Find research datasets worth reusing

opencc-by-4.0Sep 2020View details →

Synthetic COVID-19 Case Reporting Data Generated from an Agent-Based Simulation Model

This is a synthetic case reporting data set for the SARS-CoV-2 epidemic in Austria. The data set statistically reproduces and synthetically augments data on reported cases and was generated with an agent-based simulation model. References to descriptions of the model and the parameterization used to generate the data set is included in the attached PDF file. The data format is described in the README file.

opencc-by-4.0Apr 2024View details →

Synthetic Indoor Climate and Occupancy Data from Office and Meeting Room Simulations

This is the dataset used for the publication "Coddora: CO2-based Occupancy Detection model trained via DOmain RAndomization". The goal is to provide training data for occupancy detection. The dataset contains one million days of data including 10 occupied days for each of 100,000 randomized room models (50,000 rooms considering office activity and 50,000 meeting room activity). Data were generated in EnergyPlus simulations according to the methodology described in the paper. When using the dataset, please cite: <blockquote> Manuel Weber, Farzan Banihashemi, Davor Stjelja, Peter Mandl, Ruben Mayer, and Hans-Arno Jacobsen. 2024. Coddora: CO2-Based Occupancy Detection Model Trained via Domain Randomization. In International Joint Conference on Neural Networks (IJCNN). June 30 - July 5, 2024, Yokohama, Japan. </blockquote> <h2>Dataset Structure</h2> The following files are provided:     1. dataset_office_rooms.h5   (provided as zip file)     2. dataset_meeting_rooms.h5   (provided as zip file)     3. simulated_occupancy_office_rooms.csv     4. simulated_occupancy_meeting_rooms.csv Please use an archiving tool such as 7zip to unzip the hdf5 files. Both hdf5 files contain two datasets with the following keys:     1. "data": contains the simulated indoor climate and occupancy data     2. "metadata": contains the metadata that were used for each simulation The csv files contain the time series of occupancy that were used for the simulations. <h2>Data</h2> Data includes the following fields: Datetime: day of the year (may be relevant due to seasonal differences) and time of the day Zone Air CO2 Concentration: CO2 level in ppm Zone Mean Air Temperature: temperature in °C Zone Air Relative Humidity: relative humidity in % Occupancy: level of occupancy relative to the maximum capacity of the room (in the range [0-1]) Ventilation: fraction of window opening in the range [0.01, 1] SimID: foreign key to reference the room properties the simulation was based on BinaryOccupancy: 0 or 1 denoting absence or presence (for binary classification)   Example row: <table> <tbody> <tr> <th>Datetime</th> <th>Zone Air CO2 Concentration</th> <th>Zone Mean Air Temperature</th> <th>Zone Air Relative Humidity</th> <th>Occupancy</th> <th>Ventilation</th> <th>simID</th> <th>BinaryOccupancy</th> </tr> <tr> <td> 10/09 11:21:00 </td> <td> 1084.5624647371608 </td> <td> 24.545635909907148 </td> <td> 41.18393114737054 </td> <td> 0.7 </td> <td> 0.0 </td> <td>99</td> <td>1</td> </tr> </tbody> </table> <pre> </pre> <h2>Metadata</h2> Metadata includes the following fields. Underscores denote that the field was not selected during randomization but calculated from the other values. width: room width in m length: room length in m height: hoom height in m infiltration:  infiltration per exterior area in m³/m²s outdoor_co2: co2 concentration in the outdoor air in ppm (set to a random value between [300, 500]) orientation: angle between the room's facade orientation and the north direction in degrees maxOccupants: room occupation limit, i.e. the maximum number of occupants _floorArea: floor area in m² (calculated from room dimensions) _volume: room volume in m³ (calculated from room dimensions) _exteriorSurfaceArea: surface area of the facade wall (calculated from room dimensions) _winToFloorRatio: ratio between total window area and floor area (calculated from room model) firstDayUsedOfOccupancySequence: selected starting day in the sequence of occupancy data for rooms with the respective maxOccupants value simID: unique identifier of the simulation to relate between simulation metadata and resulting simulated data   Example row: <table> <tbody> <tr> <th>width</th> <th>length</th> <th>height</th> <th>infiltration</th> <th>outdoor_co2</th> <th>orientation</th> <th>maxOccupants</th> <th>_floorArea</th> <th>_volume</th> <th>_exteriorSurfaceArea</th> <th>_winToFloorRatio</th> <th>firstDayOfUsedOccupancySequence</th> <th>simID</th> </tr> <tr> <td>5.481</td> <td>5.190</td> <td>3.264</td> <td>0.000214</td> <td>438.0</td> <td>316.0</td> <td>4.0</td> <td>28.446</td> <td>92.849</td> <td>16.940</td> <td>0.216</td> <td>192</td> <td>0</td> </tr> </tbody> </table>   <h2>Occupancy Data</h2> The occupancy data provided through the separate csv files contain the data from the upfront occupancy simulations that the climate simulation was based on. For each level of considered room occupancy limit (maxOccupants), the datasets provide minute values of occupancy throughout 1000 days. Datetime, Date, Timestamp: fictive time of simulated occupancy record (sequences are in 1-minute resolution) Occupants: number of present occupants Occupancy: binary occupancy state (0=unoccupied, 1=occupied) WindowState: binary state of ventilation (0=windows closed, 1=room is ventilated) maxOccupants: maximum number of occupants considered for the simulated sequence WindowOpeningFraction: fractional extent to which windows are opened, within the interval [0.01, 1] Example row: <table> <tbody> <tr> <th>Datetime</th> <th>Date</th> <th>Timestamp</th> <th>Occupants</th> <th>Occupancy</th> <th>WindowState</th> <th>maxOccupants</th> <th>WindowOpeningFraction</th> </tr> <tr> <td>2023-01-01 00:00:00</td> <td>2023-01-01</td> <td>1.672531e+09</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> <td>0.0</td> </tr> </tbody> </table>

opencc-by-4.0Dec 2022View details →

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

Description 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. The CSV contains the following fields: * edge_id: unique identifier of the edge devices * edge_lat: latitude coordinate of the edge device * edge_lon: longitude coordinate of the edge device * time: simulation step of the observation * vehicle_id: unique identifier of the vehicle * vehicle_lat: latitude coordinate of the vehicle * vehicle_lon: longitude coordinate of the vehicle * distance: geodesic distance in meters from the vehicle and the edge device

opencc-by-4.0Nov 2022View details →

MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs obtained through electrophysiological simulations

Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess precisely known ground truth labels of the underlying disease (model parameterization) and can be employed for validation of machine learning ECG analysis tools in addition to clinical signals. Recently, synthetic ECG signals were used to enrich sparse clinical data for machine learning or even replace them completely during training leading to good performance on real-world clinical test data.      We thus generated a large synthetic database comprising a total of 16,900 12 lead ECGs based on multi-scale electrophysiological simulations equally distributed into 1 normal healthy control and 7 pathology classes. The pathological case of myocardial infraction had 6 sub-classes.  A comparison of extracted timing and amplitude features between the virtual cohort and a large publicly available clinical ECG database demonstrated that the synthetic signals represent clinical ECGs for healthy and pathological subpopulations with high fidelity. The novel dataset of simulated ECG signals is split into training, validation and test data folds for development of novel machine learning algorithms and their objective assessment.  This folder WP2_largeDataset_Noise contains the 12 lead ECGs of 10 seconds length. Each ECG is stored in a separate CSV file with one row per lead (lead order: I, II, III, aVR, aVL, aVF, V1-V6) and one sample per column (sampling rate: 500Hz). Data are split by pathologies (avblock = AV block, lbbb = left bundle branch block, rbbb = right bundle branch block, sinus = normal sinus rhythm, lae = left atrial enlargement, fam = fibrotic atrial cardiomyopathy, iab = interatrial conduction block, mi = myocardial infarction). MI data are further split into subclasses depending on the occlusion site (LAD, LCX, RCA) and transmurality (0.3 or 1.0). Each pathology subclass contains training, validation and testing data (~ 70/15/15 split). Training, validation and testing datasets were defined according to the model with which QRST complexes were simulated, i.e., ECGs calculated with the same anatomical model but different electrophysiological parameters are only present in one of the test, validation and training datasets but never in multiple. Each subfolder also contains a "siginfo.csv" file specifying the respective simulation run for the P wave and the QRST segment that was used to synthesize the 10 second ECG segment. Each signal is available in three variations: <ul> <li>*_raw.csv contains the synthesized ECG without added noise and without filtering</li> <li>*_noise.csv contains the synthesized ECG (unfiltered) with superimposed noise</li> <li>*_filtered.csv contains the filtered synthesized ECG (fiter settings: highpass cutoff frequency 0.5Hz, lowpass cutoff frequency 150Hz, butterworth filters of order 3).</li> </ul> The folder WP2_largeDataset_ParameterFiles contains the parameter files used to simulate the 12 lead ECGs. Parameters are split for atrial and ventricular simulations, which were run independently from one another.  See <a href="https://doi.org/10.48550/arXiv.2211.15997">Gillette*, Gsell*, Nagel* et al. "MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs obtained through electrophysiological simulations"</a> for a description of the model parameters.

opencc-by-4.0Oct 2023View details →

Synthetic in-situ T/S data over 1993-2018 from a NEMO-based simulation of the IMHOTEP project

"Synthetic observations" of in-situ Temperature and Salinity profiles as a function of depth have been extracted online during the  production of the global, NEMO-based experiment ** IMHOTEP-GAIc**, at every single time and location (in x,y,z dimensions) where a true in-situ profile exists in the ENACT-4 database (Good et al 2013) over the simulation period: 1980-2018. This global ocean/sea-ice/iceberg simulation uses the NEMO model, and has a horizontal resolution of 1/4°. The atmospheric forcing applied at the surface is based on the JRA reanalysis (Kobayashi et al., 2015) and varies over the full range of time-scales from 6 hours to multi-decadal. The freshwater runoff forcing applied to the experiment is fully-variable (daily to multi-decadal)  based on the ISBA-CTRIP hydrographic reanalysis for rivers (Decharme et al., 2019) and from altimeter data and regional GCM simulations for the liquid and solid discharges from the Greenland ice-sheet (Mouginot et al 2019). These runoffs are only climatological around Antarctica. The synthetic  in-situ T/S dataset from the model is available over the period 1993-2018.See the README file for more information. And online documentation is also available here: https://doc-imhotep.readthedocs.io/en/latest/6-Synthetic-Obs.html

dryad40/100

Community Land Model synthetic meteorology simulation model output

Open the record for dataset details and reuse information.

publicNov 2025View details →

opencc-by-4.0Jan 2020View details →

Synthetic dataset for the PFC3D simulation of fragmentable blocks sliding experiments

Dataset 1 - The raw data of numerical simulations, including deposit parameters of all simulations, fragments size distribution of all simulations, impact force of D1, breakage bond number of D1, velocity of D1. Simulation code - all the PFC3D simulation code for the fragmentable blocks sliding experiments.

opencc-zeroMay 2024View details →

Synthetic Microstructure Evolution in SLM Processes: 2D Slices from Potts Model Simulations in SPPARKS

The dataset comprises 2D slices of synthetic microstructures, which were simulated under various Selective Laser Melting (SLM) processing conditions. We employed the Potts kinetic Monte Carlo model, which is integrated within the open-source simulation tool, <a href="https://spparks.github.io/">SPPARKS</a>.  For the base microstructural information, we utilized a 3D Electron Backscatter Diffraction (EBSD) dataset—specifically using Inconel 100 for simplicity—to generate a representative volume element (<a href="https://www.mdpi.com/2073-4352/10/10/944">RVE</a>). This RVE serves as the initial structure from which we evolve the microstructure across different processing conditions that are pertinent to SLM techniques. The description of the parameters is provided in the <a href="https://spparks.github.io/doc/app_am_ellipsoid.html">SPPARKS Docs</a>. The outcome is a dataset of 2D slices that reflect the potential microstructural variations resulting from specific manufacturing scenarios. Please follow the instructions in the <a href="https://github.com/sara-nl/spparks_hpc">SPPARKS_HPC Repo</a> to reproduce the results.

opencc-by-4.0May 2024View details →

Synthetic Emission Line Catalog and High-Resolution SEDs for SPHEREx Galaxy Simulations

Overview: This repository contains the synthetic galaxy models derived from multi-wavelength photometry in the COSMOS field (166k galaxies over 1.27 sq. deg., 18 < i < 25) and from the GAMA survey (44k galaxies over 217 sq. deg., i < 18). These encompass a representative sample of galaxies SPHEREx will observe, and should be applicable for other surveys as well. <ul> <li>Synthetic emission line strengths in the rest-frame optical/near-infrared (H-alpha, H-beta, Paschen-alpha, [OIII], [OII], [NII], [SII])</li> <li>High-resolution SEDs (0.1 - 8 micron), which are produced by 1) fitting a library of 160 galaxy templates to multi-band photometry and 2) inserting emission lines with strengths predicted by an empirical model that depends on galaxy type, redshift and stellar mass.</li> </ul> These products were made using the modeling framework Conditional LIne Painting on Synthetic Spectra (CLIPonSS), the details of which are summarized in Feder+2023b (<a href="https://arxiv.org/abs/2312.04636">arXiv link here</a>). The synthetic emission line catalog is validated against a variety of LF measurements, line ratio trends and direct line comparisons.  Data description: High-resolution SEDs: The SEDs are stored in .FITS files, for which each galaxy SED has its own Header Data Unit (HDU). This format was chosen due to the fact that the wavelength sampling for the 160 galaxy empirical and model-based templates varies, i.e., we do not perform any interpolation onto a homogenized wavelength grid. In cases where emission lines are added but the continuum template resolution is coarse, the resolution is increased in the vicinity of the emission line(s) to adequately sample the line profiles. Each galaxy's HDU is indexed by its Farmer ID (for COSMOS) or alternatively its uberID (GAMA). The COSMOS SEDs are split into three files for relative ease of access, while the GAMA sources are all in one file. Emission line catalogs: The emission line catalogs are stored in .csv format with the following information: Tractor_ID (or uberID) [integer]: Unique identifier for each source RA/DEC [float, in degrees]: Celestial coordinates imag [AB]: i-band magnitude mass_best [float]: log-stellar masses estimated through SED fitting process ebv [float]: Galaxy intrinsic dust extinction (ranging from ebv=0 to 1) redshift [float]: Best-fit redshift from COSMOS2020 (GAMA) catalogs bfit_tid [integer]: Best fit template ID from library of templates dustlaw [integer]: Best fit dust law (1=Prevot, 2=Calzetti, 3=Seaton, 4=Allen, 5=Fitzpatrick) L_{line} [float, erg s-1]: Line luminosities from empirical model F_{line} [float, erg cm-2 s-1]: Line fluxes from empirical model ew_ha [float, Angstrom]: Equivalent width of H-alpha from empirical model Fitting templates: We include the 31 model-based templates and 129 empirical templates from Brown+2014. In the Brown templates we have removed any relevant lines that were directly measured in the initial spectra, however we do not fit for/remove PAH features. These can serve as the basis for other empirically based galaxy simulations with different emission line prescriptions. Any questions regarding the use of these data products or related issues can be directed to Richard Feder (link to <a href="https://richardfeder.github.io/">personal website</a>).

opencc-by-4.0Oct 2022View details →

Synthetic velocity profiles for simulations of blood flow in the aorta

Synthetic dataset of aortic velocity profiles, suitable to be used for numerical simulations of blood flow. Please refer to the profile number ID when using it.

opencc-by-4.0Sep 2023View details →

Synthetic along-track altimetry data over 1993-2018 from a NEMO-based simulation of the IMHOTEP project

"Synthetic observations" of along-track SSH have been extracted online during the  production of the global, NEMO-based experiment ** IMHOTEP-GAIc**, at every single time and locations where a true SLA observation exists in the AVISO database for the along-track altimetry from the TOPEX, Jason-1, Jason-2 and Jason-3 satellite continuous series over the period 1993-2018. This global ocean/sea-ice/iceberg simulation uses the NEMO model, and has a horizontal resolution of 1/4°. The atmospheric forcing applied at the surface is based on the JRA reanalysis (Kobayashi et al., 2015) and varies over the full range of time-scales from 6 hours to multi-decadal. The freshwater runoff forcing applied to the experiment is fully-variable (daily to multi-decadal)  based on the ISBA hydrographic reanalysis for rivers (Decharme et al., 2019) and from altimeter data and regional GCM simulations for the liquid and solid discharges from the Greenland ice-sheet (Mouginot et al 2019). These runoffs are only climatological around Antarctica. This synthetic along-track SSH dataset from the model is available over the altimetry period (1993-2018). It is provided there along with a time-mean model SSH (gridded model field) over the same period that can be used as a proxy for mean dynamic topography ("MDT").See the README file for more information. And online documentation is also available here: https://doc-imhotep.readthedocs.io/en/latest/6-Synthetic-Obs.html

opencc-by-4.0Apr 2020View details →

Data for synthetic simulation in "Topography curvature effects in shallow-water models"

Synthetic topographies and initial masses used in "Topography curvature effects in shallow-water models"

opencc-by-4.0Nov 2023View details →

Synthetic Observation of Column Density Maps Derived from Cloud Factory Data via Radiative Transfer Simulations

This dataset contains synthetic observation data derived from Cloud Factory data, processed through radiative transfer simulations using POLARIS. The data focuses on several star-forming regions, providing valuable insights into the evolution of filaments within these areas. The dataset is presented at three different resolutions: high (0.25 pc/pixel), medium (0.5 pc/pixel), and low (1 pc/pixel), to cater to various analytical needs. Files labeled as RA, RB, and RC correspond to three distinct regions under study. Additionally, each file is tagged with a 's' followed by a number, indicating the snapshot sequence.

opencc-by-4.0Dec 2023View details →

Molecular Dynamics Simulation Dataset for "Hydrophobic Mismatch Drives Self-Organization of Designer Proteins into Synthetic Membranes"

This repository contains molecular dynamics (MD) simulation data from the study on the self-organization of designer proteins in synthetic membranes. The data includes simulations for different single lipid compositions (DOPC, DPPC, DYPC) denoted as [lipid]-PL* where PL stands for the different TMD constructs. Multi component simulation are named accordingly. The repository provides initial (eqi.gro) and final (prod.gro) coordinates for each simulation. The 'cmd' file in each directory outlines the assembly process of each simulation, and the 'mdp' folder contains all input files for the simulations.

opencc-by-4.0Nov 2023View details →

Animation, MESA inlists, and post-processing package for "Is Betelgeuse really rotating? Synthetic ALMA observations of large-scale convection in 3D simulations of Red Supergiants"

Videos, post-processing package for mock ALMA observations, MESA (r23.05.1 with mesasdk-x86_64-linux-23.7.3) inlists and history files, Jupyter notebook and data to reproduce figures of the paper "Is Betelgeuse really rotating? Synthetic ALMA observations of large-scale convection in 3D simulations of Red Supergiants".