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942 results for “scenario”
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 4: Operational scenario with 1.5 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 4. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 3: Operational scenario with 3.0 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 3. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Main output data used in "Exploring the Greenland Ice Sheet's response to future atmospheric warming-threshold scenarios over 200 years" (Delhasse et al., 2025)
<p>Outputs used in:</p> <p>Delhasse, A., Kittel, C. and Beckmann, J.: Exploring the Greenland Ice Sheet's response to future atmospheric warming-threshold scenarios over 200 years, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-709, 2025.</p> <p>Each MAR-PISM coupling experiment (1991-2200) is related to the Greenland warming over a 10-year period compared to our reference period (1961-1990) at which climate is stabilized until 2200. The last experiment is the Reverse one, where the climate is year by year reversed after 2100 to go back to 2000-climate as forcing in 2200, the last year of the simulation. Please refer to Delhasse et al. (2024) for the coupling description.</p> <div> <table> <tbody> <tr> <th> <p>Experiment </p> </th> <th> <p>Exact Greenland warming at 600hPa (°C)</p> </th> <th> <p>10-years period</p> </th> </tr> </tbody> <tbody> <tr> <td> <p>CTRL</p> </td> <td> <p>+0.00</p> </td> <td> <p>1961-1990</p> </td> </tr> <tr> <td> <p>+1</p> </td> <td> <p>+1.04</p> </td> <td> <p>1995-2004</p> </td> </tr> <tr> <td> <p>+1.5</p> </td> <td> <p>+1.51</p> </td> <td> <p>2010-2019</p> </td> </tr> <tr> <td> <p>+2</p> </td> <td> <p>+2.04</p> </td> <td> <p>2021-2030</p> </td> </tr> <tr> <td> <p>+3</p> </td> <td> <p>+2.98</p> </td> <td> <p>2040-2049</p> </td> </tr> <tr> <td> <p>+4</p> </td> <td> <p>+4.04</p> </td> <td> <p>2058-2067</p> </td> </tr> <tr> <td> <p>+5</p> </td> <td> <p>+5.00</p> </td> <td> <p>2074-2083</p> </td> </tr> <tr> <td> <p>+6</p> </td> <td> <p>+5.96</p> </td> <td> <p>2083-2092</p> </td> </tr> <tr> <td> <p>+7</p> </td> <td> <p>+6.85</p> </td> <td> <p>2091-2100</p> </td> </tr> </tbody> </table> </div> <p><strong>Table 1. Greenland warmings at 600hPa since 1961-1990 used to define our experiments and the corresponding 10-years periods over which warmings are determined. </strong></p> <p>For each experiment, 3 types of output are available (where <em>EXP</em> corresponds to the name of the experiment as referenced in Table 1) : </p> <ul> <li> <p>EXP-PISM-thk-msk-1991-2200.nc: contain yearly ice thickness (THK) and ice mask (MASK) as simulated by PISM (PISM grid, 4.5 km);</p> </li> <li> <p>EXP-SMB-ME-RU-MAPI-CESM2-1991-2200.nc: contain yearly SMB (surface mass balance), ME (melt), and RU (runoff) on the MAR grid (25 km);</p> </li> <li> <p>EXP-ts-MB-D-SMB-1991-2200.nc: contain time series of the total MB (mass balance), D (discharge), and SMB (surface mass balance) integrated over the all ice sheet mask from PISM.</p> </li> </ul> <p>The MAR code used in this dataset is tagged as v3.11.3 on https://gitlab.com/Mar-Group/MARv3/-/tree/v3.11.3 (last access: 24 October 2024) (MARTeam, 2024). The PISM code used is tagged as PISMv1.2.2 on <a href="https://github.com/pism/pism/releases/tag/v1.2.2">https://github.com/pism/pism/releases/tag/v1.2.2</a> (last access: 24 October 2024).</p> <p>If you need other variables from MAR or PISM, send us an email (alison.delhasse@uliege.be) and we will be glad to help you. We will also be happy to share the scripts we have developed to analyze the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR-PISM outputs.<br><br><strong><em>Data usage notice:</em></strong></p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgments should be similar to the one below that contains information related to MAR and PISM. To document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact me to add their works to the list of MAR-related publications. </p> <p>"We thank A. Delhasse, C. Kittel, and J. Beckmann, as well as the MAR and PISM teams which make available the model outputs. We also thank agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR-PISM simulations. "</p> <p>You should also refer to and cite the following paper in its latest version:</p> <p>Delhasse, A., Kittel, C. and Beckmann, J.: Exploring the Greenland Ice Sheet’s response to future warming-threshold scenarios over 200 years, [JOURNAL UNDER REVIEW], 2024.</p> <p><strong><em>References</em></strong></p> <p>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</p> <p>MARTeam: MARv3.11, GitLab [data set], <a href="https://gitlab.com/Mar-Group/MARv3">https://gitlab.com/Mar-Group/MARv3#</a> (last access: 24 October 2024), 2024.</p> <p> </p>
Agriculture and food system scenarios with particular focus on organic and agro-ecological farming practices in the EU
<p>This is a comprehensive dataset of the agriculture and food system scenarios co-developed with stakeholders with the agricultural land use model BioBaM-GHG 2.0 and presented in Deliverable 4.2 of the H2020 project UNISECO. It includes sub-national (NUTS1/2-level) data on agricultural production and consumption, land use, greenhouse gas emissions from livestock and agricultural activities, etc. for the base year 2012 and the scenario years 2030 and 2050. The scenarios include a Business as usual case and four scenarios with focus on organic and agro-ecological farming practices in the EU, based on different storylines. Further information is available from the above-mentioned deliverable.</p> <p>A detailed model description is provided in the paper "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0", in which these scenarios are also presented as an exemplary application of the model BioBaM-GHG 2.0.</p> <p>This work was funded by the ERA-NET SusAn project 101243 AnimalFuture, as well as by the European Union’s Horizon 2020 research and innovation programme and its funding of the H2020 UNISECO project under grant agreement N°773901.</p>
Daily precipitation and temperature for 2021–2050 over China: multiple RCMs and emission scenarios corrected by a trend-preserving method
<p>The datasets with spatial resolution of 0.5˚×0.5˚ were corrected from CORDEX-EA regional climate models based on a trend–preserving bias correction method, including daily daily maximum and minimum temperature, and precipitation (Tmax, Tmin and Pre). The datasets cover the main land area of China and two periods, the historical period (from 1980 to 2005 ) and future period (from 2021 to 2050). The observations used in the correction were obtained from China Meteorological Administration (http://cdc.cma.gov.cn), and were derived from 2472 weather stations over China. The evaluation indicated that the corrected datasets are reliable for the investigations related to climate change across China.</p>
Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios.
<p><strong>DOCUMENTATION - Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios.</strong></p> <p>Correspondence: fschmid@ethz.ch (Franca Schmid, ORCID: <a href="https://orcid.org/0000-0002-0689-9366">0000-0002-0689-9366</a>)</p> <p><strong>1. Related references:</strong><br> The data set is published in context with the manuscript: <br> [1]<em> The severity of microstrokes depends on local vascular topology and baseline perfusion</em>. <br> F Schmid, G Conti, P Jenny and B Weber. eLife. 2021. Doi: 10.7554/eLife.60208</p> <p>The bi-phasic blood flow simulations have been performed in realistic microvascular networks (MVNs) from the mouse somatosensory cortex first published in:<br> [2]<em> The cortical angiome: an interconnected vascular network with noncolumnar patterns of blood flow</em>. P Blinder, PS Tsai, JP Kaufhold, PM Knutsen, H Suhl and D Kleinfeld. Nature Neuroscience. 2013. Doi: 10.1038/nn.3426</p> <p>The bi-phasic blood flow model for realistic MVNs has first been published in:<br> [3]<em> Depth-dependent flow and pressure characteristics in cortical microvascular networks</em>. F Schmid, PS Tsai, D Kleinfeld, P Jenny and B Weber. PLoS Computational Biology. 2017. Doi: 10.1371/journal.pcbi.1005392</p> <p><em>For further information on how to perform bi-phasic blood flow simulation, please contact the corresponding authors of [1] or [3].</em></p> <p><strong>2. Requirements (software):</strong><br> <em>All simulations and analyses have been performed in Python 2.7. To execute the analysis script the following python libraries need to be installed: cPickle, python-igraph, pandas, seaborn, scipy. The individual analyses script can then be executed by in Python (e.g. “python plot_Figure3.py”).</em></p> <p><strong>3. Content:</strong><br> <em>All folders are stored as compressed archives (*.tar.bz2). On unix-based system the folders can be unpacked by: "</em>tar –jxf ARCHIVE_NAME"<br> <br> <strong>3a. Time-averaged simulation results (python dictionaries stored as python 2.7 pickle files):</strong><br> <strong>SimulationResults_Baseline.tar.bz2:</strong><br> Folders: MVN1, MVN2<br> Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl (<em>generated from prepare_Figure4.py</em>), data_spatial_distribution_AVfactor.pkl (<em>generated from plot_Figure4.py</em>)</p> <p><strong>SimulationResults_SingleCapillaryOcclusions.tar.bz2:</strong><br> Folders: 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out, 2-in-2-out_high, 2-in-2-out_AL1, 2-in-2-out_AL2, 2-in-2-out_AL3, 2-in-2-out_AL4, 2-in-2-out_AL5, 2-in-2-out_closeToDA, 2-in-2-out_farFromDA<br> Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl (<em>only folders:</em> 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out)</p> <p><strong>SimulationResults_MultiCapillaryOcclusions.tar.bz2:</strong><br> Folders: vesselsOccluded_1, vesselsOccluded_3, vesselsOccluded_5, vesselsOccluded_7, vesselsOccluded_9<br> Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl</p> <p><strong>3b. Analysis scripts (python 2.7 scripts in folder Analyses_Scripts):</strong><br> <em>For details on the figure content see [1]. The verticesDict* and the edgesDict* are converted into graph structure (python-igraph) for all analyses. The functionality of python-igraph is used heavily throughout the various analyses.</em></p> <p><strong>helperFunctions.py</strong>: various functions used by the other analysis scripts</p> <p><strong>plot_Figure1_and_Figure1-supplement_1_a-d.py:</strong><br> <strong>Input:</strong> SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl<br> <strong>Output:</strong> Figures/Figure_1/*, Supplementary_Figures/Figure_1-supplement_1_a-d/*</p> <p><strong>plot_Figure2_and_Figure2_supplement_1_a-d.py:<br> Input:</strong> SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl<br> <strong>Output:</strong> Figures/Figure_2/*, Supplementary_Figures/Figure_2-supplement_1_a-d/*</p> <p><strong>plot_Figure3.py:<br> Input:</strong> SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_MultiCapillaryOcclusion/*/edgesDict.pkl, SimulationResults_MultiCapillaryOcclusion/*/verticesDict.pkl <br> <strong>Output</strong>: Figures/Figure_3/*</p> <p><strong>prepare_Figure4.py </strong>(<em>long execution time!):</em><br> <strong>Input:</strong> SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN*/verticesDict_baseline.pkl,<br> <strong>Output:</strong> SimulationResults_Baseline/MVN*/pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl</p> <p><strong>plot_Figure4.py </strong>(<em>long execution time!):</em><br> <strong>Input:</strong> SimulationResults_Baseline/MVN*/*<br> <strong>Output:</strong> SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl (attribute Lfactor_median added), SimulationResults_Baseline/MVN*/data_spatial_distribution_AVfactor.pkl, Figures/Figure_4/*</p> <p><strong>plot_Figure5.py:</strong><br> <strong>Input:</strong> SimulationResults_Baseline/MVN*/* <br> <strong>Output:</strong> Figures/Figure_5/*</p> <p><strong>plot_Figure6.py:</strong><br> Input: SimulationResults_Baseline/MVN1/*, SimulationResults_SingleCapillaryOcclusion/2-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl Output: Figures/Figure_6/*</p> <p><strong>4. Attributes stored in python dictionaries:</strong><br> <br> <strong>4a. Baseline:</strong><br> <strong>verticesDict: </strong><em>contains all relevant information and data stored at vertices.</em></p> <ul> <li>index: index of vertex</li> <li>pressure: time averaged pressure at vertex [mmHg]</li> <li>inflowE: list of edges delivering blood to the vertex (inflows of the vertex)</li> <li>outflowE: list of edges removing blood from the vertex (outflows of the vertex)</li> <li>coords: coordinates of the vertex [µm]</li> <li>pBC: pressure boundary conditions [mmHg], None for internal vertices</li> <li>corticalDepth: depth from cortical surface [µm]</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> </ul> <p><strong>edgesDict:</strong> <em>contains all relevant information and data stored at edges.</em></p> <ul> <li>diameter: effective vessel diameter [µm]. See [3] for details.</li> <li>htd: time averaged discharge hematocrit [-]. </li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>mainAV: identifier for ascending venule (AV) main brain. 1: is AV main brain, 0: no AV main branch</li> <li>mainDA: identifier for descending arteriole (DA) main brain. 1: is DA main brain, 0: no DA main branch</li> <li>flow: time averaged flow rate [µm<sup>3</sup> ms<sup>-1</sup>]</li> <li>length: tortuous vessel length [µm] See [1] and [3] for details.</li> <li>tissueVolume: topological tissue volume supplied by vessel [µm<sup>3</sup>]. See [1] for details.</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> <li>edgesFulfillingSelection: identifier if vessels fulfils selection criteria to qualify for analysis. 1: vessel included for analysis, 0: vessel not included for analysis. Details on the selection criteria are provided in [1].</li> <li>htt: time averaged tube hematocrit [-]</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate.</li> <li>sign: sign describing the flow direction in the vessel. +: flow direction from source (vertex with lower index) to target (vertex with higher index), -: flow direction from target to source vertex. Based on time averaged pressure values.</li> <li>points: list of tortuous vessel coordinates of the edge [µm]. Starting at the source vertex. Ending at the target vertex.</li> <li>Lfactor_median: AV-factor of the vessel. None if no AV-factor can be assigned. See [1] for details. Attribute added by plot_Figure4.py</li> </ul> <p><strong>pathsDict_allPaths_from_DA_to_AV_mainBranch: </strong><em>contains all flow path from DA main brain to AV main branch. For details see [1]</em>.</p> <ul> <li>startPoint: list of vertex indices of the end point of the DA</li> <li>endPoint: list of vertex indices of the end point of the AV</li> <li>allPaths: list of lists of vertex indices describing all paths between a the associated startPoint and endPoint.</li> </ul> <p><strong>data_spatial_distribution_AVfactor:</strong> <em>contains information on the spatial distribution of venule-sided capillaries (AV-factor > 0.5). For details see [1].</em></p> <ul> <li>edges_L_mean_50um: list of all edges for which the average AV-factor in an analysis sphere of 50 µm has been computed.</li> <li>resulting_L_mean_50um: average AV-factor for an analysis sphere for 50 µm (see Figure4/AV_factor_delta_analysisSphere50_MVN*.pkl)</li> <li>shortest_distance_to_closest_vessel: list of shortest distances to any vessel for all discretization points along all venule sided capillaries.</li> <li>shortest_distance_to_Lfactor_lt_05: list of shortest distances to an arteriole-sided capillary (AV-factor < 0.5) for all discretization points along all venule sided capillaries.</li> </ul> <p><strong>4b. Occlusion scenarios (both single- and multi-capillary occlusions):</strong></p> <p><strong>verticesDict:</strong></p> <ul> <li>index: index of vertex</li> <li>coords: coordinates of the vertex [µm]</li> <li>pressure_strokeIndex_n: time averaged pressure at vertex [mmHg] for the simulation where edge n has been occluded. For details see [1].</li> </ul> <p><strong>edgesDict:</strong></p> <ul> <li>htd_strokeIndex_n: time averaged discharge hematocrit [-] for the simulation where edge n has been occluded. For details see [1]. </li> <li>flow: time averaged flow rate [µm<sup>3</sup> ms<sup>-1</sup>] for the simulation where edge n has been occluded. For details see [1].</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate for the simulation where edge n has been occluded. For details see [1].</li> <li>htt: time averaged tube hematocrit [-] for the simulation where edge n has been occluded. For details see [1].</li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>diameter_strokeIndex_n: effective vessel diameter [µm] for the simulation where edge n has been occluded (only given for multi-capillary occlusions).</li> </ul> <p><strong>pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased:</strong> <em>contains all flow path from DA main brain to AV main branch (unique vertex sequences). For details see helperFunctions.py --></em><em> function convert_pathsDict_to_unique_vertexSequence.</em></p> <ul> <li>startPoint_strokeIndex_n: list of vertex indices of the end point of the DA for the simulation where edge n has been occluded.</li> <li>endpoint_strokeIndex_n: list of vertex indices of the end point of the AV for the simulation where edge n has been occluded.</li> <li>allPaths_strokeIndex_n: list of lists of vertex indices describing all paths between a the associated startPoint and endpoint for the simulation where edge n has been occluded.</li> </ul>
Annexes to the EFSA external scientific report "Proposed prospective scenarios for cumulative risk assessment of pesticide residues"
<p>In the context of prospective cumulative risk assessment of pesticides, different options and scenarios for a tiered approach were investigated by means of 15 case studies for the cumulative assessment group associated with an effect on the motor division of the nervous system (CAG-NAM) and 15 case studies for the cumulative assessment group associated with an effect on hypothyroidism (CAG-TCF) (doi:10.2903/sp.efsa.2021.EN-6811). The results of the prospective exposure calculations are reported in the following annexes:</p> <p><strong>Annex A</strong>: Acute exposure calculations - CAG-NAM</p> <p><strong>Annex B</strong>: Chronic exposure calculations - CAG-TCF</p> <p><strong>Annex C</strong>: Results supporting the discussion on prospective acute scenarios - CAG-NAM</p> <p><strong>Annex D</strong>: Results supporting the discussion on prospective chronic scenarios - CAG-TCF</p> <p>The case studies reported above also include fictitious data, which were included for assessing the relevance of the various parameters in these calculations. The results of these case studies do not represent real estimates of exposure or risk, nor do they represent the formal outcome of a risk assessment.</p>
Rye microgrid historical weather forecasts and stochastic scenarios
<p>This datasets connects historical weather forecasts from the Norwegian Meteorological Institute (met.no) and historical observations from Rye microgrid (https://doi.org/10.5281/zenodo.4448894).</p> <p>Each csv file represents a historical weather forecast for approximately 60 hours ahead. Each csv-file also contains the corresponding observations in the same time interval. Finally, the files also contain load, wind generation and solar PV generation predicitons.</p> <p>The predictions are generated using gradient boosting. The predictions ending with "_ls" are based on least square. The predictions ending with "_quantile_i" represent a quantile prediction. For example, "wind_quantile_2" means that there is a 20% probability the wind will be less than this value.</p> <p>The gradient boosting predictor has been trained to predict the wind power, solar power and load using the explanatory variables below:</p> <p>Solar PV: Cloud area fraction, initial production, clear sky production and forecast look-ahead time</p> <p>Wind power: wind speed, wind direction, wind power converted from wind speed forecast, initial production and forecast look-ahead time</p> <p>Load: hour of day, month of year</p>
Data from: Quantifying and linking mechanism scenarios to invasive species impact
<p>Plant species invasion represents one of the major drivers of biodiversity change globally, yet there is confusion about the nature of non-indigenous species (NIS) impact. This stems from differing notions of what constitutes invasive species impact and the scales at which it should be assessed. At local scales, the mechanisms of impact on local competitors can be classified into four scenarios: 1) minimal impact from NIS inhabiting unique niches; 2) neutral impact spread across the community and proportional to NIS abundance; 3) targeted impact on a small number of competitors with overlapping niches; and 4) pervasive impact that is disproportionate to NIS abundance and caused by modifications that filter out other species. I developed a statistical test to distinguish these four mechanism scenarios based on plant community rank-abundance curves and then created a scale-independent standardized impact score. Using an example long-term dataset, that has high native plant diversity and an abundance gradient of the invasive vine, <em>Vincetoxicum rossicum</em>, I show that impact resulted in either targeted or pervasive extirpations. Regardless of whether NIS impact is neutral, targeted, or pervasive, the net outcome will be the homogenization of ecosystems and reduced biodiversity at larger scales, perhaps reducing ecosystem resilience. The framework and statistical evaluation of impact presented in this paper provide researchers and managers with an objective approach to quantifying NIS impact and prioritizing species for further management actions.</p>
Dataset for "Narrow range of early habitable Venus scenarios permitted by modelling of oxygen loss and radiogenic argon degassing"
<p>Code and datasets required to reproduce figures in main text of Warren & Kite 2023 "Narrow range of early habitable Venus scenarios permitted by modelling of oxygen loss and radiogenic argon degassing." https://www.pnas.org/doi/full/10.1073/pnas.2209751120</p> <p>Code and usage instructions are also available at: github.com/aowarren/Venus_O2</p> <p>After downloading, code will need to be modified to find files in chosen directories for running the full model (also requires installation of VolcGases from github.com/Nicholaswogan/VolcGases) and files for re-creating plots. All .zip files contain data used to create figures in paper. Instructions to reproduce figures below:</p> <p> </p> <p><strong>1. To reproduce full dataset</strong>, download all files excluding .zip files and install VolcGases. Modify "modular_functions_clean_redox.py" to match VolcGases installation.</p> <p><em>For runaway greenhouse runs:</em> Ensure "modular_functions_clean_redox.py" line 512 is commented out. Run "adding_dissolution_clean.py" followed by "ext1line.py" to pre-run runaway greenhouse model and save output to read into full model (this speeds up running the entire suite of "melting" models, but is not strictly necessary). Next, use "gridsearch_all_redox.csv" to set model parameters, then run "input_clean_redox.py" to initiate model. </p> <p><em>For runs without runaway greenhouse melting:</em> uncomment "modular_functions_clean_redox.py" line 512. Use "gridsearch_all_redox.csv" to set model parameters, then run "input_clean_redox.py" to initiate model. </p> <p><strong>2. To reproduce Figures 2, 3, S1, and S2</strong>, either download all zip files beginning with "Fig2_" and "Fig3_", extract all files to single location, or save all new model output into a single directory and use "plot_together.py" to generate figures (instructions contained within script). Use same script to reproduce Figures S9 and S10 with data in "t_atm_sensitivity.zip" and "bH_sensitivity.zip". </p> <p><strong>3. To reproduce Figures 4 and 5</strong>, If using new model runs to generate plots, first run "gen_data_forplots.py". Alternatively, download:</p> <ul> <li>e_statistics_nomelt_CO_FMQ0.npz </li> <li>e_wd_statistics_melt_CO_FMQ0.npz</li> <li>40_Ar_hab_dlith_KU_mix_52522.csv</li> <li>40_Ar_hab_dlith_KU_nomix_52522.csv</li> </ul> <p>Then, run maintext_plotting_new.py.</p> <p><strong>4. To reproduce Figures S5 to S8</strong>, run "Ar_plots.py". Requires: "serpent_dehyd2.csv" and "eclogite_transition.csv".</p>
Graphs for indoor building scenarios for IEEE 802.11 Networks
<p>Graph files and the corresponding EPS figures for the IEEE 802.11 indoor building scenarios used in the experiments for the following publications:</p> <p>* Tejedor-Romero, M., Gimenez-Guzman, J. M., Cruz-Piris, L., Herranz-Oliveros, D., & Marsa-Maestre, I. (2024). Optimal channel assignment on dense Wi-Fi networks using Thermodynamic Threshold Accepting. <em>Engineering Science and Technology, an International Journal</em>, <em>57</em>, 101797, https://doi.org/10.1016/j.jestch.2024.101797</p> <p>* J.M. Gimenez-Guzman, I. Marsa-Maestre, L. Cruz-Piris, D. Orden, M. Tejedor-Romero, "IEEE 802.11 graph models", Alexandria Engineering Journal, https://doi.org/10.1016/j.aej.2022.12.016</p>
Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios
<p><strong>Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios</strong></p> <p>This dataset contains output ice sheet model runs forced by climate boundary conditions provided by CMIP6 climate model output. Each experiment set is archived in separate compressed tar.gz files. </p> <p>Description of the experiment sets, including the model setup, key parameters, climate forcings, and their main objectives are documented in Table 1 of Li, DeConto, Pollard (2023) Climate model differences contribute deep uncertainty in future Antarctic ice loss, Science Advances.</p> <p>Two kinds of output are included in each ice sheet run: fort.22 files contain time series of several key variables for the Antarctic Ice Sheet (area, volume, sea-level equivalent, etc.); fort.92.nc files contain 2D and 3D fields such as ice thickness and velocity at specific time slices.</p> <p> </p> <p> </p>
Test scenario datasets
<p>Test scenario datasets for SafeNcy validation.</p> <p>Each dataset contains input parameters and system outputs.</p>
PREDICTING THE HABITAT SUITABILITY OF ASIAN ELEPHANTS UNDER FUTURE CLIMATE SCENARIOS.
<p>This is the data for "PREDICTING THE HABITAT SUITABILITY OF ASIAN ELEPHANTS UNDER FUTURE CLIMATE SCENARIOS."</p>
cb-oura-1.0 : Generic climate scenarios from bias-adjusted CMIP5 global models
<p><strong>Context </strong></p> <p>The need to adapt to climate change is present in a growing number of fields, leading to an increase in the demand for climate scenarios for often interrelated sectors of activity. In order to meet this growing demand and to ensure the availability of climate scenarios responding to numerous vulnerability, impact, and adaptation (VIA) studies, <a href="https://www.ouranos.ca/">Ouranos</a> is working to create a set of operational multipurpose climate scenarios. The initial version of “Scénarios Génériques” (generic scenarios, acronym cb-oura-1.0) is used mainly in Ouranos’ work to provide a consistent image of the changing climate over the North East of North America, principally the province of Québec. Cb-oura-1.0 was produced in 2016 by downscaling and bias-adjusting a selection of global climate model simulations available through the CMIP5 program. </p> <p><strong>Climate simulations </strong></p> <table> <caption>Climate simulations in the ensemble</caption> <thead> <tr> <th scope="col">Modeling center</th> <th scope="col">Acronym</th> <th scope="col">Model</th> <th scope="col">RCP</th> <th scope="col">Status*</th> </tr> </thead> <tbody> <tr> <td><strong>College of Global Change and Earth System Science, Beijing Normal University</strong></td> <td>GCESS</td> <td>BNU-ESM</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Canadian Centre for Climate Modelling and Analysis</strong></td> <td>CCCMA</td> <td>CanESM2</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Centro Euro-Mediterraneo per I Cambiamenti Climatici</strong></td> <td>CMCC</td> <td>CMCC-CMS</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), Australia</strong></td> <td>CSIRO-BOM</td> <td>ACCESS1.3</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>a</td> </tr> <tr> <td><strong>Institute for Numerical Mathematics</strong></td> <td>INM</td> <td>INM-CM4</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>a</td> </tr> <tr> <td><strong>Institut Pierre-Simon Laplace</strong></td> <td>IPSL</td> <td>IPSL-CM5A-LR</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td>IPSL-CM5B-LR</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Met Office Hadley Centre</strong></td> <td>MOHC</td> <td>HadGem2</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology)</strong></td> <td>MPI-M</td> <td>MPI-ESM</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Norwegian Climate Centre</strong></td> <td>NCC</td> <td>NorESM</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>NOAA Geophysical Fluid Dynamics Laboratory</strong></td> <td>NOAA-GFDL</td> <td>GFDL-ESM2M</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> </tbody> </table> <p>From the complete ensemble of RCP 4.5 and 8.5 driven CMIP5 climate simulations, a selection of 22 simulations (11 per RCP) was made using a clustering ensemble reduction methodology (Casajus et al. 2016). This objective selection method identifies a reduced number of simulations that best represent the overall ensemble. Input criteria for the reduction were the monthly changes between the present (1981-2010) and two future horizons (2041-2070 and 2071-2100), at 15 regions distributed across Canada, for three variables (mean daily maximum temperature, mean daily minimum temperature and total precipitation). An initial selection of 16 simulations shows a distribution projected changes for the 12 (months) x 2 (horizons) x 15 (regions) x 3 (variables) indices that is not statistically different from the complete ensemble. A small number of simulations were subsequently added to have a complete set with both RCPs represented equally (11 members for each emission scenario). </p> <p><strong>Reference dataset </strong></p> <p>The bias-adjustment reference (or target) is a gridded observation dataset produced by Natural Resources Canada (McKenney et al., 2011; et Hutchinson et al. ,2009). It uses the ANUSPLIN interpolation method over station observations to derive daily grids of minimum and maximum temperature, as well as total precipitation for the Canadian landmass. The grid has a resolution of 10 km x 10 km and cover the time period from 1950 to 2013. </p> <p>As this dataset is not available over the United States, it was merged with another observation interpolation dataset produced by Livneh et al. (2015) in order to enable the production of bias-corrected climate scenarios covering a portion of the northern United States. </p> <p><strong>Coverage </strong></p> <p>The final version of this dataset covers a region covering the Atlantic provinces, Québec, Ontario, Manitoba and Saskatchewan and part of the northern United States: From 120°W to 54°W and from 40°N to 62°N. </p> <p>It contains the daily minimum temperature, daily maximum temperature and daily precipitation flux, covering the period 1950 to 2100. </p> <p><strong>Bias-adjustment </strong></p> <p>The global simulations where downscaled to the reference grid using bilinear interpolation and then bias-adjusted with a 1-D quantile mapping method, as described by Gennaretti et al. (2015). A moving window of 31 days was used to adjust each day of the year, using 50 quantiles to define the statistical distributions to match. The long-term linear trends of the temperature variables were preserved explicitly. </p> <p><strong>Climate indicators </strong></p> <p>This dataset is used to in the first versions (up to 1.3) of Ouranos’ <a href="https://www.ouranos.ca/en/climate-portraits">Climate Portraits </a>website. A selection of 26 seasonal and annual climate indicators were computed from the daily scenarios, using the xclim software package (Logan et al. 2022). The "virtual indicator module" used for the computation is made available here in the "indicators.yml" file.</p> <p>On the Climate Portraits website, the information is presented from three aspects: spatial, temporal and summary. This repository stores the reduced ensemble data as shown on the website. Filenames are constructed as "{aspect}_{indicator}_{season}.nc".</p> <ul> <li> <p>Maps (files "spatial_*") : Climate indicators for each bias-adjusted climate simulation and for a given RCP emission scenario are averaged over 30-year horizons. Ensemble percentiles are computed in order to summarize climate model uncertainty. In particular the 10, 25, 50, 75 and 90th percentiles over the 11 members are calculated for each RCP. </p> </li> <li> <p>Timeseries (files "temporal_*") : Climate indicators for each bias-adjusted simulation are averaged spatially over each region for every time step (annual or seasonal). The ensemble statistics are computed by first pooling all regional average values for the 11 members using within a centred 30-year window and then calculating percentile values (same as above) on the pooled data. </p> </li> </ul> <ul> <li> <p>Summary (files "summary_*") : The indicators are averaged over each region and then over 30-year horizons. The ensemble statistics (same as above) are then computed. </p> </li> </ul> <p>In versions 2.x of the app, this data will be presented as "CMIP5".</p> <p><strong>Data availability </strong></p> <p>This repository stores the climate indicator ensemble statistics as shown on the Climate Portraits website and described above. The complete daily dataset is too large for this platform.</p> <p>The complete daily dataset is available through the public THREDDS server of the PAVICS platform maintained by Ouranos. This data might be removed in the future. When this is the case, please contact us for data requests.<br> <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/bias_adjusted/cmip5/ouranos/cb-oura-1.0/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/bias_adjusted/cmip5/ouranos/cb-oura-1.0/catalog.html</a> </p> <p>The annual and season indicators of the Climate Portraits website are available on the same server, along with a few more indicators not shown on the app. <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/ouranos/portraits-clim-1.3/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/ouranos/portraits-clim-1.3/catalog.html</a></p> <p><em>Terms of use</em>: Use of this dataset should be acknowledged as 'Data produced and provided by the Ouranos Consortium on Regional Climatology and Adaptation to Climate Change'. Furthermore, the modeling groups from which the bias-adjusted climate scenarios were constructed must also be acknowledged, please refer to: The Coupled Model Intercomparison Project <a href="https://pcmdi.llnl.gov/mips/cmip5/citation.html.">https://pcmdi.llnl.gov/mips/cmip5/citation.html.</a></p>
Data repository - The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios
<p>This datasets provides regional and spatial-explicit gridded data for the analysis presented in the manuscrip "The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios" under review in "Environmental Research: Climate" with reference "ERCL-100126"</p>
All options, not silver bullets, needed to limit global warming to 1.5 °C: a scenario appraisal: high-resolution figures
<p>High resolution versions of Figure 2 and Figure S3 for the corrigendum of the paper "All options, not silver bullets, needed to limit global warming to 1.5 °C: a scenario appraisal" by Warszawski et al. (2021) published in Environmental Research Letters. </p> <p>Fig. 2: Spider plots for each of the 22 scenarios in the filtered ensemble (the corresponding model and scenario is printed above each plot), in order of increasing coverage, <em>V<sub>i</sub> </em>. Note that the AIM/CGE2.1 TERL_15D_LowCarbonTransportPolicy scenario has coverage of V<sub>i</sub>=1, despite E<sub>2050</sub> lying below themedium upper bound due to how the two energy-sector levers are combined to calculate the coverage (see Supplementary material). Each lever has been normalised to the high upper bound (the bold black inner circle on each plot; the absolute value of the upper bound is printed below the lever label). The centre of each spider plot corresponds to the minimum value across the entire ensemble of 50 scenarios for each lever. The medium upper bounds are shown as a dashed polygon. The absolute value of the lever for the given scenario is also printed on the plot. The top row contains the two scenarios singled out in figure <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abfeec#erlabfeecf1">1</a>(c), which exceed the SR1.5 remaining carbon budget for staying below 1.5 °C with a 50% likelihood; these two scenarios also have the lowest coverage of all scenarios in the filtered ensemble. For a similar plot of the complete ensemble of 1.5 °C scenarios with no or low overshoot (50 scenarios), see the supplement.</p> <p>Fig. S3: Same as Fig. 2 in main text but for all 50 scenarios. Those scenarios shaded grey are categorised as ‘Below 1.5C’ in the SR1.5. All other scenarios fall into the ‘1.5C low overshoot’ category.</p>
Net-zero CO2 emissions scenarios for Switzerland
<p>This dataset accompanies the relevant article in Communications Earth and Environment. It contains the key assumptions used in the energy system modelling with the Swiss TIMES energy systems model (STEM) for assessing net-zero carbon dioxide emissions scenarios for Switzerland. In addition, contains extensive results from STEM for each one of the core scenarios and variants assessed in the study. </p>
CCG: Morocco Coal to Clean Scenarios
<p>Data repository for the paper 'Morocco's Coal to Clean Journey: Optimised Pathways for Decarbonisation and Energy Security' (https://doi.org/10.21203/rs.3.rs-2579435/v4).</p> <p>Six clic-SAND scenario files for analysis of decarbonisation and energy security in Morocco, 'Data Note describing the scenarios' file outlining the steps to replicate the analysis and rebuild the scenarios, 'Data Annex' listing the data sources and assumptions in the scenarios, 'Instructions for running the model' outlining the steps required to re-run the scenarios on OSeMOSYS Cloud, and 'U4RIA Compliance' describing the level of compliance of the study to U4RIA principles.</p>
PyPSA-PL: High and medium ambition scenarios for RES deployment in Poland until 2030
<p>This record contains all the scripts and data from the PyPSA-PL modelling exercise that supported the report:</p> <ul> <li>Kubiczek P., Smoleń M. (2023). Polski nie stać na średnie ambicje. Oszczędności dzięki szybkiemu rozwojowi OZE do 2030 r. Instrat Policy Paper 03/2023. <a href="https://www.instrat.pl/pypsa-marzec-2023">https://www.instrat.pl/pypsa-marzec-2023</a></li> </ul> <p>The record structure is based on the PyPSA-PL repository <a href="https://github.com/instrat-pl/pypsa-pl">https://github.com/instrat-pl/pypsa-pl</a> (v2.0).</p> <p>Version 2: added data and figures that are directly presented in the report</p> <p>© Instrat Foundation 2023</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.