Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
567
datasets available to search
ShareScore release 0.9.0
Dataset results
567 results for “Simulated datasets”
Simulated NGS read datasets for novel human virus prediction
<p>This repository contains simulated Illumina read datasets for novel human virus prediction and associated metadata extracted from the Virus Host Database (<a href="https://www.genome.jp/virushostdb/">https://www.genome.jp/virushostdb/</a>). The reads are 250bp long and were simulated with Mason (<a href="https://www.seqan.de/apps/mason/">https://www.seqan.de/apps/mason/</a>) from genomes downloaded from NCBI. The training-validation-test split was done on whole viral sequences to ensure "novelty" of validation and test viruses. The training sets contain 10 million reads per class, validation sets - 1.25 million reads per class, and test sets - 1.25 million paired reads per class. The negative class sets contain reads simulated from chordate-infecting ("cho"), metazoan-infecting ("met"), eukariote-infecting ("euk") and all-nonhuman viruses. The positive class contains human-infecting viruses. The stratified dataset ("strat") contains an equal number of reads from "cho", "met but not cho", "euk but not met" and "all but not euk". </p> <p>Species-level datasets ("humspec", "allspec" and "chospec", with the corresponding fasta and *_species.rds files) are constructed analogously, but ensuring that all viruses of a given species were assigned to either training, val or test set. This is a stricter setting modelling a "novel viral species" scenario while reflecting within-species phenotype diversity.</p> <p>blast_hits.gz contains blast hits of human virome reads form Moustafa et al., 2017 (https://doi.org/10.1371/journal.ppat.1006292) blasted against our training database (see paper for details). In the second column you can find the matched label and the accession number of the matched reference. blast_labels_complete.gz contains extracted labels for all virome reads, including those without any matches. Note: one of the read headers (>3c8ac47039d32b11c8fe23f588e444e9) from Moustafa et al. is slightly corrupted with null characters. You can remove them with sed 's/\x0//g' or equivalent.</p> <p> </p>
Source code and simulation datasets for the paper 'Migration and accumulation of bacteria with chemotaxis and chemokinesis'
<p>Source code and simulation data files for the paper 'Migration and accumulation of bacteria with chemotaxis and chemokinesis'</p> <p>The zipped folder 'SimulationCode.zip' contains Matlab source code files which have been used to generate the simulations in the paper: fixed attractant gradient, axisymmetric agar-plate like migration and transient attractant source. The file 'main.m' controls all simulations run with initial conditions specified in the files with suffix '_ic'. The file 'PDEsolver.m' specifies the finite difference solver used so solve the model PDEs, while 'FourthOrderFD.m' creates the matrices that are required<br> for the finite differnce solver. The chosen scheme is of fourth order accuracy.</p> <p>The zipped folder 'SimulationData.zip' contains Matlab data files generated by running the simulation code. The results correspont to the figures in the paper.</p>
Dataset - The influence of the crowding assumptions in biofilm simulations
<p><strong>Dataset simulated for the manuscript "The influence of the crowding assumptions in biofilm simulations" by Angeles-Martinez and Hatzimanikatis.</strong></p>
Accompanying dataset for: Predicting Species Emergence in Simulated Complex Pre-Biotic Networks
<p>This is the accompanying data and code for the publication [Markovitch & Krasnogor: Predicting Species Emergence in Simulated Complex Pre-Biotic Networks] containing the full set of 10,000 lognormal networks studied, their network communities and the compotype species observed during simulations with the GARD model. Details are given in the aforementioned paper. Please see also: http://ico2s.org/</p> <p>This work was funded by the UK's Engineering and Physical Sciences Research Council (EPSRC) under projects (EP/J004111/2) "Towards a Universal Biological-Cell Operating System (AUdACiOuS)" and (EP/N031962/1) "Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies"</p> <p> </p>
Generalized Approximate Message Passing Practical 2D Phase Transition Simulations Dataset
<p>This deposition contains the results from a simulation of phase transitions for various practical 2D problem suites when using the Generalised Approximate Message Passing (GAMP) reconstruction algorithm.</p> <p>The deposition consists of:</p> <ol> <li>Five HDF5 databases containing the results from the phase transition simulations (<em>gamp_practical_2d_phase_transitions_ID_[0-4]_of_5.hdf5</em>).</li> <li>The Python script which was used to create the databases (<em>gamp_practical_2d_phase_transitions.py</em>).</li> <li>A Python module with tools needed to run the simulations (<em>gamp_pt_tools.py</em>).</li> <li>MD5 and SHA256 checksums of the databases and Python scripts (<em>gamp_practical_2d_phase_transitions.MD5SUMS / gamp_practical_2d_phase_transitions.SHA256SUMS</em>).</li> </ol> <p>The HDF5 databases are licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python scripts are licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p>
Generalized Approximate Message Passing Practical 2D Phase Transition Simulations Dataset 2
<p>This deposition contains the results from a simulation of phase transitions for various practical 2D and 3D problem suites when using the Generalised Approximate Message Passing (GAMP) reconstruction algorithm.</p> <p>The deposition consists of:</p> <ol> <li>Five HDF5 databases containing the results from the phase transition simulations (<em>gamp_practical_2d_phase_transitions_ID_[0-4]_of_5.hdf5</em>).</li> <li>The Python script which was used to create the databases (<em>gamp_practical_2d_phase_transitions.py</em>).</li> <li>A Python module with tools needed to run the simulations (<em>gamp_pt_tools.py</em>).</li> <li>MD5 and SHA256 checksums of the databases and Python scripts (<em>gamp_practical_2d_phase_transitions.MD5SUMS / gamp_practical_2d_phase_transitions.SHA256SUMS</em>).</li> </ol> <p>The HDF5 databases are licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python scripts are licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p>
Model and observational datasets used for evaluating CHASER simulated formaldehyde (HCHO) abundances in 2019 and 2020.
<p>The dataset entails the model simulation results and the observational data (satellite, aircraft, and ground-based MAX-DOAS) used for the study titled " Evaluating CHASER V.40 global formaldehyde (HCHO) simulations using satellite aircraft and ground-based remote sensing observations", submitted for peer-review in JGR: Atmospheres. </p>
Dataset to "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " by Zmijewski, Dziekan & Pawlowska
<p>The archive contains datasets, run scripts, time series and plotting scripts used when preparing the paper: P. Zmijewski, P. Dziekan and H. Pawlowska "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " submitted to Geoscientific Model Development in March 2023.</p>
An urban traffic dataset composed of visible images and their semantic segmentation generated by the CARLA simulator
<p><strong>If you use this dataset please cite this paper: Rosende, S.B.; Gavilán, D.S.J.; Fernández-Andrés, J.; Sánchez-Soriano, J. An Urban Traffic Dataset Composed of Visible Images and Their Semantic Segmentation Generated by the CARLA Simulator. <em>Data</em> 2024, <em>9</em>, 4. <a href="https://doi.org/10.3390/data9010004">https://doi.org/10.3390/data9010004</a></strong></p> <p>A dataset of aerial urban traffic images and their semantic segmentation is presented to be used to train computer vision algorithms, among which those based on convolutional neural networks stand out. The images have been generated using the CARLA simulator (but would be like those that could be obtained with fixed aerial cameras or by using AUVs) in the field of intelligent transportation management. The presented dataset is available and accessible to improve the performance of vision and road traffic management systems, especially for the detection of incorrect or dangerous maneuvers.</p>
Dataset of corrosion parameters for rebar in simulated pore solution and mortar
<p>We are pleased to announce the initial release of our dataset, "Corrosion Parameters for Rebar in Simulated Pore Solution and Mortar." This comprehensive dataset provides in-depth data on the corrosion potential, corrosion current density, and Tafel slopes of rebar subjected to various levels of chloride contamination and carbonation treatment in simulated environments.</p> <p>This release is aimed at researchers, academicians, and practitioners in civil engineering and materials science, providing valuable data for further research and practical application in understanding and mitigating rebar corrosion.</p> <p>Your feedback and contributions to this dataset are welcomed and appreciated!</p>
Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasets
<p>This archive contains the empirical data analysed in the paper 'Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasets' by Touzalin et al. (https://doi.org/10.24072/pci.ecology.100416). The dataset is provided as a .Rdata file ('TLoss_GMdata.Rdata'), and full description of the content is provided in the file 'Readme_TLdata.csv'. All additional details are available in the main text (https://doi.org/10.24072/pci.ecology.100416) or in the supporting information (https://doi.org/10.5281/zenodo.10204538).</p>
3D simulations of TRAPPIST-1e with varying CO2, CH4 and haze profiles [dataset]
<p>Dataset of 3D simulations of TRAPPIST-1e with varying CO2, CH4 and haze profiles</p><p> </p><p>Using a 3D General Circulation Model, the Unified Model, we present results from simulations of a tidally locked TRAPPIST-1e with varying CO2 and CH4 gas concentrations, and their corresponding prescribed spherical haze profiles. Our results show that the presence of CO2 leads to a warmer atmosphere globally due to its greenhouse effect, with the increase of surface temperature on the day side surface up to ∼14.1 K, and on the night side up to ∼21.2 K. The presence of CH4 can lead to a peak in the change of surface temperature on the day side due to the balance of tropospheric warming and stratospheric cooling. A thin layer of haze, formed when CH4/CO2 = 0.1, leads to a day side warming of ∼4.9 K due to a change in the water vapour and cloud distribution. The haze reaches an optical threshold thickness when CH4/CO2 ∼0.4 beyond which the day side mean surface temperature does not vary much. The planet is more favourable to habitability (surface temperature above 273.15 K) when pCO2 is high, pCH4 is low and the haze layer is thin. The effect of CO2, CH4 and haze on the day side is similar to that for a rapidly-rotating planet. On the contrary, their effect on the night side depends on the wind structure and the wind speed in the simulation.</p>
Simulation dataset and plotting scripts used for journal article "Impact of acidity and surface modulated acid dissociation on cloud response to organic aerosol" by Sengupta et al. (2024)
<p>Simulation data underlying all figures presented in "Impact of acidity and surface modulated acid dissociation on cloud response to organic aerosol" by Sengupta et al. (2024). DOI: 10.5194/acp-24-1467-2024</p> <p>The data for each figure and the plotting scripts are included in a zip file labelled by the figure number as presented in the paper and accompanying supplement.</p>
Validation and Benchmark Dataset for Discrete Element Method Simulations
<p>Verification and Benchmark Dataset for Discrete Element Method Simulations<br>v3 (05/02/2024)<br>Authors: Jose Salomon, Fernando Patino-Ramirez, Catherine O'Sullivan<br>https://doi.org/10.5281/zenodo.10160309<br>Contact: jjs19@ic.ac.uk<br>--------------------------------------------------------------------<br>Description of the repository:</p> <p>This repository contains a collection of datafiles and scripts that can be employed to validate and benchmark new or existing DEM codes. <br>Two validation cases/folders are considered "FCC_packing" and "Rolling_clump". The benchmark dataset is provided in the "Toyoura_sh" folder.<br>All datafiles and scripts are in the corresponding *.zip files. A detailed description of all cases can be found in the related article.</p> <p>In each of these folders, two sub-folders can be found: (1)"Data" and (2)"Scripts". These folders contain:</p> <p>1)"Data": contains the datafiles to perform the validation or benchmark. Two types of data/folders can be found here: "Raw" and "Filtered".<br>The "Raw" folder contains raw data only. The "Filtered" data contains the post-processed data employed to generate the plots found in the related article.<br>Plots in the related article can be reproduced by using the MATLAB files found in the corresponding data folder.</p> <p>2)"Scripts": contains the LAMMPS scripts used to generate the data files contained in "Data".<br>Indications about how to run these scripts can be found in the "README.txt" file in each folder.</p> <p>In order to reproduce the simulations of this repository, LAMMPS must be built including the "GRANULAR" and "RIGID" packages. Please check the README.txt files in each folder for details.</p>
Simulation dataset and plotting scripts used for journal article "Surface modulated dissociation of organic aerosol acids and bases in different atmospheric environments" by Sengupta and Prisle (2024)
<p>Simulation data underlying all figures presented in "Surface modulated dissociation of organic aerosol acids and bases in different atmospheric environments" by Sengupta and Prisle (2024) <a href="http://dx.doi.org/10.1080/02786826.2024.2323641" target="_blank" rel="noopener noreferrer">http://dx.doi.org/10.1080/02786826.2024.2323641</a>. </p> <p>The data for each figure and the plotting scripts are included in a zip file labelled by the figure number as presented in the paper and accompanying supplement.</p>
Simulation to optimize the laboratory diagnosis of bacteremia: event times dataset from observational and retrospective studies
<h1>Introduction</h1> <p>Dataset consisting of three .csv files generated from observational and retrospective data analysis for the manuscript titled: Simulation to optimize the laboratory diagnosis of bacteremia, by Gerada et al. Dataset consists of event time-stamps for blood cultures. All identifiers have been stripped. </p> <h1>Dataset summary</h1> <ul> <li>observational_data.csv – time-stamps generated from direct observation of blood culture processing. Note that data is in wide format, so that column names contain the event name. Also, events are not related to a particular specimen, therefore, column lengths are not equal. </li> <li>retrospecitve_audit.csv – time-stamps generated from manualk retrospective audit of laboratory information system. Column `Lab` indicates the incubator. Rows are per specimen. </li> <li>retrospective_data.csv – time-stamps pulled automatically from laboratory information system (without manual curation). Rows are per specimen.</li> </ul>
Dataset - A Python-Based Approach to Sputter Deposition Simulations in Combinatorial Materials Science
<p>This dataset accompanies the publication <em>"A Python-Based Approach to Sputter Deposition Simulations in Combinatorial Materials Science,"</em> which presents and validates pySIMTRA, a Python wrapper for the Monte Carlo-based SIMTRA simulation tool. The dataset includes all measured and simulated data shown in the publication, as well as additional animations visualizing the compositions in the multinary composition space.</p> <p>The dataset contains the compositions for each of the seven materials libraries (in at.%) in the quaternary Ni-Pd-Pt-Ru system. Additionally, it provides the simulated number of particles as outputted by SIMTRA, which serve as the basis for composition estimation. Both the compositional data and the particle counts are supplied in .csv format. To supplement the results, 3D animations of the quaternary compositional spaces are included, showing the comparison between simulated compositions (red dots) and measured compositions (blue dots). These animations offer a more intuitive visualization of the data compared to the static Figures in the publication and are supplied as .gif files.</p> <p>Due to the in-depth analysis of cathode tilt discussed in the paper, the dataset also includes simulation results for the ternary Pd-Pt-Ru library, highlighting the effect of varying the cathode tilt angle. Simulations were conducted for tilt angles of 10°, 9.5°, 9°, and 8.5°.</p>
Datasets and Simulator of the kinSoftChallenge & related article.
<p>Data described in the article:</p> <p><strong> Inferring kinetic rate constants from single-molecule FRET trajectories – a blind benchmark of kinetic analysis tools.</strong></p> <p>- MATLAB simulator scripts & configuration files incl. parameters used in the kinSoftChallenge.</p> <p>- simulated challenge datasets used in the kinSoftChallenge.</p> <p>- experimental challenge datasets (Fig. 2) were kindly provided by Ben Schuler, University of Zurich, CH.</p> <p>- experimental challenge datasets (Fig. 5) were kindly provided by Michael Schlierf, TU Dresden, D.</p>
Aeroelastic simulations of wind turbines affected by leading edge erosion: datasets for multivariate time-series classification
<p>This repository contains data generated and used for classification in the publication:<br> Duthé, G.; Abdallah, I.; Barber, S.; Chatzi, E. Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 7262. https://doi.org/10.3390/en14217262</p> <p>The data is generated via OpenFAST aeroelastic simulations coupled with a Non-Homogeneous Compound Poisson Process for degradation modelling and was used to train a Transformer deep learning model.</p> <p>One degradation run generates 1200 samples (1 sample every 6 days corresponding to a 20 year degradation period). In total 20 degradation runs are made available (20x1200 = 24'000 multivariate time-series samples). This repo can serve to benchmark long multivariate time-series classification algorithms. There are 10 possible classes of erosion severity.</p> <p>Each sample is a multivariate time-series of length 60'000, with the following 4 channels extracted from the simulations for a section at the tip of the blade:</p> <ul> <li>Inflow velocity</li> <li>Angle of attack</li> <li>Lift coefficient</li> <li>Drag coefficient</li> </ul> <p>Please see the publication above for more information as well as the included readme for information about the data and an example of how to load it into to PyTorch.</p> <p> </p>
Dataset for 'Intraocular scatter compensation with spatial light amplitude modulation for improved vision in simulated cataractous eyes'
<p>Dataset for the manuscript entitled: Intraocular scatter compensation with spatial light amplitude modulation for improved vision in simulated cataractous eyes</p> <p>includes:</p> <p>1) PSF from numerical simulations</p> <p>2) CSF measured in subjects</p> <p>3) Michelson contrast from numerical simulations</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.