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Survey Results: LP/MIP Solver Usage for Energy Modeling
<p>This report summarizes the responses to the recent solver benchmark survey conducted by Open Energy Transition. The survey aimed at better understanding energy modelers’ needs and pain points, and to gauge the usefulness of creating a new solver benchmark website for the energy modeling community. </p>
Global Cluster Test Results: Pathogenic Fungi in Decayed Norway Spruce Stands
<p><strong>Accessing the Results:</strong> Users can retrieve the test results by opening the dataset <code>Global_cluster_test_results.RData</code> in an R session and using the functions inside the script of the same name.</p> <p><strong>Description: </strong>This dataset provides global cluster test results analyzing the spatial distribution of pathogenic fungi in 273 Norway spruce stands in Norway (Lara et al., 2024). The stands, composed mainly of Norway spruce (27% to 100%), also include Scots pine and birch. It focuses on spatial patterns of decayed spruce trees, offering p-values, clustering metrics, and other parameters from statistical analyses.</p> <p><strong>Analysis:</strong> The dataset includes results from three global cluster tests (Tango, 2010):</p> <ul> <li>Tango's Nearest Neighbors (TNN)</li> <li>Tango's Double Exponential Clinal (TCN)</li> <li>Diggle and Chetwynd’s (DC)</li> </ul> <p><strong>Methodology:</strong> Cluster testing employed 1,000 Monte Carlo simulations for each test across all stands to establish null distributions and adjusted p-values, ensuring robust statistical assessments under the random labeling hypothesis: H0: the observed n0 decayed trees are a random sample from the entire sample of size n = n0 + n1 (decayed trees + healthy trees) (Tango, 2010).</p> <p> </p>
Metrics for two-sample tests: results on JetNet dataset
<p>The repository includes version 1.0 (v1.0) of the code and results corresponding to the GitHub repository <a href="https://github.com/TwoSampleTests/JetNetMetrics" target="_blank" rel="noopener">JetNetMetrics</a>.</p> <p>Publishing information and arXiv identifier will be added after publication of the main manuscript related to the data.</p>
Metrics for two-sample tests: results on Mixture of Gaussians and Correlated Gaussians models
<p>The repository includes version 1.0 (v1.0) of the code and results corresponding to the GitHub repository <a href="https://github.com/TwoSampleTests/GenerativeModelsMetrics">GenerativeModelsMetrics</a>.</p> <p>Publishing information and arXiv identifier will be added after publication of the main manuscript related to the data.</p>
Model quality assessment: Results of human ratings, prompts, models as .csv and answers of the LLM
<p>This is the dataset for the paper "<a href="https://doi.org/10.1007/978-3-031-77908-4_7" target="_blank" rel="noopener">Assessing Model Quality Using Large Language Models</a>" and contains the following files of the assessment of the model quality:</p> <ul> <li>Results of the human ratings</li> <li>Prompts</li> <li>Models as .csv</li> <li>Answers of the LLM</li> </ul> <p>The graphical representation of the models is linked under Related works.</p>
Replication Package for the Paper Titled "Emerging Results in Using Explainable AI to Improve Software Vulnerability Prediction"
<p>This is a replication package for the paper titled "Emerging Results in Using Explainable AI to Improve Software Vulnerability Prediction".</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Input files
<p>This dataset contains the parent input used to generate the simulation files of 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, see "Related work" section. A report describing this dataset will be made available on BEL-Float project website by November 2024: https://www.owi-lab.be/bel-float.</p>
SERENA Task 1.4 – Definition of scenarios - Survey results
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p>
CoVerage Results
<p>Datasets generated from GISAID metadata for the figures in the CoVerage manuscript. Data includes the results for the months of November 2021 to January 2021 and January 2023 to March 2023.</p> <p>11_2021</p> <ul> <li>11-2021_fig3_lineage_dynamics_summary_statistics.csv - summary statistics or frequency data for selected pVOIs for the month of 11-2021</li> <li>11-2021_fig4_antigenic_scores_map_visualization.csv - calculated country antigenic scores for the month of 11-2021 using the scoring technique of applying weights at known antigenic sites on the spike protein</li> <li>11-2021_fig4_antigenic_scores_ranked.csv - calculated antigenic scores of circulating lineages for the month of 11-2021 using the scoring technique of applying weights at known antigenic sites on the spike protein, ranked by antigenic score with WHO variant designation</li> <li>11-2021_fig4_antigenic_scores_ranked_all_sites.csv - calculated antigenic scores of circulating lineages for the month of 11-2021 using the scoring method of applying weights at all sites across the spike protein, ranked by antigenic score with WHO variant designation</li> <li>11-2021_fig4_antigenic_scores_map_visualization_all_sites.csv - calculated country antigenic scores for the month of 11-2021 using the scoring method of applying weights at all sites across the spike protein</li> </ul> <p>12_2021</p> <ul> <li>12-2021_fig3_lineage_dynamics_summary_statistics.csv - summary statistics or frequency data for selected pVOIs for the month of 12-2021</li> <li>12-2021_fig4_antigenic_scores_map_visualization.csv - calculated country antigenic scores for the month of 12-2021 using the scoring technique of applying weights at known antigenic sites on the spike protein</li> <li>12-2021_fig4_antigenic_scores_ranked.csv - calculated antigenic scores of circulating lineages for the month of 12-2021 using the scoring technique of applying weights at known antigenic sites on the spike protein, ranked by antigenic score with WHO variant designation</li> <li>12-2021_fig4_antigenic_scores_ranked_all_sites.csv - calculated antigenic scores of circulating lineages for the month of 12-2021 using the scoring method of applying weights at all sites across the spike protein, ranked by antigenic score with WHO variant designation</li> <li>12-2021_fig4_antigenic_scores_map_visualization_all_sites.csv - calculated country antigenic scores for the month of 12-2021 using the scoring method of applying weights at all sites across the spike protein</li> </ul> <p>01_2022</p> <ul> <li>01-2022_fig4_antigenic_scores_map_visualization.csv - calculated country antigenic scores for the month of 01-2022 using the scoring technique of applying weights at known antigenic sites on the spike protein</li> <li>01-2022_fig4_antigenic_scores_ranked.csv - calculated antigenic scores of circulating lineages for the month of 01-2022 using the scoring technique of applying weights at known antigenic sites on the spike protein, ranked by antigenic score with WHO variant designation</li> <li>01-2022_fig4_antigenic_scores_map_visualization_all_sites.csv - </li> <li>calculated country antigenic scores for the month of 01-2022 using the scoring method of applying weights at all sites across the spike protein</li> <li>01-2022_fig4_antigenic_scores_ranked_all_sites.csv - calculated antigenic scores of circulating lineages for the month of 01-2022 using the scoring method of applying weights at all sites across the spike protein, ranked by antigenic score with WHO variant designation</li> </ul> <p>01_2023</p> <ul> <li>01-2023_fig5_antigenic_scores_map_visualization.csv - calculated country antigenic scores for the month of 01-2023 using the scoring technique of applying weights at known antigenic sites on the spike protein</li> <li>01-2023_fig5_antigenic_scores_ranked.csv - calculated antigenic scores of circulating lineages for the month of 01-2023 using the scoring technique of applying weights at known antigenic sites on the spike protein, ranked by antigenic score with WHO variant designation</li> <li>01-2023_fig5_antigenic_scores_map_visualization_all_sites.csv - calculated country antigenic scores for the month of 01-2023 using the scoring method of applying weights at all sites across the spike protein</li> <li>01-2023_fig5_antigenic_scores_ranked_all_sites.csv - calculated antigenic scores of circulating lineages for the month of 01-2023 using the scoring method of applying weights at all sites across the spike protein, ranked by antigenic score with WHO variant designation</li> </ul> <p>02_2023</p> <ul> <li>02-2023_fig5_antigenic_scores_map_visualization.csv - calculated country antigenic scores for the month of 02-2023 using the scoring technique of applying weights at known antigenic sites on the spike protein</li> <li>02-2023_fig5_antigenic_scores_ranked.csv - calculated antigenic scores of circulating lineages for the month of 02-2023 using the scoring technique of applying weights at known antigenic sites on the spike protein, ranked by antigenic score with WHO variant designation</li> <li>02-2023_fig5_antigenic_scores_map_visualization_all_sites.csv - calculated country antigenic scores for the month of 02-2023 using the scoring method of applying weights at all sites across the spike protein</li> <li>02-2023_fig5_antigenic_scores_ranked_all_sites.csv - calculated antigenic scores of circulating lineages for the month of 02-2023 using the scoring method of applying weights at all sites across the spike protein, ranked by antigenic score with WHO variant designation</li> </ul> <p>03_2023</p> <ul> <li>03-2023_fig5_antigenic_scores_map_visualization.csv - calculated country antigenic scores for the month of 03-2023 using the scoring technique of applying weights at known antigenic sites on the spike protein</li> <li>03-2023_fig5_antigenic_scores_ranked.csv - calculated antigenic scores of circulating lineages for the month of 03-2023 using the scoring technique of applying weights at known antigenic sites on the spike protein, ranked by antigenic score with WHO variant designation</li> <li>03-2023_fig5_antigenic_scores_map_visualization_all_sites.csv - calculated country antigenic scores for the month of 03-2023 using the scoring method of applying weights at all sites across the spike protein</li> <li>03-2023_fig5_antigenic_scores_ranked_all_sites.csv - calculated antigenic scores of circulating lineages for the month of 03-2023 using the scoring method of applying weights at all sites across the spike protein, ranked by antigenic score with WHO variant designation</li> </ul>
Site-specific results DeltaWind + Innwind 10MW Reference Wind Turbine
<p><strong>Digitalización Offshore EU Project - CENER- Digital Twin site specific results.</strong></p> <p>Related dataset:</p> <ul> <li><a href="https://zenodo.org/records/14070345">Virtual results DeltaWind + Innwind 10MW Reference Wind Turbine</a></li> </ul> <p>Related presentation:</p> <ul> <li><a href="https://zenodo.org/records/14067010">Digitalización de parques eólicos</a></li> </ul> <p>Simulation of floating offshore wind turbine</p> <ul> <li>DeltaWind platform + Innwind 10 MW Reference Wind Turbine)</li> <li>Meteocean conditions of Canary Islands</li> <li>Depth: 350 m</li> </ul> <p>Simulations specifications:</p> <ul> <li>Simulation carried out with OpenFAST v3.4.1 version <a href="https://github.com/OpenFAST/openfast/releases/tag/v3.4.1">Release v3.4.1 · OpenFAST/openfast</a></li> <li>CENER in-house controller</li> <li>400 s transient removed</li> </ul> <p> </p> <p><strong>Dataset: </strong></p> <p>zip that contains 243 csv files.</p> <p>Each file containing one-hour time series of load simulation (time step 0.5s). </p> <p><strong>Filenames</strong> specify details about the simulation:</p> <ul> <li>dlc - Design Load Case [12 : normal power production, 64 : idling]</li> <li>Vh [wind speed]</li> <li>Y [yaw angle]</li> <li>W [wave height _ period]</li> <li>D [wind direction]</li> <li>M [wave misalignment= wave direction with respect to wind direction]</li> </ul> <p>The <strong>columns </strong>of each csv file includes followind signals according to OpenFAST nomenclature and reference frames:</p> <ul> <li>Time (s)</li> <li>PtfmSurge (m)</li> <li>PtfmSway (m)</li> <li>PtfmHeave (m)</li> <li>PtfmRoll (deg)</li> <li>PtfmPitch (deg)</li> <li>PtfmYaw (deg)</li> <li>GenPwr (kW)</li> <li>RotThrust (kN)</li> <li>GenTq (kN-m)</li> <li>RotSpeed (rpm)</li> <li>BlPitch1 (deg)</li> <li>TipDxc1 (m)</li> <li>RootMxc1 (kN-m)</li> <li>RootMyc1 (kN-m)</li> <li>TwrBsMxt (kN-m)</li> <li>TwrBsMyt (kN-m)</li> <li>FAIRTEN1 (N)</li> <li>FAIRTEN2 (N)</li> <li>FAIRTEN3 (N)</li> </ul>
Virtual results DeltaWind + Innwind 10MW Reference Wind Turbine
<p><strong>Digitalización Offshore EU Project - CENER- Digital Twin site specific results.</strong></p> <p>Related dataset:</p> <ul> <li><a href="https://zenodo.org/records/14068807">Site-specific results DeltaWind + Innwind 10MW Reference Wind Turbine</a></li> </ul> <p>Related presentation:</p> <ul> <li><a href="https://zenodo.org/records/14067010">Digitalización de parques eólicos</a></li> </ul> <p> </p> <p>Simulation of floating offshore wind turbine</p> <ul> <li>DeltaWind platform + Innwind 10 MW Reference Wind Turbine)</li> <li>Virtual Meteocean conditions </li> <li>Depth: 350 m</li> </ul> <p>Simulations specifications:</p> <ul> <li>Simulation carried out with OpenFAST v3.4.1 version <a href="https://github.com/OpenFAST/openfast/releases/tag/v3.4.1">Release v3.4.1 · OpenFAST/openfast</a></li> <li>CENER in-house controller</li> <li>400 s transient removed</li> </ul> <p> </p> <p><strong>Dataset:</strong></p> <p>csv that contains statistics from 4320 simulations</p> <p>Statistics obtained from one-hour time series of load simulations</p> <p>- The <strong>definition</strong> of the simulations are included in the csv through the <strong>columns</strong>:</p> <ul> <li>DLC - Design Load Case [12 : production, 64 : idling]</li> <li>Wind Speed </li> <li>WaveHeight</li> <li>Wave Period</li> <li>Wind Direction</li> <li>Wave Direction</li> </ul> <p>- The <strong>statistics calculated</strong> are included in columns:</p> <ul> <li> <div>BlPitch1_avg (deg)</div> </li> <li> <div>FAIRTEN1_avg (N)</div> </li> <li> <div>FAIRTEN2_avg (N)</div> </li> <li> <div>FAIRTEN3_avg (N)</div> </li> <li> <div>GenPwr_avg (kW)</div> </li> <li> <div>GenTq_avg (kN-m)</div> </li> <li> <div>PtfmHeave_avg (m)</div> </li> <li> <div>PtfmPitch_avg (deg)</div> </li> <li> <div>PtfmRoll_avg (deg)</div> </li> <li> <div>PtfmSurge_avg (m)</div> </li> <li> <div>PtfmSway_avg (m)</div> </li> <li> <div>PtfmYaw_avg (deg)</div> </li> <li> <div>RootMxc1_avg (kN-m)</div> </li> <li> <div>RootMyc1_avg (kN-m)</div> </li> <li> <div>RotSpeed_avg (rpm)</div> </li> <li> <div>RotThrust_avg (kN)</div> </li> <li> <div>TipDxc1_avg (m)</div> </li> <li> <div>TwrBsMxt_avg (kN-m)</div> </li> <li> <div>TwrBsMyt_avg (kN-m)</div> </li> <li> <div>BlPitch1_std (deg)</div> <p> </p> </li> </ul>
Danish case study simulation results (consumption, offered & accepted flexibility, revenues & expenses)
<p>This file contains results of the simulations of the Danish case study generated during Horizon 2020 MAGNITUDE project. The case study considers the provision of domestic hot water from 350 heat pumps installed in multi-storey apartment buildings. Heat pumps receive energy from electrical grid and use heat from ultra-low temperature district heating (ULTDH) as heat source. The overall system is referred to as Multi-Energy System (MES). MES can provide flexibility to either intra-day (ID) market or redispatch (RE) services to local distribution system operator. Results show how much flexibility was offered and activated, and the resulting consumption and production profiles due to that. In addition, the revenues and expenses based on the historical prices for Denmark in 2018 had been calculated.</p>
Benchmarking (multi)wavelet-based dynamic and static non-uniform grid solvers for flood inundation modelling (Simulation results)
<p>Simulation result data for Environment Agency benchmark test 5, Thamesmead hypothetical flood, and Carlisle 2005 case studies, using uniform DG2, adaptive MWDG2, adaptive HWFV1, non-uniform DG2, non-uniform FV1 and non-uniform ACC solvers. </p> <p>Model results are archived in 3 zip files:</p> <ul> <li>EA5.zip contains results of Environment Agency test 5 (Néelz and Pender, 2013)</li> <li>Thamesmead.zip contains results of Thamesmead hypothetical flood (Liang et al., 2008)</li> <li>Carlisle.zip contains results of Carlisle 2005 flooding (Neal et al., 2009)</li> </ul> <p>The results are stored with the following file extensions:</p> <ul> <li>".wd" for 2D flood inundation maps in ESRI ASCII format</li> <li>".stage" for water depth or water level time-series at staging points in tabulated text format</li> <li>".velocity" for velocity time-series at staging points in tabulated text format</li> </ul> <p>Model outputs are stored under directories named for each solver.</p> <p><strong>References</strong></p> <p>Néelz, S., & Pender, G. (2013). Benchmarking the latest generation of 2D hydraulic modelling packages. <em>Environment Agency: Bristol, UK</em>.</p> <p>Liang, Q., Du, G., Hall, J. W., & Borthwick, A. G. (2008). Flood Inundation Modeling with an Adaptive Quadtree Grid Shallow Water Equation Solver. <em>Journal of Hydraulic Engineering</em>, <em>134</em>(11), 1603–1610. https://doi.org/10.1061/(ASCE)0733-9429(2008)134:11(1603)</p> <p>Neal, J. C., Bates, P. D., Fewtrell, T. J., Hunter, N. M., Wilson, M. D., & Horritt, M. S. (2009). Distributed whole city water level measurements from the Carlisle 2005 urban flood event and comparison with hydraulic model simulations. <em>Journal of Hydrology</em>, <em>368</em>(1–4), 42–55. https://doi.org/10.1016/j.jhydrol.2009.01.026</p> <p> </p>
3D geological models of dolomitized clinoforms and flow simulation results: scenario 2 in Teoh, C.P. et al (2021)
<p>3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 2 in Teoh, C.P. et al (2021) doi:<a href="http://doi.org/10.1016/j.marpetgeo.2021.105344">10.1016/j.marpetgeo.2021.105344</a>.<br> Models are built using surface-based modelling approach (doi:<a href="https://doi.org/10.1007/s11004-018-9764-8">10.1007/s11004-018-9764-8</a>). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance.</p> <p>For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results:<br> - Only clinoforms and facies boundaries<br> - 1 dolomite body per clinothem (~20% dolomite)<br> - 2 dolomite bodies per clinothem (~40% dolomite)<br> - 3 dolomite bodies per clinothem (~60% dolomite)<br> - 4 dolomite bodies per clinothem (~80% dolomite)<br> <br> Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats.<br> Flow simulation settings are provided for IC-FERST in .mpml files (<a href="http://multifluids.github.io/">multifluids.github.io</a>)<br> Flow simulation results are provided as:</p> <ul> <li> 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month.</li> <li> In- and outflow rates and volumetric proportions per phase in .csv</li> </ul> <p>Naming of files and folders:<br> <em>Sxxxxxx_yyyyz</em> where:<br> '<em>xxxxxx</em>' is the stochastic seed number used to sample the input statistics and create the geological model<br> '<em>yyyy</em>' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies <br> '<em>z</em>' corresponds to the number of dolomite bodies per clinothem</p>
3D geological models of dolomitized clinoforms and flow simulation results: scenario 1 in Teoh, C.P. et al (2021)
<p>3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 1 in Teoh, C.P. et al (2021) doi:<a href="http://doi.org/10.1016/j.marpetgeo.2021.105344">10.1016/j.marpetgeo.2021.105344</a>.<br> Models are built using surface-based modelling approach (doi:<a href="https://doi.org/10.1007/s11004-018-9764-8">10.1007/s11004-018-9764-8</a>). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance.</p> <p>For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results:<br> - Only clinoforms and facies boundaries<br> - 1 dolomite body per clinothem (~20% dolomite)<br> - 2 dolomite bodies per clinothem (~40% dolomite)<br> - 3 dolomite bodies per clinothem (~60% dolomite)<br> - 4 dolomite bodies per clinothem (~80% dolomite)<br> <br> Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats.<br> Flow simulation settings are provided for IC-FERST in .mpml files (<a href="http://multifluids.github.io/">multifluids.github.io</a>)<br> Flow simulation results are provided as:</p> <ul> <li> 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month.</li> <li> In- and outflow rates and volumetric proportions per phase in .csv</li> </ul> <p>Naming of files and folders:<br> <em>Sxxxxxx_yyyyz</em> where:<br> '<em>xxxxxx</em>' is the stochastic seed number used to sample the input statistics and create the geological model<br> '<em>yyyy</em>' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies <br> '<em>z</em>' corresponds to the number of dolomite bodies per clinothem</p>
DRALOD D1.3 Results of performance testing of the prototype of energy recovery system data set
<p>Data set for delivery D1.3 Results of performance testing of the prototype of energy recovery system </p>
Experimental results dataset reported in the paper: "Temporal teleportation with pseudo-density operators: how dynamics emerges from temporal entanglement."
<p><strong>Temporal teleportation with pseudo-density operators: how dynamics emerges from temporal entanglement.</strong></p> <p>Experimental results data-set (as reported in Figure 2 of the main paper).</p> <p>Caption of the Figure: Classical bound violation ∆S<em><sup>(l)</sup></em>(n) =S<em><sup>(l)</sup></em>(2n)−(2n−2) (<em>l=T,H,S</em>) for the multi-parameter <em>CHSH</em> inequalities in the temporal (red), spatial (grey) and “hybrid” (blue) domain. The dots represent the experimental results, with the uncertainty bars evaluated as statistical fluctuations among repeated measurement sets, while the solid curves show the theoretically-expected values (for the correlations belonging to the spatial domain, deviations from the ideal case due to the V<em><sub>s</sub></em>= 0.982 estimated visibility of the generated |<em>ψ<sub>−</sub></em>〉state were considered)</p> <p>Data-set description:</p> <p>Column 1 = X-coordinate: n (Number of Settings)</p> <p>Column 2 = Y-coordinate: ∆S<em><sup>(S)</sup></em>(n), Space-like (S) CHSH Violation [Grey color in the figure on the paper]</p> <p>Column 3 = Uncertainty on ∆S<em><sup>(S)</sup></em>(n) (Space-like, S, Grey in the figure on the paper), </p> <p>Column 4 = Y-coordinate: ∆S<em><sup>(H)</sup></em>(n), Hybrid (H) CHSH Violation [Blue color in the figure on the paper]</p> <p>Column 5 = Uncertainty on ∆S<em><sup>(H)</sup></em>(n) (Hybrid, H, Blue in the figure on the paper)</p> <p>Column 6 = Y-coordinate: ∆S<em><sup>(T)</sup></em>(n), Time-like (T) CHSH Violation [Red color in the figure on the paper]</p> <p>Column 7 = Uncertainty on ∆S<em><sup>(T)</sup></em>(n) (Time-like, T, Red in the figure on the paper)</p>
Two-bubble envelope approximation code and results
<p>This deposit contains the code required to carry out the envelope approximation simulations used in the paper with preprint title <em>Vacuum bubble collisions: from microphysics to gravitational waves</em> by Oliver Gould, Satumaaria Sukuvaara, and David Weir.</p> <p>The method and results are based on the following papers:</p> <ul> <li><em>Gravitational Wave Production by Collisions: More Bubbles</em> by Stephan J. Huber and Thomas Konstandin [<a href="https://arxiv.org/abs/0806.1828">arXiv:0806.1828</a>].</li> <li><em>Gravitational radiation from colliding vacuum bubbles: envelope approximation to many bubble collisions</em> by Arthur Kosowsky and Michael S. Turner [<a href="https://arxiv.org/abs/astro-ph/9211004">arXiv:astro-ph/9211004</a>].</li> <li><em>Gravitational radiation from colliding vacuum bubbles </em>by Arthur Kosowsky, Michael S. Turner and Richard Watkins<em> </em>[<a href="https://inspirehep.net/literature/324187">Inspire</a>].</li> </ul> <p>In the latter paper the envelope approximation is introduced in Appendix C.</p> <p>There are two files:</p> <ul> <li><strong>dweir-envelope-b2842c4827ad.zip</strong> is a snapshot of the <a href="https://bitbucket.org/dweir/envelope/">Bitbucket-hosted Git repository for the simulation code</a>, corresponding to <a href="https://bitbucket.org/dweir/envelope/src/v1.1.0/">the commit tagged <em>v1.1.0</em></a>. Later versions of the code may exist. The snapshot was generated by the Bitbucket service and does not include the Git metadata.</li> <li><strong>two-bubbles-envelope-data.tar.gz </strong>is an archive of the simulation results used to generate the envelope approximation results seen in the preprint, as well as numerical tests and some additional numerical explorations. While the results were generated with a slightly earlier version of the code than the tagged release in this deposit, they have been confirmed to be reproducible with the code provided.</li> </ul> <p>Both archives contain README.md files with further information.</p>
Result data related to "Tröndle et al (2019) -- Home-made or imported: on the possibility for renewable electricity autarky on all scales in Europe"
<p>The files include results to out study investigating the possibility for renewable electricity autarky in Europe. For each administrative unit on the continental, national, regional, and municipal levels these files include:</p> <ol> <li>Name, country, population, current electricity demand, land cover statistics, shared coast with exclusive economic zone</li> <li>Potential in terms of area [km2], installable capacity [MW], annual electricity yield [TWh]</li> </ol> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Pfenninger, S., & Lilliestam, J. (2019). Home-made or imported: on the possibility for renewable electricity autarky on all scales in Europe. <em>Energy Strategy Reviews</em>, <em>26</em>.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 3 (2021-07-19)</p> <p>* Fix ID of EEZ in shared-coast.csv files.</p> <p>Version 2 (2019-11-08)</p> <p>* Add land cover statistics for each unit.</p>
Results of the Web-Delphi process to INAMI stakeholders (three rounds)
<p>IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 3 (Testing the framework with empirical applications), Results of the Web-Delphi process to INAMI stakeholders (three rounds), about the views of stakeholders regarding “The relevance of the following value aspects for the evaluation of new medicines in this disease context is” (2020)</p> <p>For details on the Web-Delphi process, see: IMPACT HTA, Work Package 7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 3, Deliverable 7.3 (Testing the framework with empirical applications), Testing the IMPACT-HTA Value Framework in collaboration with HTA agencies: Case studies on Non-Small Cell Lung Cancer and Spinal Muscular Atrophy (2021), Aris Angelis (LSHTM, LSE), Mónica Oliveira (IST), Teresa Rodrigues (IST), Liliana Freitas (IST), Carlos Bana e Costa (IST), Panos Kanavos (LSE)</p>
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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.