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567 results for “Simulated datasets”
Dataset for "Yield Estimation of the August 2020 Beirut Explosion by Using Physics-Based Propagation Simulations of Regional Infrasound"
<p>Dataset for “Yield Estimation of the August 2020 Beirut Explosion by Using<br> Physics-Based Propagation Simulations of Regional Infrasound”</p> <p>Authors: Keehoon Kim and Michael E. Pasyanos</p> <p>Lawrence Livermore National Laboratory, Livermore, CA, USA</p> <p>Description<br> This datset includes the infrasound waveform data recorded by the array at the<br> Mt. Meron (IMA) in Israel. A five-element array deployed by the National Data<br> Center of Israel (Fee et al., 2013), and all stations had Martec Tekelec MB2005<br> sensors which have a flat frequency response in the infrasound band (0.1–20Hz)<br> (Ponceau and Bosca, 2010). The infrasound data was provided by the National<br> Data Center of Israel, Soreq Nuclear Research Center, and the pressure<br> recordings only relevant to the 2020 Beirut explosion were uploaded to the<br> public repository (https://zenodo.org).</p> <p>File Description<br> IMA_array_infrasound_waveform.txt Pressure values in ASCII format for 5 stations</p> <p>Acknowledgments<br> This research was performed by the support from the U.S. Department of Energy,<br> National Nuclear Security Administration, Office of Defense Nuclear<br> Nonproliferation, Research and Development under the auspices of the U.S.<br> Department of Energy by the Lawrence Livermore National Laboratory under<br> Contract Number DE-AC52-07NA27344. This is LLNL Contribution LLNL-JRNL- 839419</p> <p>References<br> Fee, D., Waxler, R., Assink, J., Gitterman, Y., Given, J., Coyne, J., ... &<br> Grenard, P. (2013). Overview of the 2009 and 2011 Sayarim infrasound<br> calibration experiments. Journal of Geophysical Research Atmospheres, 118(12),<br> 6122-6143.</p> <p>Ponceau, D., & Bosca, L. (2010). Low-noise broadband microbarometers. In<br> Infrasound monitoring for atmospheric studies (pp. 119-140). Springer,<br> Dordrecht.</p>
Simulated industrial CT dataset for deep learning with dual-energy tomograms and ground truth material maps for copper and iron
<p>We use this dataset for training and evaluation of a deep learning model to discriminate multi-material systems with X-ray CT.</p> <p>The dataset consists of:</p> <ul> <li>inputs: dual-energy tomograms as binary files without a header (tensor <strong>shape for numpy: 2x128x128 @float32</strong>) <ul> <li>simulated spectra are 250kVp and 450kVp both prefiltered using 2mmCuSn</li> </ul> </li> <li>outputs: the material maps a.k.a. ground truths for the training (same shape as inputs) <ul> <li>sampled with a delaunay algorithm and randomly filled with iron and copper fractions</li> </ul> </li> </ul> <p>The <strong>dataset is normalized to [0, 1]</strong>, so you have to multiply by the mass densities of copper and iron to obtain effective fractions in g/cm^3.</p>
Simulated HPLC–DAD datasets saved as MOCCA campaigns
<p>Supplemental data for the publication: <strong>Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening, </strong>submitted by the authors to<strong> </strong><em>ACS Central Science December 2022.</em></p> <p>The data set contains simulated HPLC–DAD datasets saved as MOCCA campaigns, which were used for validation and benchmarking. The files are saved as Python Pickle Files. The zip file contains all the simulated HPLC data one-step all-files download. </p>
PMMA Pyrolysis Simulation – from Micro- to Real-Scale -- Dataset
<p>Dataset to the article "PMMA Pyrolysis Simulation – from Micro- to Real-Scale". It contains experiment data, FDS simulation and PROPTI input files, as well as the Jupyter Notebooks used to process the different data series.</p> <p> </p> <p>Published with the Fire Safety Journal: <a href="https://doi.org/10.1016/j.firesaf.2023.103926">https://doi.org/10.1016/j.firesaf.2023.103926</a><br> Preprint on arXiv: <a href="https://arxiv.org/abs/2303.17446">https://arxiv.org/abs/2303.17446</a><br> </p> <p>A video series explaining the different parts of this dataset is available on YouTube: <a href="https://www.youtube.com/playlist?list=PLWziITJoPnJKtVuaVudyO2Lz-OWaCE9Co">https://www.youtube.com/playlist?list=PLWziITJoPnJKtVuaVudyO2Lz-OWaCE9Co</a></p> <p>Recommended minimum download: "RunReports", "SmallerDirectories", "PMMA_MicroScale" and "PMMA_Cone_Aalto".</p> <p>Extract all files into the same directory, such that all the relative paths point to the correct locations.</p> <p>The data of the inverse modelling processes are stored in the "PMMA_MicroScale" and "PMMA_Cone_Aalto" archives.</p> <p>The "RunReports" archive contains all the data processing and analysis. This is, the notebooks to generate the plots from the IMP results, extracting the stoichiometry from the CHID.out files, writing the best material parameter sets into the parallel panel simulation templates and so forth.</p> <p>The data of the parallel panel simulations is distributed over a couple of *.rar archives, labelled "ParallelPanelTest_A" to "ParallelPanelTest_H". Their content should be extracted into a directory labelled "ParallelPanelTest", to allow the Python scripts to work as intended. Please refer to the video series for further information.</p> <p>The archive "SmallerDirectories" contains a couple of smaller assessments (compared to the parallel panel simulations), like the cone calorimeter GEOM to generate the flux map, flame height as well as the "GeneralInformation" directory. It is recommended to download this archive together with the "RunReports".</p> <p> </p> <p>Note: the data series labels used in the article differ from the ones used for the simulations.</p> <p>Simulations: Aalto_01, Aalto_02, Aalto_03, Aalto_04, Aalto_05, Aalto_06, Aalto_06b, Aalto_07</p> <p>Article: Cone_01, Cone_02, Cone_03, Cone_04, Cone_05, Cone_06, Cone_07, Cone_08</p> <p> </p> <p>Acknowledgments:<br> We gratefully acknowledge the computing time granted through JARA (project jjsc27) on the supercomputer JURECA at Forschungszentrum Jülich and through the project on the CoBra-system, funded by the German Federal Ministry of Education and Research with the grant number 13N15497. This research was partially funded by the German Federal Ministry of Education and Research with the grant number 13N15497.</p>
Swiss Dwellings: A large dataset of apartment models including aggregated geolocation-based simulation results covering viewshed, natural light, traffic noise, centrality and geometric analysis
<p><strong>Introduction</strong></p> <p>This dataset contains detailed data on over 45,000 apartments (370,000 rooms) in ~3,100 buildings including their geometries, room typology as well as their visual, acoustical, topological, and daylight characteristics. Additionally, we have included location-specific characteristics for the buildings, including climatic data and points of interest within walking distance.</p> <p><strong>Changelog</strong></p> <ul> <li><strong>v3.0.0 (2023-03-31):</strong> <ul> <li>Updated the dataset increasing the total number of apartments to 45176 and incorporating fixes to some of the sites. The update includes re-digitized apartments and thus alters some ID values.</li> </ul> </li> <li><strong>v2.2.1 (2023-03-10):</strong> <ul> <li>A file, <code>location_ratings.csv</code>, has been included to provide ratings of the locations in which the buildings are situated. The ratings, provided by <a href="https://en.fpre.ch/">Fahrländer Partner AG</a>, give insights into the living situation at the buildings' addresses. Details for the different dimensions are provided below.</li> <li>The file <code>location.csv</code> has been updated to include the minimum and maximum temperatures for the locations in which the buildings are situated.</li> </ul> </li> <li><strong>v2.1.0 (2022-12-23)</strong>: <ul> <li>A file, <code>locations.csv</code>, has been included to provide information on the climatic and infrastructural characteristics of the locations in which each building is situated</li> </ul> </li> <li><strong>v2.0.0 (2022-10-17):</strong> <ul> <li>Additional to the residential units, we also include the commercial and public parts (such as staircases) of the models. The field <code>unit_usage</code> describes whether an area belongs to a commercial, residential, janitor or public part of the building</li> <li>Added the fields <code>elevation</code> and <code>height</code> to <em>geometries.csv</em> to describe the elevation above the terrain surface and the height of objects.</li> <li>Added the field <code>plan_id</code> which allows identifying which floors are based on the same floor plan (in some cases multiple floors of a building share the same floor plan</li> <li>Improved the ordering of fields in the CSV files (instead of alphabetic order)</li> <li>Minor changes to individual sites</li> </ul> </li> </ul> <p><strong>Procurement</strong></p> <p>The data is sourced from commercial clients of <a href="https://www.archilyse.com/">Archilyse AG</a> specializing on the digitization and analysis of buildings. The existing building plans of clients are converted into a geo-referenced, semantically annotated representation and undergo a manual Q/A process to ensure the accuracy of the data and to ensure a maximum 5%-deviation in the apartments' areas (validated with a median deviation of 1.2%).</p> <p><strong>Geometries</strong></p> <p>The dataset contains a file <code>geometries.csv</code> which contains the geometries of all areas, walls, railings, columns, windows, doors and features (sinks, bathtubs, etc.) of an apartment.</p> <p>In total, the datasets contain the 2D geometry of ~1.7 million separators (walls, railings), ~715,000 openings (windows, doors), ca. 520,000 areas (rooms, bathrooms, kitchens, etc.), and ~315,000 features (sinks, toilets, bathtubs, etc.).</p> <p>Each row contains:</p> <ul> <li><code>apartment_id</code>: The ID of the apartment (for features, areas), <em>note</em>: an apartment id is only unique per site</li> <li><code>site_id</code>: The ID of the site</li> <li><code>building_id</code>: The ID of the building</li> <li><code>floor_id</code>: The ID of the floor</li> <li><code>plan_id</code>: The ID of the plan on which the floor is based, multiple floors of a building might be based on the same plan</li> <li><code>unit_id</code>: The ID of the unit in which the element is spatially contained (for features, areas)</li> <li><code>area_id</code>: The ID of the area in which the element is spatially contained (for features)</li> <li><code>unit_usage</code>: The usage of the unit, possible values are: RESIDENTIAL, COMMERCIAL, PUBLIC, JANITOR</li> <li><code>entity_type</code>: The entity type (<em>area, separator, opening, feature</em>)</li> <li><code>entity_subtype</code>: The entity’s sub-type (e.g. <em>WALL</em>)</li> <li><code>geometry</code>: The element’s geometry as a <a href="https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry">WKT</a> geometry in meters. The geometry is given in the site’s local coordinate system. I.e. the position between elements of the same site are correct in respect to each other. The +y direction points northwards, the +x direction points eastwards.</li> <li><code>elevation</code>: The object's elevation above the terrain surface in meters. We assume one terrain baseline per building, thus all walls in a given floor share the same elevation value. However, windows in particular might start at different elevations and have differing heights.</li> <li><code>height</code>: The height of the entity in meters, <em>note</em>: In many cases, a default height is assumed</li> </ul> <p>An example:</p> <table> <thead> <tr> <th scope="col">column</th> <th scope="col"> </th> </tr> </thead> <tbody> <tr> <td>apartment_id</td> <td> <p>d4438f2129b30290845ce7eef98a5ba7</p> </td> </tr> <tr> <td>site_id</td> <td>127</td> </tr> <tr> <td>building_id</td> <td>164</td> </tr> <tr> <td>plan_id</td> <td>492</td> </tr> <tr> <td>floor_id</td> <td>861</td> </tr> <tr> <td>unit_id</td> <td>63777</td> </tr> <tr> <td>area_id</td> <td>767676</td> </tr> <tr> <td>unit_usage</td> <td>RESIDENTIAL</td> </tr> <tr> <td>entity_type</td> <td>area</td> </tr> <tr> <td>entity_subtype</td> <td>LIVING_ROOM</td> </tr> <tr> <td>geometry</td> <td> <p>POLYGON ((-6.1501158933490139 -4.8490786654693...</p> </td> </tr> <tr> <td>elevation</td> <td>0</td> </tr> <tr> <td>height</td> <td>2.6</td> </tr> </tbody> </table> <p><strong>Simulations</strong></p> <p>Besides the geometrical model, we also provide simulation data on the visual, acoustic, solar, layout, and connectivity-related characteristics of the apartments. The file <code>simulations.csv</code> contains the simulation data aggregated on a per-area basis. Each row contains the identifier columns <code>area_id</code>, <code>unit_id</code>, <code>apartment_id</code>, <code>floor_id</code>, <code>building_id</code>, <code>site_id</code> as defined above as well as 367 simulation columns. Each simulation column is formatted as:</p> <pre><code><simulation_category>_<simulation_dimensions>_<aggregation_function></code></pre> <p>For instance. the column <code>view_buildings_median</code> describes the amount of building surface that can be seen from any point in a given room. The aggregation methods vary per simulation category and are described in detail below.</p> <p><strong>Layout</strong></p> <p>The <em>layout</em> features represent simple features based on the geometry and composition of a room, the dataset provides the following information in an unaggregated form.</p> <p>Area Basics / Geometry</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_area_type</td> <td>The area’s area type</td> </tr> <tr> <td>layout_net_area</td> <td>The area’s share of the apartment’s net area (e.g. 0 for a balcony)</td> </tr> <tr> <td>layout_area</td> <td>The area’s actual area</td> </tr> <tr> <td>layout_perimeter</td> <td>The area’s perimeter</td> </tr> <tr> <td>layout_compactness</td> <td>The area’s compactness (the Polsby–Popper score)</td> </tr> <tr> <td>layout_room_count</td> <td>The area’s share to the apartment’s room count</td> </tr> <tr> <td>layout_is_navigable</td> <td>True if the area is navigable by a wheelchair</td> </tr> </tbody> </table> <p>Area Features</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_has_sink</td> <td>True if the area has a sink</td> </tr> <tr> <td>layout_has_shower</td> <td>True if the area has a shower</td> </tr> <tr> <td>layout_has_bathtub</td> <td>True if the area has a bathtub</td> </tr> <tr> <td>layout_has_toilet</td> <td>True if the area has a toilet</td> </tr> <tr> <td>layout_has_stairs</td> <td>True if the area has stairs</td> </tr> <tr> <td>layout_has_entrance_door</td> <td>True if the area is directly leading to an exit of the apartment</td> </tr> </tbody> </table> <p>Area Windows / Doors</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_number_of_doors</td> <td>The number of doors directly leading to the area</td> </tr> <tr> <td>layout_number_of_windows</td> <td>The number of windows of the area</td> </tr> <tr> <td>layout_door_perimeter</td> <td>The sum of all door lengths directly leading to the area</td> </tr> <tr> <td>layout_window_perimeter</td> <td>The sum of all window lengths of the area</td> </tr> </tbody> </table> <p>Area Walls / Railings</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_open_perimeter</td> <td>The sum of all of the boundaries of the area that are neither walls nor railings</td> </tr> <tr> <td>layout_railing_perimeter</td> <td>The sum of all of the boundaries of the area that are railings</td> </tr> <tr> <td>layout_mean_walllengths</td> <td>The mean length of the area’s sides</td> </tr> <tr> <td>layout_std_walllengths</td> <td>The standard deviation of the lengths of the area’s sides</td> </tr> </tbody> </table> <p>Area Adjacency</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_connects_to_bathroom</td> <td>True if the area connects to a bathroom</td> </tr> <tr> <td>layout_connects_to_private_outdoor</td> <td>True if the area connects to an outside area that is private to the apartment</td> </tr> </tbody> </table> <p><strong>View</strong></p> <p>The views from an object help to understand the impact of the surroundings on the object. The view simulation calculates the visible amount of buildings, greenery, water, etc. on each individual hexagon from the analyzed object. The values are expressed in steradians (sr) and represent the amount a particular object category occupies in the spherical field of view.</p> <p>Each of the following dimensions is provided using the room-wise aggregations' <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, the column <code>view_greenery_p20</code> describes the amount of greenery that can be seen from at least 20% of the positions in the area.</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>view_buildings</td> <td>The amount of visible buildings</td> </tr> <tr> <td>view_greenery</td> <td>The amount of visible greenery</td> </tr> <tr> <td>view_ground</td> <td>The amount of visible ground</td> </tr> <tr> <td>view_isovist</td> <td>The amount of visible isovist</td> </tr> <tr> <td>view_mountains_class_2</td> <td>The amount of visible mountains of UN mountain class 2</td> </tr> <tr> <td>view_mountains_class_3</td> <td>The amount of visible mountains of UN mountain class 3</td> </tr> <tr> <td>view_mountains_class_4</td> <td>The amount of visible mountains of UN mountain class 4</td> </tr> <tr> <td>view_mountains_class_5</td> <td>The amount of visible mountains of UN mountain class 5</td> </tr> <tr> <td>view_mountains_class_6</td> <td>The amount of visible mountains of UN mountain class 6</td> </tr> <tr> <td>view_railway_tracks</td> <td>The amount of visible railway_tracks</td> </tr> <tr> <td>view_site</td> <td>The amount of visible site</td> </tr> <tr> <td>view_sky</td> <td>The amount of visible sky</td> </tr> <tr> <td>view_tertiary_streets</td> <td>The amount of visible tertiary_streets</td> </tr> <tr> <td>view_secondary_streets</td> <td>The amount of visible secondary_streets</td> </tr> <tr> <td>view_primary_streets</td> <td>The amount of visible primary_streets</td> </tr> <tr> <td>view_pedestrians</td> <td>The amount of visible pedestrians</td> </tr> <tr> <td>view_highways</td> <td>The amount of visible highways</td> </tr> <tr> <td>view_water</td> <td>The amount of visible water</td> </tr> </tbody> </table> <p><strong>Sun</strong></p> <p>Sun simulations help to understand the impact of solar radiation on the object. The outcome of the sun simulations helps to identify surfaces that have great solar potential. Sun simulations are defined by the amount of solar radiation on each individual hexagon from the analyzed object. The sun simulation not only includes direct sun but also considers scattered light. The sun simulation values are given in Kilolux (klx). Simulations are performed for the days of the summer solstice, winter solstice, and the vernal equinox.</p> <p>Each of the following dimensions is provided using the room-wise aggregations' <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, column <code>sun_201806211200_median</code> describes the median amount of direct daylight received on the positions in the area.</p> <p>Vernal Equinox</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>sun_201803210800</td> <td>Daylight at 08:00 on 21st of March</td> </tr> <tr> <td>sun_201803211000</td> <td>Daylight at 10:00 on 21st of March</td> </tr> <tr> <td>sun_201803211200</td> <td>Daylight at 12:00 on 21st of March</td> </tr> <tr> <td>sun_201803211400</td> <td>Daylight at 14:00 on 21st of March</td> </tr> <tr> <td>sun_201803211600</td> <td>Daylight at 16:00 on 21st of March</td> </tr> <tr> <td>sun_201803211800</td> <td>Daylight at 18:00 on 21st of March</td> </tr> </tbody> </table> <p>Summer Solstice</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>sun_201806210600</td> <td>Daylight at 06:00 on 21st of June</td> </tr> <tr> <td>sun_201806210800</td> <td>Daylight at 08:00 on 21st of June</td> </tr> <tr> <td>sun_201806211000</td> <td>Daylight at 10:00 on 21st of June</td> </tr> <tr> <td>sun_201806211200</td> <td>Daylight at 12:00 on 21st of June</td> </tr> <tr> <td>sun_201806211400</td> <td>Daylight at 14:00 on 21st of June</td> </tr> <tr> <td>sun_201806211600</td> <td>Daylight at 16:00 on 21st of June</td> </tr> <tr> <td>sun_201806211800</td> <td>Daylight at 18:00 on 21st of June</td> </tr> <tr> <td>sun_201806212000</td> <td>Daylight at 20:00 on 21st of June</td> </tr> </tbody> </table> <p>Winter Solstice</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>sun_201812211000</td> <td>Daylight at 10:00 on 21st of December</td> </tr> <tr> <td>sun_201812211200</td> <td>Daylight at 12:00 on 21st of December</td> </tr> <tr> <td>sun_201812211400</td> <td>Daylight at 14:00 on 21st of December</td> </tr> <tr> <td>sun_201812211600</td> <td>Daylight at 16:00 on 21st of December</td> </tr> </tbody> </table> <p><strong>Noise / Window Noise</strong></p> <p>Noise level and the distribution of elements from an area help to understand how an object is exposed to the acoustics of this area. The acoustic simulation calculates the noise intensity on each individual hexagon from the analyzed object considering traffic and train noise datasets. Adjacent buildings are considered noise-blocking elements. The values are expressed in dBA (decibels).</p> <p>Window Noise</p> <p>The noise per window of a given area is aggregated via <code>min</code> and <code>max</code>. For instance, <code>window_noise_train_day_max</code> represents the maximum amount of noise received on any window of the area.</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>window_noise_traffic_day</td> <td>The amount of noise received on the area’s windows from daytime car traffic</td> </tr> <tr> <td>window_noise_traffic_night</td> <td>The amount of noise received on the area’s windows from night-time car traffic</td> </tr> <tr> <td>window_noise_train_day</td> <td>The amount of noise received on the area’s windows from daytime train traffic</td> </tr> <tr> <td>window_noise_train_night</td> <td>The amount of noise received on the area’s windows from night-time train traffic</td> </tr> </tbody> </table> <p>Area-Wise Noise</p> <p>The area-wise noise describes the amount of noise received from a noise source aggregated over the whole area in an unaggregated form. For instance, <code>noise_traffic_night</code> describes the dBA of noise received in the area from car traffic at night when propagating noise from all windows.</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>noise_traffic_day</td> <td>The amount of noise received in the area from daytime car traffic</td> </tr> <tr> <td>noise_traffic_night</td> <td>The amount of noise received in the area from night-time car traffic</td> </tr> <tr> <td>noise_train_day</td> <td>The amount of noise received in the area from daytime train traffic</td> </tr> <tr> <td>noise_train_night</td> <td>The amount of noise received in the area from night-time train traffic</td> </tr> </tbody> </table> <p><br> <strong>Connectivity</strong></p> <p>Centrality simulations help to analyze a floor plan, whether it’s a shopping mall and you want to identify prominent areas in order to select the most prominent spot or it’s an interior design circulation path and you want to determine open floor plan areas. Centrality simulations are done using topological measures that score grid cells by their importance as a part of a grid cell network.</p> <p>The distances and centralities are aggregated via <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, <code>connectivity_balcony_distance_min</code> describes the shortest distance to the next balcony from the point closest to the balcony in the area.</p> <p>Distances</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>connectivity_room_distance</td> <td>Distance to the next area of type ROOM</td> </tr> <tr> <td>connectivity_living_dining_distance</td> <td>Distance to the next area of type LIVING_DINING</td> </tr> <tr> <td>connectivity_bathroom_distance</td> <td>Distance to the next area of type BATHROOM</td> </tr> <tr> <td>connectivity_kitchen_distance</td> <td>Distance to the next area of type KITCHEN</td> </tr> <tr> <td>connectivity_balcony_distance</td> <td>Distance to the next area of type BALCONY</td> </tr> <tr> <td>connectivity_loggia_distance</td> <td>Distance to the next area of type LOGGIA</td> </tr> <tr> <td>connectivity_entrance_door_distance</td> <td>Distance to the next apartment exit</td> </tr> </tbody> </table> <p>Centralities</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>connectivity_eigen_centrality</td> <td>The Eigen-Centrality value</td> </tr> <tr> <td>connectivity_betweenness_centrality</td> <td>The Betweenness-Centrality value</td> </tr> <tr> <td>connectivity_closeness_centrality</td> <td>The Closeness-Centrality value</td> </tr> </tbody> </table> <p><strong>Location Properties</strong></p> <p>In addition to the apartment-related data, we also provide simulation data on the climatic, and infrastructural characteristics of the locations. The file <code>locations.csv</code> contains the simulation data aggregated on a per-building basis. Each row contains the identifier <code>building_id</code> corresponding to the building ids referenced in <code>geometries.csv</code> and <code>simulations.csv</code>.</p> <p><strong>Climate</strong></p> <p>The climate features represent 39 simple features based on the spatial climate analysis of Meteo Swiss as derived from <a href="https://www.meteoswiss.admin.ch/climate/the-climate-of-switzerland/spatial-climate-analyses.html.">MeteoSwiss</a>. Each column is formatted as <code>climate_<category>_<period>. </code>For instance, the column <code>climate_tnorm_january</code> describes the monthly mean temperature in degrees Celsius (from the norm period of 1991-2020) at the location of the building. The aggregation methods vary per simulation category and are described in detail below.</p> <p>Temperature Normals</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>climate_tnorm_year</td> <td>The yearly mean temperature in degrees Celsius of the current norm period from 1991 to 2020 (TnormY9120)</td> </tr> <tr> <td>climate_tnorm_january</td> <td>The monthly mean temperature in January in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>climate_tnorm_februry</td> <td>The monthly mean temperature in February in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>climate_tnorm_december</td> <td>The monthly mean temperature in December in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>climate_tminnorm_january</td> <td>The monthly minimum temperature in January in degrees Celsius of the current norm period from 1991 to 2020 (TminnormM9120)</td> </tr> <tr> <td>...</td> <td> </td> </tr> <tr> <td>climate_tminnorm_december</td> <td>The monthly minimum temperature in December in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>climate_tmaxnorm_january</td> <td>The monthly maximum temperature in January in degrees Celcius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>...</td> <td> </td> </tr> <tr> <td>climate_tmaxnorm_december</td> <td>The monthly maximum temperature in December in degrees Celcius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> </tbody> </table> <p>Sunshine Duration Normals</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>climate_snorm_year</td> <td>The yearly mean relative sunshine duration in percent of the current norm period from 1991 to 2020 (SnormY9120). Relative sunshine duration (RSD) is the ratio between the effective sunshine duration and the duration maximally possible if no clouds were covering the sun. A period with sunshine is defined as a period when the direct solar irradiance exceeds 200 W/m²</td> </tr> <tr> <td>climate_snorm_january</td> <td>The monthly mean relative sunshine duration for January in percent of the current norm period</td> </tr> <tr> <td>climate_snorm_februry</td> <td>The monthly mean relative sunshine duration for February in percent of the current norm period</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>climate_snorm_december</td> <td>The monthly mean relative sunshine duration for December in percent of the current norm period</td> </tr> </tbody> </table> <p>Precipitation Normals</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>climate_rnorm_year</td> <td>The yearly mean precipitation in mm of the current norm period (RnormY9120)</td> </tr> <tr> <td>climate_rnorm_january</td> <td>The monthly mean precipitation for January in mm of the current norm period (RnormM9120)</td> </tr> <tr> <td>climate_rnorm_februry</td> <td>The monthly mean precipitation for February in mm of the current norm period (RnormM9120)</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>climate_rnorm_december</td> <td>The monthly mean precipitation for December mm of the current norm period (RnormM9120)</td> </tr> </tbody> </table> <p><strong>10-Minute Walkshed Infrastructure</strong></p> <p>Based on OpenStreetMap data and its tagging system we counted all 465 tags (key and value tuples as listed here: https://wiki.openstreetmap.org/wiki/Map_features) which can be reached within a 10-minute walk from the location of the building. Each column is formatted as <code>walkshed_<poi_category>_<poi_type>. </code>For instance, the column <code>walkshed_shop_coffee</code> describes the number of coffee shops located within 10 minutes of walking from the building.</p> <p>The following is an excerpt of support categories and their corresponding types.</p> <ul> <li><code>shop: antique, art, ...</code></li> <li><code>amenity: art, atm, ...</code></li> <li><code>tourism: alpine, attraction, ...</code></li> <li><code>leisure: amusement, beach, ...</code></li> <li><code>healthcare: clinic, dentist, ...</code></li> <li><code>historic: archaeological, battlefield, ...</code></li> <li><code>ariaelway: station</code></li> </ul> <p><strong>Location Ratings</strong></p> <p>The location ratings, provided by <a href="https://en.fpre.ch/">Fahrländer Partner AG</a>, give insights into the living situation at locations in which the buildings are situated. The file location_ratings.csv provides the following information:</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>location_rating_MIKRAT_W</td> <td>Living situation - Overall (1.0=worst, 5.0=best)</td> </tr> <tr> <td>location_rating_IMAGE_W</td> <td>Living situation - Image (1.0=worst, 5.0=best)</td> </tr> <tr> <td>location_rating_DL_W</td> <td>Living situation - Service Quality (1.0=worst, 5.0=best)</td> </tr> <tr> <td>location_rating_FZ_W</td> <td> <p>Living situation - Leisure Quality (1.0=worst, 5.0=best)</p> </td> </tr> <tr> <td>location_rating_NASE_W_DOM</td> <td>The most dominant segment of demand:<br> <br> 1 Rural-traditional<br> 2 Modern worker<br> 3 Transitional-alternative<br> 4 Traditional middle class<br> 5 Liberal middle class<br> 6 Established alternative<br> 7 Upper middle class<br> 8 Professional elite<br> 9 Urban elite<br> 10 Unknown<br> <br> <a href="https://en.fpre.ch/marktdaten/nachfragersegmente/nachfragersegmente-im-wohnungsmarkt/">More Information</a></td> </tr> <tr> <td>location_rating_FGFRQZ</td> <td> <p>The mean number of pedestrians per hour throughout a day between 7 am and 8 pm of an average working day.<br> <br> 1 <50<br> 2 50-100<br> 3 100-200<br> 4 200-500<br> 5 >500</p> </td> </tr> </tbody> </table>
Dataset to Report on skill of CMIP6 models to simulate alkalinity and improved parameterizations for large scale alkalinity distribution
<p>Six sensitivity experiments were conducted with the ocean-sea ice-biochemistry model FESOM2.1-REcoM3 to investigate the sensitivity of total alkalinity to changes in parametrizations. The results are reported in part two of the report:</p> <p>Hinrichs, C., & Hauck, J. (2022). OceanNETS Task 4.4 – Report on skill of CMIP6 models to simulate alkalinity and improved parameterizations for large scale alkalinity distribution. <a href="https://oceanrep.geomar.de/id/eprint/56871/1/Deliverable_4.4_vfinal.pdf">https://oceanrep.geomar.de/id/eprint/56871/1/Deliverable_4.4_vfinal.pdf</a></p> <p>This item contains the data and plotting routine to reproduce Figure 14 in the report. The model data has been post-processed into global mean depth profiles.</p>
DATA7: A dataset that uses synthetic trajectories of vehicles and real cellular tower locations to simulate the workload of Edge nodes in the city of Pisa
<p><strong>Description</strong></p> <p>The dataset contains observations of vehicles in the range of edge nodes (cellular towers). The trajectories of vehicles are synthetically generated with <a href="https://www.eclipse.org/sumo/">SUMO</a>. The cellular tower positions have been taken from <a href="https://opencellid.org/">OpenCelliD</a>. The dataset is in the comma-separated values (CSV) format, and is around 220MB decompressed.</p> <p><br> The CSV contains the following fields:<br> * edge_id: unique identifier of the edge devices<br> * edge_lat: latitude coordinate of the edge device<br> * edge_lon: longitude coordinate of the edge device<br> * time: simulation step of the observation<br> * vehicle_id: unique identifier of the vehicle<br> * vehicle_lat: latitude coordinate of the vehicle<br> * vehicle_lon: longitude coordinate of the vehicle<br> * distance: geodesic distance in meters from the vehicle and the edge device</p>
Dataset of the Article "Reconstruction of the unbinding pathways of new inhibitors of the SARS-CoV-2 Papain-like protease using molecular dynamics simulation"
<p>This dataset contains concatenated trajectory files of the SuMD simulation of the unbinding pathways of the new inhibitors for SARS-CoV-2 papain-like protease. This data will be published in an article titled: "<strong>Reconstruction of the unbinding pathways of new inhibitors of the SARS-CoV-2 Papain-like protease using molecular dynamics simulation".</strong></p>
Multicenter dataset of simulated neuroimaging features
<p>Let <span class="math-tex">\(y_{ijf}\)</span> be the one-dimensional array of the simulated feature <span class="math-tex">\(f\)</span>, for the single-center <span class="math-tex">\(i\)</span>, and participant <span class="math-tex">\(j\)</span>, for a total of <span class="math-tex">\(k\)</span> single-center datasets,<span class="math-tex">\(n_i\)</span> participants for each center, and <span class="math-tex">\(V\)</span> features. We simulated MRI-derived cortical thickness (CT) and fractal dimension (FD) data for <span class="math-tex">\(k = 3, 10, 36\)</span> single-centers. Each single-center dataset provided the same number of participants (i.e., <span class="math-tex">\(n_i = n\)</span>), with <span class="math-tex">\(n\)</span> assuming the values <span class="math-tex">\(25, 50, 100, 250\)</span>. Additionally, for <span class="math-tex">\(k = 3\)</span>, we simulated CT and FD data with <span class="math-tex">\(n = 500, 100, 5000, 10000\)</span>. Totally, we did 32 experiments, i.e., we simulated 32 different multicenter datasets (16 for the CT features and 16 for the FD measures).</p> <p>Each <span class="math-tex">\(y_{ijf}\)</span> was generated using the model proposed by Johnson and colleagues [1] and recently used for neuroimaging features’ simulation by Chen and collaborators [2]:</p> <p><span class="math-tex">\(y_{ijf} = \alpha_f + \beta_fx_{ij} + \gamma_{if} + \delta_{if}\epsilon_{ijf}\)</span></p> <p>where <span class="math-tex">\(\alpha_f\)</span> is the average value of the feature <span class="math-tex">\(f\)</span> in the single-center ICBM dataset [3], <span class="math-tex">\(\beta_f\)</span> is the effect of the age on the feature <span class="math-tex">\(f\)</span>estimated through a linear regression between actual age and feature <span class="math-tex">\(f\)</span> in the single-center ICBM dataset, <span class="math-tex">\(x_{ij}\)</span> is a simulated age variable drawn from a uniform distribution <span class="math-tex">\(X \sim uniform([20, 90])\)</span>. The mean site effect <span class="math-tex">\(\gamma_{if}\)</span> was drawn from a normal distribution with zero mean and standard deviation equal to 0.1, while the variance site effect <span class="math-tex">\(\delta_{if}\)</span> was drawn from a center-specific inverse gamma distribution with chosen parameters. For our simulations, we chose to distinguish the site-specific location factors by assuming independent and identically distributed (i.i.d.) normal distributions and scaling factors using the parameters described as follows. We set the value of the inverse gamma shape, for each center, as {46, 51, 56}, respectively, when k = 3, as {40, 42, .., 58} when k = 10, and as {10, 12, .., 70} when k = 36. In all cases, the inverse gamma scale was set to 50.</p> <p>Each CSV file contains the following columns:</p> <ul> <li>SITE: simulated imaging site label (i.e., a, b, c, ..., j)</li> <li>age: each subject's simulated age, expressed in years</li> <li>cortex_CT (FD): CT (or FD) of the cerebral cortical gray matter (GM).</li> <li>lh_cortex_CT (FD), rh_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM.</li> <li>lh_frontal_cortex_CT (FD), rh_frontal_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the frontal lobe.</li> <li>lh_temporal_cortex_CT (FD), rh_temporal_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the temporal lobe.</li> <li>lh_parietal_cortex_CT (FD), rh_parietal_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the parietal lobe.</li> <li>lh_occipital_cortex_CT (FD), rh_occipital_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the occipital lobe.</li> </ul> <p><em>References</em></p> <p>[1] Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. <em>Biostat. Oxf. Engl.</em> <strong>8</strong>, 118–127 (2007).</p> <p>[2] Chen, A. A. <em>et al.</em> Mitigating site effects in covariance for machine learning in neuroimaging data. <em>Hum. Brain Mapp.</em> <strong>43</strong>, 1179–1195 (2022).</p> <p>[3] 1000 Functional Connectomes Project (FCP) – <a href="http://fcon_1000.projects.nitrc.org/fcpClassic/FcpTable.html">ICBM dataset</a>.</p>
Dataset used for "Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia"
<p><strong>General Info</strong></p> <p>This dataset contains monthly output from two 20-year (1979-1998) variable-resolution (VR) CESM2 simulations (HMA_VR7a and HMA_VR7b). The coupled atmosphere-land simulations were run with a newly generated VR grid that has regional grid refinements up to 7 km over High Mountain Asia. The HMA_VR7b simulation was performed with an updated glacier-cover dataset (<a href="https://doi.org/10.5281/zenodo.7864689">https://doi.org/10.5281/zenodo.7864689</a>) and includes snow and glacier model modifications. Further, monthly output from a globally uniform 1-degree CESM simulation (NE30), used for evaluation of the HMA VR simulations, is also included. The monthly output have been used for analysis and discussion in the paper “<em>Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia” </em>that is currently under review in the Cryosphere Discussions, <a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>.</p> <p><strong>Contact</strong></p> <p>René Wijngaard (<a href="mailto:r.r.wijngaard.uu@gmail.com">r.r.wijngaard.uu@gmail.com</a> / <a href="mailto:r.r.wijngaard@uu.nl">r.r.wijngaard@uu.nl</a>) </p> <p><strong>Raw Data</strong></p> <p>Raw monthly and daily unstructured HMA VR model output are available on request. </p> <p><strong>Dataset Contents</strong></p> <pre><code>NE30.tar HMA_VR7a.tar HMA_VR7b.tar </code></pre> <p>These files contain atmosphere (CAM) and land (CLM) model output that are regridded to a 1-degree finite volume (0.9 x 1.25 degrees latitude/longitude) grid. The following variables are included: CLDLIQ, OMEGA, Q, STEND_CLUBB, SWCF, T, Z3, EFLX_LH_TOT, FGR, FIRE, FLDS, FSA, FSDS, FSH, FSM, FSNO, FSM, FSR, H2OSNO, PCT_LANDUNIT, QICE_MELT, QSNOFRZ, RAIN, SNOW, and TSA. </p> <pre><code>SMB_HMA_VR7a.tar SMB_HMA_VR7b.tar</code></pre> <p>These files contain unstructured SMB-related CLM model output (i.e., on the HMA VR grid). The following variables are included: PCT_LANDUNIT, QRUNOFF_ICE, QSNOFRZ_ICE, QSNOMELT_ICE, QSOIL_ICE, RAIN_ICE, and SNOW_ICE.</p>
Simulating the Laurentide Ice Sheet of the LGM (Datasets from Yelmo_v1.751 output simulations)
<p>Model output presented in Moreno-Parada, D., Alvarez-Solas, J., Blasco, J., Montoya, M., and Robinson, A.: Simulating the Laurentide ice sheet of the Last Glacial Maximum.</p>
Simulation Datasets of 5HT3AR Bound to Two Partial Agonists
<p>Simulation Datasets Reported in the manuscript <strong>Structural Basis for Partial Agonism in 5HT3AR Receptors:</strong></p> <p>1. SMP100 PDB and MOL2 files: SMP.pdb (4.7 KB) and SMP.mol2 (4.7 KB)<br> 2. SMP100 topology and parameter files: SMP.top (2.8 KB) and SMP.prm (4.7 KB)<br> 3. SMP-bound 5-HT3AR initial conformation from cryo-EM and prepared in Maestro: 5HT3AR-SMP_init.pdb (2.7 MB)<br> 4. SMP-bound 5-HT3AR system PDB file and trajectory: 5HT3AR-SMP.pdb (14.4 MB) and 5HT3AR-SMP.xtc (135.7 MB)<br> 5. ALB148471 PDB and MOL2 files: ALB.pdb (4.7 KB) and ALB.mol2 (4.7 KB)<br> 6. ALB148471 topology and parameter files: ALB.top (2.8 KB) and ALB.prm (4.7 KB)<br> 7. ALB-bound 5-HT3AR initial conformation from cryo-EM and prepared in Maestro: 5HT3AR-ALB_init.pdb (2.7 MB)<br> 8. ALB-bound 5-HT3AR system PDB file and trajectory: 5HT3AR-ALB.pdb (14.4 MB) and 5HT3AR-ALB.xtc (135.7 MB)</p>
Datasets for simulating heat and CO2 fluxes in Beijing using SUEWS V2020b
<p>Data, model runs and codes used in the manuscript "<em>Simulating heat and CO<sub>2</sub> fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance</em>". The source code for Surface Urban Energy and Water balance Scheme (SUEWS) V2020b is also included.</p> <p> </p> <p><strong>[Latest Version]</strong></p> <p>June 9, 2023: Version 2 was created. (1) Modifications in <strong>LAI</strong>: the input data of LAI (<em>LAI/source_data/Beijing_LAI.csv</em>) was previously derived from a MODIS product MOD15A2H; it was replaced with the LAI time series at a higher spatial resolution derived from data provided by Landsat 7 ETM+; the code for data retrieval (<em>LAI/LAI_from_GEE.txt</em>) was added. (2) Modifications in <strong>ModelRuns</strong>: some of the external parameters given to SUEWS were adjusted, including the optimized LAI parameters (for case LAI and case gs_LAI), and Fr<sub>nonheat</sub> (from 0.2 to 0.5); a <em>read_me.txt</em> was added. (3) Modifications in <strong>Fig&Statistics</strong>: the shaded area for the line charts were changed from standard deviation to the interquartile range.</p> <p> </p> <p><strong>[Data&Code Description]</strong></p> <p>The folders are:</p> <p><strong>1. Observations</strong><br> The units for CO2 flux (Fc), latent heat flux (QE), and sensible heat flux (QH) are μmol m<sup>-2</sup> s<sup>-1</sup>, W m<sup>-2</sup>, and W m<sup>-2</sup>, respectively, unless otherwise stated.<br> <strong>1.1. Observation datasets</strong><br> 1.1.1. <em>radiation_flux_2010-2011.csv</em> is radiation flux observations from May 2010 to July 2011 at 140 m on the IAP tower, Beijing.<br> 1.1.2. <em>turbulent_flux_before_QC_2016.csv</em> is turbulent flux observations before quality control (QC) obtained with the eddy covariance (EC) technique for the year 2016 at 47 m on the IAP tower.<br> 1.1.3. <em>turbulent_flux_after_QC_2016.csv</em> is turbulent flux observations after quality control (QC).<br> 1.1.4. <em>Fc_mean_diurnal_by_season.csv</em> is the mean diurnal seasonal cycle of Fc observations.<br> 1.1.5. <em>Fc_gapfilled_with_MDC.csv</em> is the Fc time series for the year 2016 gap-filled with Mean Diurnal Cycle (MDC) method.<br> 1.1.6. <em>meteorological_observations </em>is a folder including meteorological observations at different heights measured at the IAP tower for the years 2010-2012.<br> <strong>1.2 Codes for observations processing</strong><br> 1.2.1. <em>turbulent_flux_QC.py</em> is for turbulent flux QC covering wind direction filtering, stationarity test, friction velocity filtering, and nighttime filtering.<br> 1.2.2. <em>mean_diurnal_cycle_of_flux_observations.py</em> is for obtaining the mean diurnal cycle of flux observations.<br> 1.2.3. <em>Fc_gap_filling.py</em> is for filling the gaps in the Fc time series with the MDC method.</p> <p><strong>2. LAI</strong><br> This folder includes a project aiming to obtain the parameters for the leaf area index (LAI) model adopted by SUEWS. In this LAI model, the LAI is related to air temperature only. Incorporating hourly air temperature observations and the 'real' LAI (e.g. observed LAI, remotely sensed LAI), the optimization method Covariance Matrix Adaptation Evolutionary Strategies (CMA-ES) will derive the optimized parameters for the LAI model. For more details, please read the description text file (<em>read_me.txt</em>) under this folder.<br> 2.1. <em>read_me.txt.</em><br> 2.2. <em>source_data</em> is a folder including the input for CMA-ES optimization. The input files of Beijing are included, allowing a quick run to reproduce the results demonstrated in the manuscript.<br> 2.3. <em>result_data</em> is a folder including the output for CMA-ES optimization.<br> 2.4. The code for data retrieval from the Google Earth Engine platform (<em>LAI/LAI_from_GEE.txt</em>) was added. The others are Python scripts regarding CMA-ES, including the LAI model, data de-spiking, interpolation, CMA-ES training, and result visualization.</p> <p><strong>3. ModelRuns</strong><br> This folder includes SUEWS source code, four model runs conducted to test the sensitivity to vegetation-related parameters (i.e., maximum conductance, LAI model parameters), and four model runs to test sensitivity to the radius of the model domain.<br> 3.1. <em>SUEWS_SourceCode</em> is a folder including SUEWS V2020b source Fortran codes. For detailed descriptions, readers are referred to SUEWS webpage (https://suews.readthedocs.io/en/latest/).<br> 3.2. <em>Input</em> is a folder including the necessary input files shared by each of the model runs. Please copy all the files to a model run folder (e.g. <em>VegetationSensitivity\case-base\Input\</em>) before starting a SUEWS run.<br> 3.3. <em>VegetationSensitivity</em> is a folder including four SUEWS runs to test the model sensitivity to vegetation-related parameters: <em>case-base</em> is the control run, <em>case-gs</em> is a model run where maximum conductance of vegetation has been modified to more site-specific values, <em>case-LAI</em> is a model run where LAI parameters have been optimized with CMA-ES, and <em>case-gs_LAI</em> is a model run where both maximum conductance and LAI parameters have been modified. Note that all the model runs have selected 1000 m as the radius of the model domain. To conduct a quick model run to reproduce the output, enter the command "./SUEWS_V2020b" in the model run folder (e.g. <em>VegetationSensitivity\case-base\</em>) under a Linux environment (e.g., Ubuntu).<br> 3.4. <em>RadiusSensitivity</em> is a folder including four SUEWS runs to test the model sensitivity to the size of the model domain, covering circles with a 500 m, 750 m, 1000 m, 1500 m radius, respectively. Note that the vegetation-related parameters are the same as the model run <em>VegetationSensitivity\case-gs_LAI</em>.</p> <p> 3.5. read_me.txt is a note on how to reproduce the SUEWS cases shown in the manuscript.</p> <p><strong>4. Fig&Statistics</strong><br> This folder includes Python scripts and data to reproduce a portion of the figures and statistics in the manuscript quickly. They are sorted into the following sub-folders:<br> 4.1 <em>validation_WFDE5</em>,<br> 4.2 <em>evaluation_radiation</em>,<br> 4.3 <em>evaluation_turbulent</em>,<br> 4.4 <em>accumulated_Fc</em>,<br> 4.5 <em>Fc_component_MDC</em>.</p>
Fast Calorimeter Simulation Challenge 2022 - Dataset 1
<p>This is dataset 1 of the “Fast Calorimeter Simulation Challenge 2022”. It is based on the ATLAS GEANT4 open datasets that were published <a href="http://opendata-qa.cern.ch/record/15012">here</a>. There are four files, two for photons and two for charged pions. Each dataset contains the voxelised shower information obtained from single particles produced at the calorimeter surface in the η range (0.2-0.25) and simulated in the ATLAS detector. Each file contains "incident_energies" of shape (num_showers, 1) and "showers" of shape (num_showers, num_voxels). There are 15 incident energies from 256 MeV up to 4 TeV produced in powers of two. 10k events are available in each sample with the exception of those at higher energies that have a lower statistics. These samples were used to train the corresponding two GANs presented in the AtlFast3 paper <a href="https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/SIMU-2018-04/">SIMU-2018-04</a> and in the FastCaloGAN note <a href="https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-SOFT-PUB-2020-006/">ATL-SOFT-PUB-2020-006</a>. The number of radial and angular bins varies from layer to layer and is also different for photons and pions, resulting in 368 voxels for photons and 533 for pions.</p> <p>dataset_1_photons_1.hdf5 and dataset_1_pions_1.hdf5 should be used for training, dataset_1_photons_2.hdf5 and dataset_1_pions_2.hdf5 for evaluation.</p> <p>More details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at <a href="https://calochallenge.github.io/homepage/">https://calochallenge.github.io/homepage/</a></p>
Multicenter dataset of simulated neuroimaging features - quadratic relationship with age
<p>Let <span class="math-tex">\(y_{ijf}\)</span> be the one-dimensional array of the simulated feature <span class="math-tex">\(f\)</span>, for the single-center <span class="math-tex">\(i\)</span>, and participant <span class="math-tex">\(j\)</span>, for a total of <span class="math-tex">\(k\)</span> single-center datasets,<span class="math-tex">\(n_i\)</span> participants for each center, and <span class="math-tex">\(V\)</span> features. We simulated MRI-derived cortical thickness (CT) and fractal dimension (FD) data for <span class="math-tex">\(k = 3, 10, 36\)</span> single-centers. Each single-center dataset provided the same number of participants (i.e., <span class="math-tex">\(n_i = n\)</span>), with <span class="math-tex">\(n\)</span> assuming the values <span class="math-tex">\(25, 50, 100, 250\)</span>. Totally, we did 24 experiments, i.e., we simulated 24 different multicenter datasets (12 for the CT features and 12 for the FD measures).</p> <p>Each <span class="math-tex">\(y_{ijf}\)</span> was generated using the model proposed by Johnson and colleagues [1] and recently used for neuroimaging features’ simulation by Chen and collaborators [2]:</p> <p><span class="math-tex">\(y_{ijf} = \alpha_f + \beta_{f1}x_{ij} + \beta_{f2}x_{ij}^2 + \gamma_{if} + \delta_{if}\epsilon_{ijf}\)</span></p> <p>where <span class="math-tex">\(\alpha_f\)</span> is the average value of the feature <span class="math-tex">\(f\)</span> in the single-center ICBM dataset [3], <span class="math-tex">\(\beta_{f1}\)</span>and <span class="math-tex">\(\beta_{f2}\)</span> are the linear and quadratic effects of the age on the feature <span class="math-tex">\(f\)</span>, respectively, and <span class="math-tex">\(x_{ij}\)</span> is a simulated age variable drawn from a uniform distribution <span class="math-tex">\(X \sim uniform([20, 90])\)</span>. The mean site effect <span class="math-tex">\(\gamma_{if}\)</span> was drawn from a normal distribution with zero mean and standard deviation equal to 0.1, while the variance site effect <span class="math-tex">\(\delta_{if}\)</span> was drawn from a center-specific inverse gamma distribution with chosen parameters. For our simulations, we chose to distinguish the site-specific location factors by assuming independent and identically distributed (i.i.d.) normal distributions and scaling factors using the parameters described as follows. We set the value of the inverse gamma shape, for each center, as {46, 51, 56}, respectively, when k = 3, as {40, 42, .., 58} when k = 10, and as {10, 12, .., 40, 41, .., 50, .., 70} when k = 36. In all cases, the inverse gamma scale was set to 50.</p> <p>Each CSV file contains the following columns:</p> <ul> <li>SITE: simulated imaging site label (i.e., a, b, c, ..., j)</li> <li>age: each subject's simulated age, expressed in years</li> <li>cortex_CT (FD): CT (or FD) of the cerebral cortical gray matter (GM).</li> <li>lh_cortex_CT (FD), rh_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM.</li> <li>lh_frontal_cortex_CT (FD), rh_frontal_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the frontal lobe.</li> <li>lh_temporal_cortex_CT (FD), rh_temporal_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the temporal lobe.</li> <li>lh_parietal_cortex_CT (FD), rh_parietal_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the parietal lobe.</li> <li>lh_occipital_cortex_CT (FD), rh_occipital_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the occipital lobe.</li> </ul> <p><em>References</em></p> <p>[1] Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. <em>Biostat. Oxf. Engl.</em> <strong>8</strong>, 118–127 (2007).</p> <p>[2] Chen, A. A. <em>et al.</em> Mitigating site effects in covariance for machine learning in neuroimaging data. <em>Hum. Brain Mapp.</em> <strong>43</strong>, 1179–1195 (2022).</p> <p>[3] 1000 Functional Connectomes Project (FCP) – <a href="http://fcon_1000.projects.nitrc.org/fcpClassic/FcpTable.html">ICBM dataset</a>.</p>
Dataset of sound field simulations above finite absorbers
<p>The authors' documentation on the training, validation, and testing datasets in their paper "<em>Sound absorption estimation of finite porous samples with deep residual learning</em>."</p> <p>The sound fields are generated with a simplified boundary element method (BEM) of a baffled porous layer on a rigid backing using the Delany–Bazley–Miki model. Further information on the contents of this database can be found in <em>documentation.pdf</em>.</p> <p>More details on the models and reproduction of results of the paper using this database can be found in the GitHub repo: <a href="https://github.com/eliaszea/finite-absorber-ML">https://github.com/eliaszea/finite-absorber-ML</a>. </p>
MARitime SIMulated (MARSIM) Dataset for GPS Spoofing Detection
<p>MARitime SIMulated (MARSIM) is a comprehensive and labeled dataset created to test and evaluate NMEA-based anomaly detection approaches related to GNSS/GPS spoofing attacks in the maritime context, e.g. <a href="https://github.com/fkie-cad/mana">MANA</a>. It consists of multiple scenarios (benign and attacked) with a duration of 120 s, provided as network traffic recordings, i.e., in packet capture (pcap) format. Each recording contains the NMEA 0183 sentences transmitted within the network of a single vessel heading north from a fixed point with constant velocity. The recordings are reduced to two PNT transmitting devices, namely the pair of GPS receivers, which are placed at a fixed distance of 4 m from each other.</p> <p>Working with the provided packet captures can be facilitated by using our <a href="https://github.com/fkie-cad/maritime-dissector">Wireshark dissector</a>.</p> <p>For more information, please see Spravil J, Hemminghaus C, von Rechenberg M, Padilla E, Bauer J. Detecting Maritime GPS Spoofing Attacks Based on NMEA Sentence Integrity Monitoring. Journal of Marine Science and Engineering. 2023; 11(5):928. DOI: <a href="https://doi.org/10.3390/jmse11050928">10.3390/jmse11050928</a></p>
Direct Numeric Simulation dataset archives
<p>Cloud droplets are analyzed using DNS data. Let's first discuss the many types of cloud representation and the cloud's structure before getting into the data. Later, the World Meteorological Organization adopted this classification, and eleven cloud genera are now recognized according to height and appearance. The middle clouds in the center, low clouds in the bottom panel, and high clouds in the top panel. We used simulated data of cumulus-type low clouds. Fair weather clouds that resemble white cotton balls are present. Clouds have a number of effects on the atmosphere, including the hydrological cycle and the radiation imbalance.</p>
Dataset for "Active buckling of pressurized spherical shells : Monte Carlo simulation"
<p>We study the buckling of pressurized spherical shells by <br> Monte Carlo simulations in which the<br> detailed balance is explicitly broken -- thereby driving the shell<br> active, out of thermal equilibrium.<br> Such a shell typically has either higher (active) or<br> lower (quiescent) fluctuations<br> compared to one in thermal equilibrium depending on how the detailed balance<br> is broken.<br> We show that, for the same set of elastic parameters, a shell<br> that is not buckled in thermal equilibrium can be buckled if turned<br> active. Similarly a shell that is buckled in thermal equilibrium<br> can unbuckle if turned quiescent.<br> Based on this result, we suggest that it is possible to experimentally design<br> microscopic elastic shells whose buckling can be optically controlled.</p>
Simulated nanoCT Datasets
<p>Simulated parallel-beam and fan-beam nano-CT datasets with per-angle random phantom shifts and rotations.</p> <p>The dataset generation is described in the paper <a href="https://arxiv.org/abs/2307.10474">Learning-based approaches for reconstructions with inexact operators in nanoCT applications</a>.</p> <p>Code to use the data is available at <a href="https://gitlab.informatik.uni-bremen.de/inn4ip/cond-inn4nanoct">https://gitlab.informatik.uni-bremen.de/inn4ip/cond-inn4nanoct</a> (in particular the module src/learned_reco/data.py).</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.