ul> <li>JCMsuite (version 5.2.1 or newer)</li> <li>MATLAB (tested with version R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holders in the files by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>. </p> <p><strong>References</strong></p> <p>[1] Fridtjof Betz, Felix Binkowski, Martin Hammerschmidt, Lin Zschiedrich, Sven Burger: Resonance expansion of quadratic quantities with regularized quasinormal modes, Physica Status Solidi A <strong>220</strong>, 2370013 (2023)</p> <p>[2] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763</p> <p>[3] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192</p>
Additional results for article "A new approach for the generation of real-time GNSS low-latitude ionospheric scintillation maps"
<p>Complete set of interpolation error and correlation metrics for the approaches GDA, IDW, RBF and GPR for all the 12 pre-processing options using the SSS cross-validation scheme for the 10-hour dataset (40 maps with 16-minute interval for each approach and each pre-processing options) - file “Complete table of interpolation errors and correlation.csv”.</p> <p>Complete set of scintillation maps for the approaches GDA, IDW, RBF and GPR for all the 12 pre-processing options covering the 10-hour dataset (40 maps with 16-minute interval for each approach and each pre-processing options) - file “Scintillation maps for the 10-hour dataset.zip”.</p> <p>Comparison plots of the scintillation maps generated by the approaches GDA, IDW, RBF and GPR with the pre-processing options SAR, SMR and VQI for each of the 40 intervals of time of 16 minutes covering the 10-hour dataset - file “Set of maps for all 4 approaches with the SAR, SMR and VQI sets of options.zip”.</p> <p>Sequence of scintillation maps for the 8-hour dataset generated by the GPR(VQI) approach for the three time resolutions (1, 2, and 16-minute) - file “Scintillation maps for the 8-hour comparison dataset.zip”.</p> <p>Animations corresponding to the sequence of maps generated by the GPR(VQI) approach for the 8-hour dataset, and for the three time resolutions (1, 2, and 16-minute) - file “Animations of GPR(VQI) maps for the 8-hour comparison dataset.zip”.</p>
Measuring Bulk Crystallographic Texture from Ti-6Al-4V Hot-Rolled Sample Matrices using Synchrotron X-ray Diffraction (Results Dataset)
<p>A dataset of crystallographic texture results for both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases, measured from 31 different hot-rolled Ti-6Al-4V (Ti-64) materials and 3 differently orientated samples using synchrotron X-ray diffraction (SXRD). The aim of the work was to accurately quantify bulk macro-texture for both the α and β phases across a range of different processing conditions, and to compare results with electron backscatter diffraction (EBSD) measurements. The synchrotron intensities were extracted using a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package, and then directly used to calculate the pole figures, orientation distribution functions (ODFs) and numerical values for the texture indices in <a href="https://mtex-toolbox.github.io">MTEX</a></p> <p><strong>Material </strong></p> <p>The Ti-64 materials had been hot-rolled at a range of different temperatures, and to different reductions, followed by air-cooling. Three samples of different orientation were cut from the centre of these rolled blocks, and from the starting material. The material and hot-rolling conditions are recorded in this <a href="https://doi.org/10.5281/zenodo.7438090">analysis dataset</a> as an excel spreadsheet and summarised in the table below.</p> <table align="center"> <caption>A table recording the sample number and associated hot-rolling condition.</caption> <tbody> <tr> <td> <p><em><strong>Sample Number</strong></em></p> </td> <td> <p><em><strong>Rolling Condition</strong></em></p> </td> </tr> <tr> <td>1</td> <td>825ºC, 87.5% Reduction</td> </tr> <tr> <td>2</td> <td>865ºC, 87.5% Reduction</td> </tr> <tr> <td>3</td> <td>895ºC, 87.5% Reduction</td> </tr> <tr> <td>4</td> <td>915ºC, 87.5% Reduction</td> </tr> <tr> <td>5</td> <td>935ºC, 87.5% Reduction</td> </tr> <tr> <td>6</td> <td>950ºC, 87.5% Reduction</td> </tr> <tr> <td>7</td> <td>960ºC, 87.5% Reduction</td> </tr> <tr> <td>8</td> <td>975ºC, 87.5% Reduction</td> </tr> <tr> <td>9</td> <td>1020ºC, 87.5% Reduction</td> </tr> <tr> <td>10</td> <td>β-annealed, 825ºC, 87.5% Reduction</td> </tr> <tr> <td>11</td> <td>β-annealed, 915ºC, 87.5% Reduction</td> </tr> <tr> <td>12</td> <td>β-annealed, 975ºC, 87.5% Reduction</td> </tr> <tr> <td>13</td> <td>Reduced heating from 915ºC, 87.5% Reduction</td> </tr> <tr> <td>14</td> <td>Reduced heating from 975ºC, 87.5% Reduction</td> </tr> <tr> <td>15</td> <td>825ºC, 75% Reduction</td> </tr> <tr> <td>16</td> <td>865ºC, 75% Reduction</td> </tr> <tr> <td>17</td> <td>895ºC, 75% Reduction</td> </tr> <tr> <td>18</td> <td>915ºC, 75% Reduction</td> </tr> <tr> <td>19</td> <td>935ºC, 75% Reduction</td> </tr> <tr> <td>20</td> <td>950ºC, 75% Reduction</td> </tr> <tr> <td>21</td> <td>960ºC, 75% Reduction</td> </tr> <tr> <td>22</td> <td>975ºC, 75% Reduction</td> </tr> <tr> <td>23</td> <td>1020ºC, 75% Reduction</td> </tr> <tr> <td>24</td> <td>β-annealed, 825ºC, 75% Reduction</td> </tr> <tr> <td>25</td> <td>β-annealed, 915ºC, 75% Reduction</td> </tr> <tr> <td>26</td> <td>β-annealed, 975ºC, 75% Reduction</td> </tr> <tr> <td>27</td> <td>Reduced heating from 915ºC, 75% Reduction</td> </tr> <tr> <td>28</td> <td>Reduced heating from 975ºC, 75% Reduction</td> </tr> <tr> <td>29</td> <td>As-received</td> </tr> <tr> <td>30</td> <td>As-received, β-annealed</td> </tr> <tr> <td>31</td> <td>975ºC, 50% Reduction</td> </tr> </tbody> </table> <p><strong>MTEX Data Analysis</strong></p> <p>The lattice plane intensities for 22 α and 4 β phase peaks were extracted from the Continuous-Peak-Fit analysis, also included in this <a href="https://doi.org/10.5281/zenodo.7438090">analysis dataset</a>, and saved as text files in the form of pole figures. The lattice intensity text files were analysed in MTEX using scripts from the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package, to plot pole figures and ODF slices, and to calculate pole figure maxima, ODF maxima, texture indices and texture component phase fractions. A kernel half-width of 10° was found to produce optimal data fitting, for highly accurate texture strength intensity values.</p> <p><strong>Metadata </strong></p> <p>An accompanying YAML text file contains associated processing metadata for the SXRD analysis, recording information about the packages used to process the data, along with details about the different files contained within this results dataset.</p>
Result data related to "Bersalli et al. (2023) -- Most industrialised countries have peaked carbon dioxide emissions during economic crises through strengthened structural change"
<p>This repository contains the result data of our study investigating the relationship between emission peaks and economic crises. The repository contains mainly two datasets:</p> <ul> <li>multiplicative-contributions.csv / .nc</li> <li>prepost-growth-rates.csv / .nc</li> </ul> <p>Both datasets exist in CSV and NetCDF file format for convenience. The dataset <em>multiplicative-contributions</em> contains year-to-year change factors of GDP, population, energy-intensity, and carbon-intensity for every country in our study. The dataset <em>prepost-growth-rates</em> contains growth over a multi-year period pre- and post- crisis for each country and each crises in our study.</p>
TRANSIT long-distance multimodal trips model results - a Spanish case study
<p>The files downloaded present the results per scenario considered obtained with the agent-based model developed for the assessment of the Intermodal Timetable Synchronisation solution proposed in the scope of the TRANSIT project (<a href="https://www.transit-h2020.eu/">https://www.transit-h2020.eu/</a>).</p> <p>An agent-based modelling framework called <a href="https://github.com/StefanoPenazzi/jtap/tree/main">J-TAP</a> has been developed and put at work to implement a Spanish long-distance multimodal trips model. The enhanced version of J-TAP used in this work can be found on a <a href="https://github.com/NommonSolutionsAndTechnologies/jtap">github repository</a>.</p> <p>The case study is focused on modelling the long-distance travel patterns of the residents in the Valencia (Spain) area. The destinations considered include the whole of Spain. The period under study is a full year from March 2019 to February 2020 (both inclusive).The files are structured in three different scenarios:</p> <ol> <li><strong>CS01 - Baseline</strong>. The current state of the network is considered and the actual long-distance travel patterns are obtained.</li> <li><strong>CS02 - HSR connection with Madrid-Barajas airport</strong>. The long-distance travel patterns are modelled with hard measures, the high-speed rail is connected to Madrid-Barajas airport.</li> <li><strong>CS03 - HSR connection with Madrid-Barajas airport and timetable synchronisation</strong>. The effects of the timetable synchronisation are modelled.</li> </ol> <p>Each scenario includes the following files:</p> <ul> <li>ctapModelParameters. A folder containing all the information extracted from the <a href="https://neo4j.com/product/graph-data-science/?utm_program=emea-prospecting&utm_source=google&utm_medium=cpc&utm_campaign=emea-search-offers&utm_adgroup=dynamic&utm_content=dynamic&utm_placement=&utm_network=g&gclid=Cj0KCQiAwJWdBhCYARIsAJc4idAo4CEi9lU8TXwmBym8MNHpEIZHPBs3x_4phxbu76y1XKbYlFoZCjIaAiGhEALw_wcB">neo4j</a> graph database created to model the multimodal network and the agents. J-TAP contains packages that simplify network creation in the graph database. This information is stored in .json files (e.g., "Os2DsTravelCostParameter.json" contains the generalised cost for each OD pair and transport mode, "AttractivenessParameter.json" contains the attractiveness by destination, activity, time of the year and agent, etc.). The solver included in the J-TAP framework uses this information to calculate the agents plans.</li> <li>population.json. The result of the J-TAP optimisation. It includes the fitness value for each agent plan evaluated during the J-TAP execution. The best plan for each agent is selected as the plan performed by the agent. An agent plan includes: <ul> <li>activities - Sequence of activities.</li> <li>locations - Sequence of locations</li> <li>ts - Initial time of the activity</li> <li>te - Final time of the activity</li> </ul> </li> <li>LinkTimeFlow.csv. It is obtained after processing the previous file. It contains the number of agents using each link in the network (i.e., road, rail, air and cross links) in each time interval. The first column represents the link id and the rest of columns indicates the number of agents in each interval.</li> </ul> <p>The J-TAP simulation framework is explained in detail in TRANSIT's deliverable <a href="http://www.nommon-files.es/transit/TRANSIT-D5.1_Modelling_Framework_v02.00.00.pdf">D5.1. TRANSIT Modelling and Simulation Framework</a> and the complete description of the case studies and scenarios tested is included in TRANSIT's deliverable <a href="http://www.nommon-files.es/transit/TRANSIT-D6.1_Assessment_of_Intermodal_Concepts_00.02.00.pdf">D6.1. Impact Assessment of New Intermodal Concepts and Passenger Information Services: Conclusions and Recommendations</a>.</p> <p>Thank you for downloading the dataset! It would be very helpful if you share your view on the data show with us. </p>
Data to reproduce the results: Statistical power of spatial earthquake forecast tests
<p>We provide data needed to reproduce the figures from the publication titled "Statistical power of spatial earthquake forecast tests".</p>
COALMOD-World 2.0 data, results, figures for: Stranded Assets in the Coal Export Industry? The Case of the Australian Galilee Basin
<p>This dataset contains all COALMOD-World 2.0 data for Hauenstein et al. (2023): New coal mines in the Australian Galilee Basin are not economically viable and are prone to become stranded assets (doi.org/10.1016/j.oneear.2023.07.005).</p> <p>With the input data files and the GAMS scenario file the model (https://github.com/chauenstein/COALMOD-World_v2.0) can be run to reproduce the model results.</p> <p>Furthermore, the output.zip folder contains the results file, the R code to compile the figures, and PDFs of the figures.</p>
SS-DBEM model results for main tuna commercial species
<p>Projected body size and potential biomass changes (in %) for the main commercial tuna species and swordfish by each RFMO by the mid- and the end-of-the-century. The changes have been estimated as the difference between the future and the reference period. A multi-species ecosystem model which integrates a species-based model (DBEM, Dynamic Bioclimatic Envelope Model) with the size-spectrum approach (SS) was used in this study. The code for the model is available <a href="https://zenodo.org/record/7548113#.Y8kwxBfMKUk">here</a>.</p>
steinbock results of IMC example data
<p>If you are working with these files, please cite them as follows:<br><br>Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). <a href="https://doi.org/10.1038/s41596-023-00881-0">https://doi.org/10.1038/s41596-023-00881-0</a></p><p>This repository hosts the results of processing example imaging mass cytometry (IMC) data hosted at <a href="https://zenodo.org/record/5949116">zenodo.org/record/5949116</a> using the <i>steinbock </i>framework available at <a href="https://github.com/BodenmillerGroup/steinbock">github.com/BodenmillerGroup/steinbock</a>. Please refer to <strong>steinbock.sh </strong>for how these data were generated from the raw data.</p><p>The following files are part of this repository:</p><ul><li><strong>panel.csv</strong>: contains channel information regarding the used antibodies in <i>steinbock</i> format</li><li><strong>img.zip</strong>: contains hot pixel filtered multi-channel images derived from the IMC raw data. One file per acquisition is generated</li><li><strong>images.csv</strong>: contains metadata per acquisition</li><li><strong>pixel_classifier.ilp</strong>: ilastik pixel classifier (same as the one in <a href="https://zenodo.org/record/6043544">zenodo.org/record/6043544</a>)</li><li><strong>ilastik_crops.zip</strong>: image crops on which the ilastik classifier was trained (same as the ones in <a href="https://zenodo.org/record/6043544">zenodo.org/record/6043544</a>)</li><li><strong>ilastik_img.zip</strong>: contains multi-channel images (one per acquisition) in .h5 format for ilastik pixel classification</li><li><strong>ilastik_probabilities.zip</strong>: 3 channel images containing the pixel probabilities after pixel classification</li><li><strong>masks_ilastik.zip</strong>: segmentation masks derived from the ilastik pixel probabilities using the <strong>cell_segmentation.cppipe</strong> pipeline</li><li><strong>masks_deepcell.zip</strong>: segmentation masks derived by <i>deepcell</i> segmentation</li><li><strong>intensities.zip</strong>: Contains one .csv file per acquisition. Each file contains single-cell measures of the mean pixel intensity per cell and channel based on the files in <strong>img.zip </strong>and <strong>masks_deepcell.zip</strong>.</li><li><strong>regionprops.zip</strong>: Contains one .csv file per acquisition. Each file contains single-cell measures of the morphological features and location of cells based on <strong>masks_deepcell.zip</strong>.</li><li><strong>neighbors.zip</strong>: Contains one .csv file per acquisition. Each file contains an edge list of cell IDs indicating cells in close proximity based on <strong>masks_deepcell.zip</strong>.</li><li><strong>ome.zip</strong>: contains .ome.tiff files derived from img.zip; one file per acquisition</li><li><strong>histocat.zip</strong>: contains single-channel .tiff files with segmentation masks derived from <strong>masks_deepcell.zip</strong> for upload to histoCAT (<a href="https://bodenmillergroup.github.io/histoCAT/">bodenmillergroup.github.io/histoCAT</a>)</li><li><strong>cells.csv</strong>: contains intensity and regionprop measurements of all cells</li><li><strong>cells_csv.zip</strong>: contains intensity and regionprop measurements of all cells per acquisition</li><li><strong>cells.fcs</strong>: contains intensity and regionprop measurements of all cells in fcs format</li><li><strong>cells_fcs.zip</strong>: contains intensity and regionprop measurements of all cells per acquisition in fcs format</li><li><strong>cells.h5ad</strong>: contains intensity, regionprop and neighbor measurements of all cells in <i>anndata </i>format</li><li><strong>cells_h5ad</strong>: contains intensity regionprop and neighbor measurements of all cells per acquisition in <i>anndata </i>format</li><li><strong>graphs.zip</strong>: contains spatial object graphs in .graphml format; one file per acquisition</li></ul>
Model results and configuration files for "Large modeling uncertainty in projecting decadal surface ozone changes over urban and industrial regions of China"
<p>This repository includes files as described below:</p> <p><strong>1. namelist_CBMZ09_example.input, namelist_MOZART202_example.input:</strong></p> <p>Two WRF-chem namelist files for CBMZ and MOZART simulation.</p> <p>They are modified according to the namelist from <a href="https://github.com/wrfchem-leeds/WRFotron">https://github.com/wrfchem-leeds/WRFotron</a>.</p> <p><strong>2. wps_namelist_example.wps:</strong></p> <p>namelist for WRF Preprocessing System (WPS)</p> <p><strong>3. temporal_hourly_scale_factor_emission.csv:</strong></p> <p>Hourly scale factors for emissions.</p> <p>Hourly allocation is applied to all emission data (i.e., emissions for 2017, 2030 and perturbated emissions of NOx, VOCs).</p> <p><strong>4. vertical_emission_ratio.csv</strong></p> <p>Vertical shares (ratios) of emissions.</p> <p>Emissions from sectors of power and industry are vertically allocated based on this file. Vertical allocation is conducted for all emission data.</p> <p>These shares are suggested by MICS-ASIA III intercomparison framework.</p> <p><strong>5. 01_2030_2017_simulations.zip: </strong></p> <p>Simulated MDA8 ozone under future (2030) and 2017 emission scenarios by the two chemical mechanisms (i.e., CBMZ, MOZART).</p> <p><strong>6. 02_perturbations_of_NOxVOCs.zip:</strong></p> <p>Simulated MDA8 ozone given perturbations of NOx and VOCs emissions by the two chemical mechanisms.</p> <p><strong>7. 03_hourly_diff_O3_NOx_OH_HNO3.zip: </strong></p> <p>Differences of hourly simulated concentrations of O3, NOx, OH and HNO3 during July in the Base-2017 scenario between CBMZ and MOZART (CBMZ - MOZART).</p>
GC model results
<p>Results from the GC model software that accompanies the publication <strong>Understanding repertoire sequencing data through a computational model of the germinal center </strong>(Rodrigo García-Valiente, Elena Merino Tejero, Maria Stratigopoulou, Daria Balashova, Aldo Jongejan, Danial Lashgari, Aurélien Pélissier, Tom G. Caniels, Mathieu A. F. Claireaux, Aram Al-Soudi, Marit J. van Gils, María Rodríguez Martínez, Niek de Vries, Michael Meyer-Hermann, Jeroen E.J. Guikema, Huub Hoefsloot, Antoine H.C. van Kampen).<br> <br> Source code can be found in <a href="https://github.com/EDS-Bioinformatics-Laboratory/GC_ABM_SHM_network ">GitHub</a>.</p>
Results of the crowd-mapping action within the project TeRRIFICA [Dataset No. 1 dated 2022-09-19]
<p>The dataset includes the results of the crowd-mapping action within the project "Territorial RRI fostering innovative climate action" - TeRRIFICA (Horizon 2020 under GA 824489) dated 2022-09-19. The data are points added to the map by the users (volunteers) and represent locations where climate change-related issues occur regarding air temperature, air quality, water, soil, and wind (SPOTS). The second part of the dataset is related to the crowd-mapping users and their anonymized characteristics (USERS). More details are available at https://terrifica.eu/.</p>
NI4OS-Europe landscaping survey results
<p>The dataset includes 575 complete responses collected in the NI4OS-Europe landscaping survey between 21 October and 10 December 2019. The survey was run in the following countries: Albania, Armenia, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Georgia, Greece, Hungary, Moldova, Montenegro, North Macedonia, Romania, Serbia and Slovenia.<br> The activity was coordinated in collaboration with the four regional EOSC projects (EOSC- Pillar, EOSC-Nordic, EOSC Synergy, ExPaNDS) and its results were used by the EOSC Landscape Working Group.</p> <p>The starting point for developing the survey was the questionnaire designed by the EOSC-Pillar project team, but Policy Mapping Survey by Regional Cooperation Council, EUA’s Open Access Survey and the FAIRsFAIR Policy and Practice Survey 2019 were also used. Some of the questions that were included had to be rephrased to adapt to regional stakeholders’ needs. NI4OS-Europe work package leaders suggested additional questions, some of which were included in the survey. The file NI4OS-Europe_survey_questions.ods contains all the questions from the survey mapped according to topics. For each question it is indicated whether it was adopted from an existing survey or was suggested by a NI4OS work package.</p> <p>Five questionnaires were defined – one for each stakeholder group identified by the NI4OS-Europe partners based on their role in the research ecosystem: FUND (research funders and policymakers), CREATE (universities, research institutes, etc.), SUPPORT (libraries, repositories, research infrastructures, etc.), CONSUME (organizations using research results in their work, e.g. SMEs) and FACILITATE (individuals and organizations involved in promoting the principles of open science). There were 82 questions in all questionnaires.</p> <p>LimeSurvey was used to collect responses. As some questions appeared across multiple surveys, the five questionnaires were merged into one survey with forking paths: after answering the preliminary questions, a respondent was redirected to the appropriate questionnaire.</p> <p>The survey collected 575 complete responses from 482 distinct entities in 15 countries. The incomplete responses have been removed from the dataset.</p> <p>****Dataset contents****<br> NI4OS-Europe_survey-results_2019_completed.ods, data file, survey responses, OpenDocument Spreadsheet (.ods) file generated in Apache OpenOffice Calc<br> NI4OS-Europe_survey_questions.ods, survey questions, OpenDocument Spreadsheet (.ods) file generated in Apache OpenOffice Calc<br> NI4OS-Europe_survey-questions.pdf, survey questions exported as a PDF file from LimeSurvey<br> NI4OS-Europe_survey-README.txt<br> The dataset was used to create visualizations on the NI4OS website: https://ni4os.eu/survey-results/<br> ****Dataset license****<br> The dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0), <a href="https://creativecommons.org/licenses/by/4.0">https://creativecommons.org/licenses/by/4.0</a></p>
Research Data for System test results for the R290 systems
<p>This dataset provides the research data system test results of R-290 (propane) systems. In the TRI-HP project, a new heat pump system with R-290 was integrated into multiple renewable energy sources. There were two experimental campaigns. After analysing the results of the first experimental campaign, the prototype was further improved. The second experimental campaign was conducted with the improved prototype.</p> <p>The detailed information can be found in ZENODO:</p> <p><a href="https://zenodo.org/record/7324246">Refined heat pump design and results of final testing</a></p> <p><a href="https://zenodo.org/record/7285499">Critical review of heat pump prototype operation and required modications</a></p> <p><a href="https://zenodo.org/record/5937065">Experimental results of dual source heat exchanger</a></p> <p>The background of this dataset:</p> <p>During the first experimental campaign, the heat pump prototypes were tested in the laboratories of the project<br> partners in static conditions. These conditions are defined in standards like, for example, EN 14511 [1], providing<br> specific temperatures on the evaporator and condenser sides. These tests enable us to characterize the COP and<br> EER in the nominal conditions of the equipment. However, when such heat pumps are installed in a real building<br> in a specific climate, they will be subject to varying conditions, depending on the load, the weather along<br> the seasons, intermediate storage vessels etc. For this reason, it is important also to carry out</p> <p>dynamic tests.<br> The dynamic tests presented in this deliverable were performed in two laboratories, following the principles of the<br> hardware-in-the-loop where part of the systems is installed in the lab (e.g. the heat pump), and part of the systems<br> is simulated and emulated to provide to real installed systems the response of the virtual element (e.g. building,<br> ground source, weather, solar thermal and PV). The full methodology and setups are described in detail in the<br> deliverable D7.1 [2].<br> The solar-ice system was tested in SPF-OST facilities in Rapperswil, Switzerland. The applied methodology was<br> the Concise Cycle Test (CCT), where 7 individual days along the year for the given climate of Bern were selected.<br> The complete system includes solar thermal, ice storage and water storage. The system was assembled in<br> the laboratory, and the actual setup is described in deliverable D7.3 [3]. The electrical part, including<br> household electricity, PV and the battery, will be tested for the CO2 slurry system due to time constraints since this<br> part does not affect the thermal behaviour of the installed system. The full efficiency of the propane solar-ice<br> slurry system, including the electrical part, will be provided in D7.9, "Demonstrated energetic performance and cost<br> competitiveness of systems".<br> The dual-source system was tested in the IREC laboratory SEILAB in Tarragona, Spain. The methodology is slightly<br> different since the selected periods are not individual days but a series of four consecutive days in<br> the summer and winter seasons. The overall system includes storage tanks, a geothermal loop as well as<br> Photovoltaics (PV) and an electrical battery. The assembled system and the lab setup were described in <br> deliverable D7.2 [4].<br> The present deliverable compiles the results of these two series of dynamic experiments with the propane heat<br> pumps. The results of the solar ice system are first analyzed in section 2, including a summary of the laboratory<br> setup and the control strategies used. The same structure is followed for the dual source system in section 3.<br> 2 Deliverable D7.4</p>
Research data for Refined heat pump design and results of final testing
<p>In this dataset, the data of the second experimental test campaign of the CO2-ice heat pump is shared. The report, which analyzes the data and makes the necessary explanations, has already been shared as a "Refined heat pump design and results of final testing (Deliverable: D5.6)". The report has already been published on ZENODO.</p>
Energy-Saving Strategies for Mobile Web Apps and their Measurement: Results from a Decade of Research - Dataset
<p>In 2022, over half of the web traffic was accessed through mobile devices. By reducing the energy consumption of mobile web apps, we can not only extend the battery life of our devices, but also make a significant contribution to energy conservation efforts. For example, if we could save only 5% of the energy used by web apps, we estimate that it would be enough to shut down one of the nuclear reactors in Fukushima. This paper presents a comprehensive overview of energy-saving experiments and related approaches for mobile web apps, relevant for researchers and practitioners. To achieve this objective, we conducted a systematic literature review and identified 44 primary studies for inclusion. Through the mapping and analysis of scientific papers, this work contributes: (1) an overview of the energy-draining aspects of mobile web apps, (2) a comprehensive description of the methodology used for the energy-saving experiments, and (3) a categorization and synthesis of various energy-saving approaches.</p>
ADS v4 results for MY 34 & 35
<p>Results from the Aerosol Determination Strategy v4:</p> <p>*Extinction values comes from RCP retrievals</p> <p>*Covers MY 34 and first half of MY 35</p> <p>Columns description:</p> <p>Reff_dust : Effective radius dust (unit in μm)<br> Reff_dust_err : Effective radius error dust (unit in μm)<br> Reff_H2Oice : Effective radius water ice (unit in μm)<br> Reff_H2Oice_err : Effective radius error water ice (unit in μm)<br> Gamma : Ratio of the number densities of water ice over dust (no unit)<br> Gamma_err : Ratio of the number densities of water ice over dust (no unit)<br> Veff_dust : Effective variance of dust (no unit)<br> Veff_dust_err : Effective variance error of dust (no unit)<br> Veff_H2Oice : Effective variance of water ice (no unit)<br> Veff_H2Oice_err : Effective variance error of water ice (no unit)<br> M_Dust : Mass loading of dust (unit in g/cm^-3)<br> M_Dust_err : Mass loading error of dust (unit in g/cm^-3)<br> M_Dust_0Veff_err : Mass loading error considering ν_eff exact of dust (unit in g/cm^-3)<br> M_Dust_0VeffGamma_err : Mass loading error considering ν_eff and γ exact of dust (unit in g/cm^-3)<br> M_H2Oice : Mass loading of water ice (unit in g/cm^-3)<br> M_H2Oice_err : Mass loading error of water ice (unit in g/cm^-3)<br> M_H2Oice_0Veff_err : Mass loading error considering ν_eff exact of water ice (unit in g/cm^-3)<br> M_H2Oice_0VeffGamma_err: Mass loading error considering ν_eff and γ exact of water ice (unit in g/cm^-3)<br> N_Dust : Number density of dust (unit in #/cm^-3)<br> N_Dust_err : Number density error of dust (unit in #/cm^-3)<br> N_Dust_0Veff_err : Number density error considering ν_eff exact of dust (unit in #/cm^-3)<br> N_Dust_0VeffGamma_err : Number density error considering ν_eff and γ exact of dust (unit in #/cm^-3)<br> N_H2Oice : Number density of water ice (unit in #/cm^-3)<br> N_H2Oice_err : Number density error of water ice (unit in #/cm^-3)<br> N_H2Oice_0Veff_err : Number density error considering ν_eff exact of water ice (unit in #/cm^-3)<br> N_H2Oice_0VeffGamma_err: Number density error considering ν_eff and γ exact of water ice (unit in #/cm^-3)<br> MeritF : Value of the Merit Function<br> aerosol_type : Type of the aerosol, H2Oice (poure water ice), Dust (pure dust), Mixture_H2Oice (mixture with water ice as MSC), Mixture_Dust (mixture with dust as MSC), No_Aerosols<br> Vertical_res : Vertical resolution (unit in km)<br> extinction_121 : Extinction for order 121 (unit in km^-1)<br> extinction_134 : Extinction for order 134 (unit in km^-1)<br> extinction_149 : Extinction for order 149 (unit in km^-1)<br> extinction_168 : Extinction for order 168 (unit in km^-1)<br> extinction_190 : Extinction for order 190 (unit in km^-1)<br> altitude : Altitude above Mars' surface (unit in km)</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.