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358 results for “dataset generation”
NIFECG synthetic signals generated with fecgsym by PhysioNet: Dataset 2/2
<p>First part</p> <p>https://zenodo.org/records/8415709</p> <p>Second part</p> <p>https://zenodo.org/records/8429286</p> <p>Non-invasive fetal electrocardiogram (NIFECG) signals.</p> <p>Fetal's heart rate: 60 - 200 bpm</p> <p>Mother's heart rate: 65 - 120 bpm</p> <p>Sample frequency 1000 Hz</p> <p>8,008 signals in total</p> <p>Download the files and join them as follows:</p> <p>cat tmp2.tar_part* > nifecg_signals.tar</p> <p>Untar the file with the following command:</p> <p>tar xvf nifecg_signals.tar</p>
Raw data for the generation of the FluPRINT dataset
<p>Here you can find raw data downloaded from the Stanford Data Miner (https://datamt.net) that were used to build the FluPRINT database.</p> <p>Please note that in total 121 CSV files are provided in 8 folders (one for each clinical study).</p> <p>Files are provided in two formats: zip and 7zip (7z).</p> <p>7zip is a free and open-source file archiver available for download here: https://www.7-zip.org.</p>
A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics
Open the record for dataset details and reuse information.
RosettaAntibody generated models for a dataset of 49 antibody-Fv structures
<p><strong>Structures of antibody-Fv domains computationally generated by RosettaAntibody based on the protocol of </strong><a href="https://www.nature.com/articles/nprot.2016.180?proof=true&draft=marketing">Weitzner, Jeliazkov, Lyskov et al.</a><strong> (Nature Protocols 12, 401–416, 2017). There are 49 antibody targets, with about 2800 decoy structures provided per antibody. A table is also provided with the H3-loop rmsd of each structure from the experimental crystal structure.</strong></p> <p><strong>These structures can be used to evaluate whether a score function can identify the near-native structures from the pool of decoys. These structures can also be used for comparison with other sets of structures generated by other antibody structure prediction programs.</strong></p> <p><strong>The research study on this set is unpublished and a manuscript is under preparation. Please cite Jeliazkov, Frick, Zhou & Gray, “Robustification of RosettaAntibody and Rosetta SnugDock,” in preparation, 2020.</strong></p> <p><strong>The homology modeling stage of RosettaAntibody was run with stringent homolog exclusion settings, excluding CDR templates of over 95% identity and FR templates of over 90% identity. The H3 modeling stage was run as described by <a href="https://www.nature.com/articles/nprot.2016.180">Weitzner, Jeliazkov, Lyskov et al.</a> (Nature Protocols 12, 401–416, 2017). </strong></p> <p><strong>The set of 49 antibody-Fv domains was originally compiled by <a href="https://academic.oup.com/peds/article/29/10/409/2462315">Marze et al. </a>(Protein Eng. Des. Sel. 29(10), 409-418, 2016). The dataset was first used to evaluate CDR-H3 loop rmsds in <a href="https://www.jimmunol.org/content/early/2016/11/18/jimmunol.1601137">Weitzner and Gray </a>(J. Immunology 198(1):505-515, 2017).</strong></p> <p><strong>The dataset can be extracted on a linux interface using: </strong></p> <p><strong> tar -xvzf <a href="https://www.zenodo.org/api/files/c2ba9da2-0d46-4aba-adbf-8ddd39b52b29/decoys_rosettaantibody_20200323.tar.gz?versionId=df264fc5-c79c-47b4-82bf-d1cda1e025e2">decoys_rosettaantibody_20200323.tar.gz</a> </strong></p> <p><strong>When extracted the output comprises RosettaAntibody generated models for 49 antibodies in the format:</strong></p> <p><strong><Antibody pdb id> / model-<id 1>.relaxed_<id 2>.pdb.gz</strong></p> <p><strong>The Rosetta "ref2015" scores (ref2015_score) and rmsd of the H3 loop (h3_rmsd) for every model (model) is provided in model_scores_and_rmsds.txt . RMSDs are calculated with respect to the corresponding crystal structure (pdb id) over all heavy atoms in the h3 loop (93–102 in Chothia numbering) after superposition of the framework residues. ID 1 comes from the homology model source (lower is better). ID 2 indicates the loop model.</strong></p> <p><strong>The decoy PDB files contain additional metrics following the ATOM records such as VH–VL relative orientation metrics (from Marze et al.) and per-residue Rosetta scores. Caveat: the “RMS” values reported within the decoy files were calculated against the input homology model and not the crystal structures (whereas the model_rmsds.txt file contains the H3 rmsds w.r.t. crystal).</strong></p>
Dataset used for "Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data"
<p>Each simulated dataset was generated using the AbSim R package (version 0.2.6) in a B cell single-lineage fashion. Each B cell clone simulation begins with a random selection from sets of IGHV, IGHD, and IGHJ germline sequences to produce a unique V(D)J recombination event. Then, clones are made by introducing mutations using a local nucleotide context-dependent model (S5F model) along a phylogenetic tree in which branching events occur stochastically. </p>
Unit test generation for common and uncommon behaviors: dataset
<p>The data used for the 'Unit test generation for common and uncommon behaviors' Master's thesis.</p> <p>The folder structure used in the archives is the same as the one used by the linked evaluation tool.</p> <p>The archives contain the following:</p> <ul> <li>cubtg-eval-1.4.tar.gz: tests generated by EvoSuite</li> <li>stadard.log.tar.gz: EvoSuite log files created during test generation</li> <li>cubtg-eval-1.4-pit-1.6.tar.gz: result of running PIT on the generated tests</li> </ul>
Dataset for: Generation of model tissues with dendritic vascular networks via sacrificial laser-sintered carbohydrate templates
<p>Published in:<br> Nature Biomedical Engineering. doi: 10.1038/s41551-020-0566-1.</p> <p>Generation of model tissues with dendritic vascular networks via sacrificial laser-sintered carbohydrate templates</p> <p>Ian S. Kinstlinger (1), Sarah H. Saxton (2), Gisele A. Calderon (1), Karen Vasquez Ruiz (1), David R. Yalacki (1), Palvasha R. Deme (1), Jessica E. Rosenkrantz (3), Jesse D. Louis-Rosenberg (3), Fredrik Johansson (2), Kevin D. Janson (1), Daniel W. Sazer (1), Saarang S. Panchavati (1), Karl-Dimiter Bissig (4), Kelly R. Stevens (2,5), and Jordan S. Miller (1)</p> <p>1 Department of Bioengineering, Rice University, Houston, TX, USA.<br> 2 Department of Bioengineering, University of Washington, Seattle, WA, USA.<br> 3 Nervous System, Palenville, NY, USA.<br> 4 Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA.<br> 5 Department of Pathology, University of Washington, Seattle, WA, USA</p> <p>Sacrificial templates for patterning perfusable vascular networks in engineered tissues have been constrained in architectural complexity, owing to the limitations of extrusion-based 3D-printing techniques. Here we show that cell-laden hydrogels can be patterned with algorithmically generated dendritic vessel networks and other complex hierarchical networks by using sacrificial templates made from laser-sintered carbohydrate powders. We quantified and modulated gradients of cell proliferation and cell metabolism emerging as a result of fluid convection through these networks and of diffusion of oxygen and metabolites out of them. We also show scalable strategies for the fabrication, perfusion culture and volumetric analysis of large tissue-like constructs with complex and heterogeneous internal vascular architectures. Perfusable dendritic networks in cell-laden hydrogels may help sustain thick and densely cellularized engineered tissues, and assist interrogations of the interplay between mass transport and tissue function.</p>
CusVarDB: A tool for building customized sample-specific variant protein database from Next-generation sequencing datasets
<p>CusVarDB is a windows based tool for creating a variant protein database from Next-generation sequencing datasets. The program supports variant calling for Genome, RNA-Seq and exome datasets.</p> <p>This repository will provide the resultant variant peptides identified in our study and its corresponding information. The detailed information of the table is given below.</p> <p>Supplementary Table 1. This table contains the resultant variant peptides along with its wild-type peptides from BT474, MDMAB157, MFM223, and HCC38 datasets. Along with mutant peptides, this section also provides additional information such as peptide-spectrum match (PSM), Protein accession, cross-correlation value from the search (Xcorr), and retention time (RT).</p> <p>Supplementary Table 2. This table provides the complete details of the resultant peptides. Here the mutant and corresponding wild-type peptides are mentioned in different sheets. For a given mutant peptide its wild-type peptide and corresponding information can be mapped using the VLOOKUP function in Excel by keeping column A (Sl.No) as lookup parameter.</p> <p>Supplementary Table 3. This table briefs about the variants which are already reported in other cancers.</p>
Dataset accompanying paper submission for "Toward data-driven generation and evaluation of model structure for integrated representations of human behavior in water resources systems"
<p>This data set accompanies code archived at DOI: <a href="https://doi.org/10.5281/zenodo.3833186">10.5281/zenodo.3833186</a>, which was used in the experiments for the paper submission "Toward data-driven generation and evaluation of model structure for integrated representations of human behavior in water resources systems"</p>
Self-generated Fitbit dataset 10.22.2011-09.20.2014
<p>This dataset was generated using public APIs to export self-generated physical activity observations in 24h epochs using Fitbit tracking devices between 10.22.2011 to 09.20.2014</p>
A Dataset of User Generated Videos from Edinburgh Festival 2016
<p>Files include footage from performances of the trEd Dance group (edfest8, edfest9 and edfest10) and Rebecca Wilson’s ‘The Strawberry Show’ (edfest6 and edfest7) taken during Edinburgh Festival 2016 by BBC R&D as part of the COGNITUS project funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 687605. BBC©2016. Further details about the content are available in the accompanying paper <em>"An Open Access Dataset of User Generated Videos from Edinburgh Festival 2016"</em>. If you have any queries please contact BBC R&D at cognitus-h2020@rd.bbc.co.uk. This notice must remain attached to any copy of the content.</p> <p> </p>
A dataset of synthetically generated code blocks for the learning of WCET on Cortex A53
<p>WE-HML Dataset WCET code block with different pollution factors on data cache for Cortex A53</p><p>Git : https://gitlab.inria.fr/puaut/we-hml</p><p>Paper : https://ieeexplore.ieee.org/document/9545301</p>
PhiPiPi Dataset for our paper "Event Generation and Consistence Test for Physics with Sliced Wasserstein Distance"
<p>The PhiPiPi dataset is used to train and test our fast event simulation model for our paper "Event Generation and Consistence Test for Physics with Sliced Wasserstein Distance" in <a href="https://arxiv.org/abs/2310.17897">https://arxiv.org/abs/2310.17897</a>. The dataset is generated by BES III simulation framework to simulate phase-space Monte Carlo events, focusing on the production of psi(2S) in electron-positron annihilation. This intricate process entails the psi(2S) decay into a trio of particles: phi, pi+, and pi-, with the phi particle subsequently decaying into a K+ and K- pair.</p><p>We have meticulously partitioned the data into distinct sets for training, validation, and testing to facilitate a robust machine learning workflow. Each set is systematically saved in the convenient npy format for ease of access and integration.</p><p>The dataset contains the four-momentum components (px, py, pz, E) of the final-state particles: K+, K-, pi+, and pi-. This comprehensive inclusion ensures a detailed representation of the kinematic properties of the particles for accurate model training.</p><p>Researchers and practitioners are invited to use this dataset by incorporating it into our public code repository: <a href="https://github.com/caihao/SWD-EvtGen">https://github.com/caihao/SWD-EvtGen</a>. Doing so will enable the training of our fast event simulation model, enhancing the fidelity and efficiency of high energy physics simulations.</p>
Dataset and figure generator for Variational Monte Carlo approach applied to the model describing WSe2 homo-bilayer
<p>This data set contains post-processed data obtained from variational Monte-Carlo approach for Hubbard model with complex, spin and direction dependent phase. This model is believed to properly describe the eseential features of WSe2 twisted homo-bilayer. The python notebook included, allows to generate figures regsarding formation of Mott insulating phase and spin ordering.</p>
Code generation for classical-quantum software systems modelled in UML - Dataset and EGL Transformation
<p>This dataset contains all the elements necessary for carry out the EGL transformation from UML models to Hybrid and Quantum code, as well as to carry its validation. </p> <blockquote> <p><em>Quantum computing is gaining an increasing interest since it can solve certain problems exponentially faster than classical computing. Thus, many organizations are researching and launching investments for integrating quantum software into their existing systems. Software modernization (as based on Model-Driven Engineering) has been proposed to migrate from/to the so-called hybrid software systems, which integrate classical and quantum software. In that process, both, reverse engineering and restructuring phases, have already been investigated. However, forward engineering phase for generating hybrid source code from high-level design models has not yet been addressed. Thus, this research proposes a quantum code generation technique from extended UML design models. It consists of a set of Model-to-Text transformations (defined through Epsilon Generation Language) to generate both Python and Qiskit code, which respectively integrate classical and quantum code. The transformation has been validated through a multi-case study with 7 hybrid software systems modelled in UML, which demonstrated that the transformation is effective and efficient. The implication of this work is that the software modernization process for hybrid software systems can be completed by tackling forward engineering phase, and that Model-Driven Engineering can therefore globally facilitate industry adoption of quantum software.</em></p> </blockquote>
Datasets generated in the ConvAE-RF modelling of grain yield in the mid-lower Yangtze plains
<p><em>formatted_grid.zip </em>is the preprocessed input meteorological dataset to a ConvAE-RF model proposed by the author.</p> <p><em>final_output.zip</em> is 2016-2100 output yield projections from ScenarioMIP experiments SSP126, SSP245, SSP370 and SSP585 of 25 AMES NEX GDDP CMIP6 GCMs downscaled by <a title="NASA Global Daily Downscaled Projections, CMIP6" href="https://www.nature.com/articles/s41597-022-01393-4" target="_blank" rel="noopener">Thrasher et al., 2022.</a></p> <p><em>coldwave_order.csv</em> contains 4 lists of 25 AMES NEX GDDP CMIP6 GCMs (one for each ScenarioMIP experiment) ranked according to mean coldwave frequency predicted in a unit area (0.25° * 0.25°) in the mid-lower Yangtze plain provinces.</p> <p><em>heatwave_order.csv</em> contains 4 lists of 25 AMES NEX GDDP CMIP6 GCMs (one for each ScenarioMIP experiment) ranked according to mean heatwave frequency predicted in a unit area (0.25° * 0.25°) in the mid-lower Yangtze plain provinces.</p> <p><em>tmax_thr90.nc</em> is 2d and records the threshold daily maximum temperature for each point on the spatial grid above which a day would be qualified as a heatwave candidate.</p> <p><em>tmax_thr90.nc</em> is 2d and records the threshold daily minimum temperature for each point on the spatial grid below which a day would be qualified as a coldwave candidate.</p> <p>Please reference the README in this <a href="https://github.com/zjmagou/MLYPGrain2024">GitHub Repository</a> for data usage.</p> <p> </p>
Dataset for Tunable on-chip electro-optic frequency-comb generation at 8 µm wavelength
<p>This dataset contains the information contained in Figures 2, 3, 4, 6, 7, 8, 9 of the related manuscript. This research dataset should be interpreted and understood in the context of the corresponding manuscript, which has been published in Laser & Photonics Reviews with DOI: 10.1002/lpor.202300961. All relevant information regarding the dataset, how it was obtained and its context is contained in the manuscript. The data correspond to the information shown in the figures of the manuscript. </p> <p>Each file is in .txt format, the decimal separator is a point '.' and the column separator is a tab '\t'.</p>
Intermediate files used for generating co-register result on the Xenium Breast Cancer Dataset with Giotto Suite
Open the record for dataset details and reuse information.
MST NDSI Collection: A Cloud-free MODIS NDSI Dataset (2001–2022) for Tibetan Plateau Generated by a LightGBM-Based Method Using Multivariate Features
<p>1. Cloud-free MODIS normalized difference snow index (NDSI) dataset for Tibetan Plateau from 2001 to 2022 is generated.</p> <p>2. This dataset is derived from daily 500-m MODIS NDSI datasets (MOD10A1 and MYD10A1).</p> <p>3. This dataset is provided using a WGS_1984_UTM_45N projection, with the data format of TIFF images. </p> <p>4. The NDSI value ranges from -10,000-10,000, corresponding to the raw NDSI band of MOD10A1 and MYD10A1. The fill value is set to -32768.</p> <p>5. Due to the large amount of data, the data for each year has been divided into several split archives (YYYY.partX.rar).</p> <p>6. Each RAR contains NDSI data for one year (January to December). After uncompressing into the TIFF format, the files are named as "NDSI_Daily_YYYY_mm_dd.tif" for NDSI data and "NDSI_Daily_YYYY_mm_dd.tif".</p> <p>7. The accuracy of this collection has been well validated by simulation experiments.</p> <p>8. This version includes data from 2017 to 2022 (https://doi.org/10.5281/zenodo.14177009). Version 1 includes data from 2001 to 2004 (https://doi.org/10.5281/zenodo.14027672). Version 2 includes data from 2005 to 2010(https://doi.org/10.5281/zenodo.14038846). Version3 includes data from 2011 to 2016 (https://doi.org/10.5281/zenodo.14089925).<br><br><br></p>
Dataset - Unlocking Aerobatic Potential of Quadcopters: Autonomous Freestyle Flight Generation and Execution
<h1>Dataset - Unlocking Aerobatic Potential of Quadcopters: Autonomous Freestyle Flight Generation and Execution</h1> <p>Dataset for manuscripts "Unlocking Aerobatic Potential of Quadcopters: Autonomous Freestyle Flight Generation and Execution".</p> <p>This dataset includes the raw data for the images in the manuscript except for the schematic drawings.</p> <p>The source code is released as ROS packages at <a title="https://github.com/ZJU-FAST-Lab/Aerobatic-Planner" href="https://github.com/ZJU-FAST-Lab/Aerobatic-Planner" target="_blank" rel="noopener">GitHub</a>.</p> <h2>Large-scale aerobatic flight</h2> <p>Code: outdoor.mlx</p> <p>Data: outdoor_data/</p> <ul> <li> <p>outdoor_final.bag: This rosbag captures all essential data during outdoor flight in real-world experiments.</p> </li> <li> <p>outdoor_map.pcd: Pointcloud map file of the outdoor flight environment.</p> </li> <li> <p>[ManeuverName]View[id].mp4: Video files showcasing different maneuvers from various perspectives during rosbag playback in Rviz visualization software.</p> </li> </ul> <h2>Successive aerobatic maneuvers in confined spaces</h2> <p>Code: indoor.mlx</p> <p>Data: indoor_data/</p> <ul> <li> <p>nokov[id].bag: The rosbag captures all essential data during indoor flight in real-world experiments. Each bag representing a single aerobatic flight. Data package representing indoor experimental flights, with each package representing a single flight.</p> </li> <li> <p>indoor_map.pcd: Pointcloud map file of the indoor flight environment.</p> </li> </ul> <h2>Combination of aerobatic intentions</h2> <p>Data: intention_simulation/</p> <ul> <li> <p>[ManeuverName]_Intention.png: Input aerobatic intentions.</p> </li> <li> <p>[ManeuverName]_Action.png: Maneuvers generated based on intentions.</p> </li> </ul> <p>These pictures are rendered using Blender.</p> <h2>Ablation analyses</h2> <h3>Yaw compensation</h3> <p>Code:</p> <ul> <li> <p>ablation_yaw_comp.mlx</p> </li> <li> <p>yaw_sensitivity.mlx</p> </li> </ul> <p>Data: ablation_data/YawComp/</p> <ul> <li> <p>[with/without]YCM.mp4: Slow-motion footage of the drone executing flight trajectories in the yaw-sensitive area at 1/50th of the original speed.</p> </li> <li> <p>[with/without]YCM.txt: State computation of the entire trajectory, with a time interval of 0.2 microseconds for each line of state.</p> </li> </ul> <h3>Trajectory optimization</h3> <p>Code:</p> <ul> <li> <p>ablation_traj_opt.mlx</p> </li> <li> <p>attitude_penalty.mlx</p> </li> </ul> <p>Data: ablation_data/TrajOpt/</p> <ul> <li> <p>OptTrajRet.txt: Various evaluation metrics obtained from 90 trajectory generations.</p> </li> <li> <p>[ManeuverName]_[OptimizeCondition].png: Typical optimized trajectories under different optimization conditions for various maneuvers.</p> </li> </ul> <h1>Additional data in the rebuttal</h1> <h2>Matching and surpassing human pilots</h2> <p>Code:</p> <ul> <li> <p>human_single.mlx</p> </li> <li> <p>human_multi.mlx</p> </li> </ul> <p>Data: human_competition/[single/multi]/[auto/manual]/[id].bag: The rosbag captures all essential data about drone flight in competition.</p> <h2>Accurate thrust module fitting</h2> <p>Code: thrust_fitting.mlx</p> <p>Data: thrust_fitting/[lidar/nokov].csv: measured calibration data of two drones.</p> <h2>Optimization of 1000 aerobatic trajectories</h2> <p>Code: repeat_trajopt.mlx</p> <p>Data: repeat_traj_opt/</p> <ul> <li> <p>opt_result.txt: final output results of the trajectory optimization problem.</p> </li> <li> <p>optBoundValue.txt: boundary values of trajectories.</p> </li> <li> <p>trajpos.txt: position, velocity, and orientation of the trajectories.</p> </li> </ul> <h2>Aerobatics vs. Non-Aerobatics</h2> <p>Code: nonaero_compare.mlx</p> <p>Data: nonaero_compare/</p> <ul> <li> <p>aerotraj_[aero/non].txt: position and orientation of the trajectories.</p> </li> <li> <p>opt_result_[aero/non].txt: final output results of the trajectory optimization problem.</p> </li> <li> <p>optBoundValue_[aero/non].txt: boundary values of trajectories.</p> </li> <li> <p>trajpos_[aero/non].txt: position, velocity, and orientation of the trajectories.</p> </li> </ul>
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.