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567 results for “Simulated datasets”
Dataset: Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction
<p>This dataset contains the data used in the paper</p> <blockquote> <p>de Dumast, P., Sanchez, T., Lajous, H., Bach Cuadra, M. (2023). Simulation-Based Parameter Optimization for Fetal Brain MRI Super-Resolution Reconstruction. MICCAI 2023. LNCS, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_32</p> </blockquote> <p>A preprint can also be found on <a href="https://arxiv.org/abs/2211.14274">arXiv</a>. If you found this dataset useful or used it in your research, please cite this reference.</p> <p>This paper studied the impact of the regularization parameter <span class="math-tex">\(\alpha \)</span> on the super-resolution reconstruction of fetal brain magnetic resonance (MR) images. It used simulated T2-weighted data MR images generated using FaBiAN v2.0, a Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates fast spin echo (FSE) sequences of the developing fetal brain throughout gestation. The dataset contains the raw simulated data, the corresponding ground truths as well as corresponding super-resolution (SR) reconstructions using MIALSRTK and NiftyMIC with varying regularization parameters <span class="math-tex">\(\alpha \)</span>.</p> <p>Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023.</p>
Pre-training with simulated ultrasound images for breast mass segmentation and classification - dataset
<p>Dataset assosiated with the MICCAI Workshop on Data Engineering in Medical Imaging paper: "Pre-training with Simulated Ultrasound Images for Breast Mass Segmentation and Classification"</p>
STAR2D simulation and Hydrogen photodissociation cross sections dataset
<p>This dataset consists of two tables: extreme ultraviolet (XUV) and vacuum ultraviolet (VUV) emission spectra from a laser produced, 1 micron wavelength plasma. The emission spectra was calculated from STAR-2D simulation results. </p><p>The data consists of wavelength-dependent spectra at varying drive laser intensities from 1E11 to 1E9 W/cm2. More information on the STAR-2D code can be cited here: A. Sunahara, A. Sasaki, and K. Nishihara, J. Phys.: Conf. Ser. 112, 042048 (2008). </p><p>The second dataset consists of hydrogen photodissociation and photoionization cross section data from Heays et al: A. N. Heays, A. D. Bosman, and E. F. van Dishoeck, Astronomy &Astrophysics 602, A105 (2017).</p>
Simulated calibration dataset for 4D scanning transmission electron microscopy
<p>4D-STEM data frequently requires a number of calibrations in order to make accurate measurement: for instance, in various cases, it can be essential to measure and correct for diffraction shifts, account for ellipticity in the diffraction patterns, or determine the rotational offset between the real and diffraction planes.</p> <p>We've prepared a simulated 4D-STEM dataset which includes diffraction shifting, elliptical distortion, and an r-space/k-space rotational offset. Two HDF5 files each include the simulated data for two different electron probes: a standard probe, using a circular probe-forming aperture, and a 'bullseye' probe, using a patterned aperture. Each HDF5 file contains the following data objects:</p> <p>(a) the 'experimental' 4D-STEM scan of a strained single-crystal gold nanoparticle (size: (100,84,250,250) )</p> <p>(b) a 4D-STEM scan of a calibration sample of polycrystalline gold (size: (100,84,250,250) )</p> <p>(c) a stack of diffraction images of the electron probe over vacuum (size: (250,250,20) )</p> <p>(d) a single image of the electron probe over the sample and far from focus, such that the CBED forms a shadow image (size: (512,512) )</p>
Reference Dataset for Benchmarking Organ Doses Derived from Monte Carlo Simulations of CT Exams
<p>This reference dataset contains CT scanner x-ray source characteristics, filtration profile, de-identified patient image data and size characteristics, voxelized patient models, exam characteristics, x-ray tube current data, and organ dose results in tabular form from Monte Carlo (MC) simulations of abdominal/pelvis CT exams of pregnant patients. This dataset can be used for benchmarking MC simulation codes for CT dosimetry.</p>
Datasets For "Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data", Blake et al. (2020), JGR
<p>Datasets and sample Python codes for the 2020 paper <em>"Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data"</em>, by Blake et al., submitted to the Journal of Gephysical Research, Space Physics. </p> <p>Up-to-date Python codes can be found at <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p>The complete SWMF simulation folders (including parameter and log files etc.) can be requested from <a href="https://ccmc.gsfc.nasa.gov/index.php">NASA's Community Coordinated Modeling Center</a>.</p> <p>#########</p> <p><strong>Data/ </strong>contains the following:</p> <p><strong>Data/HIST_DATA.txt </strong>contains the minimum Dst values and calculated maximum extents of the auroral equatorward boundaries for 25 years of INTERMAGNET data (1991-2016). The fourth column is the standard deviation of the calculated auroral boundary in degrees. </p> <p><strong>Data/Boundary_Fits.csv </strong>contains the calculated minimum Dst values, and calculated auroral boundaries using Method 1 and Method 2 (see main paper's ttext), for each of the 15 SWMF simulations. Also included are the uncertainties for each calculation.</p> <p><strong>Data/SWMF_outputs/ </strong>contains 15<strong> </strong>.txt files,<strong> </strong>each of which correspond to an SWMF simulation of the same name given in Table 1 in the main text. These data are for the magnetic longitude, magnetic latitude and maximum calculated <em>E<sub>H</sub> </em>(V/km) for each simulation.</p> <p>#########</p> <p><strong>Codes/ </strong>contains two python scripts, and some sample data. These scripts correspond to Section 2 in the main text:</p> <p>1) <strong>Boundary_Calc.py</strong> calculates the extent of the auroral boundary using magnetic latitudes and maximum calculated <em>E<sub>H</sub></em> values from multiple INTERMAGNET sites. </p> <p>2) <strong>Efield_Calc.py </strong>calculates the E-field for a single INTERMAGNET site using the Quebec 1-D resistivity model.</p> <p>A more detailed description of these codes can be found here: <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p> </p>
Simulated NGS datasets for real-time detection of novel pathogens
<p>Datasets based on the <a href="https://doi.org/10.5281/zenodo.3678563">bacterial</a> and <a href="https://doi.org/10.5281/zenodo.4312525">viral</a> simulated NGS datasets. Fastq files correspond to tests sets of those datasets. Basecall files were generated based on the fastq files with an 8nt simulated barcode between the mates of a read pair. The "rn" datasets containg random length subreads (25-250bp) of the original validation and training reads.</p> <p>The Nanopore datasets were resimulated with <a href="https://github.com/liyu95/DeepSimulator">DeepSimulator 1.5</a> (Li et al., 2020) based on the original datasets (i.e. using the same species composition as the original data). The test Nanopore dataset contains full reads (target average length: 8kb) and the training and validation datasets - 250bp subreads.</p>
Simulated TSCH dataset using different slotframe matrix configurations
<p>The current dataset was obtained by using a specifically developed simulator, to analyze the behavior of a time slotted channel hopping (TSCH) wireless sensor network (WSN), under different operating conditions.</p> <p>The configuration of the simulator, the characteristics of the network, and the generated traffic patterns are reported in [1].<br>Different configurations of the slotframe matrix, which allows slots to be reserved for specific pairs of nodes, are analyzed.</p> <p>File "<code>network_topology.pdf</code>" reports the topology of the simulated wireless network.</p> <p>The four analyzed configurations, which are deeply described in [1], are:</p> <ul> <li><strong>Star</strong>, whose slotframe matrix configuration is reported in the "<code>Star.conf</code>" file.</li> <li><strong>Load</strong>, whose slotframe matrix configuration is reported in the "<code>Load.conf</code>" file.</li> <li><strong>Parallel</strong>, whose slotframe matrix configuration is reported in the "<code>Parallel.conf</code>" file.</li> <li><strong>LPR</strong>, whose slotframe matrix configuration is reported in the "<code>LPR.conf</code>" file.</li> </ul> <p>A typical "<code>.conf</code>" file has the following format:<br><code># Offset src dest</code><br><code>0 4 1</code><br><code>0 6 2</code><br><code>0 8 3</code><br><code>0 10 9</code><br><code>1 1 0</code><br>where the first column represents the slot offset, i.e., the time slot in the slotframe matrix (which repeats periodically over time), in which a transmission opportunity is scheduled. Since many concurrent transmissions between different couple of nodes and different channels are possible simultaneously, more than one transmission could be scheduled at the same time. In the example, four transmission opportunities are scheduled in slot offset number 0.<br>The second column of each row represents the source node, while the third column represents the destination node. For instance, the schedule "<code>0 10 9</code>" represents the scheduled transmission at slot offset 0 from the source node 10 to the destination node 9.</p> <p> </p> <p>For each configuration, a corresponding file with the extension "<code>.dat</code>" contains the log generated in the simulation. An example is the following: <br><code>00083 72204 FLOW: 6 11 -> 0 LOST: 0 TRIES: 3 LATENCY: 540</code><br><code>00082 72156 FLOW: 5 10 -> 0 LOST: 0 TRIES: 4 LATENCY: 3460</code><br><code>00084 78013 FLOW: 0 4 -> 0 LOST: 0 TRIES: 2 LATENCY: 1240</code><br>where the transmission in a path from the source node (e.g., 11) to a destination node (e.g., the root node 0) is summarized with a single line in the log.</p> <p>Each line is composed of the following fields:</p> <ul> <li><em><packet number></em>: an integer number (e.g., <code>00083</code>) that uniquely identifies a packet transmitted in a multi-hop fashion from the source node to the destination node.</li> <li><em><queuing_time></em>: the queuing time expressed in terms of number of slots. In the simulation, slots have a length of 20 ms.</li> <li><em><flow_index></em>: the word "<code>FLOW:</code>" followed by an integer number identifying the flow. The simulation contains seven periodic flows with periods 6001, 6003, 6005, 6007, 6011, 6013, and 6017 expressed in terms of number of slots, for flows with index 0, 1, 2, 3, 4, 5, 6, respectively. For instance, "FLOW: 6" has a period of 6017 slots, which corresponds to 120.34 s (i.e., about 2 minutes).</li> <li><em><path></em>: an integer value representing the source node of the path, followed by the characters "<code>-></code>", followed by another integer value representing the destination node. For instance, "<code>11 -> 0</code>" represents the transmission in the path between node 11 and node 0.</li> <li><em><lost></em>: is an indication if the packet was lost in the path ("<code>LOST: 1</code>") or the packet arrived correctly at the destination ("<code>LOST: 0</code>"). A packet is lost if on a given link reached the maximum number of retransmissions.</li> <li><em><tries></em>: is the sum of the transmissions performed in each link. For instance, the link "<code>10 -> 0</code>" is composed of 3 hops. The value "<code>TRIES: 4</code>" means that a retransmission was performed for one of the links in the path.</li> <li><em><latency></em>: the transmission latency of the packet from when it was queued to when it reached its destination. The latency is expressed in ms.</li> </ul> <p>For each condition, the number of logged packets (i.e., lines) is 36,742,162, corresponding to 20 years of simulation.</p> <p> </p> <p>In addition, the code of the simulator is provided in the file "<code>TSCHmodeler.zip</code>".</p> <p>To run the simulations reported in [1], you have to execute the command:</p> <ul> <li>For experiment in Section IV.A <ul> <li><code>python3 -m TSCHmodeler conf/simple.conf</code></li> <li><code>python3 -m TSCHmodeler conf/simple_1week.conf</code></li> </ul> </li> <li>For experiment in Section IV.B <ul> <li><code>python3 -m TSCHmodeler conf/star_minimal.conf</code> for the <strong>star</strong> minimal configuration</li> <li><code>python3 -m TSCHmodeler conf/star_load.conf</code> for the <strong>load</strong> minimal configuration</li> <li><code>python3 -m TSCHmodeler conf/star_parallel.conf</code> for the <strong>parallel</strong> minimal configuration</li> <li><code>python3 -m TSCHmodeler conf/star_LPR.conf</code>for the <strong>LPR</strong> minimal configuration</li> </ul> </li> <li>For experiment in Section IV.C <ul> <li><code>python3 -m TSCHmodeler conf/large_40_nodes.conf</code></li> <li><code>python3 -m TSCHmodeler conf/large_121_nodes.conf</code></li> </ul> </li> </ul> <p> </p> <p>References:<br>[1] S. Scanzio, P. Chiavassa, G. Formis, G. Paolini and G. Cena, “A Lightweight Simulation Environment for TSCH-Based Wireless Sensor Networks,” in IEEE Transactions on Industrial Cyber-Physical Systems, 2025. doi: <a title="https://doi.org/10.1109/TICPS.2025.3620370" href="https://doi.org/10.1109/TICPS.2025.3620370" target="_blank" rel="noopener">10.1109/TICPS.2025.3620370</a></p>
Simulated Ancient Genomic Kinship Dataset: VCF and BAM (1x) Files for Related (including inbred) Pairs
<p>Simulated Ancient Genomic Kinship Dataset: VCF and BAM (1x and 5x) Files for Related (including inbred) Pairs</p><p><strong>Description:</strong></p><p>This dataset comprises simulated pedigrees (VCF files containing 8,677,101 autosomal biallelic and 298,625 X chromosomal SNP positions) generated using Ped-sim (v1.3) and comprising pairs of diverse familial relationship types up to third-degree. The first-degree relationships are parent-offspring and siblings; the second-degree relationships are half-siblings, grandparent-grandchild, and avuncular pairs; and third-degree relationships are first cousins, great-grandparent-great-grandchild, and grand avuncular pairs. For each of these 8 relationship types, our dataset includes 48 pairs of individuals. It also contains unrelated pairs. Additionally, the dataset includes first- and second-degree relatives, with inbreeding (parent-offspring pairs where the parents of the offspring are the first cousins and grandparent-grandchild pairs where the grandchild is the offspring of first cousins). Our simulations encompass all combinations of kinship types regarding sex. The dataset was further enriched by simulating ancient DNA-like sequencing data (5x and 1x BAM files) of Ped-sim simulated individuals using the gargammel tool, employing procedures akin to standard paleogenomic sequencing libraries. Note that the BAM files contain only randomly chosen 200K autosomal SNP positions. Positions can be found in the "200K_positions" file. Details can be found in Aktürk, Mapelli and Güler et al. 2023.</p><p><strong>Data Sources and Generation:</strong></p><p>Founder genotypes for pedigree simulation were created from the Tuscany (TSI) population SNPs within the 1000 Genomes Dataset v3. Notably, the founder genotypes lack background relatedness or runs of homozygosity (ROH).</p><p><strong>Description of File Naming Conventions:</strong></p><p>The naming conventions of the BAM files in this dataset are designed to convey key information regarding the specifics of each file.</p><p><strong>cov1x or cov5x:</strong> This segment denotes the coverage level of the BAM files, indicating whether the sequencing coverage for the individuals in the files is 1x or 5x.</p><p><strong>run_*:</strong> Signifies the particular batch from which the pedigree and individuals are derived. This name segment also applies to VCF files.</p><p><strong>parent-offspring_* or similar identifiers:</strong> Reflects the origin of the individual from the corresponding VCF file. For instance, "parent-offspring_1" corresponds to the individuals present in the "run_*_parent-offspring_1.vcf" file.</p><p><strong>parent-offspring* or similar identifiers:</strong></p><p> Indicates the origin of the individual from the sets within the VCF files. For example, "parent-offspring1" signifies the first set of parent-offspring pedigrees within the VCF file. Note that parent-offspring, grandparent-grandchild, and great-grandparent-great-grandchild and the inbreeding VCFs contain only one set, so this identifier is always 1. This convention can be 1 or 2 for the rest of the pedigrees, as the VCF files contain two sets of related pairs.</p><p><strong>_g*-b*-: </strong>Provides information about the individual's generational level within the VCF. This follows the Ped-sim syntax. For example, for parent-offspring type, "_g1-b1-" indicates the first parent (generation 1) within a specific pedigree, and "_g1-b2-" indicates the second parent (generation 1) while "_g2-b1-" represents the offspring (generation 2).</p><p><strong>Example Naming Structure:</strong></p><p>For instance, the file "cov1x_run1_parent-offspring_1_parent-offspring1_g1-b1-i1.all.hs37d5.cons.90perc.trimBAM.bam" signifies a BAM file with 1x coverage, originating from "run1," containing individuals from the "run_*_parent-offspring_1.vcf" file (first set of parent-offspring pairs) where "_g1-b1-" designates the first parent in the first generation. The latter half of the name "hs37d5.cons.90perc.trimBAM.bam" is the same across all files. </p><p><strong>Note1:</strong> Segments such as <strong>parent-offspring*_g*-b*- </strong>can also be tracked in the naming of the genotype columns in the VCF.</p><p><strong>Note2: </strong>Sexual information within the VCF files is discernible from the genetic data present at X chromosome positions. Individuals carrying two genotypes on the X chromosome are female, while those with a single genotype are male.</p><p><strong>Note3: Some of the individuals from distinct pedigrees may</strong>,<strong> in fact</strong>,<strong> be related due to shared ancestry through common founders. To suit specific research objectives, researchers may need to identify and exclude such relatives if the full dataset is used for kinship estimation.</strong></p><p>For more details about the dataset's generation process, unique characteristics, or any specific inquiries, our team is available for further information. We welcome and encourage inquiries, aiming to provide comprehensive support and additional details that might aid researchers in utilizing this dataset effectively. Please don't hesitate to contact us for any specific information you may need.</p><p>This repository contains only VCFs and cov1x BAM and 200K_positions files. The rest of the files can be found at <strong>10.5281/zenodo.10079625 </strong>and<strong> 10.5281/zenodo.10079685.</strong></p><p> </p>
Simulated Ancient Genomic Kinship Dataset: BAM (5x run1-6) Files for Related (including inbred) Pairs
<p>Simulated Ancient Genomic Kinship Dataset: VCF and BAM (5x (run1-6)) Files for Related (including inbred) Pairs</p> <p><strong>Description:</strong></p> <p>This dataset comprises simulated pedigrees (VCF files containing 8,677,101 autosomal biallelic and 298,625 X chromosomal SNP positions) generated using Ped-sim (v1.3) and comprising pairs of diverse familial relationship types up to third-degree. The first-degree relationships are parent-offspring and siblings; the second-degree relationships are half-siblings, grandparent-grandchild, and avuncular pairs; and third-degree relationships are first cousins, great-grandparent-great-grandchild, and grand avuncular pairs. For each of these 8 relationship types, our dataset includes 48 pairs of individuals. It also contains unrelated pairs. Additionally, the dataset includes first- and second-degree relatives, with inbreeding (parent-offspring pairs where the parents of the offspring are the first cousins and grandparent-grandchild pairs where the grandchild is the offspring of first cousins). Our simulations encompass all combinations of kinship types regarding sex. The dataset was further enriched by simulating ancient DNA-like sequencing data (5x and 1x BAM files) of Ped-sim simulated individuals using the gargammel tool, employing procedures akin to standard paleogenomic sequencing libraries. Note that the BAM files contain only randomly chosen 200K autosomal SNP positions. Positions can be found in the "200K_positions" file. Details can be found in Aktürk, Mapelli and Güler et al. 2023.</p> <p><strong>Data Sources and Generation:</strong></p> <p>Founder genotypes for pedigree simulation were created from the Tuscany (TSI) population SNPs within the 1000 Genomes Dataset v3. Notably, the founder genotypes lack background relatedness or runs of homozygosity (ROH).</p> <p><strong>Description of File Naming Conventions:</strong></p> <p>The naming conventions of the BAM files in this dataset are designed to convey key information regarding the specifics of each file.</p> <p><strong>cov1x or cov5x:</strong> This segment denotes the coverage level of the BAM files, indicating whether the sequencing coverage for the individuals in the files is 1x or 5x.</p> <p><strong>run_*:</strong> Signifies the particular batch from which the pedigree and individuals are derived. This name segment also applies to VCF files.</p> <p><strong>parent-offspring_* or similar identifiers:</strong> Reflects the origin of the individual from the corresponding VCF file. For instance, "parent-offspring_1" corresponds to the individuals present in the "run_*_parent-offspring_1.vcf" file.</p> <p><strong>parent-offspring* or similar identifiers: </strong>Indicates the origin of the individual from the sets within the VCF files. For example, "parent-offspring1" signifies the first set of parent-offspring pedigrees within the VCF file. Note that parent-offspring, grandparent-grandchild, and great-grandparent-great-grandchild and the inbreeding VCFs contain only one set, so this identifier is always 1. This convention can be 1 or 2 for the rest of the pedigrees, as the VCF files contain two sets of related pairs.</p> <p><strong>_g*-b*-: </strong>Provides information about the individual's generational level within the VCF. This follows the Ped-sim syntax. For example, for parent-offspring type, "_g1-b1-" indicates the first parent (generation 1) within a specific pedigree, and "_g1-b2-" indicates the second parent (generation 1) while "_g2-b1-" represents the offspring (generation 2).</p> <p><strong>Example Naming Structure:</strong></p> <p>For instance, the file "cov1x_run1_parent-offspring_1_parent-offspring1_g1-b1-i1.all.hs37d5.cons.90perc.trimBAM.bam" signifies a BAM file with 1x coverage, originating from "run1," containing individuals from the "run_*_parent-offspring_1.vcf" file (first set of parent-offspring pairs) where "_g1-b1-" designates the first parent in the first generation. The latter half of the name "hs37d5.cons.90perc.trimBAM.bam" is the same across all files. </p> <p><strong>Note1:</strong> Segments such as <strong>parent-offspring*_g*-b*- </strong>can also be tracked in the naming of the genotype columns in the VCF.</p> <p><strong>Note2: </strong>Sexual information within the VCF files is discernible from the genetic data present at X chromosome positions. Individuals carrying two genotypes on the X chromosome are female, while those with a single genotype are male.</p> <p><strong>Note3: Some of the individuals from distinct pedigrees may</strong>,<strong> in fact</strong>,<strong> be related due to shared ancestry through common founders. To suit specific research objectives, researchers may need to identify and exclude such relatives if the full dataset is used for kinship estimation.</strong></p> <p>For more details about the dataset's generation process, unique characteristics, or any specific inquiries, our team is available for further information. We welcome and encourage inquiries, aiming to provide comprehensive support and additional details that might aid researchers in utilizing this dataset effectively. Please don't hesitate to contact us for any specific information you may need.</p> <p>This repository contains only cov5x BAM files (run1-6). The rest of the files can be found at <strong>10.5281/zenodo.10079625 </strong>and<strong> 10.5281/zenodo.10070958.</strong></p>
Dataset and neural network weights to the paper: "Generative diffusion for regional surrogate models from sea-ice simulations"
<p>All the needed code and data to reproduce the results from the paper: "Generative diffusion for regional surrogate models from sea-ice simulations".<br>While most of the code is a frozen clone of the original <a href="https://github.com/cerea-daml/diffusion-nextsim-regional">Repository</a>, this capsule also includes the dataset and neural network weights to train and apply the surrogate models.</p> <p>The <strong>dataset</strong> for training and evaluation can be found at <em>data/nextsim</em>, which includes three different Zarr folders for training/validation/testing. The dataset is based on neXtSIM simulation data and ERA5 forcing data and extracted from the <a href="https://ige-meom-opendap.univ-grenoble-alpes.fr/thredds/catalog/meomopendap/extract/catalog.html">SASIP shared data OpenDAP server</a>:</p> <ul> <li>The neXtSIM simulations were performed by Gauillaume Boutin and published in the paper "<a href="https://doi.org/10.5194/tc-17-617-2023">Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework</a>" (Boutin et al., 2023) and available as Zenodo <a href="../records/7277523">dataset</a> (Boutin et al., 2022).</li> <li>The forcing data is based on the ERA5 reanalysis dataset published in the paper: "<a href="https://doi.org/10.1002/qj.3803">The ERA5 global reanalysis</a>" (Hersbach et al., 2020) and available as dataset from the Copernicus Climate Change Service (C3S, Copernicus Climate Change Service, 2023). The here used forcing data is based on the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels">hourly reanalysis data on single levels</a> and interpolated with nearest neighbors to the curvilinear grid as used in the output from the neXtSIM simulations. <strong>Disclaimer:</strong> The results contain modified Copernicus Climate Change Service information, 2023. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</li> </ul> <p>The <strong>neural network weights</strong> are included under <em>data/models </em>and split into weights for the deterministic models and the diffusion models.<br>These neural network weights have been used to generate the results presented in the paper.</p> <p>In this capsule, the <em>notebooks</em> folder includes also the figures used within the paper and additional trajectory data used in the qualitative analysis of the paper.</p> <p>Generally, we recommend to just download the <em>data.tar.gz </em>file and use otherwise the original <a href="https://github.com/cerea-daml/diffusion-nextsim-regional">Repository</a>, since the here included code can be outdated. We further refer to the repository for additional information.</p> <p> </p> <p>Contained in this capsule:</p> <ul> <li>configs.tar.gz: The configuration files for the experiments.</li> <li>data.tar.gz: The dataset and neural network weights.</li> <li>diffusion_nextsim.tar.gz: The main code for the neural network etc.</li> <li>environment.yaml: The anaconda environment file, can be used to install the needed packages.</li> <li>notebooks.tar.gz: The notebooks that were used to create the figures in the paper. The figures from the paper and the data from the qualitative analysis are included as well.</li> <li>readme.md: The readme file from the repository.</li> <li>scripts.tar.gz: The scripts used for the experiments.</li> <li>setup.py: the file to install the <em>diffusion_nextsim</em> package in a python environment.</li> </ul> <p>References:</p> <p>Guillaume Boutin, Heather Regan, Einar Ólason, Laurent Brodeau, Claude Talandier, Camille Lique, & Pierre Rampal. (2022). Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7277523</p> <p>Boutin, G., Ólason, E., Rampal, P., Regan, H., Lique, C., Talandier, C., Brodeau, L., and Ricker, R.: Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework, The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, 2023.</p> <p>Copernicus Climate Change Service (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.adbb2d47">10.24381/cds.adbb2d47</a>.</p> <p>Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis. <em>Q J R Meteorol Soc</em>. 2020; 146: 1999–2049. <a href="https://doi.org/10.1002/qj.3803">https://doi.org/10.1002/qj.3803</a></p> <p> </p>
Dataset for article - Dislocation dynamics in Ni: Parameterising dislocation trajectories from atomistic simulations
<p>Data supporting findings in "Dislocation dynamics in Ni: Parameterising dislocation trajectories from atomistic simulations". In this work, 8 identical Molecular Dynamics (MD) simulations of edge dislocations in pure Nickel were run and analysed, where dislocation positions were extracted and each dislocation is tracked for further analysis. Each simulation only differs in the random seed used for setting the initial atom velocities. Files needed to reproduce a specific simulation can be found in the directories provided in <em>data.zip</em>:</p> <ul> <li>fr_18430</li> <li>fr_27743</li> <li>fr_44345</li> <li>fr_46165</li> <li>fr_54382</li> <li>fr_54992</li> <li>fr_97181</li> <li>fr_98232</li> </ul> <p>The random seed needed to set the initial velocities of the atoms are set in the LAMMPS script. The log files generated by LAMMPS are provided for the different stages of a given simulation in the <em>LAMMPS_logs</em> sub-directories. The corresponding output files from the OVITO DXA are also included in the sub-directory <em>Ni_disloc_const</em>. The files <em>disloc_data_*.ca</em> contain the raw output from the OVITO DXA and can be opened directly in OVITO. The <em>disloc_data_*.txt </em>files contain information extracted from the OVITO DXA in a format that can be read in and processed by the dislocation tracking code (<a href="https://github.com/geraldineanis/DislocCode/tree/v1.0.0">https://github.com/geraldineanis/DislocCode/tree/v1.0.0</a>). The wildcard character "*" is replaced with the simulation timestep. In each directory, the dislocation position vs. time data is included as a text file<em> </em>named<em> </em><em>perfect_pos_<seed>.txt</em>. The Mishin 2004 EAM interatomic potential file <em>NiAl_Mishin_2004.eam.alloy</em> used to generate the data is also provided.</p> <p>Additional simulations were carried out at a range of applied shear stresses (20 MPa - 50 MPa). Files needed to reproduce theses simulations are provided in <em>data_stress.zip </em>and follow the same format described above.</p> <p>The data provided here was generated with the <code>LAMMPS/29Sep2021-kokkos</code><em> </em>module on the SULIS Tier 2 HPC platform, which has been built with the OpenMP backend of the <code>kokkos</code> package and uses the <code>foss-2021b</code> toolchain (<a href="https://docs.easybuild.io/common-toolchains/">https://docs.easybuild.io/common-toolchains/</a>). For further information, please refer to <a href="https://sulis-hpc.github.io/appnotes/lammps.html">https://sulis-hpc.github.io/appnotes/lammps.html</a>.</p> <p>For more details on the calculations, please refer to the publication (in preparation). Please refer to the GitHub repository at <a href="https://github.com/geraldineanis/DislocCode/tree/v1.0.0">https://github.com/geraldineanis/DislocCode/tree/v1.0.0</a> for the analysis tools developed and for detailed instructuctions for their use.</p>
Simulated datasets for detector and particle flow reconstruction: CLIC detector, machine learning format
<p><strong>Synopsis</strong></p> <p>Machine-learning friendly format of tracks, clusters and target particles in electron-positron events, simulated with the CLIC detector. Ready to be used with <a href="https://zenodo.org/records/14930299">jpata/particleflow:v2.3.0</a>. Derived from the EDM4HEP ROOT files in <a href="https://zenodo.org/record/8260741">https://zenodo.org/record/8260741</a>.</p> <ul> <li>clic_edm_ttbar_pf.zip: e+e- -> ttbar, center of mass energy at 380 GeV</li> <li>clic_edm_qq_pf.zip: e+e- -> Z* -> qqbar, center of mass energy at 380 GeV</li> <li>clic_edm_ww_fullhad_pf.zip: e+e- -> WW -> W decaying hadronically, center of mass energy at 380 GeV</li> <li>clic-tfds.ipynb: an example notebook on how to load the files</li> </ul> <p><strong>Contents</strong></p> <p>Each .zip file contains the dataset in the <a href="https://github.com/tensorflow/datasets">tensorflow-datasets</a>, <a href="https://github.com/google/array_record">array_record</a> format. We have split the full datasets into 10 subsets, due to space considerations on zenodo, two subsets from each dataset are uploaded. Each dataset contains a train and test split of events.</p> <p><strong>Dataset semantics (to be updated)</strong></p> <p>Each dataset consists of events that can be iterated over using the tensorflow-datasets library and used in either tensorflow or pytorch. Each event has the following information available:</p> <ul> <li>X: the reconstruction input features, i.e. tracks and clusters</li> <li>ytarget: the ground truth particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> <li>ycand: the baseline Pandora PF particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> </ul> <p>The full semantics, including the list of features for X, are available at https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/heptfds/clic_pf_edm4hep/utils_edm.py and https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/data/key4hep/postprocessing.py.</p>
Dataset of optimal cement-based panels enhanced with microencapsulated phase change material for EnergyPlus simulations
<p>This dataset includes:<br> - A series of EnergyPlus models of the BESTEST - Case 900 - from ANSI/ASHRAE Standard 140-2011 for the original (Baseline_Case900) and three enhanced designs (Opt-1, Opt-2, Opt-5) by using a cement-based panel containing microencapsulated phase change material.<br> - All the optimal solutions (parameters and corresponding performance) in XLSX format, which were obtained for two multiobjective optimization studies of the thermophysical properties (ParetoFront_CaseA and ParetoFront_CaseB).<br> - The typical meteorological year (TMY) of Sofia city employed to obtain the results, which is freely provided by Climate.One.Building.Org repository (https://climate.onebuilding.org/)</p>
Simulated NGS read datasets for prediction of novel fungal pathogens and multiple pathogen classes
<p>This repository contains simulated Illumina read datasets for novel fungal pathogen prediction and real-time detection of multiple pathogen classes. They were used to train the models hosted at <a href="https://zenodo.org/record/5711877">https://zenodo.org/record/5711877</a>.<br> The reads were simulated with Mason (<a href="https://www.seqan.de/apps/mason/">https://www.seqan.de/apps/mason/</a>) from genomes downloaded from NCBI, based on metadata stored in a manually curated database (<a href="https://zenodo.org/record/5846345">https://zenodo.org/record/5846345</a>).</p> <p>We provide the following:</p> <p>1) An rds file describing assignment of fungal species from the database (<a href="https://zenodo.org/record/5846345">https://zenodo.org/record/5846345</a>) to training, validation and test sets (TrainValTest_fungi.rds). A second rds file (TrainValTest_temporal.rds) includes species added within 12 weeks after the original datasets were compiled. Those species were used for a temporal benchmark.</p> <p>2) Fungal validation and test sets. Each contains 1.25 million, 250bp-long reads simulated from non-overlapping sets of human ("pathogenic") or non-human ("nonpathogenic") pathogens. The test set contains paired reads ("_1" and "_2" for the first and second mate). The number of reads per species is proportional to the respective genome length. An additional, temporal test set (*temporal*fasta.gz) includes 15 species added after 12 weeks from the consturction of the original datasets.</p> <p>3) Fungal training sets. They contain 250bp-long reads simulated from species not present in the validation or test sets. There are four variants:<br> 3a) "low-coverage, linear" - 20 million reads, number of reads per species proportional to genome length<br> 3b) "low-coverage, logarithmic" - 20 million reads, number of reads per species proportional to the logarithm of genome length ("log")<br> 3c) "high-coverage, linear" - 240 million reads, number of reads per species proportional to genome length ("24")<br> 3d) "high-coverage, logarithmic" - 240 million reads, number of reads per species proportional to the logarithm of genome length ("24log")</p> <p>4) Training, validation and test sets for the multiclass models. They should be used together with the "pathogenic" read sets hosted at <a href="https://zenodo.org/record/4456857">https://zenodo.org/record/4456857</a>. Here, we share sets for two of the four total classes:<br> 4a) The 'non-pathogen' class is a mixture of "nonpathogenic" biacterial and viral read sets, concatenated and downsampled to the original read number (20M for training, 1.25M for validation and test). The training and validation sets contain mixed-length (25-20bp) simulated subreads (original sets hosted here: <a href="https://zenodo.org/record/4456857">https://zenodo.org/record/4456857</a>). The test set contains 250bp long reads based on the test sets from here: <a href="https://zenodo.org/record/3678563">https://zenodo.org/record/3678563</a> and here: <a href="https://zenodo.org/record/4312525">https://zenodo.org/record/4312525</a>; it was also sorted by species.<br> 4b) Mixed-length versions of the "pathogenic" fungal training and validation sets, prepared by random shortening of the "low-coverage" read sets in the "linear" (_rn_) and "logarithmic" (_rn_*log_) flavours.</p> <p>See also the preprint: <a href="https://www.biorxiv.org/content/10.1101/2021.11.30.470625">https://www.biorxiv.org/content/10.1101/2021.11.30.470625</a></p>
Post-processed dataset from 50000 numerical simulations of monopile-supported NREL 5MW wind turbine in OpenFAST
<p>The dataset contains two separate files: NREL_Trainset40000.mat and NREL_Testset10000.mat.</p> <p>The stored input enviormental and operational parameters are:</p> <ul> <li>Significant wave height, m (Hs), peak period, s (Tp), wave direction, deg (Wave_dir);</li> <li>Wind speed, m/s (Vw_mean, Vw_std), wind direction, deg (Wdir_mean, Wdir_std);</li> <li>Turbine rotational speed, rpm (Rpm_mean, Rpm_std), blade pitch, deg (Pitch_mean, Pitch_std), turbine yaw angle, deg (Yaw_mean, Yaw_std).</li> </ul> <p>The output of the simulations includes the time series, sampled at 50 Hz, of the reaction force and bending moments at the mudline:</p> <ul> <li>Fzz, N</li> <li>Mxx, Nm</li> <li>Myy, Nm</li> </ul> <p>contact: nandar.hlaing@uliege.be</p>
Dataset for Electron Precipitation Curtains – Simulating the Microburst Origin Hypothesis by T.P. O'Brien et al. submitted to J. Geophysical Res.
<p>Technical reports and data sets for the the paper Electron Precipitation Curtains – Simulating the Microburst Origin Hypothesis. Additional AC6 information can be found at rbspgwy.jhuapl.edu/ac6 and at spdf.gsfc.nasa.gov/pub/data/aaa_smallsats_cubesats/aerocube/aerocube-6/. AC6 data have also been ingested into the main CDAWeb database at cdaweb.gsfc.nasa.gov. Source code related to this data set can be found at https://github.com/tpoiii/dipole_tracer_ac6, or DOI: 10.5281/zenodo.6011631.</p>
Microgrid Simulation Datasets
<p>Simulation results (raw data) for a wide-range time-domain simulation environment for stand-alone microgrids. The contents are as follows:</p> <p>1. py_psim_hw_merged_50ns.parquet: Simulation data comparing results obtained with Python, PSIM, and FPGA accelerated simulation program.</p> <p>2. hw_realtime.parquet: FPGA real-time simulation data.</p> <p> </p>
Simulated X-ray micro-computed tomography based particle tracking velocimetry dataset for validation purposes
<p>Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium)<br> Date: 8th February 2022<br> For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022).<br> https://doi.org/10.1063/5.0088000<br> -----------------------------</p> <p>Validation dataset for micro-computed tomography based particle tracking velocimetry: a simulated micro-CT based velocimetry experiment with associated ground-truth particle trajectories</p> <p>- The ground truth trajectories were based on randomly dropping virtual particles in the pore space, and tracking their movement through a CFD-based velocity field (see below). The positions were calculated for the time corresponding to each radiograph of a micro-CT experiment. The folder "GroundTruthData" contains the locations of all particles at the central time of each micro-CT scan, as well as their radii. Check the associated readme file to read the data file.</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 7 time steps (70 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ.</p> <p>- The directory "clearFrame" contains an image of the pore space without particles, matching with the time frame images, in the same format and with the same voxel size as the time frame images.</p> <p>- The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage).</p> <p>- The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/</p> <p>- The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 6 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
ConFiRMa dataset_01: simulation of CRM characterization tests with the OOFEM code (detailed level modelling)
<p>The Dataset collects the input files developed for the simulation of characterization tests performed on Composite Reinforced Mortar samples with the free open-source code OOFEM (detailed level modelling). The description of the numerical models and the analysis and comparison of the results can be found in paper "Characterization of Textile Reinforced Mortar: state of the art and detailed modelling with a free open source finite element code" (<a href="https://doi.org/10.1061/(ASCE)CC.1943-5614.0001240">https://doi.org/10.1061/(ASCE)CC.1943-5614.0001240</a>).</p> <p>OOFEM Version 2.5 (https://doi.org/10.5281/zenodo.4339630) was used for running the analyzes.</p> <p>ReadMe file provide a description of the different input files.</p> <p> </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.